Business Intelligence Archives | TechnologyAdvice We're On IT. Mon, 13 Feb 2023 15:26:36 +0000 en-US hourly 1 https://cdn.technologyadvice.com/wp-content/uploads/2021/09/ta-favicon-45x45.png Business Intelligence Archives | TechnologyAdvice 32 32 MQL vs. SQL: Differences & Comparison in 2023 https://technologyadvice.com/blog/information-technology/mql-vs-sql/ https://technologyadvice.com/blog/information-technology/mql-vs-sql/#comments Fri, 10 Feb 2023 22:14:39 +0000 https://technologyadvice.com/?p=40811 Does your company know the difference between an MQL vs SQL? Explore the top differences now so you can generate more revenue with fewer leads.

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Key takeaways
  • Now more than ever, sales and marketing need cross-discipline support, and they need quantifiable proof of the value of their efforts. MQLs and SQLs can offer a lot in this regard.
  • When the two teams work together to define their qualification criteria, both benefit from better leads, and both avoid the frustrations of wasted efforts. 

Some industry terms have definitions with precise, universally agreed meanings. Others are more linguistic guidelines than actual rules. And some are listed in the dictionary under “it depends.” Marketing Qualified Leads (MQL) and Sales Qualified Leads (SQL) fall into the latter category. 

SQLs and MQLs are metrics that can be either meaningful or meaningless depending on how they’re used. And missteps or masterstrokes for either begin with what the term actually means.

What Are MQL & SQL?

Here are the best general-purpose descriptions that can be offered for MQL vs SQL:

A lead is a potential sale, especially in B2B. A “qualified” lead is one marked as heightened interest, and thus more likely to convert to a customer/client for the brand. In other words, it’s a label instructing the marketing and sales teams to track, nurture, and follow up with the lead.

If the lead was vetted by the marketing team, it’s labeled as an MQL. If it was qualified by sales, it’s marked as an SQL.

That’s it. Those are the only commonalities that apply across the board. Anything more granular or specific will vary by organization, team, strategy, and a host of other factors. 

ALSO READ: Finding B2B Sales Leads That Don’t Suck 

Common Threads

Some of those factors are popular enough to merit mentioning, however, and may help organizations that want to set their own MQL vs SQL standards. 

Lead Priority

Most teams that have defined criteria for one type of qualified lead will usually have criteria for the other as well. SQLs and MQLs tend to go hand in hand. However they are not the same, and in some organizations one will have primacy over the other.

Differences in department functions are the main reason for this viewpoint. Marketing teams are tasked with increasing brand visibility and reach, but typically don’t see the end of the process (where a sale is made).

Conversely, sales teams interact with leads more directly, but usually on a one-on-one basis. They can see what concerns and details contribute to a lead’s final decision, but often don’t see the beginning of that journey for any customer they contact.

The divide-and-conquer approach can either be a strength or a weakness here (more on that below), but either way it tends to create a hierarchy between the two lead types, whether that’s part of the official process or not.

A more formalized sales funnel may have MQLs serve as a handoff point. Once a lead becomes an MQL, their information is passed on to the sales team for follow-up. If they respond to further sales outreach, they’re upgraded to an SQL, and sales reps work to determine if a deal can be reached.

For organizations that are more siloed in their approach, SQLs and MQLs may not be part of a sales funnel at all. Instead, it may be more a matter of interdepartmental politics. If marketers send along MQLs that don’t convert well, sales staff may instead prioritize their internally generated leads whenever they can.

Qualifiers

Qualifiers are the benchmarks used to separate the warm leads from the cold. When discussing SQLs and MQLs, this is the aspect that makes them impossible to define concretely. Different organizations—and different teams—will use different benchmarks in their qualifying process.

That said, the majority of qualifiers are tied to specific engagement metrics, both for sales and marketing. Below are some examples of popular qualifiers.

Marketing might qualify leads that:

  • Sign up for a newsletter.
  • Engage with the brand on social.
  • Share brand content on their own social account.
  • Download an ebook or other digital asset.
  • Click on CTA buttons in emails, on landing pages, or on ads.
  • Visit a set number of site pages (e.g. blogs or product pages) or return to the site a set number of times.

Sales might qualify leads that:

  • Respond to email outreach.
  • Sign up for a demo or free trial.
  • Call the company directly.
  • Engage with the website chatbot.
  • Connect with sales reps on social platforms, or respond positively to messages there.

Marking a lead as qualified may happen after just one benchmark has been reached, or it may require crossing multiple thresholds before the lead is tagged for follow-up. Qualifiers may prove more or less effective over time if leads tied to them are consistently resulting in the same outcomes.=

Making the Most of MQL vs SQL

Marketing and sales both bring important insights and expertise to the table, but when the two teams don’t work in concert, that ineffectiveness can result in frustration and wasted efforts. MQLs and SQLs, regardless of how leads are classified or what the process does with them, are only valuable as metrics when they are calibrated to accurately gauge a lead’s interest.

Below are some guidelines on how to achieve that cooperative calibration, and some common mistakes that complicate the process.

ALSO READ: Defining Qualified Leads: What Marketing and Sales Need to Agree On

Effective Lead Qualifying Strategies

Leads, even qualified leads, are just a metric—a key performance indicator (KPI). Ultimately, it’s a value in a spreadsheet cell, and doesn’t mean anything beyond its numerical value. As a result, many teams find that the metric fails to measure or promote real success.

The ones that do see success recognize that the leads metric is a placeholder; one that represents actual human beings.

The Human Factor

Leveraging MQL vs SQL to build an effective sales funnel is an exercise in psychology and empathy. These aren’t numbers or email addresses moving down the funnel. They’re people. They have agendas and interests all their own. So, benchmarks need to indicate alignment between the lead’s use case and the brand’s offerings.

This all starts with questions. Why did they read the blog, download the ebook, or sign up for the newsletter? What questions do leads ask on sales calls? Who’s choosing not to convert, and were there quantifiable reasons for their decision?

Some of this can be accomplished internally, via empathy mapping, consulting experts among the staff, and looking back on previous experiences and data. Getting those answers directly from the target audience, however, is the more reliable strategy in the long run.

Surveys, solicited customer feedback, focus groups, social media polls, the list of possible tactics here runs pretty long. Regardless of how the information is gathered, the goal is to understand three things:

  1. How users discover your brand.
  2. Why they interact or engage with a given touchpoint.
  3. What do they find compelling or repulsive?

As this information is gathered, it should provide guidance toward benchmarks that are stronger indications of interest and alignment, allowing those to be used as the qualifiers for leads. The process can then be repeated as more leads and new touchpoints start to add to the stack. It’s a feedback loop that ensures conversion rate optimization efforts actually optimize anything.

Done right, this data will aggregate into concrete answers regarding who is being qualified as a lead that shouldn’t be, and how to prevent those false leads from moving down the funnel.

Cooperation

Sales funnels that struggle to find value in their lead generation efforts typically suffer from a sales-marketing segregation. Siloing the departments prevents either side from being bolstered by the insights of their counterpart. The result is poor lead gen, poor conversion rates, and often, poor retention rates for sticky business models.

Marketing professionals are trained to reach a broad audience. They look for ways to broadcast the brand message so that it will find more of the right people, so that anyone who might find the brand’s offerings beneficial will know how and where to get those offerings.

Sales professionals are trained to help people weigh the costs and benefits of a purchase. Where marketers are usually one-to-many, salespeople are more practiced in having a direct dialogue with their audience. They hear the feelings and concerns of their audience straight from the source, and usually have a better idea of what benefits or features are real linchpins.

With a little collaboration, marketers can leverage the expertise of sales teams to refine branding and messaging. If an experienced sales pro knows exactly what to say, an experienced marketer gives them the right megaphone and points them in the right direction, metaphorically speaking.

Sales can help marketers know what details to focus on, while marketers can build processes that effectively pre-screen leads. Done properly, sales can focus on only the most valuable leads, and marketers can minimize efforts spent on ineffective tactics.

When the two teams work together to define their qualification criteria, both benefit from better leads, and both avoid the frustrations of wasted efforts. 

Lead Qualifying Pitfalls

Much of this has already been pointed to, but these missteps bear repeating, at least in summary.

Don’t:

  • Define MQL vs SQL standards in isolation.
  • Set qualifying benchmarks at random.
  • Assume the other department just doesn’t know what they’re doing.
  • Ignore customer/lead feedback.

Do:

  • Plan metrics around direct audience insights.
  • Measure lead performance and optimize over time.
  • Build sales funnels via marketing-sales collaboration.
  • Prioritize methods that produce brand evangelists.
  • Leave room for mistakes that can be lessons learned.

Unless carefully crafted, lead generation processes struggle to produce a measurable impact on long-term growth.

Reaping the Rewards

Current economic situations have prompted many brands and organizations to cut back on spending. In most cases, the first budget cuts are assigned to marketing and sales teams.

Now more than ever, sales and marketing need cross-discipline support, and they need quantifiable proof of the value of their efforts. The MQL vs SQL debate can offer a lot in this regard.

When done right, MQLs can:

  • Prove the value of marketing investments.
  • Those writers, SEO pros, and social experts are more than just another line on the HR budget sheet, and MQLs can help prove it.
  • Help sales pros focus their time on more profitable leads.
  • Weed out the false leads (like the story above).
  • Provide a system of measuring and optimizing demand- and lead-generation efforts.

Ultimately this can lead to improved conversion rates, sales numbers, and customer retention.

SQLs, when done right, can:

  • Align sales and marketing priorities, so everyone knows which green flags for which to be on the lookout.
  • Connect marketing efforts to sales wins, so the whole team succeeds together.
  • Set benchmarks for marketing, so they know when to pass leads on to sales, and when to let them incubate a little longer.
  • Identify methods, tactics, strategies, and efforts that are providing reduced ROI.

Done together, lead quality can be improved dramatically, providing a catalyst for accelerated growth in the market (without leaving a bunch of unhappy former customers in the wake).

The bottom line is that progress is easier to achieve when it’s measured accurately. Improvement is easier to achieve when results are quantifiable and attributable. The right processes can give us those numbers, and can validate our efforts to those in the company responsible for deciding what budget items are “indispensable.”

But these numbers still represent people. Management may not always be intimately familiar with the humans behind the figures, but those of us in sales in marketing should be. It’s our job to make sure the brand is offered to the right people.

Looking for the latest in CRM solutions? Check out our CRM Software Buyer’s Guide.

FAQ 

What are Marketing Qualified Leads?

A marketing qualified lead, or MQL, is a lead identified by standards established by a marketing team.

What are Sales Qualified Leads?

A sales qualified lead, or SQL, is a lead identified by standards established by a sales team.


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What is Data Visualization & Why is it Important? https://technologyadvice.com/blog/information-technology/importance-data-visualizations/ https://technologyadvice.com/blog/information-technology/importance-data-visualizations/#respond Fri, 10 Feb 2023 19:34:59 +0000 https://technologyadvice.com/?p=68460 Data visualizations represent vast amounts of complex data in a way that is easy to interpret and understand. Read why they are so important in 2023.

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Key takeaways
  • Have proof that a minor product adjustment will reduce overhead by 50%? Visualization is the difference between more numbers on a spreadsheet and being impossible to ignore.
  • Visualizations are key to helping share information that would be otherwise difficult for most people to process.

Numbers are great. People aren’t always great at making sense of them (or putting them to good use). Ensuring you have quality data visualizations can help your employees and clients better understand your business. So, why is data visualization so important?

Computers are far better at this than humans. Their base language is literally numeric in nature. Which can make words, images, and sounds harder for them to parse.

What is data visualization?

The same dilemma faces businesses that want to leverage data and analytics to improve their processes. The key here is using the machine to pre-digest the data, and output it in a format that the wetware can grasp with ease. We call this Data Visualization.

Here are three compelling reasons your business needs stunning data visualizations.

ALSO READ: How to Optimize Your Data Visualizations

Data visualizations are easier to understand

Playing with blocks

Our brains have limits on how much they can learn, think about, and remember. So, humans evolved to use some hacks to compensate. The go-to tactic is “putting things in boxes.”

Phone numbers illustrate this well. Long strings of numbers aren’t very user-friendly, so we break them up into blocks to make memorizing them easier. In the US, for example, numbers are hyphenated into three sections: 555-555-5555.

Making numbers matter

Even the most practiced mathematicians will start to see spreadsheet cells blend together after a thousand rows or so. No one can be expected to be handed a mountain of unorganized data and understand what it all means.

Data visualizations provide the means to more efficiently communicate insights from all the number crunching. Thousands of data points might not be easy to remember, but a single stat or pie chart is more likely to be treated as its own block in working memory.

Context and relevance

Here’s how data visualization can make this happen.

First, the process uses graphs, charts, and other forms of visualization, shifting the burden of parsing the information from the reader to the machine (a system better suited for comparing countless abstract numerical figures). This frees up the reader to simply consider the ramifications of the data. In other words, it provides context.

Without benchmarks against which we can measure numbers, understanding their value is difficult. This is the same problem experienced when temperature is being compared between the Fahrenheit and Celcius scales. Forty degrees can either be sweltering or chilly depending on your frame of reference.

Second, data visualizations let the machine handle all the parsing. Using visualization tools to present that information, it’s much easier to strip away irrelevant data and findings — because not everyone will benefit from every pie chart. Presenting the data that’s most valuable to a given audience highlights the most important or urgent info.

Bottom line: Data visualizations provide context and relevance, simplifying your message, and promoting comprehension across a wider audience.

ALSO READ: Top Tableau Alternatives for Data Visualization and Analysis

Data visualizations are easier to share

Images, catchy quotes, and well-chosen sound bites are a lot easier to share than lengthy reports, articles, or rants. Social media pros have been leveraging this fact for years, and data visualizations offer the same solution to analytics teams.

Whether you’re sharing findings with your internal team, an entire organization, or an even wider external audience, the data will have greater reach if it’s easy to reference. No employee is going to pin a 12-page report to their office wall. Even if they did, there’s no way for them to effectively leverage that information in their day-to-day efforts.

An easy-to-read graph, however, or a short list of powerful statistics … that’s a different story.

Data visualizations are easier to use

The goal here is for the data to affect and guide future decisions. Data visualizations help here, too, and largely for the reasons already discussed. Removing the excuses of “I didn’t know” and “it’s too complex” drastically improves the odds that the insights will actually generate positive results.

When data is visualized in an approachable format:

  • It’s easier to understand.
  • Audiences digest it faster.
  • Sharing happens more readily.
  • It remains top of mind.
  • Professionals will reference it more frequently in their own efforts.

Visualizations are key to helping share information that would be otherwise difficult for most people to process. Not everyone has a head for numbers (especially that many numbers). The visuals present the information in a format more conducive to consumption by the general audience, which often means achieving better results, faster.

Here’s the whole argument in a nutshell: Have proof that a minor product adjustment will reduce overhead by 50%? Visualization is the difference between more numbers on a spreadsheet and being impossible to ignore.

The dark side of data visualizations

A word of caution here for anyone who wants to achieve more than short-term goals with their data. Numbers don’t lie, but people often do. The problem with abstracting data into a graphic or visual is that the numbers can lose their context — and flashy visuals can be used to misdirect viewers.

When used unscrupulously, data can facilitate the manipulation of information and present a version of the facts that doesn’t align with reality.

For example, “Vanity metrics” are heavily maligned in marketing despite their usefulness in the right circumstances. The bad reputation is well-earned, though, in some settings. Traffic to the site might look like progress, but if none of those users are members of the target audience, the high traffic volume may simply be hiding poor conversion rates.

Presented this way, data can point to lots of busy work on the part of certain teams, but very little actual performance or progress. So when you start putting together all of the graphs you need, make sure to illustrate the appropriate context.

In summary

Data can be a powerful tool in business. It can improve business strategies. It can prove ROI of efforts and resources. It can highlight issues with the market, the business, the team, or even the data itself. Data can also be formatted to transmit important insights quickly and compellingly.

Most of this is accomplished through visualizations. Business intelligence tools are excellent for such purposes and can make short work of quality analytics. 

Looking for the latest in Business Intelligence solutions? Check out our Business Intelligence Software Buyer’s Guide

FAQ 

What is data visualization?

Data Visualization is a software process that takes raw data from spreadsheets and other sources, then turns it into colorful graphs, charts, and tables to make it more easily understood.

Why is data visualization so important?

Data visualizations provide context and relevance, simplifying your message, and promoting comprehension across a wider audience.

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Structured vs Unstructured Data Comparison 2023 https://technologyadvice.com/blog/information-technology/structured-vs-unstructured-data/ https://technologyadvice.com/blog/information-technology/structured-vs-unstructured-data/#respond Fri, 20 Jan 2023 23:25:25 +0000 https://technologyadvice.com/?p=83070 Key Takeaways Structured data is stored in a schema, a plan of how the data will be stored and used by software that manipulates structured data. Unstructured data is not defined and stored in its native format.  Microsoft Word saves its file with a .doc extension, and an adobe file is saved with a .pdf... Read more »

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Key Takeaways
  • Structured data is stored in a schema, a plan of how the data will be stored and used by software that manipulates structured data.
  • Unstructured data is not defined and stored in its native format. 
  • Microsoft Word saves its file with a .doc extension, and an adobe file is saved with a .pdf extension, the native format for these software tools.

To get the most out of an organization’s data, Information Technology (IT) managers and executives need to understand the types of data used by an organization. IT managers that understand the types of structured vs. unstructured data available will help them make better decisions when aggregate data is used from both data categories.

What are structured and unstructured data types?

Structured data is created from a pre-defined format when a user has some idea of what data columns will be included in the structured data. Structured data is stored in a tabular form with specific columns that can be text, numeric, or date columns. Each of these columns can be formatted to accept data in particular formats. 

Relational Database Management Systems (RDBMS) such as Microsoft (MS) SQL Server or an Oracle Database are popular software tools for structured data for large organizations. SQLite or MySQL are RDBMS tools that small businesses can use. 

ALSO READ: How Businesses use Structured vs. Unstructured Data

Unstructured data types are word documents, emails, adobe PDF files, social media posts, and video or audio files. Any data that is not considered structured data can fall into the category of unstructured data, such as presentations, chats, sensor data, and satellite imagery. 

Unstructured data is stored in its native format. All unstructured data is saved in its native format by the software that created the file, and 80-90 percent of all business data is unstructured. 

Structured vs. unstructured data advantages and disadvantages

Regardless of the type of data businesses use, IT managers need to know the strength and weaknesses of the data types to exploit the advantages of structured and unstructured data.

The advantages and disadvantages of structured data

The advantage of structured data is it’s generated by a variety of business applications that are used daily in a business environment. Entry-level users can use basic software tools like MS Access, Excel, and more experienced users can use MS SQL Server or business intelligence (BI) tools to manipulate data. 

Structured data has a wider variety of RDBMS software and analytical tools available to support it since it has been around for decades. Artificial Intelligence (AI) tools can quickly generate queries due to how structured data is stored.

Structured data is not flexible and can only be used for its intended purpose, which  is a significant disadvantage. Another pain point of structured data is the complex alteration it must go through before a flexible data store can use it. As a business grows, the number of databases and tables proliferates, lending itself to overlapping datasets and redundant data with complex relationships between tables.

The advantages and disadvantages of unstructured data

Since unstructured data is in its native format, no processing is required before using it. As a result, unstructured data is flexible and can be used for different purposes. 

Another advantage of unstructured data is the low overhead associated with storing and processing the data. When appropriately used, unstructured data can provide better insight into a business’s overall progress that can become a competitive advantage.

A disadvantage of unstructured data is that it requires advanced analytics to derive meaningful information for a business. In addition, retrieving, processing, and analyzing unstructured data requires advanced tools and data science skills to generate insightful information.

Quick summary:  

  • Structured data has been around for a long time and is easy to use.
  • Getting relevant information from unstructured data requires an experienced data scientist familiar with the latest AI tools.

ALSO READ: 5 Best Data Storage Solutions for BI

Similarities and differences between structured and unstructured data

All data belongs to a business and can add value to a company using quantitative or qualitative data. Each data type can represent a comprehensive business overview from an employee, supplier, and customer perspective. 

Similarities between the two data types

The similarity between the structured vs. unstructured data is they belong to a business. The proprietary business data needs to be securely protected by the owning organization with the proper cybersecurity controls in place. 

Both data types can be used to improve the business through continuous process improvement (CPI) practices, making the data valuable to businesses.

Differences between the two data types

The main difference between structured and unstructured data is that structured data uses a defined format, and unstructured is saved in its native format. Structured data is quantitative and is used to show monetary gains and losses for organizations, which is numbers-based, countable, or measurable. 

Unstructured data is qualitative and generally descriptive or interpretation-based, so it can tell the why, how, and what happened in certain business situations.

Another difference between the two data types is that structured data is easier to search by a person or a created algorithm. However, to exploit and retrieve meaningful data from unstructured data, businesses will require a person with data scientist skills that can use advanced analytical techniques like data mining and data stacking

Additionally, structured data is stored in a data warehouse, while unstructured data is stored in a data lake, which has more storage capacity for all data types.

Quick summary:  

  • Structured and unstructured data add value to a business but are used differently.
  • Combining quantitative and qualitative data allows management to make decisions beyond raw Return on Investment (ROI) gains.
  • A third category of data that may be stored in a data lake is called semi-structured data. Semi-structured data does not have a fixed schema but uses tags and business metadata to help define its semi-structured data. HTML code and XML documents are examples of semi-structured data.

Software tools used to manipulate structured vs. unstructured data

Relational database management tools have been around as long as structured data. Businesses can use analytical tools to manipulate and analyze structured and unstructured data in today’s environment. 

Artificial Intelligence and its associated cohorts under AI are Machine Learning (ML) and Natural Language Processing (NLP), which play a crucial role in extracting insightful data from structured and unstructured data. 

Software tools used for structured data

Any RDBMS software like Microsoft SQL Server can manipulate structured data. Zoho Analytics can connect to an organization’s structured and unstructured data and blend the two data types to provide meaningful information to executives and IT managers. Zoho Analytics also offers the following features:

  • Dashboards – visual analysis information with drag-and-drop options.
  • Zai AI – Zia intelligent assistant that uses ML and NLP to generate responses.
  • Library of mathematical and statistical functions – uses a user-friendly formula engine to help extract business metrics.

Software tools used for unstructured data

Over eighty percent of business data is unstructured. Since unstructured data is stored in multiple formats, a business can use an Extract, Transform, and Load (ETL) software tool to extract structured and unstructured data onto a data lake platform. ETL software can also store data in a data warehouse, a centralized database, or an analytical database for faster queries. Hadoop is one of the many software tools that use ELT to transform and load large amounts of data to a designated repository for further analysis. 

Quick summary:  

  • Voluminous amounts of structured, semi-structured, and unstructured data are known as big data. 
  • Big data software tools such as Sisense have built-in ETL tools, and Google Cloud Platform uses its embedded set of big data tools to analyze data in data lakes or warehouses.

Characteristics of Structured vs. Unstructured Data

  Structured Data Unstructured Data
Characteristics
  • Pre-defined schema or data model
  • Text or numeric
  • Searchable
  • No pre-defined data model
  • Text, images, audio, video, or other file formats<
  • Difficult to search
Stored In
  • RDBMS
  • Data Warehouses
  • Applications
  • NoSQL databases
  • Data warehouses
  • Data lakes
Created by
  • Humans or machines
  • Humans or machines
Types of Applications
  • Train reservation system
  • Supply Chain Management system
  • ERP systems
  • HRIS systems
  • Word Processing
  • Email software
  • Presentation software
  • Audio and visual software tools
Examples
  • Employee ID number
  • Social Security number
  • Dates
  • Credit card numbers
  • Addresses
  • Text files
  • Reports
  • Emails
  • Images
  • Audio and visual files

Selecting the best tools for structured and unstructured data

Emails, reports, and surveys are as important as structured data in an RDBMS system. The benefit of equating unstructured data as valuable as structured data is realizing the value of unstructured data, especially with it being eighty percent of all business data. With a better understanding of all data types in a business, executives and IT managers can formulate a plan to eliminate any data silos. When deciding on a data storage solution, management needs to consider these key features:

  • Space and scalability – as a business identifies more unstructured data, additional space may be needed, so scalability is necessary.
  • Accessibility – a data storage solution must be easily accessible and able to interface with any other analytical tools used in your business environment.
  • Management – understand how the selected data storage vendor will manage your organization’s data and what self-service options are available for a user.
  • Security – ensure the selected data storage vendor uses updated security features, including end-to-end encryption.

To help IT managers get started with research for a data storage solution, here are some of the best data storage vendors identified in 2022.

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The Top 4 Benefits of Using Embedded Analytics https://technologyadvice.com/blog/information-technology/competitive-advantages-embedded-analytics/ https://technologyadvice.com/blog/information-technology/competitive-advantages-embedded-analytics/#respond Fri, 30 Dec 2022 17:45:02 +0000 https://technologyadvice.com/?p=68851 Embedded analytics make apps look beautiful, but other than aesthetics, are there competitive reasons to embed analytics on preexisting applications or internal web portals? Or given its startup costs, would there only be costs on your balance — and would analytics be too much of an expense? Far from it. Aside from guaranteeing your survival... Read more »

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Embedded analytics make apps look beautiful, but other than aesthetics, are there competitive reasons to embed analytics on preexisting applications or internal web portals? Or given its startup costs, would there only be costs on your balance — and would analytics be too much of an expense?

Far from it.

Aside from guaranteeing your survival in a bloated marketplace, top performers gain up to 20% of their total annual revenue from embedded offerings. Reduced churn rate, increased revenue, improved product adoption, increased sales, and a sustainable unique selling proposition (USP) are your top five competitive advantages of embedded analytics.

Survival Strategy

Embedding analytics in commercial software is your key to staying relevant in a crowded market. That’s particularly so since over 90% of software companies embed some form of analytics in their applications — often for free. As Reveal, a purpose-built platform for embedded analytics, notes:

“Today’s app developers are competing in a crowded market. With all this availability, end users have little patience for apps that fail to bring them any particular value. If an app provides only short-term worth, users are likely to uninstall it fast; this is as true in the enterprise app space as it is in the consumer market.”

So, the expense of not investing in analytics is likely much bigger than the cost of adding analytics to your software-as-a-service (SaaS) product.

Users love data and have come to expect embedded features. At the same time, since 78% of commercial application teams charge more for those added integrated analytics, there’s no reason you need not profit from your embedded analytics solution either.

ALSO READ: Driving Business Results with Embedded Analytics

Stickier Applications

Prospective customers are more than ready to pay more for this functionality. As 87% of 1,931 analytics end users recently told Hanover Research, they use their analytics “often or very often” to make business decisions. The result? Embedded applications become an asset to their business.

Take BoldBI, for one, which helps the telecommunications industry integrate embedded dashboards in their applications. In 2022, the company noted that its telecom clients value their embedded offerings for reasons that include the following:

  • Increased Revenue: Telecom managers can track and monitor internal company revenue performance over a period of time without needing to consult external BI for information. (This “swivel chair effect” wastes 2 hours of productivity per worker each week.)
  • Reduced Churn Rate and Increased Usage: Telecom operators can plumb analytics to identify when and why customers leave their services.
  • Improved Communication Efficiency: Embedded analytics helps telecom vendors track text, voice, data service utilization, subscriber segmentation by service type, and more to determine which communication services need improvement.
  • Increased Connectivity: For industries like telecommunications this is critical. End users rely on them to provide uninterrupted swift connections for fast and smooth work.

As long as BoldBI helps its telecom clients monitor their operations through its embedded tools, BoldBI retains their business — and grows.

Attract New Users

Almost 90% of users appreciate the far-reaching effects of embedded analytics. That’s especially true if you do it well. Suggested best practices include:

  • Consider the Overall User Experience: Provide a seamless user experience where analytics are embedded deep in the platform layer and seem a part of the UI experience.
  • Integrate Workflow Actions With Embedded Workflow Capabilities: This way users are not only fed information but are also enabled to respond. For example, they can share report findings with others.
  • Reduce Security Friction Points: This enables users to access dashboards, for example, without needing to regularly update their passwords.
  • Embed Self-Service Analytics With Open-Source Tools: This can enable users to add their own contributions to the application.

Also, think of the responsibilities of target and prospective users and how these end users will likely use your application. Then, embed useful analytics at those relevant touchpoints.

Similarly, think of the user’s persona, and embed analytics in those spots. For example, content creators want to query certain data sources, create dashboards and reports, and share what they’ve created with colleagues — embed data in those key places. Data analysts, on the other hand, are more exploratory. They like to start with a blank canvas and make their way to their own data sources. Embed your analytics accordingly.

Differentiate Your Product

There’s still the opportunity to stand out and dominate your software niche, which you can accomplish through resourcefulness and creativity. Some examples:

  • More collaborative analytics that engage multiple users
  • An original and forward-looking dashboard that differentiates you as an innovation-minded OEM (original equipment manufacturer)
  • Highly actionable analytics, allowing users to take action, such as to share reports with others
  • Supplementary “pay to play” modules, where you can charge on point of sale or on a subscription-basis

Google Cloud suggests you could also differentiate your embedded software with functional features that make users’ lives easier. One example is you can add third-party data for additional insights into customer behaviors and preferences. For instance, if you’re working on enterprise resource planning (ERP), you may want to consider integrating anonymized consumer spending data from U.S. credit card companies.

Another helpful tip is to integrate your own insights to help users with their objectives and needs. For instance, if you offer a website application, you can embed a functionality that identifies peak traffic times to publish blogs.

Monetize

Even in today’s glutted embedded software marketplace, there’s still the opportunity to gain revenue from monetizing your embedded product. Models for data monetization include:

  • Upselling dashboards from one tier to the next
  • Packaging analytics as an add-on
  • Offering customized dashboards to your customers
  • Proffering updates to your solution

Top performers regularly earn up to 20% of their total annual revenue from embedded offerings from ideas such as these.

Grow Your Application Revenue With Embedded Analytics

Even in a glutted marketplace, you can still profit from embedding analytics in your commercial applications through working on metrics that measure SaaS return, such as improving LTV (lifetime value),  reducing customer churn, upsell, increasing sales efficiency, and the like.

Proof?

98% of commercial software companies report definite ROI (return on investment) from their analytics offerings, while application teams estimate those analytics contribute more than half of the total application’s value.

Looking for the latest in Business Analytics solutions? Check out our Business Analytics Software guide.

Trending Business Intelligence Software

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Build a modern business, driven by data. Connect to any data source to bring your data together into one unified view, then make analytics available to drive insight-based actions—all while maintaining security and control. Domo serves enterprise customers in all industries looking to manage their entire organization from a single platform.

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Top Tableau Alternatives For Visualizing & Analyzing Data https://technologyadvice.com/blog/information-technology/tableau-alternatives/ https://technologyadvice.com/blog/information-technology/tableau-alternatives/#respond Tue, 27 Dec 2022 16:06:51 +0000 https://technologyadvice.com/?p=53339 Quick Summary This high level of functionality means that Tableau comes with a high price tag relative to some other options on the BI market. If your organization needs a lot of data analysis and visualization, then it might be worth the investment. But if your business intelligence needs are more occasional, the platform might... Read more »

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Quick Summary
  • This high level of functionality means that Tableau comes with a high price tag relative to some other options on the BI market. If your organization needs a lot of data analysis and visualization, then it might be worth the investment. But if your business intelligence needs are more occasional, the platform might not offer the return on investment you would like.
  • These products are by no means the only Tableau alternatives. There are plenty of viable solutions in the business intelligence software market, but a high learning curve keeps many buyers from doing enough research.

Tableau is a data visualization tool and one of the market leaders in data analysis and business intelligence. It offers numerous products, all of which do something slightly different but each of which still falls underneath the umbrella of data discovery, analysis, visualization, and business intelligence. Tableau’s main offerings are:

  • Tableau Desktop: The flagship product, Tableau Desktop, is a downloadable computer app that lets users analyze large amounts of complex data and turn it into charts, graphs, and other visualizations.
  • Tableau Server: This function is used to share workbooks, reports, and other files created in Tableau Desktop across an organization.
  • Tableau Cloud: Formerly known as Tableau Online, this product is a fully cloud-based version of Tableau that allows users to do everything without having to download an additional app.
  • Tableau Public: This free version of Tableau allows users to create data visualizations at no cost and then publish them to Tableau’s public cloud; although, files cannot be saved locally or privately due to the free nature of the service.
  • Tableau Prep: This self-service data discovery and preparation tool helps to clean, sort, and combine data to get it ready for use in one of Tableau’s other products.
  • Tableau Reader: This free desktop application allows users to open and interact with data visualizations made in Tableau Desktop.

What Does Tableau Do?

Tableau is a data visualization tool and one of the market leaders in data analysis and business intelligence. It offers numerous products, all of which do slightly different things but still fall underneath the umbrella of data discovery, analysis, visualization, and business intelligence. Tableau’s main offerings are:

  • Tableau Desktop: The flagship product, Tableau Desktop is a downloadable computer app that lets users analyze large amounts of complex data and turn it into charts, graphs, and other visualizations.
  • Tableau Server: This function is used to share workbooks, reports, and other files created in Tableau Desktop across an organization.
  • Tableau Cloud: Formerly known as Tableau Online, this product is a fully cloud-based version of Tableau that allows users to do everything without having to download an additional app.
  • Tableau Public: This free version of Tableau allows users to create data visualizations at no cost and then publish them to Tableau’s public cloud; although, files cannot be saved locally or privately due to the free nature of the service.
  • Tableau Prep: This self-service data discovery and preparation tool helps to clean, sort, and combine data to get it ready for use in one of Tableau’s other products.
  • Tableau Reader: This free desktop application allows users to open and interact with data visualizations made in Tableau Desktop.

Most businesses opt for the Tableau Creator plan, which bundles together Tableau Desktop, Tableau Prep Builder, and one one license for either Tableau Cloud or Tableau Server for a comprehensive solution.

Why Tableau May Not Work For Your Company

Tableau is a business intelligence industry leader for a reason, and it regularly receives high marks for its eye-catching data visualizations, powerful analytics, and relative ease of use. Given the platform’s popularity, you may be wondering why it’s even worth it to consider a Tableau competitor, but there are plenty of reasons to look at other BI software.

For starters, Tableau has a less steep learning curve than some other competitors in the BI space. However, it still requires a certain level of technological know-how to make the most of the product, most notably with an understanding of SQL queries. This is a big roadblock for small companies or teams which don’t have access to an in-house SQL expert.

In fact, Tableau’s offerings are often too robust for companies just starting to experiment with data analysis and visualization. Tableau competes with BI offerings from Microsoft, SAP Analytics Cloud, and other enterprise-level software designed for multi-national companies. If you’re working with huge datasets and need to run complicated analytics on them, Tableau is a great option. But if you just need to turn last quarter’s metrics into a simple chart, it will likely be overkill for your business’s needs.

This high level of functionality means that Tableau comes with a high price tag relative to some other options on the BI market. If your organization needs a lot of data analysis and visualization, then it might be worth the investment. But if your business intelligence needs are more occasional, the platform might not offer the return on investment you would like. On the other hand, a Tableau competitor such as Qlik Sense or Zoho Analytics could make a better option.

 

Best Alternatives to Tableau

GoodData

GoodData offers two major products: an end-to-end data platform and embedded analytics. These two work on the GoodData security structure that runs the gamut from HIPAA to GDPR and financial data regulations. The platform gives companies a full data management and analysis system that runs on any data source. This software provides the architecture for data integration, cleansing, analysis, and publication to reporting tools and apps. The scalable enterprise platform lets companies use their data to grow in new, stable, and innovative ways that protect the company’s data but drive analytics toward insight.

Why choose GoodData over Tableau

GoodData’s embedded analytics are perfect for smaller corporations looking to improve data analysis and access but who can’t commit to a full end-to-end data management system. The analytics use the GoodData platform as a service model to get companies up and running quickly, sometimes within a matter of days, and without hiring data scientists. Because GoodData encourages quick setups with minimal additional staff, it makes for a great upgrade for businesses looking to expand regardless of limited access to resources.

InsightSquared

InsightSquared is a sales-focused data visualization company that aggregates and provides a platform for business users to better understand their data. The software is split into two main products, Tiles and Slate, which are built to scale to a business’s needs.

Why choose InsightSquared over Tableau

InsightSquared, as a sales-focused data visualization tool gives teams incredibly detailed insights into their sales metrics. Small businesses and enterprises looking to make data-driven growth will find utility in the predictive analysis tools that InsightSquared provides. These insights use the rates at which team members close sales, the quality of leads that have been gathered, and historical data to project out the rate at which a business can expect its revenue to grow or contract. InsightSquared can very easily aid in building quarterly business plans and market redirections.

SAS

SAS Business Intelligence and Analytics (also called Visual Analytics) is just one of the many data processing and analytics tools available from SAS, which specializes in building enterprise-ready tools for users across industries. Choose the right tool, or set of tools, from SAS based on your business and technology needs and the maturity of your data program.

Why choose SAS over Tableau

Look to the Visual Analytics program to gain business analytics without tying up IT resources. The system is built for business leaders and technical data analysts to work in tandem, with IT controlling governance but business guiding the data visualization process. SAS also integrates with MS Office programs like Excel and Outlook to keep data in the hands of those who need it most.

The focus on AI-aided forecasting and data analysis will be the main draw over Tableau to users looking for something new. While Tableau is easy to use and generates perfectly serviceable data views, it cannot be understated how the additional processing power provided by the AI can help along the data analysis process. Staff members finding themselves bogged down in the volume of data they have to comb through for work may find that SAS and its AI-aided visual analytics program will ease the burden of work.

TIBCO Spotfire

Spotfire aims to democratize data across an organization by giving access to all employees, rather than forcing you to make requests through your IT or data teams. They provide cloud, platform, enterprise, and AWS-focused systems for BI from lots of disparate data sources.

Why choose TIBCO Spotfire over Tableau

Spotfire’s extensive capabilities include both big data and what they call “big content.” With big content searching, analysts can easily view all user-generated text across multiple platforms, including email, chat, and search terms. Big content searches help identify customer pain points and possible solutions. To sweeten this deal even further, Spotfire provides predictive analysis from your data, so businesses can be proactive rather than reactive. 

Tableau users would generally be able to spot these same trends using its visualization tools, but Spotfire’s proactive predictive analytics make this process less time-consuming and labor-intensive. This gives analysts more time to work on their actual analysis rather than spending their work days on finding insights.

Viur

Viur’s major strength comes in the form of dashboard-to-email automation that gives users scheduling capabilities over your report data. With Viur, data runs and aggregates silently and publishes when needed based on preset rules, rather than forcing analysts to run a new report.

Why choose Viur over Tableau

Like other options in this category, Viur offers responsive reports that look good on every device, as well as a visual dashboard creator that offers a wide range of report types. This software requires some SQL expertise for greater customization than the out-of-the-box reporting options, but educational resources and contract analysts will help you learn and maximize use of the software. In the short run, this makes Viur a little less user-friendly than Tableau upon installation, but once the learning curve has been overcome, Viur offers a much more robust customization experience.

The Viur team understands security, so data is stored in a personalized secure location that suits the needs of each specific company. The software encrypts connections from those user-selected databases all the way through the reporting process to keep every client’s information safe.

Zebra BI

Zebra BI’s interface resembles a plug-in more than a separate platform that connects to data sources. The entire system bolts onto Excel, so a spreadsheet-heavy workplace doesn’t have to learn new tools. On the downside, if data does not exist in an Excel format, analysts will need to go through the process of importing it.

Why choose Zebra BI over Tableau

Zebra provides new chart types that can’t be found in Excel, which means generating these views is as simple as selecting data and choosing the chart from the Tables option. Zebra also offers data scaling to ensure data isn’t skewed when generating these new views. The data skewing Zebra BI uses is a nice touch that works to avoid common chart distortions and detrimental chart misrepresentations. 

Presenters that need to update a chart shortly before a meeting will have no problem at all. Zebra automatically updates linked PowerPoint slides when changes have been made to a chart in Zebra BI. Zebra works for companies new to BI or those who do a majority of their work in Microsoft Office. 

Tableau may be more complex than a business needs for its data analytics, and companies that primarily use Microsoft Office will find a transition to Zebra BI fits right into their workflow.

Infor Birst

Birst’s multi-tenant cloud architecture aggregates data in the cloud, with AWS, or locally. Infor Birst also offers a desktop client. The company employs strict security standards throughout its  data centers and encryption in the cloud to keep client data safe.

Why choose Infor Birst over Tableau

Because data travels between the cloud and your local databases, analysts can access all of their data and visualizations from any device. Run your analyses with visual discovery dashboards that make data more accessible. This reduces the need for IT interventions or extensive training to use the product. Tableau offers similar mobile access, but Infor Birst’s increased focus on security and ease of use makes it a more attractive choice for security-minded organizations.

Microsoft Power BI

Part of the Microsoft suite of business tools, Power BI is built specifically for data analysis and visualization. While providing the usual dashboards and reporting features, Microsoft’s Power BI also gives analysts the ability to embed data within their apps—an integration that others on this list don’t offer.

Why choose Microsoft Power BI over Tableau

With 60 data sources, the native connectors aren’t overwhelming, but analysts can search and combine data with question-based analytics. Microsoft Power BI offers a free trial with an upgrade to the business version. This tool can be used in conjunction with other Microsoft Business Application systems including PowerApps. The integration with familiar Microsoft tools also makes it an easy fit in many offices that already use Microsoft tools in their daily operations. There is much that can be said about a gentle learning curve and familiar software design.

Looking for a new BI software? We’re here to help

These products are by no means the only Tableau alternatives. There are plenty of viable solutions in the business intelligence software market, but a high learning curve keeps many buyers from doing enough research. Feeling confused? Looking for the latest in Data Visualization solutions? Check out our Data Visualization Software guide.

Which Business Intelligence solution is right for your company?

Featured Business Intelligence Software

If you need a way to jumpstart your research into business intelligence software, here’s a quick list of the top BI tools.

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Top Data Integration Strategies for Business Intelligence (BI) https://technologyadvice.com/blog/information-technology/bi-data-integration-strategies/ https://technologyadvice.com/blog/information-technology/bi-data-integration-strategies/#respond Fri, 12 Aug 2022 16:35:33 +0000 https://technologyadvice.com/?p=93957 Today’s technology can enable businesses and organizations to harness their data to its fullest potential and gain insights to help boost performance and success. That is, if they use all of their data. Data can be sourced from many different tools and systems involved in a business’s processes. Therefore, being unable to develop insights from... Read more »

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Today’s technology can enable businesses and organizations to harness their data to its fullest potential and gain insights to help boost performance and success. That is, if they use all of their data.

Data can be sourced from many different tools and systems involved in a business’s processes. Therefore, being unable to develop insights from data sources could mean missing out on some vital information that could otherwise propel a business to success.

You never know what data could be monumental in transforming business practices for the better, which is why it is so important to be able to access and analyze as much as possible. In order to do this, data integration strategies can facilitate the transfer and use of data between locations.

What are the must-have predictive analytics tools for your business? | TechnologyAdvice.com

How Does Integrated Data Impact BI a Company?

Integrated data can support BI for companies, as it can enable them to gain insights to help them make actionable decisions. These insights inform organizations of the best possible methods for achieving their desired results and help them determine ways to adjust their business strategies to incorporate this knowledge.

What outcomes are possible?

Businesses that integrate their data can find ways to improve their business decisions and processes to generate beneficial outcomes.

For example, suppose a business was to integrate its data to enable it to use the data for analysis within a software system. This could help the business determine insights and develop methods to improve its return on investment, promote its services and products, and predict future business scenarios.

Data integration can also help to improve communication and collaboration among decision-makers, enabling them to determine methods for reaching better business outcomes.

What Important Data Integration Speedbumps Might Impact Your BI?

Of course, the value of an insight brought on by data analysis depends on the accuracy of the data being analyzed. Unfortunately, speed bumps can pop up throughout the data integration process, which could compromise the value and usefulness of the integrated data.

Data silos

Data silos are usually accessible by a select group of individuals and can create issues with data sharing and integration. In addition, the inaccessibility and limited visibility of this data mean that it can often be misunderstood or result in poor data quality. This can also cause inconsistencies in data that may overlap across other locations, threatening data integrity.

Data inconsistency

Data inconsistency refers to a situation where different versions of identical data exist in multiple places, creating an incorrect representation of the information within a database. This can cause significant issues with analyzing data. Some forms of data inconsistency can include temporal, semantic, structural, and transformation inconsistency.

Disorganized data

Disorganized data, or unstructured data, is data that does not follow any predefined structure or organization hierarchy. Disorganized data can be problematic when integrating data from various sources for use within a separate system.

Compromised data integrity 

Many of the aforementioned issues can result in compromised data integrity, which means the data is no longer accurate or consistent. Compromised data serves no value to BI, as it cannot be analyzed to gain real insights about the business.

Strategies for Integrating Data Into BI

Application-based integration

Many enterprise applications use prebuilt connections to facilitate the transfer of information from the source into the desired location. These applications can usually automate the retrieval, transformation, and movement of this data information, making it an easy integration option.

While this may be an ideal method for data integration, built-in connections are not always supported between software systems. Therefore, using another data integration method in these cases may be helpful.

Middleware data integration

Middleware enables the sharing of data between multiple applications. For data integration, businesses can use middleware to transfer data from source systems and into a central data repository, where it can be accessed for data analysis.

A helpful aspect of middleware data integration is that middleware platforms can validate and format the data before transferring it to the data repository. This ensures businesses won’t end up with compromised data integrity or disorganized data.

Common storage (data warehousing)

Common storage integration, otherwise known as data warehousing, is a data integration method where data is copied from the source location, and the copied data is then transferred to a data warehouse. The data warehouse will store the information and display it in a consistent format.

The data is transformed prior to being copied and stored, so all of the information in the data warehouse has a consistent uniformed appearance. This integration method is also beneficial for supporting data integrity, as all data information can be accessed from the data warehouse as one single source.

Data consolidation

Data consolidation is a method where information from multiple data sources are combined within a system, which acts as a new single source of truth for the organization. The data consolidation technique can enable organizations to maintain less storage locations for data.

ETL (extract, transform, load) technology is an example of a system that uses data consolidation to move large amounts of data. It does so by pulling data from sources, cleaning, filtering, transforming, and applying business rules to the data before finally transferring it to the end location.

Hand-coding (manual data integration)

Hand-coding is the manual data integration process where humans will evaluate and categorize data without using a software system. This way, the business can develop its own strategies and custom code for organizing and integrating data information.

While this method may provide more control over the integration process, it has several drawbacks. Hand-coding can be a slow and tedious job, and the lack of automation also means a greater likelihood of human error throughout the process. Manually integrating data also means needing to manually change code when integrating new data, which can make it challenging to scale and maintain the information for larger datasets.

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Power BI vs Tableau: A Data Analytics Duel https://technologyadvice.com/blog/information-technology/power-bi-vs-tableau/ https://technologyadvice.com/blog/information-technology/power-bi-vs-tableau/#comments Tue, 14 Sep 2021 19:00:36 +0000 https://technologyadvice.com/?p=62394 The contenders for data visualization & analytics are Microsoft Power BI vs. Tableau. Click here to see who wins.

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Key Takeaways
  • Power BI comes at a lower price point than Tableau, but scaled features and additional users will increase that price.
  • Tableau is built for data analysts, while Power BI is better suited to a general audience that needs business intelligence to enhance their analytics.

The world of data visualization and analytics is moving fast with new players hitting the market and established brands absorbing smaller up and comers every day. To stay at the forefront of the data analytics field, a tool must have that special mix of power, ease of use, brand recognition, and price. Both of these tools have this secret sauce, which is why many teams find themselves comparing Microsoft Power BI vs Tableau when looking for the perfect data analytics tool.

Also Read: Top Tableau Alternatives For Visualizing & Analyzing Data

Power BI and Tableau aren’t the only market leaders in the business intelligence space. Looking for the latest in Business Intelligence solutions? Check out our Business Intelligence Software Buyer’s Guide.

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Learn more about Domo

How is Tableau Different from Power BI?

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a dashboard in power BI from Microsoft.

Power BI uses the existing Microsoft systems like Azure, SQL, and Excel to build data visualizations that don’t break the bank. This is a great choice for those who already work within the Microsoft products like Azure, Office 365, and Excel. It’s also a fairly good low-price option for SMBs and startups that need data visualization but don’t have a lot of extra capital.

Tableau sales and marketing dasbhoard.

Tableau specializes in making beautiful visualizations, but much of its advertising is focused on corporate environments with data engineers and bigger budgets. There’s a public (free) version of the tool, but with limited capabilities. The more you pay the more you can access with Tableau, including benchmarked data from third parties. The software also has a non-profit tool and versions for academic settings.

Price Comparison Between Power BI and Tableau

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Overall, Power BI sits at a lower price point than Tableau, with a free version, a monthly subscription, and a scalable premium version with a higher price. Although it’s a Microsoft product, Power BI users don’t have to pay directly for Office365 to gain access to the tool’s admin center interface. However, there will be charges for subscriptions and users. The way Power BI is set up within the Microsoft ecosystem makes it pretty affordable, especially for those companies who are already deeply invested in Microsoft software.

Tableau’s pricing is a little more confusing, likely because they recently moved from a bulk purchase to a subscription model. The current pricing is a tiered system that distinguishes between different user types. There are creators that can make visualization models and add data sources, explorers that can edit existing visualizations to answer their own questions, and viewers that can look at the models that others have created. If you already have a lot of data on spreadsheets and want to spend the time exporting your data from third party tools before uploading to Tableau, the pricing per user is fairly reasonable but still higher than what you get with Power BI. However, if you want direct connections to your third party apps like Marketo, Google Analytics, Hadoop, or any Microsoft product, you’ll need to pay for the Professional edition.

Deployment Options

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Power BI comes in several forms: desktop, pro, premium, mobile, embedded, and report server. Depending on your role and needs, you might use one or all of these services to build and publish visualizations. The most basic setup is an Azure tenant (which you can keep even after your trial is over) that you connect to your Power BI through an Office365 Admin interface. Although that sounds daunting, most companies who use the software will already have the framework in place to get the server running quickly. Power BI is fairly easy to use, and you can quickly connect existing spreadsheets, data sources, and apps via built-in connections and APIs.

In addition to the free public product, Tableau also comes in several forms: individual, team, and embedded analytics plans, which are available on-premises, via a public cloud server, or a private cloud server. Tableau lets you set up your initial instance through a free trial, which gives you full access to the parts of the tool. From the opening dashboard, you’ll see a list of all of your available connections. Connect your data sources, and then you can start building a worksheet where your visualizations will live. If you’ve built your visualizations in Tableau Desktop, you can share them with your team via Tableau Server or Tableau Online.

Integrations and Key Connections

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Power bi vs tableau

Power BI has API access and pre-built dashboards for speedy insights for some of the most-used technology out there like Salesforce, Google Analytics, email marketing, and of course Microsoft products. You can also connect to services within your organization or download files to build your visualizations. In order to connect any data to Power BI, use the “Get Data” button. You’ll need to go through a short authorization process in order to get fully connected.

Tableau invested heavily in integrations with popular enterprise tools and widely-used connections. You can view all of the connections included with your account level right when you log into the tool. Tableau’s connection interface is a little more involved than Power BI because you’ll need to identify which data to pull into the tool when you make the connection. It might be helpful to understand what data you want to look at and why before you start making those connections.

Can Power BI connect to Tableau?

Some companies elect to use both Tableau and Power BI to improve their data visualizations. If that describes your company, you may want the option to examine Power BI models or datasets in Tableau. You can connect the two, although you may run into some issues if you have multi-factor authentication enabled or if a session remains idle for too long. Before attempting to connect the two, you’ll also need to make sure you have the latest versions of both platforms installed so that they can communicate correctly.

Dashboards and BI Reporting in Tableau vs. Power BI

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power bi vs. tableau

Power BI has real-time data access and some pretty handy drag-and-drop features. The whole tool is built to speed up time to visualizations, and it gives even the most novice users access to powerful data analytics and discovery without a whole lot of prior knowledge and experience.

Real-time data access means that teams can react instantly to business changes fed to Power BI from the CRM, project management, sales, and financial tools. Considering live data access is where most SaaS products and especially most dashboard products are moving toward, Power BI certainly has the leg up here.

Tableau’s features are just as powerful, but some of them are a little less intuitive, being hidden behind menus. Use the dashboards and reports to forecast revenue based on past customer behavior, and employ calculations to transform existing data based on your requirements. Tableau gives you live query capabilities and extracts, which is particularly helpful for data analysts who are used to stopping all work for the query process.

The interface uses a drag-and-drop table view to ask questions of the data. You put your data types in the x and y axes, and then Tableau instantly builds your visualization. The company line is that they “keep the focus on your questions,” but this really feels like Tableau lives somewhere in between query-based (and developer-dependent) data visualization and drag and drop. They balance it nicely, however, because, despite the UX’s somewhat cluttered appearance, Tableau is fairly easy to use, as long as you’re familiar with your data sets or are willing to spend some time studying.

Extra BI and Productivity Features

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Power BI has native apps so you can access data from anywhere, alerts about changes. You can also use the publish to web feature that lets you add your visualizations directly to your blog or website. And don’t worry if the tool doesn’t make sense at first: there’s extensive online support with guided learning and documentation including the Power BI YouTube channel, webinars.

One of the coolest features included in Power BI is the natural language query tool. This is like Google for your data. You can literally ask questions of the data like “how much do we invest in each customer?” or “where do our highest value customers live” and the natural language query tool will find answers to the questions.

Tableau also has extensive support tools that teach you everything from the basics of setting up the software through initial data analysis. You can access and manipulate data via the mobile app, and whole teams can collaborate around shared dashboards. Tableau doesn’t have a natural language query, but when the company was acquired by Salesforce, the tool integrated the Einstein AI for data discovery.

What Is Better: Tableau or Power BI?

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When comparing Microsoft Power BI vs. Tableau, you really have to think about who will be using these tools. Power BI is built for the common stakeholder, not necessarily a data analyst. The interface relies more on drag-and-drop and intuitive features to help teams build their visualizations. It’s a great addition to any team that needs data analysis software but without getting a degree in data analysis first.

Tableau is similarly powerful, but the interface isn’t quite as intuitive, which makes it more difficult to use and learn. Those with data analysis experience will have less trouble cleaning and transforming data into visualizations, but those just getting their feet wet will likely feel overwhelmed with the uphill battle to learn some data science before making visualizations.

Overall, we call this Power BI vs Tableau duel a draw. Power BI wins for ease of use, but Tableau wins in speed and capabilities. Small businesses with limited financial and human resources should start out with Power BI, especially if they already invest in Microsoft products. However, medium and enterprise companies that prioritize data analytics and have the human capital to support them will be better off with Tableau.

Power BI vs. Tableau aren’t the only options for data visualization and data analysis tools. If you’re ready to search for your next business intelligence tool, fill out our Business Intelligence Product Selection Tool for a free, 5-minute consultation.

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Expert Panel: A Data Governance Policy That Pleases BI & SecOps https://technologyadvice.com/blog/information-technology/data-governance-bi-secops/ https://technologyadvice.com/blog/information-technology/data-governance-bi-secops/#respond Fri, 13 Aug 2021 15:50:56 +0000 https://technologyadvice.com/?p=78722 Business intelligence and security teams may feel like they are at opposing ends of the data pipeline. Security teams guard the data the company produces, and metes it out to the BI team as needed. BI will always want more data and context to give the business more complete recommendations. If either of these groups... Read more »

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Business intelligence and security teams may feel like they are at opposing ends of the data pipeline. Security teams guard the data the company produces, and metes it out to the BI team as needed. BI will always want more data and context to give the business more complete recommendations. If either of these groups were to build a data compliance policy on their own, the result is likely to be weighted by the needs of the writers. But how do we get these teams to cooperate on a policy that will please everyone?

As Adam Nathan, Director of Solutions Engineering at CoEnterprise puts it, “The compliance officers would like users to have zero access, the data analysts 100% access. Data security engineers, data engineers and database administrators are in the accountable middle of all of this.”

The word “policy” often implies restrictions and consequences. But a data governance policy can actually make everyone happier, the data more secure, and business intelligence easier to come by. Understanding what everyone needs from the policy can ease the process of writing a policy that pleases everyone.

What do data security engineers want from a data governance policy?

Data security teams can be fairly single-minded in their goal to protect the company’s data from prying eyes and accidental leaks. Rich Hale, Chief Technology Officer of ActiveNav, says, “They want policies that empower them to act to protect the interests of the business by preventing data misuse and enforcing measures to control access to sensitive classes of data or, indeed, to constrain the inappropriate aggregation of such data.”

But when data security is implemented consistently with a data governance policy, security engineers are empowered to identify and stop data practices that put the company’s information security at risk. Hale goes on, “Smart security engineers recognize that they need to work with data owners and stewards to enable appropriately controlled data opportunities. Policies can enable this by defining how data ownership is determined and the responsibilities of data owners.”

In an ideal world, security teams would either securely store all data or destroy it completely as soon as possible. Barring those options, finding a data governance policy that allows for safe use of data by analysts and business users that protects both the company and its customers is vital.

What do data analysts and scientists want from a data governance policy?

On the other hand, data analysts want and need access to data from across the organization to get the best possible understanding of business needs. According to Hale, “Data engineers and analysts want their data governance policies to eliminate data silos and improve data quality. Data silos cause problems throughout an organization, no matter the size. Without centralized organization, multiple versions of information can be used, causing error and poor decision making and ultimately, lead to a loss of business value. Successful data governance equates to more accurate analytics, which ideally leads to positive business outcomes such as increased revenue.”

Breaking down these silos, or at least finding ways to transfer the data into a centralized location, is key to building a clear BI picture. Missing information from a single cloud software or standalone app can throw off key forecasts and impede decision-making.

What data governance policy will please both security and BI teams?

At their core, security and BI teams have similar goals for data governance: To centralize and control the business data for the good of the company. And building a policy that meets those needs may be easier than expected. According to these experts, a good data governance policy will provide transparency, speed, and consistency across the enterprise.

Transparency

Analysts and engineers need to know where the data comes from, what might be left out, and how the company can best use the information securely. Transparency requires understanding what kinds of data each department creates and which of those datasets it deems important.

Anthony Habayeb Co-founder and CEO of Monitaur says that a key way to increase transparency is through documentation. “Poor quality data can put the entire business at risk, so data governance policies should emphasize data quality, reliable service, and internal access controls that ensure consumer data privacy. To meet that bar, data engineers and analysts must maintain fully documented datasets with dictionaries and lineage so that they know what data they are looking at — and the quality of the data.” Building a living map of the company’s data that is routinely updated will then help the governance team better communicate their policies to the rest of the company.

Robin Bell, CIO of Egress Software says that data governance doesn’t stop at BI and security teams. “Data governance is an organization-wide policy that must be supported from the executive level, but also communicated well, so that all employees understand why it’s needed by all roles in an organization. Without buy-in at all levels, no policy will deliver on its stated purpose. At Egress, we engaged architecture, development, security, data engineering, legal and DPO teams to define our data governance policy , supported by the Chief Technology Officer and Chief Information Officer. This has resulted in a data platform that we are now increasingly democratizing access to across different business areas, reviewing and evaluating the policies as we go.”

The data governance policy should ensure transparency from both sides. Data and security teams need to understand the company’s data landscape to better secure and use the information on hand. And ideally, the business users should be able to understand their role in data security to ensure continued compliance.

Speed

The speed at which data is ingested, transformed, and stored is a delicate balance. BI teams want all the information right now, but security teams would rather no one have access to data ever. A governance policy can define how data is ingested into the system and how that data is translated and used. These policies reduce gray areas that would otherwise slow outputs for BI teams and their business clients.

Speed is always going to be a trade-off, however. Immediate access to data without oversight or cleansing may ultimately put business users behind schedule, says Cedric Dussud co-founder of Narrator. “When operational data changes, or new operational data becomes available, a good policy will ensure that it’s modeled and classified correctly for data security before allowing broad access. Though this can sometimes slow things down, the up-front investment in data quality pays off for all downstream use.”

Adam Nathan of CoEnterprise says that the policy should also increase the speed of requests across many teams. “When a compliance request is made to validate that the governance model has been implemented faithfully, a database administrator can quickly provide a structured demonstration of compliance. To not have a compliance request turn into weeks of auditing, a deeply connected approach to security and data will make this almost effortless.”

By standardizing practices and data ingestion, BI and security can increase the overall speed of business, reduce the amount of work they do twice, and ensure the consistency of data across teams.

Consistency

A good data policy will ensure that all usable metrics and data types are defined and those definitions are adhered to across data sources. This consistency makes sure that everyone is looking at the same metrics, everyone speaks the same data language, and outcomes are clearly defined.

David Mariani, founder and Chief Technology Officer of AtScale points out that “Raw data can be interpreted differently, which can lead to conflicting metrics and analysis that erodes trust in data. Data teams should collaborate with analysts to build a governed set of enterprise metrics (e.g. revenue, cost, quantities) and a governed set of analysis dimensions on which to categorize, sort, and group (e.g. time, geography, product). Implementing a semantic layer can eliminate analytics inconsistencies while providing a layer of security and governance across all forms of data consumption.” Adding a data management software is one way to govern data ingestions and dispersal, and can ensure consistency if implemented correctly.

Ravi Hulasi, Chief Cloud Evangelist at Tamr agrees that a policy that employs software for governance can provide the consistency a company needs. “Arriving at this 360-degree coverage requires companies to first understand where their data exists, usually by deploying a data catalog. The next step is to implement a mastering solution to cleanse and unify the data into a known state and central location; making it useable for both the security engineer and analyst, while maintaining the lineage back to the source for auditing purposes.”

Write a governance policy that stands the test of time

Understanding the shifting needs of the technical teams and the business users may actually be easier than you think, if you get everyone in the same (virtual) room. Define the goals and desired outcomes of your policy and work backwards.

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How To Use Tableau For Project Management https://technologyadvice.com/blog/information-technology/how-to-use-tableau-for-project-management/ https://technologyadvice.com/blog/information-technology/how-to-use-tableau-for-project-management/#respond Thu, 22 Jul 2021 14:00:35 +0000 https://technologyadvice.com/?p=71127 You’ve been looking at Tableau for embedded analytics and data viz, but can your project management team benefit from the software as well? Sure, Tableau probably isn’t the first software system that comes to mind when you think project management, but you might be surprised by how useful it can be for visualizing the work... Read more »

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You’ve been looking at Tableau for embedded analytics and data viz, but can your project management team benefit from the software as well? Sure, Tableau probably isn’t the first software system that comes to mind when you think project management, but you might be surprised by how useful it can be for visualizing the work you do in spreadsheets or lending more customizability to the reporting tools in your project management system.

But if you’re looking to get out of spreadsheets entirely, we can help. Use the Business Intelligence Product Selection Tool on our business intelligence software page to match with five different business intelligence vendors that we think you’ll love. For more tips on getting the most out of Tableau for your project management needs, read on.

Table of contents

 

Which business intelligence tool
is right for your organization?

 

Building Gantt charts in Tableau

If you currently use spreadsheets for project management, the ability to build Gantt charts might be one of the more exciting prospects of using Tableau. Gantt charts have been a staple of project management since World War I, showing not only a list of tasks but also their estimated time to complete, progress, and interdependence on other tasks. Using Tableau, you can import data from spreadsheets to build helpful Gantt charts.

Also read: 16 Tableau Alternatives For Visualizing And Analyzing Data

Start by making an Excel sheet. You’ll want to make columns for team members, tasks, beginning and end dates, and date of completion. Next, import your spreadsheet to Tableau and start customizing it. You can either build the Gantt chart out manually by dragging and dropping dimensions to the appropriate rows and columns, or you can apply the Gantt Bar mark type to visualize your project.

Screenshot of a Gantt chart built in Tableau.

From here, you can publish your visualization to Tableau Online, Desktop, or Public. You can also embed the Gantt chart wherever you want to so others can view and interact with it.

Also Read: Power BI vs. Tableau: A Data Analytics Duel

Gaining deeper insight with donut charts

Donut charts are a favorite visualization among Tableau users, and it’s easy to see why. This slight improvement on the pie chart makes a great addition to almost any dashboard you’re working on, showing you quick snapshots of revenue goals, marketing signup sources, and more.

Screenshot of a donut chart in Tableau.

You can also use them to track project management stats. Here are some ideas of the different ways you can use donut charts for project management:

  • Display late versus on-time tasks
  • See how close individual team members are to completing their tasks
  • Identify unassigned versus assigned tasks
  • View individual team members’ bandwidth based on the number of tasks they’ve been assigned
  • Get a snapshot of your project portfolio’s health by seeing how many projects are running on-time or on-budget

Once you start using Tableau for more project management-related uses, you’ll probably start to identify other areas where donut charts can be helpful. Also take into consideration who will be able to see these donut charts. Will only project managers be able to see which team members are making better progress than others? Could this visualization be used to encourage some friendly competition among team members?

Connecting your project management tool to Tableau

But what if you don’t use spreadsheets for project management? Many companies use project management software applications like Wrike, Microsoft Project, or Monday.com, but for this example, we’ll take a look at Tableau’s integration for Asana.

Asana is one of the most popular project management tools on the market. And while it does offer some of its own reporting features, you may want to import your Asana data into Tableau. Doing so allows you to create more customized views or to more easily share a project’s status with stakeholders who don’t have an Asana seat. To import your data in Asana to Tableau, Asana Business and Enterprise customers can easily add Asana as a web data connector by copying and pasting Asana’s web data connector URL into Tableau.

Screenshot of a dashboard built in Tableau using data from Asana.

From there, you can create multiple visualizations using your Asana data. This is especially useful if you use custom fields in Asana or if you want to foster more collaboration between departments. (Note: Not all Asana data — like task dependencies — is available for use in Tableau.)

Asana isn’t the only project management tool that integrates with Tableau. If you use project management software, check your tool’s documentation to see if it supplies a web connector URL. In many cases, project management tools will also let you export data as a CSV or JSON file, which you can then import to Tableau for reporting and visualizing.

Get more out of your project management data with the right business intelligence (BI) tool

Tableau is a market leader when it comes to reporting and data visualization, but it isn’t the only one that can help you dig deeper into your project management data. Use the BI Product Selection Tool on our business intelligence software page to get free recommendations on which applications will work the best for you. In less than five minutes, we’ll ask you some questions to discover what specifications are most important to you, then we’ll send you a free shortlist of BI vendors that match your needs the best.

Top Business Intelligence Software Recommendations

1 Domo

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Build a modern business, driven by data. Connect to any data source to bring your data together into one unified view, then make analytics available to drive insight-based actions—all while maintaining security and control. Domo serves enterprise customers in all industries looking to manage their entire organization from a single platform.

Learn more about Domo


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Building a Strong Executive Team: Working on the Business Instead of In It https://technologyadvice.com/blog/human-resources/working-on-the-business/ https://technologyadvice.com/blog/human-resources/working-on-the-business/#respond Thu, 29 Apr 2021 18:00:19 +0000 https://technologyadvice.com/?p=76583 This is part two of our Building a Strong Executive Team series. To start at the beginning, click here. As an executive, you should be working on the business instead of in the business. But what does that mean? Basically, executives should be doing less of the day-to-day work that keeps the business going and... Read more »

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This is part two of our Building a Strong Executive Team series. To start at the beginning, click here.

As an executive, you should be working on the business instead of in the business. But what does that mean? Basically, executives should be doing less of the day-to-day work that keeps the business going and doing more work on product development, overall business strategy, and creating a vision for your company’s future. In this article, we’ll talk about some of the ways to get yourself out of the daily grind and strengthen your business through careful planning.

How to work on the business

Delegate whenever possible

As an executive, you can’t be personally responsible for all of the work your department does. You don’t have enough time for all the work that the business needs and your skills won’t align well with every task. You need to delegate whenever possible, so you have time for planning for the company’s future and can ensure that all the work gets done properly.

When delegating, try to align tasks with your employees’ interests and skillsets. Obviously, there are some jobs that no one wants to do, so make sure you spread those around evenly. You can even ask for volunteers on certain projects. This allows you to gauge your employees’ interest and not just assume what they would and wouldn’t enjoy working on.

John Ross, CEO of Test Prep Insight shares how he handles delegation. “You need to empower your direct reports to handle decisions themselves, while also setting explicit boundaries. For me, this has meant setting dollar thresholds. I have essentially told my direct reports that I don’t want to be hassled about a business decision unless it has the potential of crossing the $10,000 mark. If it has the potential to earn us more than $10,000 or may cost us more than $10,000, then I want to be looped in, at least initially. Other than that, I have given my team full power to handle matters on behalf of the business. This gives me more time to focus on big ticket items and removes more mundane tasks from my daily agenda.” Empowering your employees to make decisions for the business not only takes work off your plate, but it can also give them a greater feeling of ownership over their work.

“You need to empower your direct reports to handle decisions themselves, while also setting explicit boundaries.” – John Ross, CEO of Test Prep Insight

Delegating also sets a good example for managers that you might one day like to promote to the executive level. By delegating your own work, you show them that they don’t have to handle everything on their own. This allows them to manage their team effectively, instead of getting buried in work.

Also read: Building a Strong Executive Team: Benefits of Using eLearning to Train Millennials

Schedule time for planning

The truth for most executives is that if it’s not on their calendar, it doesn’t get done. If this is the same for you, make sure you block out time on your calendar specifically for planning. You may even consider having these planning sessions off-site in a coffee shop or the library to prevent interruptions. You can do this planning alone or with other executives and key stakeholders. Christine McKay, CEO of Venn Negotiation says, “Dedicate an hour at the beginning of the week to strategically plan what must get done, and an hour at the end of the week for evaluation, data analysis, and most importantly: celebration of your team’s accomplishments.”

In these planning sessions, you should look over company data and customer feedback to determine how your business is performing. Then, decide what the next reasonable steps would be. These sessions are also a good time to review employee performance. Determine if it’s time to hire and who you might consider for promotions as new positions become available.

Make SMART goals

When planning for your company’s future, ensure that you’re making SMART (specific, measurable, achievable, relevant, and time-bound) goals. By aligning your goals with these criteria, it’s easier to determine the steps you need to take to achieve them. Using business intelligence software and sales intelligence software can help you measure your current performance and figure out what your goals should be.

Once you’ve made your SMART goals, you need to cascade them down through the rest of the company. Everyone on your team should know what your goals are and how their work contributes to achieving those goals. This can be especially helpful when you’re delegating work no one wants to do but is crucial for the business.

Create processes and systems

Use your planned goals as an outline for creating processes and systems within your business, if they don’t already exist. You might consider using business process management software to make mapping out these processes easier. Processes and systems should always be a priority in your business. Although they don’t contribute directly to client value, they’re still important to improving the business overall.

Addressing some of the problems executives face when creating processes and systems, Brad Touesnard, CEO of SpinupWP says, “You need to narrow the scope of what you want to do to figure out what you need to do. There will always be appealing, futuristic innovations coming out in the tech world that you can see your business implementing, because these products are designed to make you imagine. At the end of the day, however, it goes back to improving the gaps in your vital business functions rather than trying to build new ones through a digital bauble.”

Adding processes can help your employees optimize their workflows and free up some of their time. This allows them to respond to client requests more quickly or gives them more time to spend on innovative projects. These processes can also make training and performance reviews easier because everyone is following the same playbook.

Also read: 16 Tableau Alternatives for Visualizing and Analyzing Data

Work on the business to achieve your goals

Working on the business instead of in it can be difficult, but it’s important if you want your business to remain competitive and continue growing. It allows you to take a step back and actually spend time and effort on planning for the future, rather than getting caught up in the day-to-day. Schedule your planning sessions, delegate tasks when you can, and create processes and systems that keep you on track with your goals. Each of these steps can help your business reach new milestones and allow you to better prepare for the future.

Next in series: Building a Strong Executive Team: What Good Executive to Company Communication Looks Like

Top Business Intelligence Software Recommendations

1 Domo

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Build a modern business, driven by data. Connect to any data source to bring your data together into one unified view, then make analytics available to drive insight-based actions—all while maintaining security and control. Domo serves enterprise customers in all industries looking to manage their entire organization from a single platform.

Learn more about Domo


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