• If you’ve currently been using an Excel spreadsheet as your CRM, migrating to an actual CRM can make a huge difference for your sales team.
• CRMs offer significantly more functionality than a basic spreadsheet, such as automated workflows and preset email templates, and they might be less expensive than you think.

Customer relationship management (CRM) systems offer many benefits for salespeople, so there are lots of reasons why you may want to migrate from a spreadsheet to a legitimate CRM. However, switching from either a spreadsheet or a different CRM does present certain data migration challenges. In this guide, we’ll cover the seven essential steps for a successful CRM data migration.

What is CRM Data Migration?

CRM data migration refers to importing data from a spreadsheet into a CRM system or moving data from one CRM system to another. Most make it easy to import a spreadsheet as long as it’s properly formatted. Many CRM systems also offer tools to both import and export data, making moves between software easier.

There are many reasons you might want to perform a CRM migration:

• You’ve grown beyond a simple spreadsheet.
• You maxed out your current CRM system’s capabilities and want more marketing automation or customization.
• You’re paying too much for your current CRM software and wish to seek out a more affordable alternative.
• You need more integration with third-party apps.
• Your company is switching to a full-service enterprise software stack that will move everything under one roof.

Whatever your reasons for performing a CRM data migration, this guide will give you a high-level overview of the most critical steps to help you put together a CRM migration strategy that fits your business needs.

How to Implement a CRM Migration Strategy?

Ready to get started with a CRM data migration, so you can improve customer engagement? Here are the seven steps you need to follow in order to create a successful data migration template checklist.

1. Audit your current data

Reducing the data you import can reduce both the complexity and cost of the migration, especially if you have a lot of data to migrate. Consider whether you really need old contacts that haven’t interacted with your company in years or email templates you used before your company rebranded and changed logos.

With that in mind, you should begin your data migration checklist by sorting through your existing data to determine what should and should not be imported. Once you’ve made that decision, clean up the existing data you plan to keep. If you’re worried about accidentally deleting something old but necessary, then make a copy of the data before cleaning it up. If you’re not concerned, do the backup anyway in case you stumble into a crisis.

2. Understand your new CRM system

Before you go any further with the data migration, you need to get to know your new CRM system. Each CRM has a unique way of formatting and storing data and may have a unique process for importing data. Once you decide on a CRM, you need to understand what your data will look like once you get it into the system.

Identify where all of your old data will be stored after the migration, and categorize it accordingly. For instance, existing customers will likely go somewhere else in the CRM than prospective leads that are currently being nurtured by the sales team. There will likely be some differences from your existing CRM, and you may need to import the categories separately to ensure they all end up in the correct places.

3. Engage in data mapping

Once you have identified the high level categories where your data will go, it’s time to move onto data mapping. Data mapping refers to the practice of matching the fields in one database to another. Mapping your data before the migration will ensure everything is imported correctly and that you don’t lose any custom fields. Each CRM should provide more information about what data mapping to perform before initiating the transfer process.

4. Consider your migration support tools options

If performing a CRM data migration sounds intimidating, then you’ll be happy to hear there are plenty of tools to help you. Most CRMs provide built-in data migration tools that will take care of transferring data from a spreadsheet into your new CRM. This is typically the easiest and most seamless option, and you should always begin your search for migration support tools there.

There are also third-party data migration tools such as Pentaho and Talend Open Studio you can check out if the native tools fall short for some reason. Beyond that, complex migration situations can require creating your own in-house data migration tools based on the CRM API (application programming interface). However, this is more common for enterprise companies and shouldn’t be necessary if all you’re importing is a spreadsheet.

5. Back up your migration data

Once your data is ready for importing, you need to make a backup first to keep things safe. If you are migrating a spreadsheet, create an identical copy of it before testing the migration. Store this backup safely in the cloud and on a hardware drive, so you have a backup if anything goes wrong.

If you’ve got an old CRM, you should leave it up and running for a couple months in case you need to go back and retrieve legacy data for some reason. If you want extra peace of mind, you can store the exported data separately in the cloud or on a hard drive.

6. Do a test migration first

When migrating data to a CRM, never move everything over until you have performed a smaller test migration first. To do this, take a small dataset and import it into the new CRM, then check it over carefully for errors.

This will allow you to identify errors and issues before you import the entire dataset and potentially have to fix the whole thing. If you do identify a problem, try to figure out what went wrong, and report and test the migration again until the import is successful.

7. Perform the final migration

Once you have perfected the migration process, you are ready to import all of the data over. If you have a lot of data to move, you may wish to do it in sections, so you can check it again at the end of each import just to be safe. Once you are done, clean and validate all of the data to ensure the migration was successful. Now, you are finally ready to use your new CRM.

Choosing a CRM for Data Migration

If you’ve currently been using an Excel spreadsheet as your CRM, migrating to an actual CRM can make a huge difference for your sales team. CRMs offer significantly more functionality than a basic spreadsheet, such as automated workflows and preset email templates, and they might be less expensive than you think. If you are ready to discover the CRM possibilities, check out our CRM software guide.

Top CRM Software Recommendations

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To maximize its value, decision makers need to be aware of its challenges, also known as its five V’s. These are its sheer amount of volume, its exponential velocity, its variety, the need to verify it, and the issue on how to extract value from its content.

The 5 V’s of Big Data

Understanding the challenges of Big Data and knowing which BI tools to use to solve those issues can help you answer questions that were previously considered beyond your reach.

Volume

Volume refers to the colossal amount of data that inundates organizations. We’re well past the days when companies resourced their data internally and stored it in local servers. Companies of 15 years ago handled terabytes of data.

Today, data has grown to petabytes if not exabytes of bytes (that’s 1,000–1 million TB) that come from sources such as transaction processing systems, emails, social networks, customer databases, website lead captures, monitoring devices and mobile apps.

To handle all of this data, managers use data lakes and warehouses or data management systems. They store it on clouds or use service providers such as Google Cloud. And as global data grows from two zettabytes at the beginning of the decade to 181 zettabytes a day by 2025, even these may be insufficient.

An example of data volume

Walmart operates approximately 10,500 stores in 24 countries, handling more than 1 million customer transactions every hour. The result? Walmart imports more than 2.5 petabytes of data per hour, storing it internally on what happens to be the world’s biggest private cloud.

How software can help: Apache’s Hadoop splits big data into chunks, saving it across clusters of servers. Check out Cloudera Enterprise 4.0 with its high-availability features that makes Java-encoded Hadoop more secure.

Velocity

Big data grows fast. Consider that, according to Zettaspere, there are around 3,400,000 emails, 4,595 SMS, 740,741 WhatsApp messages, almost 69,000 Google searches, 55,000 Facebook posts, and 5,700 tweets made per minute.

Around five years ago, data scientists measured incoming data with computerized batch processing that read large files and generated reports. Today, batch processes are unable to handle the continuous rush of real-time data from a growing number of sources.

More critical still, data ages fast. As Walmart’s former senior statistical analyst Naveen Peddamail said, “If you can’t get insights until you’ve analyzed your sales for a week or a month, then you’ve lost sales within that time.”

Competitive companies need some capable business intelligence (BI) tools to make timely decisions.

An example of data velocity

Using real-time alerting, Walmart sales analysts noted that a particular, rather popular, Halloween novelty cookie was not selling in two stores. A quick investigation showed that, due to a stocking oversight, those cookies hadn’t been put on the shelves. By receiving automated alerts, Walmart was quickly able to rectify the situation and save its sales.

How software can help: Splunk Enterprise monitors operational flows in real time, helping business leaders make timely decisions. That said, it’s expensive for large data volumes.

Variety

Variety refers to the different types of digitized data that inundate organizations and how to process and mine these various types of data for insights. At one time, organizations mostly gained their information from structured data that fit into internal databases like Excel.

Today, you also have unstructured information that evades management and comes in diverse forms such as emails, customer comments, SMS, social media posts, sensor data, audio, images, and video. Companies struggle with digesting, processing, and analyzing this type of data and doing so in real time.

An example of data variety

Walmart tracks each one of its 145 million American consumers individually, resulting in accrued data per hour that’s equivalent to 167 times the books in America’s Library of Congress. Most of that is unstructured data that comes from its videos, tweets, Facebook posts, call-center conversations, closed-circuit TV footage, mobile phone calls and texts, and website clicks.

How software can help: Walmart uses a 250-node Hadoop. For small to midsize companies, Tableau is ideal since it is also designed for non-technical users.

Veracity

Veracity is arguably the most important factor of all the five Vs because it serves as the premise for business success. You can only generate business profit and impact change with thorough and correct information.

Data can only help organizations if it’s clean. That’s if it’s accurate, error-free, reliable, consistent, bias-free, and complete. Contaminating factors include:

• Statistical data that misrepresents the information of a particular market
• Meaningless information that creeps into and distorts the data
• Outliers in the dataset that make it deviate from the normal behavior
• Bugs in software that produce distorted information
• Software vulnerabilities that could cause bad actors to hack into and hijack data
• Human agents that make mistakes in reading, processing, or analyzing data, resulting in incorrect information

Example of data veracity

According to Jaya Kolhatkar, vice president of global data for Walmart labs, Walmart’s priority is making sure its data is correct and of high quality. Clean data helps with privacy issues, ensuring sensitive details are encrypted while customer contact information is segregated.

How software can help: Multilingual and scalable Apache Spark is good for quick queries across data sizes. However, it’s expensive and has latency issues.

Value

Big data is the new competitive advantage. But, that’s only if you convert your information into useful insight.

Users can capture value from that data through:

• Making their enterprise information transparent for trust
• Making better management decisions by collecting more accurate and detailed performance information across their business
• Fine-tuning their products or services to narrowly segmented customers
• Minimizing risks and unearthing hidden insights
• Developing the next generation of products and services

Example of data value

Walmart uses its big data to make its pharmacies more efficient, help it improve store checkout, personalize its shopping experience, manage its supply chain, and optimize product assortment among other ends.

How software can help: Splunk enterprise helps businesses analyze data from different points of view and has advanced monitoring features that come at a price.

Using Big Data Management Tools to Optimize the 5 V’s

Savvy companies use robust big data management tools to maximize the value of their big data. Tools like Hadoop help companies store that massive data, clean it, and rapidly process it real time. Such data management tools also help leaders handle unstructured data and extract insights to benefit their companies.

The post What are the 5 V’s of Big Data? appeared first on TechnologyAdvice.

Both solutions source data from multiple data connectors to provide businesses with meaningful and actionable insights. However, each system is best for different kinds of businesses.

Qlik is best for quickly growing large enterprises that handle a significant volume of data on a regular basis. Its AI-backed data presentation services and multiple node deployments make it an excellent fit for sprawling workplaces. Tableau, however, is better suited to smaller businesses with local operations and agile processing needs.

If neither of these BI solutions seems like the best fit for your organization, you can explore more solutions on our comprehensive list of BI software.

What Features Does Qlik Offer?

Qlik strives to make data literacy more accessible to everyone in an organization. The software does this by leveraging many different options for visualizing data, which users can build using drag-and-drop features.

These visualizations allow a surprising amount of granularity, considering how well they summarize large groups of data. Users are able to request data views as specific as “quarterly sales in a given region” in plain conversational, requests.

Qlik also uses an associative engine to help reveal insights that can be easily lost to human error. This focus on showing associative data tends to surface insights a human would not have on their own. While this is good for viewing the same data sources in different ways, some users might find the experience overwhelming.

What Features Does Tableau Offer?

Tableau helps users tell stories with data by making it easier to find and share insights. Customizable, user-friendly dashboard tools let analysts generate graphs and reports for forecasting, spotting trends, and more.

Tableau’s query-based approach gives users a more hands-on experience and allows for exacting deployment of only the data points a user has personally sought out. Although this empowers individual users to find answers for themselves, it may do so at the cost of unexpected insights.

Tableau also lets users present data analysis as a story, using slideshow tools like Microsoft PowerPoint to help analysts create narratives. This simplifies the process of parsing data and translating it for broader business audiences.

Qlik vs. Tableau: Deployment

Depending on your organization’s needs, Qlik and Tableau offer different deployment options. For Qlik, businesses can choose from a selection of software as a service (SaaS), on-premises, or private cloud for deployment. Similarly, Tableau comes as an on-premises or cloud-based solution.

Qlik prioritizes scalability with its multi-node deployment options, allowing massive enterprises to locally house and access their own instance of Qlik. For smaller operations, though, this may be excessive.

Tableau deployment is usually faster and simpler compared to Qlik. However, large businesses may experience bottlenecks when bloated servers are forced to manage more requests than they are equipped to handle.

Qlik vs. Tableau: HR Analytics

Both Qlik and Tableau handle the less obvious number-crunching that human resource (HR) analytics requires.

Qlik aids talent acquisition initiatives by monitoring data at large businesses that would otherwise be a time-consuming process for an individual. The data Qlik provides can be anything from departmental headcounts to the precise spending areas for hiring budgets. By utilizing its AI to display data like employee development and training costs, Qlik becomes a useful and responsive HR companion.

Tableau carefully monitors turnover rates, retention headcounts, and intracompany movements. Crucial data points like slowly increasing turnover rates or steady intracompany movement are revealed with very little coaxing from Tableau’s HR tracking capabilities. This data is also easily collated by Tableau for stakeholder presentations, keeping everyone in the loop no matter their proximity to the hiring process or stake in HR budgets.

Qlik vs. Tableau: Security

Both Qlik and Tableau support multiple users with role-based permissions, so companies don’t have to worry about the wrong people viewing data they shouldn’t be able to access.

Additionally, system administrators can also enable multi-factor authentication (MFA) as an extra safeguard against weaker passwords. For Qlik, an enterprise can set up MFA using Okta, and Tableau users can use Duo.

For maximum security, both solutions offer single sign-on (SSO) capabilities. This is ideal for organizations looking to minimize the threat compromised user passwords pose for companies’ most valuable data.

Qlik vs. Tableau: How to Choose the Right BI Solution

Qlik and Tableau are powerful BI software solutions that offer numerous advantages, but one may meet your organization’s unique needs better than the other.

Qlik comes equipped with robust, AI-backed data parsing capabilities. The automatic report visualization and intelligent query features make it an invaluable tool for large enterprises working with large volumes of raw information.

Tableau, on the other hand, is an excellent fit for single-office enterprises and small businesses. From its deployment to its data management, Tableau is agile and powerful enough to handle complex workloads without being overwhelming for small teams.

Qlik and Tableau are some of the most popular business intelligence systems on the market, but that doesn’t necessarily mean your search should end here. If you want to save hours on the search for the right business intelligence solution, explore our complete list of business intelligence software. Our advisors can help you narrow down the top options for your business’s unique needs.

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What is Data Cleaning?

Data cleaning is the process of removing incorrect, duplicate, or otherwise erroneous data from a dataset. These errors can include incorrectly formatted data, redundant entries, mislabeled data, and other issues; they often arise when two or more datasets are combined together. Data cleaning improves the quality of your data as well as any business decisions that you draw based on the data.

There is no one right way to clean a dataset, as every set is different and presents its own unique slate of errors that need to be corrected. Many data cleaning techniques can now be automated with the help of dedicated software, but some portion of the work must be done manually to ensure the greatest accuracy. Usually this work is done by data quality analysts, BI analysts, and business users.

Data Cleaning vs. Data Cleansing vs. Data Scrubbing

You might sometimes hear the terms data cleansing or data scrubbing used instead of data cleaning. In most situations, these terms are all being used interchangeably and refer to the exact same thing. Data scrubbing may sometimes be used to refer to a specific aspect of data cleaning—namely, removing duplicate or bad data from datasets.

You should also know that data scrubbing can have a slightly different meaning within the specific context of data storage; in this case, it refers to an automated function that evaluates storage systems and disk drives to identify any bad sectors or blocks and to confirm the data in them can be read.

Note that all three of these terms—data cleaning, data cleansing, and data scrubbing—are different from data transformation, which is the act of taking clean data and converting it into a new format or structure. Data transformation is a separate process that comes after data cleaning.

What are the Steps of Data Cleaning?

Every organization’s data cleaning methods will vary according to their individual needs as well as the particular constraints of the dataset. However, most data cleaning steps follow a standard framework:

1. Determine the critical data values you need for your analysis.
2. Collect the data you need, then sort and organize it.
3. Identify duplicate or irrelevant values and remove them.
4. Search for missing values and fill them in, so you have a complete dataset.
5. Fix any remaining structural or repetitive errors in the dataset.
6. Identify outliers and remove them, so they will not interfere with your analysis.
7. Validate your dataset to ensure it is ready for data transformation and analysis.
8. Once the set has been validated, perform your transformation and analysis.

Periodically, you should evaluate your data cleaning processes and tweak it as necessary. While each dataset is unique, it’s still important to develop a somewhat standardized process for your data management team to use as a starting point. This will ensure no crucial data cleaning steps accidentally get skipped while providing enough flexibility to adjust the framework as needed.

What are the Benefits of Data Cleaning?

Not having clean data exacts a high price: IBM estimates that bad data costs the U.S. over $3 trillion each year. That’s because data-driven decisions are only as good as the data you are relying on. Bad quality data leads to equally bad quality decisions. If the data you are basing your strategy on is inaccurate, then your strategy will have the same issues present in the data, even if it seems sound. In fact, sometimes no data at all is better than bad data.

Cleaning your data results in many benefits for your organization in both the short- and long-term. It leads to better decision making, which can boost your efficiency and your customer satisfaction, in turn giving your business a competitive edge. Over time, it also reduces your costs of data management by preemptively removing errors and other mistakes that would necessitate performing analysis over and over again.

Improving Data-Driven Decisions with Data Cleaning

Cleaning your data takes time and effort, not to mention purchasing software as well. However, it’s well worth the investment to ensure your data is accurate and your analysis is grounded in reality. If your organization claims to be data-driven, then data cleaning absolutely needs to be a foundational part of your data management process.

Need help deciding on the best data cleaning and business intelligence software for your businesses? Our TechnologyAdvice experts can help with that. Reach out to us today to schedule a free consultation to discuss your needs and receive customized software recommendations.

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Both programs are categorized as SDS, or “software-defined storage.” Because Ceph and Gluster are open-source, they provide certain advantages over proprietary solutions. Open-source SDS gives users the flexibility to connect any supported software or hardware without the restrictions a provider might impose on operating system or usage. 

Looking for recommendations other than Ceph vs. Guster for your big data storage? Check out the cloud backup and storage software category for more information about cloud backup software, or click on the image below to get free recommendations that fit your needs straight from our Technology Advisors.

ALSO READ: Top 5 Security-as-a-Service Providers

VP and general manager Ranga Rangachari at RedHat describes the difference between the two programs:

“Ceph is part and parcel to the OpenStack story. In the community, [the majority] of the OpenStack implementations were using Ceph as the storage substrate. Gluster is classic file serving, second-tier storage, and deep archiving.”

In simpler terms, Ceph and Gluster both provide powerful storage, but Gluster performs well at higher scales that could multiply from tera to petabytes in a short time. Ceph does provides rapid storage scaling, but the storage format lends itself to shorter-term storage that users access more frequently.

Overview

Ceph: scalable object storage with block and file capabilities

Gluster: scalable file storage with object capabilities

The differences, of course, are more nuanced than this, based on they way each program handles the data it stores.

Ceph uses object storage, which means it stores data in binary objects spread out across lots of computers. It builds a private cloud system with OpenStack technology, and users can mix unstructured and structured data in the same system.

Gluster uses block storage, which stores a set of data in chunks on open space in connected Linux computers. It builds a highly scalable system with access to more traditional storage and file transfer protocols, and can scale quickly and without a single point of failure. That means you can store huge amounts of older data without losing accessibility or security. An April 2014 study by IOP Science showed that Gluster outperformed Ceph, but still showed some instabilities that resulted in partial or total data loss.

Interaction with Files

Both use a standard POSIX or NFS interface, and users can interact with data as though through a standard file system. Both provide search and retrieval interfaces for the data you store. But if your team plans on doing anything with big data, you’ll  want to know which of these to choose.

Ceph distributes data across computers in the cluster and allows the user to access all of the data at once through the interface. On the backend, CephFS communicates with the disparate parts of the cluster and stores data without much user intervention. Multiple clients can also access the store without intervention.

Ceph dashboard, via the Calamari management and monitoring system.

Gluster also distributes data to connected computers, but data storage happens in blocks, keeping everything together. The GlusterFS finds appropriately sized storage areas for the data in any one of the storage locations, places the data for storage, and creates an identifying hash. The program stores data on kernel systems and doesn’t produce another metadata system, instead creating a unique hash for the file. Without the interference of a metadata server, Gluster reacts and scales more quickly than its competitors, but still maintains usability. From the interface, users see their data blocks as directories. Because each file has a unique hash, a user must make a copy before renaming, or else lose access to the data.

GDash — the GlusterFS Dashboard.

Complications

Ceph requires monitor nodes in an odd number distributed throughout your system to obtain a quorum and reduce the likelihood of “split-brain” and resulting data loss.

Gluster runs at a default block size twice that of Ceph: 128k for Gluster and 64k for Ceph. Gluster claims that their increased block size makes for faster processing, but with a little work, you can increase Ceph’s block size and increase capabilities as well.

Both of these programs are open-source, but companies can purchase third-party management solutions that connect to Ceph and Gluster. The most popular management tools for each are:

Ceph: InkTank, RedHat, Decapod, Intel,

Gluster: RedHat

Conclusions

Deciding whether to use Ceph vs. Gluster depends on numerous factors, but either can provide extendable and stable storage of your data. Companies looking for easily accessible storage that can quickly scale up or down may find that Ceph works well. Those who plan on storing massive amounts of data without too much movement should probably look into Gluster.

Looking for more storage and big data solutions? Check out our Cloud Backup and Storage Product Selection Tool for comparisons, reviews, and suggestions.

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When shopping for a business intelligence solution, some people try to compare Alteryx vs. Tableau. This is understandable — both products fall under the umbrella of business intelligence software. But Tableau primarily functions as a data visualization system with few tools for data cleansing while Alteryx works great for data cleansing but lacks good data viz capabilities.

Instead of trying to decide whether you should go with one of these business intelligence solutions over the other, use them both. They complement one another, which helps you get the best insights from your data.

If you’re still looking around for the best business intelligence systems for your needs, we can send you a free list of recommendations. Use our BI Product Selection Tool to request your shortlist of the top five BI solutions for your organization. Getting started is easy and takes less than five minutes.

Topics

1. Alteryx: what it does best
2. Tableau: what it does best
3. Using Alteryx and Tableau together

Alteryx: what it does best

Back to topics ‚Üë

Alteryx is an ETL system, which means it’s designed to extract, transform, and load massive amounts of data from a variety of different data sources. This system is designed for use by BI analysts, but it works just as well regardless of whether or not you use SQL.

Alteryx uses workflows to engage with data throughout the ETL process, and the system allows you to make these repeatable so you don’t waste time on manual processes. A scalable and intuitive user interface (UI) makes working in the software fast and easy to learn, and flexible and diverse data discovery and management tools let you access dozens of data connectors and make edits to the incoming data.

One major shortcoming with Alteryx is data visualization. You can use Alteryx to generate reports, but these aren’t accessible to employees who don’t work in data or business intelligence. Instead, Alteryx offers Analytic Templates for loading data into third-party visualization platforms.

Also read: Qlik vs. Tableau: Comparison Of Key Differences

If you use Qlik or Tableau for data visualization, Alteryx also supports direct data integration. Using this feature, you can load your data directly into Qlik, Tableau, or Microsoft Power BI once you’ve cleansed and prepared it in Alteryx.

Tableau: what it does best

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Tableau is a data visualization platform that uses drag-and-drop functionality to create a variety of interactive graphs and charts. People know Tableau for offering some of the best data visualization tools on the market, and they use this system to reveal hidden insights and to tell stories with data.

Using Tableau, you can create forecasts, spot trends and outliers, generate maps, and more. Similar to Alteryx, Tableau uses drag-and-drop functionality, but in Tableau, you apply visuals to data segments to see data in different ways instead of imposing various transformations on data to clean it. This means people with any amount of coding know-how can use Tableau, but the system also supports natural language in addition to custom SQL queries.

Also read: The TechnologyAdvice 2019 Best Business Intelligence Software Awards

Tableau’s biggest blind spot is data cleansing and preparation. It does an excellent job of visualizing the data you load into the system, but it doesn’t come with tools for data blending and cleansing. For this, Tableau offers an Alteryx Starter Kit for Tableau, which makes it easier for you to prepare your data in Alteryx and then load it directly into Tableau for visualization.

Using Alteryx and Tableau together

Back to topics ‚Üë

For data scientists and business intelligence analysts alike, the combination of Alteryx and Tableau is a lifesaver. If you spend a lot of time writing code to prepare, cleanse, and analyze data, Alteryx will save you hours of writing SQL and R code. If you have no idea how to code, Alteryx empowers you to work in data using drag-and-drop features. Once you load your clean data into Tableau, you can uncover hidden insights and make your organization’s data more accessible to everyone using high quality visualizations.

Generating Tableau Data Extracts

Perhaps the biggest value-add Alteryx offers is the ability to convert datasets to Tableau Data Extract (.tde) files. Making a Tableau Data Extract compresses data to reduce storage requirements and to optimize it for visualization in Tableau.

These extracts are columnar stores, which means they aggregate data into columns instead of rows. This improves performance in Tableau and speeds up load times, not to mention that it also eases file sharing and collaboration.

Cleansing data for more insightful visualizations

Using Alteryx with Tableau together goes beyond simply making data visualization easier. Clean data is valuable data. By corollary, data with missing values, structural errors, and other impurities is not. Even worse — impure data costs you money, both via opportunity cost and bad decisions.

This reminds me of a famous quote from Bill Gates: “The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.”

By tweaking some of the wording, we can also apply this lesson to business intelligence software:

“The first rule of any business intelligence software is that visualization applied to clean data reveals good insights. The second is that visualization applied to messy data reveals poor insights.”

Tableau does a great job at visualizing data, but if your data still needs some TLC, you’re wasting your time. Cleansing your data in Alteryx before loading it into Tableau will save you time and make you more money.

Not convinced Alteryx and Tableau are right for you? We can help.

At TechnologyAdvice, we work with BI analysts every day. We know your pain points, and we want to help connect you with the right software. To get a free, personalized list of recommended business intelligence products, use our Product Selection Tool for Business Intelligence.

We’ll send you a shortlist of the five best tools for your specific needs so you don’t have to waste any more time searching for the right solution. Getting started is easy and takes less than five minutes.

Top Business Intelligence Software Recommendations

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Business intelligence (BI) software aids this process by pulling data from your various client-facing and back-end systems and providing visualization and analysis tools. By transforming your raw data into intelligible reports, dashboards, and illustrations, you can gain quicker insights, make better decisions about your business, and move toward positive revenue goals faster.

If you’re shopping for a business intelligence solution, you’ve probably come across Domo and Tableau — two of the most prominent vendors in the space. Both offer powerful BI solutions that process data from numerous sources for any job role, but they don’t offer the same utility and value in every area.

Domo and Tableau are popular brands, but they’re not the right BI tool for every company. Use our BI Product Selection Tool to get a short list of business intelligence software recommendations with the features and benefits your company needs.

To help you decide between Domo vs. Tableau, we’ll compare the two systems based on pricing, dashboards, reporting capabilities, and data integrations. Let’s take a look.

Also read: Tableau vs. Spotfire: Business Intelligence for the Non-IT Guru

Domo vs. Tableau: systems and pricing

In many cases, the way a product is packaged and priced will determine whether it’s a good fit for your business. This is especially true with business intelligence apps. It’s also one of the biggest points of differentiation between Tableau and Domo.

Beyond their free trial, Domo offers pricing based on numbers of users and data refresh rates. Its pricing is subscription based and offers over 1,000 data connectors and support for 250 million rows of data. In 2018, Domo offered three pricing tiers, however, they no longer publicly display pricing information.

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Tableau, on the other hand, divides its product tiers by implementation and user needs. First, decide whether you’d like to host Tableau on-premise, in the public cloud, or have Tableau host your server. Then decide how many of each user type you need:

• Creator: data analyst user role, will load and standardize data. Every account needs at least one Creator
• Explorer: general business user role, can make and edit visualizations. A minimum of 5 explorers are required.
• Viewer: can view and interact with visualizations Explorers and Creators have made. A minimum of 100 viewers are required

Tableau’s pricing differs between users of on-premise/public cloud and Tableau hosted, and it bills annually.

As is usually the case, your best value option will depend on your number of users and implementation plans.

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Domo vs. Tableau: dashboards

The ability to create custom dashboards is arguably one of the most important features in any business intelligence solution. Dashboards give you the ability to organize data sources, reports, and custom objects in a central location that (ideally) stays updated in real time.

Tableau doesn’t disappoint here. Users can easily create interactive dashboards using custom filters and drag-and-drop functionality. Dashboards can be shared internally through Tableau Online or Server or embedded into your own wikis, corporate portals, or web pages via API connection. Embedded Analytics does cost extra, but for those who consistently distribute BI data across their organization, it’s well worth the cost. Dashboard options are only limited by your creativity and the types of data you upload to Tableau.

To give you a better idea of Tableau’s interface, here’s an example of a dashboard that details customer data:

Domo, at its core, is a cloud-based dashboarding tool. When they say their platform “creates a truly digitally connected organization,” they mean it can provide insight and visibility into all of your data sources. Any sources that do not have a native connection can be accessed via API or through the Domo Workbench Connector, which allows you to bring data into Domo via a CSV file.

Domo dashboards are up to par with Tableau in terms of user experience and visualizations. They offer a number of different pre-built pages that can self-assemble based on data inputs (e.g. Finance, HR, Marketing, Sales, Retail), or you can drag and drop to build custom visualizations with the Card Builder tool.

Also read: 16 Tableau Alternatives For Visualizing And Analyzing Data

Domo vs. Tableau: reporting and analytics capabilities

One of the major selling points of both platforms is that they make enterprise-class analytics accessible to the common line-of-business user. That means companies can process and understand their data without going through the IT department.

Ignorance is bliss, but if you’re signing a year-long contract, you should at least have a basic idea of what’s under the hood.

Tableau connects to your servers, databases, and web tools to gather data from across your enterprise. Their analytics features cover data discovery, data visualization, geocoding, survey analysis, time-series analysis, social analytics, and more. It integrates with R statistical programming language and provides mobile BI access with touch-optimized features for tablets.

Tableau’s unique data preparation feature lets your data analysts connect to “messy” spreadsheets and fix/configure data while you sync. Pivot cross-tab data back into normalized columns, remove extraneous titles, text, and images, reconcile metadata fields, all as you build out your data.

Domo gives companies the ability to analyze and cleanse their data, no matter the source. It simplifies ETL processing (extract, transform, load), so you can find the value in your data, even without formal SQL training. The DataFusion feature also lets you merge data from multiple sources.

Domo vs. Tableau: data connectors

Business intelligence applications are only useful insomuch as they integrate with outside data sources like business systems (CRM, marketing automation, FSM, supply chain), servers, and databases.

Tableau and Domo provide a set of native data connectors, which means they can seamlessly pull data from those sources without custom configuration or coding. An advantage in this area can make a BI platform vastly more usable.

Tableau offers native connectors for hundreds of different sources.

Domo boasts over 1,000 pre-build connectors in their connector library. They also have a proprietary app store with pre-built solutions for different roles and industries, which could make a big difference if you’re looking for a flexible solution.

Domo can connect to nearly any data source, virtual or physical, but it does all of its processing in the cloud. That will certainly reduce the load on your own servers, but it may mean slower speeds in some scenarios.

Keep in mind, any databases or applications not listed on the company site will presumably require API integration and/or middleware to sync.

Domo vs. Tableau: making your final decision

Both platforms can help businesses mine and visualize data and make better decisions across departments. Both offer broad horizontal integration and the ability to scale as your business grows. They both even have mobile apps.

The biggest differences center around implementation and pricing. If you’re looking for a strictly cloud-based app to build attractive dashboards and share access among the whole team, Domo may be the better choice. If you work in a hybrid environment and would like to provide desktop access to a few power users, consider Tableau.

And don’t forget, Tableau and Domo aren’t the only BI solutions on the market. Use our Product Selection Tool for Business Intelligence for BI software, or click on the image below to get started with a customized list of software recommendations based on your needs.

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The post Domo vs Tableau: Features & Pricing appeared first on TechnologyAdvice.

The Nashville Analytics Summit started in 2013 as a way for businesses and thought leaders to share knowledge and insights about the role big data and analytics play in the modern organization. Created by Nashville Technology Council members, the 2019 Summit lasts for two days from September 9-10 at the Omni Hotel Nashville.

Notable speakers and sessions

• Lena Winfree: Rachel + Winfree Data Analytics Consulting, Data Strategy for Small Businesses
• Cristina Ingram: Vanderbilt University Medical Center, VUMC Case Study: Analytics as the Art of Combining Soft Skills and Technology
• Anika Vinze: Accenture, Tackling Infant Mortality with Big Data Analytics
• Zack Burch: Asurion, Creating a Self-Service NLP Application for Decoding the Voice of the Customer
• Eric Siegel: Predictive Analytics World, Keynote – Predictive Analytics: Delivering on the Promise of Data Science
• Michael Holloway: Nashville Software School, Exploring the Availability of Affordable Housing in Davidson County Using Python
• Tara Aaron: Aaron | Sanders PLLC, When Big Data is Personal Data – Data Analytics in the Age of Privacy Laws

Where to dine around downtown Nashville

You’re bound to be hungry after a long day learning and talking about analytics, so be sure to check out some of our favorite spots from Nashville’s flourishing food scene. The best part? All of these eats are within about a ten-minute walk from the Omni Nashville Hotel.

The Green Pheasant

$$

Chic lunch and dinner spot facing the Cumberland River Greenway that serves seasonal Japanese cuisine sourced directly from Japan.

• Where: 6-minute walk from the Omni Hotel Nashville
• Tip: Reservations recommended
• Website: http://thegreenpheasant.com/

Etch

$$$

Located in the ground floor of the Encore Tower, Etch serves international lunch and dinner fare from award-winning chef Deb Paquette.

• Where: 5- minute walk from the Omni Hotel Nashville
• Tip: Reservations recommended
• Website: https://etchrestaurant.com/

Liberty Common

$$

Neighbor to The Green Pheasant, Liberty Common serves up American Southern and French cuisine for brunch and dinner.

• Where: 7-minute walk from the Omni Hotel Nashville
• Tip: Arrive early
• Website: https://www.liberty-common.com/

Pinewood Social

$$

Featured in Condé Nast Traveler and once host to the late, great Anthony Bourdain, Pinewood Social is a trendy late night bar, restaurant, bowling alley, and more that offers craft cocktails and New American eats.

• Where: 11-minute walk from the Omni Hotel Nashville
• Tip: Reservations recommended for dinner
• Website: https://www.pinewoodsocial.com/

Where to hear live music in Nashville

If you go to Nashville but don’t catch some live music, have you really been to Nashville? Broadway is famous (or infamous, if you ask locals) for dive bars and honky tonks. That’s great if you’re into Guns N’ Roses and Neil Diamond cover tunes and bachelorette parties, but you might also consider these mainstays of Nashville’s world-famous music scene.

Ryman Auditorium

Affectionately known as the “Mother Church of Country Music,” the Ryman Auditorium is a historic live music staple of Nashville that continues to feature the best country, Americana, indie, folk, and rock acts around.

• Where: 9-minute walk from the Omni Hotel Nashville
• Concert: Dwight Yoakam on Monday, September 9 at 7:30 pm
• Website: https://ryman.com/

The Bluebird Cafe

Long regarded as one of the best spots around town for Nashville-style writers rounds and more recently famous from the TV series Nashville, The Bluebird Cafe is still one of the best listening rooms in town to hear music from established and up-and-coming talent alike.

• Where: 15-minute rideshare from the Omni Hotel Nashville
• Tip: Tickets go fast, so purchase yours well in advance.
• Website: https://bluebirdcafe.com/

Puckett’s Grocery & Restaurant

A popular destination serving Southern classics like the meat-and-three and barbecue, visit Puckett’s Grocery & Restaurant on the corner of Church and Fifth to catch lively writers rounds and solo musical performances.

• Where: 6-minute rideshare, 15-minute walk from the Omni Hotel Nashville
• Tip: Hang a left on Fifth Avenue out of Puckett’s to grab after-dinner coffee or reasonably-priced cocktails at the downtown Frothy Monkey.
• Website: https://puckettsgro.com/nashville/

Belcourt Taps

For one of the best writers rounds in town, take a Lyft or Uber over to Hillsboro Village near Vanderbilt University to get a table at Belcourt Taps. This casual restaurant and bar pours cold beer on tap and features local songwriters every night of the week.

• Where: 11-minute rideshare from the Omni Hotel Nashville
• Tip: Take a walk down 21st Avenue South to catch indie, foreign, and arthouse films at the Belcourt Theatre.
• Website: https://www.belcourttaps.com/

Woolworth on Fifth

Known as the site of some of the first lunch counter sit-in protests during the Civil Rights Movement, Woolworth on Fifth pays homage to the cultural significance of the former F.W. Woolworth store by serving inspired, fresh takes on Southern cuisine and hosting live music downstairs from Motown and big band jazz artists.

• Where: 7-minute rideshare, 15-minute walk from the Omni Hotel Nashville
• Tip: Check out The Nashville Arcade and some of the art galleries on Fifth Avenue while you’re here.
• Website: https://woolworthonfifth.com

The post Join Us In September For The 2019 Nashville Analytics Summit appeared first on TechnologyAdvice.

Also Read: 3 Ways Healthcare Management Revolutionizes Healthcare Tech

Even social networks are providing input for wellness, caregiving, and health insurance. The challenge is in handling the new diversity and volume of health-related data to make sense of it all and apply the conclusions in ways that improve the care given to patients.

Business intelligence (BI) solutions and specifically data analytics can give healthcare staff valuable support. Different types of data analytics can help hospitals, wellness centers, and other healthcare organizations better serve patients in several ways:

• Descriptive analytics. Shows what is happening or has been happening. For example statistics on the latest round of flu outbreaks by state, by age group, or other demographics.
• Diagnostic analytics. Helps to see why an event happens. Points to relationships and causes like tracing heart disease back to poor diets or lack of exercise.
• Predictive analytics. Indicates what will likely happen. Spots trends and assesses the chances of events happening, such as probable success rates of treatment on new populations of patients.
• Prescriptive analytics. Tells you what to do. Recommends specific actions in response to individual patient symptoms, rising levels of healthcare expenses, and much more.

Indeed, with names like “diagnostic” and “prescriptive,” data analytics almost seem to have been invented with healthcare in mind. I’ve selected four more detailed examples below that show how health analytics can contribute to improved patient care.

Evaluate Patient Caregiver Performance

aregivers largely determine the quality of patient care. For example, their performance can be measured individually regarding effectiveness, punctuality, and costs generated. It can also be measured collectively by patient surveys and social media sentiment. Levels of care can be defined using these different parameters. Care that falls below a given benchmark standard can generate an alert. The organization can then resolve the issue in a timely way.

The analytics can be generated in real time as physicians consult with patients, as people enter a hospital for care, as health insurers reimburse medical expenses, and so on. Intuitive graphs and charts can be generated for at-a-glance understanding of how well care is being provided. Caregivers are not obliged to continually stare at a screen to see what is happening either. In a hospital, an important KPI can be displayed by a light bulb that changes color and is easy to see for all staff. For example, a green color means caregiver performance is globally on track, while a red color means an issue needs to be resolved.

Diagnose Patient Illnesses and Health Problems

While the symptoms of a patient’s illness may be plain to see, identifying the illness itself may be more complex. Different ailments may have symptoms in common but be fundamentally different from one another and require different treatments. Combinations of illnesses or side effects caused by medication already taken by the patient can make the situation still more complicated.

Diagnostic data analytics works backward from the symptoms to suggest the cause of what has happened. While physicians and other caregivers continue to be responsible for the final diagnosis, they can use data analytics to save time and to avoid possible errors of judgment. Afterward, the results of each diagnosis together with a description of the symptoms and any additional factors can be added to the database being used for the analytics. This helps the diagnostic data analytics to be increasingly accurate.

Predict Patient Risk

As the saying goes, prevention is better than cure. Predictive data analytics can forecast which patients are at greater risk for disease. Factors driving the analytics have traditionally been age, blood pressure, cholesterol, blood glucose levels, and family history of illness. Now, health habits, employment conditions, and physical environment can also be added to the mix, thanks to developments like fitness wearables that can monitor patients round the clock.

High-risk patients are candidates for early intervention. Problems can be avoided or treated before they become serious. Patients avoid the discomfort and danger of more advanced stages of illnesses, while the healthcare system reduces the costs of returning a patient to better health. As more data is added for the predictive data analytics to use models of patient conditions and ways of estimating risk can be adjusted accordingly. This keeps the analytics in step with communities and populations as they evolve.

Reduce Costs of Patient Care

On the condition that levels of quality and results are maintained, lower care costs can be advantageous for both patients and healthcare organizations. For a given health condition, prescriptive analytics can select the treatments and medications that work best and cost the least. These analytics can also calculate individual patient costs and define suitable allocations of caregiver personnel and resources so that healthcare organizations can cut waste and boost efficiency.

More advanced use of prescriptive data analytics can lead to surprising, but effective recommendations. For example, diabetes is a handicap afflicting many people that brings with it high recurring costs of treatment. However, diabetes can also be prevented through diet and exercise programs. Prescriptive analytics can prompt health insurers to pay now for health counseling of people at high risk, rather than being obliged to fund ongoing costs of treatment later.

Analytics , An Affordable Way Forward

Technology for handling big healthcare data and generating actionable analytics has become very affordable. Solutions are now available to suit the budgets of small and medium-sized healthcare organizations, as well as the larger ones. Easy to set up and easy to use, these business intelligence and analytics systems need little or no technical expertise. They let caregivers explore and ask questions about data on the fly without the assistance of IT departments. As healthcare data and patient populations grow so do opportunities to improve patient care, thanks to healthcare analytics.

Ilan Hertz is head of lead generation at Sisense, the leader in simplifying business intelligence for complex data, offering a powerful business intelligence software. Ilan uses his domain expertise and data-driven methodologies to lead digital marketing efforts at Sisense.

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The post Healthcare and BI: How Can Analytics Improve Patient Care? appeared first on TechnologyAdvice.

But why not all three? You can gain exclusivity by producing a product that offers a high degree of functionality and is also affordably priced. How? The answer is by employing degrees of automation that enable the product to be produced and distributed at the highest efficiency and at minimal cost. It starts with the implementation of an enterprise resource planning (ERP) solution.

The Impact of an ERP Solution

An ERP software solution enables manufacturers to differentiate their product offerings and streamline processes and operations in many ways. In order to gain executive buy-in to purchase such a solution, you need to demonstrate those capabilities. Here are four ways an ERP will benefit your business. Use these to get your entire organization on board.

ALSO READ: 5 Factors to Weigh During Your ERP Software Comparison

Increased automation

An excellent way to gain executive buy-in for an ERP solution is by emphasizing the numerous manufacturing processes it can manage. One of the most beneficial aspects of automation is the elimination of manual data entry. This drastically decreases the chance of errors, duplication, and mistakes. Automation also gives employees the ability to let the ERP system take over mundane tasks and focus on more pertinent areas of the business.

Better, faster data

Because the data produced integrates in real-time with other ERP software modules, an ERP makes it possible for management to obtain up-to-the-minute “snapshots” of operations to facilitate new operational efficiencies as well as better decision making.

Cross-departmental usage

A major challenge in justifying the implementation of an ERP solution at a manufacturing or distribution facility is that it may not always be viewed as being beneficial at all levels of the organization. However, every function in the company, human resources, sales, distribution, accounting, supply chain, purchasing, manufacturing and shipping, and more, will benefit from the new ERP software. Therefore, a way to achieve total acceptance of a new solution is to continually reference the benefits each worker will gain.

Increased synergies and more efficient employees

New software must be viewed as an investment that produces efficiencies and effectiveness. One of the most important aspects is how the employees actually utilize the system to support day-to-day activities. With ERP, gone are the days of silos of information and disparate departments. ERP increases synergy within a company by integrating all aspects of the business. This gives employees the ability to work off of the same, real-time data. With ERP, employees have a single source of truth for company information, making every employee and the company more productive.

As we enter Q4 and the last remaining months of 2017, now is the time to reflect on the year and focus on how to refine your strategy to align with your 2018 business goals. An ERP solution is an excellent tool to help boost your company’s drive to succeed. Not sure where to start? Our free ERP Product Selection Tool is a great resource to help narrow down your options.

Joey Benadretti is the president and joint managing director of SYSPRO USA. He is a champion of U.S. manufacturing and is a strong advocator of the rebuilding of the U.S. manufacturing base.

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The post Why and How to Gain Executive Buy-In to an ERP Solution appeared first on TechnologyAdvice.