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Choosing the Right BI Tool, Part 1

Key pillars to consider include business scope, data environment

In today’s business environment, choosing the right BI tool to fit your organization, department, or project’s implementation needs can be daunting. BI reporting and analytic tool choices are expanding at a rapid pace and taking on a myriad of flavors with growth areas like cloud BI tools and analytic specialty tools. No longer do you just focus on major enterprise players such as MicroStrategy, Business Objects, Cognos, Microsoft, and SAS. Specialty or Niche players like Tableau, Qlickview, Spotfire, Domo, BIRST, and Good among others are challenging the traditional BI landscape offering viable alternatives depending on your needs. Expand your search into marketing analytics specialty tools, and your choices grow immensely.  

Looking back throughout my career in IT and BI, I’ve had the opportunity to learn what’s important. There is no one single tool that will meet all of your needs in fact, having multiple tools can increase your BI launch success. But how do you choose the right BI and analytics tool for your organization? In my experience, it’s critical that you focus on understanding five pillars of information or hire someone to help you. In this first part of a  two-part blog series, I will focus on the first two.

1. Business Scope

This is the core requirements or objectives the BI tool(s) needs to accomplish within the business. These include both the short term and more strategic needs. Are you supporting a one-time need, a single project, a departmental, or enterprise BI capability? Needs should be driven by documenting your business use cases across all stakeholders. Knowing this will greatly influence whether you need a basic capability or robust suite of tools to satisfy a diverse set of users.  

2. The Data Environment

This is critical to supporting any BI initiative — failure to understand this and you are doomed from the beginning. The data environment is comprised of four elements: volume of data, velocity of data change, the completeness or quality of the data, and the structure of the data you are going to be working with. Each tool has sweet spots for handling different data types or align well in certain data ecosystems.

Volume and Velocity Data: Volume of data refers to how much data you need to report on. Are you dealing with data in the tens of thousands of records, hundreds of millions of rows, or more? Velocity refers to how fast the data changes, grows, and is updated. This also influences the frequency that you might need to support or enhance your BI implementation.

Completeness of data: Refers to both the quality and level of detail you have to support your project’s needs. Do you need to clean up and augment data from many sources, or is it integrated into a single source?

Data Structure: This refers to how the data will be structured and made available to the tool. Is the data coming from an optimized BI or analytics mart (Star Schema) or transactional database, spreadsheet, flat file, OLAP type Cube, messaging bus, web service, or some other variant that you will need to connect into? This drives the complexity and structures the tool’s needs to support.  

In addition to the above, consider what the vendors at the top of your list have in their future releases, their vision, and how their after-sales support can help you move forward. Most of the vendors offer a free trial of their software for a few days or weeks. I’d recommend that you test it out with your own sample data to explore key use cases in your business.

At the end of the day, your business intelligence solution must be built on the right tool(s) to deliver actionable insights to users. Come back on Wednesday for Part 2 when I'll look at required capabilities, culture, and budget.