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Top Mistakes to Avoid in Analytics Implementations

November 16, 2012 No Comments

Featured Article by Bernard Wehbe, Managing Partner, StatSlice Systems (

Business analytics allow a company to make better decisions and just plain be smarter—but it has to be done the right way.  This article talks about important factors in implementing analytics correctly—from picking the right dashboard to understanding what kind of analyst is right for the project.

Mistake 1.  Not putting a strong interdisciplinary team together.

It is impossible to put together an analytics platform without understanding the needs of the customers who will use it.  Sounds simple, right?  Who wouldn’t do that?  You’d be surprised how many analytics projects are wrapped up by IT because “they think” they know the customer needs.  Not assembling the right team is clearly the biggest mistake companies make.  Many times what is on your mind (and if you’re an IT person willing to admit it) is that you are considering converting all those favorite company reports.  Your goal should not be that.  Your goal is to create a system—human engineered with customers, financial people, IT folks, analysts, and others—that give people new and exciting ways to look at information.  It should give you new insights. New competitive information.  If you don’t get the right team put together, you’ll find someone longing for the good old days and their old dusty reports.  Or worse yet, still finding ways to generate those old dusty reports.

Mistake 2.  Not having the right talent to design, build, run and update your analytics system.

It is undeniable that there is now high demand for business analytics specialists.  There are not a lot of them out there that really know what to do unless they’ve been burned a few times and have survived and then built successful BA systems.  This is reflected by the fact you see so many analytics vendors offer, or often recommend, third-party consulting and training to help the organization develop their business analytic skills.  Work hard to build a three-way partnership between the vendor, your own team, and an implementation partner.  If you develop those relationships, risk of failure goes way down.

Mistake 3.  Putting the wrong kind of analyst or designer on the project.

This is somewhat related to Mistake 2 but with some subtle differences.  People have different skillsets so you need to make sure the person you’re considering to put on the project is the right “kind.”  For example, when you put the design together you need both drill-down and summary models.  Both have different types of users.  Does this person know how to do both?  Or, for example, inexperience in an analyst might lead to them believing vendor claims and not be able to verify them as to functionality or time to implement.

Mistake 4.  Not understanding how clean the data is you are getting and the time frame to get it clean.

Profile your data to understand the quality of your source data.  This will allow you to adjust your system accordingly to compensate for some of those issues or more importantly push data fixes to your source systems.  Ensure high quality data or your risk upsetting your customers.  If you don’t have a good understanding of the quality of your data, you could easily find yourself way behind schedule even though the actual analytics and business intelligence framework you are building is coming along fine.

Mistake 5.  Picking the wrong tools.

How often do organizations buy software tools that just sit on the shelve?  This often comes from management rushing into a quick decision based on a few demos they have seen.  Picking the right analytics tools requires an in-depth understanding of your requirements as well as the strengths and weaknesses of the tools you are evaluating.  The best way to achieve this understanding is by getting an unbiased implementation partner to build a proof of concept with a subset of your own data and prove out the functionality of the tools you are considering.

Bottom Line.  Think things through carefully.  Make sure you put the right team together.  Have a data cleansing plan.  If the hype sounds too good to be true—have someone prove it to you.

Bernard Wehbe has over fourteen years of consulting experience focused exclusively on data warehousing and business intelligence. His experience includes data warehousing architecture, OLAP, data modeling, ETL, reporting, business analysis, team leadership, and project management. His business systems expertise is very broad-based and he is in high demand for his consulting expertise. Prior to founding StatSlice, Bernard served as a Technical Architect for Hitachi Consulting and The Sabre Group in the Dallas, TX area.  Mr. Wehbe received a Bachelor of Science as well as a Master of Science in Industrial Engineering & Management from Oklahoma State University.


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