Starting and running an analytics/BI project in any organization can be a daunting task. However, when “analytics nirvana” takes hold on teams, it can be especially difficult to foresee potential roadblocks amidst the excitement and glamour of delivering BI solutions. I get it- BI is all the rage in medium-large companies looking to get an edge over competitors and to realize untapped opportunities. My word of advice: don’t let the glamour of your BI project blind you to these 4 major pitfalls.
Underestimating or Overlooking Data Silos
Especially in large organizations, it’s not uncommon to have many sources of what at first glance is “similar data”. It’s very likely the data you plan to start using for business intelligence analysis has many different versions stored in different “data silos”. For example, the marketing team may be using, storing, and analyzing the customer data your team is planning to use in your brand new BI project. However, without doing your homework and speaking with others in the organization, you would totally overlook the fact that the marketing team uses a simplified version of the data stored in databases used by the sales team. The marketing team may in reality use old customer addresses and their data doesn’t record refund quantities. Next time you’re setting out to gather data requirements for your next BI project, make sure the silos you’re using are the most accurate, up to date, and the most comprehensive your organization has access to.
Foregoing Communication for Delivery
Similar to the common misconception made by entrepreneurs: “build it and they will come”, forgoing communication for delivering “results” can lead to building solutions not needed by the business teams you’re serving. Spending regular status update sessions with members of the business units that will most benefit from your BI implementation can work wonders for your project. Spending even half-an-hour a week gives your stakeholders the chance to give you real-time feedback, provide specific subject matter expertise to the meaning behind the data, and give the stakeholders a sense of some ownership in the outcome of the project. Meet regularly with the “customers” of your data, and always have a finger on the pulse of the business value you are providing to guarantee your deliverables go above and beyond expectations.
“Curse of Expertise”
The curse of knowledge is generally known as a cognitive bias that occurs when, in predicting others’ forecasts or behaviors, individuals are unable to ignore the knowledge they have that others do not have, or when they are unable to disregard information already processed. Never assume too much knowledge on the part of your business stakeholders. Make every meeting a small learning opportunity to give your stakeholders a better grasp of the value of business intelligence. Set a good example for your team whether you are the Sr. project manager or an entry level coordinator. Never assume. Always educate.
Data: Garbage In, Garbage Out
Business intelligence dashboards and tools are only as good as the quality of their underlying data. It’s my opinion that many BI project teammates frequently overlook the importance of quality assurance for building “fancy vizes”. Quality assurance is the foundation upon which BI teams can build data tools and dashboards. As someone who has often played the role of QA point person, I can certainly attest to the benefits of selecting an individual to be responsible for QA on your project and making that quality assurance their core areas of focus. Depending on the project and the data being used, this responsibility could fall on the business analyst, development lead, or on another project teammate. This can be a difficult role to play, so giving credit and showing appreciation for your “QA Advocate” can go a long way to keeping the team in good spirits and ensuring data accuracy and integrity across deliverables for your BI project.
Even if you’ve got the “easiest” BI project assignment with the big data dream team on your side, keep your eyes peeled for these common project pitfalls. I wish you all the best with your big data and business intelligence pursuits!