TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

Why BI Isn't a Technology Issue (Part 1 in a Series)

Implementing a BI program without a data-centric culture is bound to disappoint users and waste resources.

• By Jonathan Fowler
• April 2, 2018

Companies and organizations in the early stages of business intelligence maturity can fall into the trap of assuming BI and analytics efforts are a matter of technology first. In these instances, discussion of BI goals revolves around the tools necessary to get the job done, and the goals are assumed to be short-term and exclusive to the IT domain.

For Further Reading: Great Data Stories Will Always Be About People 5 Tips for Getting Your Team Thinking About Data How and Why to Build an Analytics-Driven Culture

Imagine this conversation at Acme Widgets Corporation:

Manager: "We need to get into advanced analytics for our customers."

Employee 1: "We have SQL Server Reporting Services and the analytics available in Excel."

Manager: "Sure, but I mean next-generation stuff with dashboards and visualizations."

Employee 2: "Oh, like Tableau or Power BI. I think SAS is even more robust."

Manager: "Yep, like that. Ask IT what we can get licensed. I'd like to see some dashboards by end of quarter."

What's wrong with this picture?

No one in the room has asked what the analytics efforts are supposed to achieve for the customers, what data will be reported in the visualizations, or whether the data environment at present can support an analytics solution. Now imagine a third employee in the room raising these issues:

Employee 3: "I'm excited to start this project, but shouldn't we step back and make sure we have the right infrastructure first? I mean, we have issues month to month with being able to validate numbers. Beyond all that, we don't know how these visualizations are supposed to help customers. We would be presumptuous to just throw dashboards at them."

Manager: "We don't have time to do that. Our focus is on billable work, and we should be able to figure out what everyone else wants on our own."

Employee 3 has a broader view of this effort and understands the old GIGO adage of database development: garbage in, garbage out. Acme isn't ready to engage an analytics solution. The others in the room are focused on a specific technology package and somehow assume it will make them much more effective and productive without actually defining what the needs are or making sure the existing environment would support it.

Unfortunately, these conversations happen often, and Employee 3's voice is ignored for the sake of "just getting it done." Conventional wisdom at Acme rewards staying focused on billable work. Anything that involves taking the foot off the gas and evaluating where the work efforts are going (and why they matter) is frowned upon.

What will happen? In this case, Acme will license a very capable analytics solution. Someone will be tasked with learning it. A few dashboards will be made without any real stakeholder input, and the analytics initiative will be underutilized. Ultimately, Acme will question why such an effort was made to begin with, and "analytics" will carry a negative stigma within the corporation.

This is a symptom of seeing BI as a technology issue rather than a cultural one. This view is tempting, to be sure. There are plenty of capable and sophisticated BI software solutions available, and used properly, they can be critical pieces of an organization's data environment.

However, it takes more than adding a software package to make meaningful sense of an organization's data. In Part 2, we will look at the importance and characteristics of a data-centric culture.