4 Bad Business Intelligence Habits to Avoid at All Costs
Get more out of your business intelligence by breaking these bad habits.
With organizations struggling to sort through and derive value from ever-increasing mountains of data, business intelligence (BI) has become a vital strategy. The strategies and methodologies of BI have evolved alongside the technologies it analyzes — and through the evolution, many organizations have developed some poor BI habits.
Business intelligence arose as an effort to collect, store and analyze data in an age of cloud computing, digital marketing and big data. However, most businesses who use some form of BI aren’t realizing the significant gains that should be possible. The reasons behind this failure to capitalize with BI are varied, but many of them boil down to bad habits that must be broken.
Here are four ways organizations demonstrate bad habits in the actual implementation of business intelligence.
Lack of Quality Control for Source Data
Big data is a "hot, new thing" with incredible potential, and many organizations are enthusiastic about using it. However, a lot of businesses have developed the bad habit of dumping every data stream they can access into a data warehousing structure — often custom built at significant expense to the company — and then trying to sift through every last byte, looking for the tiniest flecks of digital marketing gold.
What organizations should be concentrating on is finding a way to sort relevant data from irrelevant noise, before the streams are dumped into warehousing. Big data may have limitless potential, but it’s not all usable for every company, in every industry. By implementing more stringent quality control for data processing, organizations can save themselves significant time, money and hassle.
Relying on Oversimplified Visualizations
Data visualizations, from the classic flowchart to the relatively new format of infographics, are staple tools for business intelligence. Visualizations allow for complex BI data to be presented in readable and digestible ways for business users who may lack sufficient technical understanding to make sense of the data in raw form. However, many organizations take the idea of simplification too far.
Today’s workforce increasingly consists of tech-savvy individuals who’ve grown up in the digital era — in fact, many of them have never known life without the Internet. These individuals are well equipped to view and understand more advanced features. The problem with oversimplification is that crucial data can easily be left out, which would have changed the nuances of the results and allowed for a more effective interpretation.
While organizations should keep user-friendly features and interfaces, they should also realize that it’s entirely within the capacity of the modern workforce to handle customizations, developer’s kits and other advanced components of BI visualizations.
Lack of True Business Value
This bad BI habit ties into the lack of quality control most organizations have when it comes to big data. Innovations in data warehousing and analytical tools have changed the way companies collect and manage information, but many end users aren’t sufficiently informed about just how this technology is supposed to work.
In many cases, a significant portion of big data stems from machine-generated event data, while the percentage of actionable business data remains low. End users who aren’t familiar with the particular system they’re working with are often forced to use older, slower tools to access and understand practically limitless amounts of stored data — and as a result, analytical progress is significantly slowed.
Over-Reliance on the Cloud
Another shiny new tool for business, cloud-based storage and applications, have come to represent convenience and cost-effectiveness. The issue here is that many big data systems and tools are already flawed — and moving them to the cloud doesn’t fix the underlying problems.
Relying on cloud platforms to somehow make big data more manageable is an unproductive habit. The traditional approaches to data analytics simply aren’t sufficient on the vastly larger scale of big data, and organizations need a better way to sort, track, extract and present data — with or without cloud solutions.
Business intelligence is a field with enormous potential for organizations willing to break these bad habits and seek more efficient solutions.