Top 10 worst practices to avoid in Business Intelligence

Published August 31, 2014   |   
Raghav Ravisankar

Business intelligence (BI) has great potential in transforming the raw data into meaningful and useful information for businesses to identify, develop and create new strategic business opportunities. But, the right use technology plays a critical role in the success of BI. The following are the top 10 worst practices to avoid in business intelligence.

10 worst practices in business intelligence.

1. Using BI as a tool to justify decisions

Business Intelligence is a tool to help to make decisions rather than make decisions and justify them. Business intelligence highlights different factors which helps us to take business decisions.

2. Having non-actionable data or not taking action on data

Having BI data is like having a tool to find out what to implement. But not implementing based on the information is as good as not getting the data in the first place. Also data which is not actionable, which is not a measure of employee’s efforts, should not be obtained.

3. Using Excel as a tool for BI

Excel is very useful as a simple interface to do basic numerical functions like adding, subtracting, etc. Although Excel provides much utility for business users, it wreaks havoc on the quality and consistency of information. It is also very insecure. And as each professional uses different spreadsheets, it contributes to more complexity and failure to accurately represent the information collected. This is especially damaging in heavily regulated industries that need to adhere to strict compliance legislation.

4. Failure to incorporate automation

It is absolutely wrong to have data in numbers and looking through it manually. It makes the situation prone to a lot of errors. Instead, if the data were to be automatically organized with the use of different tools, it would save a lot of errors in the data and help in making better business decisions.

5. Assuming a data warehouse is the one-size-fits all solution

Sometimes having the data stored in a data warehouse is not the final solution. Without a proper mechanism to deliver the data, having a data warehouse is not productive. In addition to that, a data warehouse should be designed in such a way that it is structured around the requirement of the company.

6. Not choosing a BI tool based on business need

Often, companies end up choosing a BI tool which doesn’t suit their business need. Companies have to use a BI tool which highlights each problem and simplifies that problem for the user to interpret the data easily. This goes a long way in highlighting the things that matter in the functioning of the organization.

7. Overemphasis on forecasting at the expense of understanding

Forecasting something based on BI without understanding what is causing the effect is something to be avoided. Without clear understanding what is causing the problem or creating the positive effect.

8. Not utilising the BI information to empower business

BI helps us to chart the positive and negative effects of the business decisions undertaken by the management. But not utilising BI to implement changes which would help in making the business better is counterproductive.

9. Centralising BI to the IT Department

The IT Department has a lot of different functions in an organization or a company. However, centralising all BI data to the IT Department causes major delays in retrieving BI information about the company and the way its run. These kinds of delays can lead to major losses. Allowing each department to easily view the BI information through their own interfaces is way better because in that way each department can targets its flaws and work towards  better productivity.

10. Being archaic in BI information.

In today’s world, man hours no longer have much value in many sectors and organizations. But continuing to collect such data and using that data to assess company performance is completely damaging to the companies strategies. A company should adapt itself to the changing world and the output of each worker through new parameters and bring it forward to the BI system.