Don’t scrutinize data in a vacuum.
It has never been harder to compete than it is today. Consumers are more demanding. Competitors are more numerous and more specialized. Supply chains are more complex and markets are global. How in the world can any of us expect to make a profit in that kind of environment?
Listen to enough analysts and they’ll tell you that the answer is some form of data analysis tool. Being in the business of business intelligence (BI) software, it is hard to disagree. But business intelligence alone can never substitute for good management. Good data analysis, just like good management, always begins with asking questions.What Do You Want to Know?
Answering these questions was the key to establishing a BI infrastructure that created process improvements that, in turn, led to higher profits and more effective promotions. Bigelow proves how data analysis can be successful when questions inform the deployment.
What To Do Next
Data analysis software is best used as a key ingredient for a business improvement plan. Here are five steps to get started:
1. Examine Existing Processes
Every business has break points, but they often go unseen. That’s because so many remain buried in processes that are born and nurtured through culture rather than reason. Ironically, this problem can be alleviated through the often-onerous Sarbanes-Oxley requirements, which require an assessment of internal controls having an impact on financial performance. Going through that exercise can create a useful blueprint for the deployment of further data analysis.
2. Determine Questions To Be Answered
Once the break points are identified, the next step is to question how they affect operations and what changes might be made to improve performance. Tests can then be carried out.
3. Design BI Backbone To Monitor Processes
It’s at this point that data analysis tools become truly useful. New processes can be monitored and compared with historical norms to predict a performance impact, which can also be used to measure the return on investment of both the software and the new processes.
4. Make Improvements
This is pretty straightforward. Once you’ve proved the impact of a new process, widespread change is the next step. Just be sure the changes don’t move in front of the data analysis infrastructure, otherwise measurement techniques will quickly become irrelevant.
5. Refine BI Basis To Track New Processes
Ongoing testing, tracking, and deployment of process improvements are the ultimate goals of data analysis. In that sense, the technology infrastructure you put in place to track, understand, and manage data will always be in flux, moving in lockstep as business improvements are put in place. As with all things in business, data analysis can never live in a vacuum. So don’t think of it as static technology. Think of it instead as a critical piece of the business strategy puzzle that gets examined and refined each and every quarter. Indeed, in the fast-moving retail and consumer products markets, that may be the key not just to profits, but also to survival.