As result of “big data” mania, there is an explosion of interest in business intelligence solutions and advanced analytics techniques. In particular, organizations of all sizes want to sharpen their ability to track the health of customer relationship management (CRM) business processes. A common question that I get from my clients is: "What are the best sales metrics that we should track, and how do we do it?"
Recently, my colleague Boris Evelsonand I responded to an inquiry on this topic. Our answer is summarized below.
"How do we set up BI dashboards for a sales-focused company? We currently have Cognos, IBI, and various cubes around a 6 (+) year old Teradata warehouse. We are upgrading our Teradata to its latest technology and have purchased IBI's BI suite to use in conjunction. Our focus is on sales -- How did other organizations start out? We would like to know what works best for different roles from the CEO down to an inside sales rep?"
We believe the answer to your question relies in adopting best practices around analytical sales performance management. You should take a top-down approach that has five steps:
1. First, define the overall sales strategy.
2. Then, identify goals and objectives that you need to achieve in order to make your sales strategy successful.
Do you think you are ready to tackle Big Data because you are pushing the limits of your data Volume, Velocity, Variety and Variability? Take a deep breath (and maybe a cold shower) before you plunge full speed ahead into unchartered territories and murky waters of Big Data. Now that you are calm, cool and collected, ask yourself the following key questions:
What’s the business use case? What are some of the business pain points, challenges and opportunities you are trying to address with Big Data? Are your business users coming to you with such requests or are you in the doomed-for-failure realm of technology looking for a solution?
Are you sure it’s not just BI 101? Once you identify specific business requirements, ask whether Big Data is really the answer you are looking for. In the majority of my Big Data client inquiries, after a few probing questions I typically find out that it's really BI 101: data governance, data integration, data modeling and architecture, org structures, responsibilities, budgets, priorities, etc. Not Big Data.
Why can’t your current environment handle it? Next comes another sanity check. If you are still thinking you are dealing with Big Data challenges, are you sure you need to do something different, technology-wise? Are you really sure your existing ETL/DW/BI/Advanced Analytics environment can't address the pain points in question? Would just adding another node, another server, more memory (if these are all within your acceptable budget ranges) do the trick?