During my tenure in both Indian and American organizations as a consultant, I have observed that some very simple things hold back the implementation of analytics in organizations. This is despite the fact that the opportunity cost of not implementing analytics is much higher than the upfront cost of investment. I will discuss both the top most constraints as well as some work-around to getting around these challenges.
Some of these constraints that seem to act more as mental blocks to adopting business analytics are-
1) Licensing norms of analytical software
Some analytical software believe in hefty annual licenses, some deal with one off perpetual licensing fees, while some have a subscription model. Business Analytics software needs much more transparency in benchmarking and testing as well as fixed pricing.
All software claim to be the fastest and most reliable leading to confusion in the minds of customers.
Many services organizations prohibit freeware and open source software because of lack of education on these licensing norms.
2) Capital expenditure on hardware and software required-
Traditionally business intelligence and business analytics software projects required huge upfront capital investment in analytical infrastructure. The Return on Investment on these projects is either not measured or is not committed to by software vendors.
3) Training skills and needs of existing human resources-
Even though a short training course for human resources costs much less than the opportunity cost of not using business analytics, organizations are reluctant to engage in vendor for training on business analytics unless they have a clearly identified need. One reason for this is, training departments of analytical software providers charge a huge premium and rarely take the effort to cater to every need of the businesses (like advising when to use and when not to use their software). The shortage of resources in analytics can be matched by both off-shoring as well as consultants, but this again requires a change in comfort level of the client organization.
4) Data governance of input business data-
Even if you have the software license, and the people, many organizations are effectively running on piles of spreadsheets linking to each other. These spreadsheet marts need a lot of pain and effort to transition to a seamless business analytics organization, and the transition pains lead to holding back of fresh investment in analytics and continuing business as usual than business intelligence units (BAU vs BIU)
5) Flexible work force for analytics driven campaigns
Organizations and managers adopting analytics for the first time are reluctant to commit to continuing series of year long analytic projects. This is because initial analysis generally reveals a need for more detailed analysis, specific deep dives as well as further prioritization of tasks that can be given to analytics. Rather than focus on the savings generated, many organizations view this as additional costs, by discounting the opportunity costs of streamlined analysis.
6) Previous bad experiences with legacy analytics or business intelligence software-
Business analytics vendors are partly to blame for this, by rampant mis-selling software, add-ons, packages, and by promising everything by their own software. This destroys the trust and credibility of the industry, and has been a case in point for declining IT budgets or under investment in these areas.
A thinking organization will pick the software it needs, run the projects it needs to, by a mixture of training, part time consultants, and optimized mix of software. As technology and media make the consumer more discerning, organizations need analytics as business edge, and any reluctance or bias in hesitating in following through insights generated by their own data, will have costly ramifications in their market place.