After spending millions over couple of years, the CEO of a largest retail company says he is not convinced if Analytics added any value to the company & if the investments were really needed. Does it sound a familiar scenario? I am sure to the most of you it does.
The same CEO had backed the investment & initiative while launching, congratulated everyone on the Go-Live. He had onboarded everyone under his team to get this going. Unfortunately, the case is not the same today. Interesting it is but this is far too common Analytics world.
According to Gartner, more than half of all Analytics projects don’t get completed within budget or on time, or they fail to deliver the expected wow factor. As a consultant delivering Analytics over last 18 years, there are some trends I have observed with our clients. Some are very obvious & some are interesting to show up there…..
The major one & most common of all, is the misalignment of leadership on client’s end, simply put, the internal politics. Everyone knows with their experience that Analytics today is the same league as all life necessities like Air, Water, Food & Wifi. Still people find politics to be more valued than Analytics. We have observed that Analytics creates a bit of ‘fear factor’ at senior levels – Fear of someone else becoming a hero, fear of ‘actual’ results being exposed or fear of losing significance from Finance department standpoint. The moment this fear factor weighs in, people lose their common sense & they end up wasting their energies into protecting their turf or stopping the hero. That’s the primary cause of misalignment & hence the eventual failure of Analytics initiatives. There is no cheat-sheet to address this, at least I don’t want to be preachy in this – The best way is to acknowledge this risk right at Kick-off, discuss with the CxOs & be aware of what we are up to.
Another one is the lack of user adoption post launch. Either the CxOs don’t find good enough a reason to log in or they get the same info in their mailbox as PDF or old style excel. There are many factors that builds this up – A cascading effect of the 1st one could be a factor. Lackluster response from business in defining what is needed & during the overall execution process is a significant cause contributing to this failure. We forget a simple psychological trick here – We hold on tight & close to the heart to the things we feel we own, we feel ours. It is our collective responsibility to get everyone including the business to have the ownership feel about Analytics initiatives. Sounds cliché but having the business to champion the analytics initiative than anyone else helps drive the user adoption & avoids failures. There are many tricks up our sleeves as consultants in achieving this & we do use them to the best of our judgement, but there is no straightforward formula for this as well.
A third one in this series is having inferior quality of data at source. Unlike other applications, Analytics does not generate data, it just converts the data which you have into information. It’s a mirror – Shows exactly what you have in your data sources. Many a times the customer facing applications do not collect quality data or the users are left with their own choices while entering data & then you are staring at a sizeable amount of bad data to deal with. The funny aspect here is that organizations only realize the ‘bad data’ issue when they take up Analytics initiatives – Like you realize your looks when you face a mirror. Expecting analytics to clean data or show great KPIs despite source data issues is like expecting the mirror to show someone else when we face it. Setting expectations & socializing the data issues across user spectrum becomes a focus area for a great beginning. Several solutions can be evaluated & planned while we as consultants execute such initiatives based on specific situations.
The last one which I wanted to highlight in this write up is choosing the right hands to execute Analytics initiatives, choosing the right talent. Let’s acknowledge that analytics is a niche area & needs special treatment. Unlike ERPs, CRMs & any pre-built applications, analytics is kind of ‘Build your own Pizza’ type work which needs a lot of design & integration factor coming in. The packaged solutions never work with analytics. There are many who work on just visualizations & still be in the fray for evaluations, but the crux of analytics is wrangling & massaging data with a solid platform for visualization to sit on. Here as well there are no straightforward answers but certainly there are processes which can be followed.