Are we spending our Business Intelligence and Analytics Budget where it matters most? The gap between the haves and have-nots only seems to be widening. While a lot of progress has been made, many organisations rush to adopt the latest technology without laying appropriate foundations. Others spend so much time laying the foundations that they lose momentum.
It is not through a lack of spending. Analytics has never had as much C-Level “support” as it does today. But is this investment providing the return on investment expected?
I have spent a lot of time leading analytics, business intelligence and visualization projects (and working with vendors). As is often the case, the rush to deploy the latest technology only results in the true benefits being overlooked. I have seen many organisations simply convert spreadsheets to “shiny” dashboards with limited value added (99.999% replication of the spreadsheet).
Shouldn’t we hold our data and analytics investments up to the same standard that we apply to other parts of the business? Of course, we should. Let’s face it, the outputs and deliverables from analytics projects often face the same challenges that other projects encounter. We ship a dashboard into production but don’t follow up to make sure it is used, that it is used as intended and if there is opportunity to improve the business outcome with just a few small tweaks.
The problem likely has nothing to do with the technology. So, what can you do? Benchmarking is popular. There is no shortage of maturity models from consulting firms. The most famous being the Gartner Analytics Maturity Model. Thomas Davenport has established the DELTA model. There are many other variations and extensions to these models and many have common elements.
Gartner’s early analytics model (they have iterated on this a few times now) focuses primarily on an organisations ability to progress through levels of technical competency to achieve increasing value). Davenport’s DELTA model is more wholistic and focused on aspects of an organisations ability to execute (across Data, Enterprise, Leadership, Target and Analyst). Another lens to consider is your organisation’s ability to manage a portfolio of analytics projects and measuring the organisation’s ability to capture value from these investments.
I would argue that on almost any maturity model it would be best to build confidence in your ability to capture value from your analytics investment than to prematurely move to the next level (being able to solve a problem with analytics is not the same as enabling the business to benefit from that knowledge). Being able to demonstrate the link between analytics investments and business outcomes is essential to convince stakeholders to give you permission to solve the organisations most valuable problems. It will also enable you to establish the capability to embed more advanced capabilities into business systems and processes, compounding your investment.
Does your analytics team know how to make good investments? Are they able to effectively manage a portfolio of initiatives that are aligned to business strategy? Can they demonstrate that their investments provide an ROI and do the business stakeholders agree with this assertion?