Gartner to the rescue. Their survey of 2015 on this subject is telling. While we wait for the 2016 update here are some of the realities we can evaluate.
Big Data Industry Insights – Gartner 2015
- Investments in Big Data technology and insights has grown from 58% in 2012 to about 76% in 2015 among the companies surveyed. That is good.
- Only about 14% of firms were able to get their Big Data investment to deployment. Up from 8% in 2013. Not so good.
- The challenge for these firms [56% say this is the biggest challenge] is determining how to get value from their Big Data investments. Nothing else was close and even funding was down the list at only 31%. So they are getting the money they need but not certain how to substantiate value/ROI. Not so good.
- The most important measurement criteria was financial returns at 38%. But 41% of those firms surveyed don’t know if the ROI on their efforts will be positive or negative. That sounds precarious.
Here is How We See It
My partner, and our firm’s Chief Analyst, Karl Ottolini says this boils down to an internal struggle that has multiple goals, few of them shared across the organization:
This is a pervasive struggle to link application of big data to a cogent articulation of expected financial outcomes.
We have worked in the Big Data field for many years and have many shared success stories with clients. But we have also observed client organizations who struggle with these efforts. Here is where we see the problems and opportunities to successful Big Data deployment and meeting established goals/criteria:
- Because each department has its own goals the only common ground they can agree upon is financial outcome.
- This is too far down the food chain to be accessible and manageable on an organization-wide basis. Relegating the majority of Big Data outcomes down to an impact on financial results is like trying to find the proverbial needle in a haystack.
- Big Data is powerful and can directly impact someaspects of outright financial improvement such as Risk Management, Cost Containment and Operational Efficiency, to name only a few. However, attribution to financial results is incredibly difficult given the size and scope of these factors. Where we have found success is breaking down the components that impact results and then attacking them independently so the sum is greater than any of the individual parts.
- This approach provides organizational focus across the entire group of stakeholders. For instance, focusing Big Data predictive analytics efforts against its most powerful capability, predicting the correlation betweenexisting internal data and external behavioral data [banking, credit cards, geographic location, social media], can have dramatic results across multiple components of the business.
- Analyzing both internaland external data sets enables us to dramatically improve the following: cost of acquisition models [which result in cost savings or improved revenue streams], customer experience [which drives satisfaction and share of customer] and microtargeting [which shifts the customer base to increased loyalty].
- Once these types of initiatives are agreed to, each department has its goals to enable the process. They all then participate in the attainment of a shared set of goals.
- Our experience has shown that the practice of implementing Big Data in discrete phases – each designed to deliver its own measurable metric benefit – can go a long way toward reducing project risk, improving time to market and accelerating expected returns.
This is an exciting time to be navigating these new and enabling technologies. But there is a struggle, whether between departments, the CFO’s office or the shareholder. The true potential and impact of Big Data is being obscured by a lack of focus. Our suggestion is to break it down to identify the key variables that can be impacted in the shortest time frame. Get successes recognized and have the next Big Data application planned and ready to implement. Good luck.
ShareShare Monetizing Big Data Assets, an Internal Tug-of-War