Written by Roman Stanek |
A new generation of insight tools are poised to change the way that business is conducted at every level, in every industry. These ‘systems of insight’ will finally make good on the promise of big data by closing the loop between data, insights and action. But while the technology itself is certainly exciting, there is a key consideration behind unlocking the true business impact of these systems, and that is getting to value quickly.
In this case, achieving time to value doesn’t just mean getting a technology platform in place as fast as possible. It means developing and deploying an operating model that includes the technology and experts that can get the right data to your frontline decision makers. Furthermore, that data has to be in a format that makes it easy for them to identify the information they need, and the best action they should take in order to solve real business problems. In other words, you must operationalize your data as quickly and effectively as possible.
I recently participated in a webinar with Brian Hopkins, the principal analyst at Forrester Research. We discussed how too often, organizations spend huge amounts of time and money building vast data lakes, only to discover that adoption rates are terrible. This is because they are based on an outdated premise of building analytics systems to give visibility into all possible data, which makes deciphering clear insights extremely difficult. All that time spent in development is wasted, because ultimately your end users are still left sifting through too much information for anyone who isn’t a data scientist to glean insights from.
Most workers are part of what we might call the production environment within a business. These are the professionals who make or break your business, and they need to focus on their core job functions. The notion that hundreds of employees have the time, inclination and expertise to slice and dice data on a regular basis isn’t realistic, but at the same time your business still wants its workers to be taking data-driven actions.
The ability of a system of insight to learn and iterate quickly is key to the value equation. Let’s take the example of insurance underwriting, a decision process that isn’t a closed loop today. It’s possible to radically overhaul this process with intelligent recommendations that help your underwriters assess pricing, risks, and coverage when making a policy decision. By delivering these recommendations to underwriters - with a system that is instrumented so you know when these were used and the outcome – you can determine whether providing that recommendation at that point in the underwriting process had the desired outcome. The underwriter’s interactions are then fed back into the system, tuning your insight platform so that the process gets better and more effective over time.
If implemented correctly, the potential impact of these systems is staggering, with forecasts projecting that insights-driven companies will take in $1.2 trillion in revenue by 2020. But as enterprises from every industry rush to implement these solutions, it’s important to remember that this is an early adopter market. Expertise in developing, implementing, and deploying these systems is very limited, and the clock is ticking for companies to either become truly insight-driven or be left behind by their more agile competition.
Written by Roman Stanek |