Data itself is quite often inconsequential in its own right. Measuring the value of data is a boundless process with endless options and approaches – whether structured or unstructured, data is only as valuable as the business outcomes it makes possible.
It is how we make use of data that allows us to fully recognise its true value and potential to improve our decision making capabilities and, from a business stand point, measure it against the result of positive business outcomes.
There are multiple approaches to improving a business’s decision-making process and to determine the ultimate value of data, including data warehouses, business intelligence systems, and analytics sandboxes and solutions.
These approaches place high emphasis on the importance of every individual data item that goes into these systems and, as a result, highlight the importance of every single outcome linking to business impacts delivered.
Big data characteristics are defined popularly through the four Vs: volume, velocity, variety and veracity. Adapting these four characteristics provides multiple dimensions to the value of data at hand.
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Essentially, there is an assumption that the data has great potential, but no one has explored where that might be. Unlike a business intelligence system, where analysts know what information they are seeking, the possibilities of exploring big data are all linked to identifying connections between things we don’t know. It is all about designing the system to decipher this information.
A possible approach could be to take the four Vs into prime consideration and determine what kind of value they deliver while solving a particular business problem.
Now that organisations have the ability to store as much data as possible in a cost-effective manner, they have the capabilities to do broader analysis across different data dimensions and also deeper analysis going back to multiple years of historical context behind data.
In essence, they no longer need to do sampling of data – we can carry out their analysis on the entire data set. The scenario applies heavily into developing true customer-centric profiles, as well as richer customer centric offerings at a micro level.
The more data businesses have on the customers, both recent and historical, the greater the insights. This will in turn lead to generating better decisions around acquiring, retaining, increasing and managing those customer relationships.
This is all about speed, which is now more important than ever. The faster businesses can inject data into their data and analytics platform, the more time they will have to ask the right questions and seek answers. Rapid analysis capabilities provide businesses with the right decision in time to achieve their customer relationship management objectives.
In the digital era, capability to acquire and analyse varied data is extremely valuable, as the more diverse customer data businesses have, the more multi-faceted view they develop about their customers.
This in turn provides deep insights into successfully developing and personalising customer journey maps, and provides a platform for businesses to be more engaged and aware of customer needs and expectations.
While many question the quality and accuracy of data in the big data context, but for innovative business offerings the accuracy of data is not that critical – at least in the early stages of concept design and validations. Thus the more business hypotheses that can be churned out from this vast amount of data, the greater the potential for business differentiation edge.
>See also: The big data phenomenon is broken: 5 tips for doing analytics the right way
Developing a framework of measurement taking these aspects into account allows businesses to easily measure the value of data in their most important metric – money.
Once implementing a big data analytics platform, which measures along the four Vs, businesses can utilise and extend the outcomes to directly impact on customer acquisition, onboarding, retention, upsell, cross-sell and other revenue generating indicators.
This can also lead to measuring the value of parallel improvements in operational productivity and the influence of data across the enterprise for other initiatives.
On the other side of the spectrum, however, it is important to note that amassing a lot of data does not necessarily deliver insights. Businesses now have access to more data than ever before, but having access to more data can make it harder to distill insights, since the bigger the datasets, the harder it becomes to search, visualise, and analyse.
It is not the amount of data that matters, it’s how smart organisations are with the data they have. In reality, they can have tons of data, but if they’re not using it intelligently it seldom delivers what they are looking for.
Sourced from Soumendra Mohanty, SVP, global data and analytics, Mindtree