Modern customers are savvy, and want to choose where, when and how they wish to interact.
We now have the capacity to capture every interaction customers make and deliver exactly what they want on a silver platter.
This mundane information allows the delivery of a service which is anything but mundane.
That ability to look at the whole customer journey provides the opportunity to understand the importance of every tiny interaction. We can open our eyes for the first time to reveal a full day in the life of a customer. Big Data opens up a thousand eyes to a new invisible dimension: author Rick Smolan in The Human Face of Big Data likens the new developments to the planet growing a new nervous system with which to accumulate all of this real time data. The amount of data one modern person grapples with in one day is equivalent to the data perceived by one person in the 1400s across their whole life. This provides an enormous bedrock of information to potentially mine.
Data Mining is essentially data archaeology, and in traditional archaeology, archaeology is literally rubbish: what past generations threw away. Big Data is not merely Garbage In, Garbage Out: one person's garbage is another person's gold. The 2.5 exabytes of data generated every day – set to become zettabytes (trillions of gigabytes) by 2020 – does have the capacity to yield huge returns. But this nontraditional, unstructured data has to be collated.
Quantitative versus Qualitative
What does it mean to a Decision-Maker when a huge pile of data is jettisoned into his inbox? It is all very well having the data, but performance improvement and competitive advantage result from Analytics models which actually allow managers to predict and optimize outcomes.
This is the time when managers must get creative. On the one hand analytics must be advanced, but on the other it cannot be so complex that it loses all practical application. This leads us to Occam's Razor: What is the least complex model that would still dramatically improve our performance?
"Most companies spend too much time at the altar of big data. And not nearly enough time thinking about what the right data is to seek out."
– Maxwell Wessel, general manager, SAP.io
Executives don't understand or trust Big Data-based models and consequently, don't use them. Yet we use electricity every day without really understanding what it is; and we don't need to know Google's algorithms inside-out to use a search bar. In the same way, an Analytics dream team with the right tools is necessary to navigate through the clouds of data. They must quickly identify and connect the most important data for Analytics usage and mount a cleanup operation to synchronize and merge overlapping data to work around missing information.
Managers should concentrate on targeted efforts to source data, build models and transform organizational culture. Data is not just about Data, but also about people. Tools must complement existing decision-making processes, analytics tools must be embedded in simple interfaces that the ordinary end user can use, and executives must drive change from above and below to promote a cultural change where Analytics becomes a company's bread and butter. In this way, we end up with people taking back control of the customer journey.