Over the past five years, the drum beat of discussion around big data has been incessant. As a result, the business community is now largely convinced that we MUST leverage big data or risk irrelevance in the data-driven future. While that may be true, “keeping up with the Joneses” is not a valid reason to launch a big data initiative. There is much more work to be done to determine what data is valuable and how best to leverage it.
Behind closed doors, managers will often admit that they don’t really understand what to do with all of their data and, more importantly, don’t know how to determine what value it will provide. Given the nature of data analysis, the question of value may never be fully answered up front, but here’s a way to look at big data that can steer you to success while setting proper expectations along the way.
The first thing is to ensure that that company is ready for the challenge. As Andrew McAfee and Erik Brynjolfsson point out in their Harvard Business Review article on Big Data: The Management Revolution, “companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like and ask the right questions. Big data’s power does not erase the need for vision or human insight.”
At the same time organizations need a data-driven culture, as opposed to one that relies on a HiPPO (highest paid person’s opinion) approach, to act on the insights that big data can provide.
Everyone involved must understand that leveraging big data follows the scientific method. Whether it is figuring out the factors that are driving climate change or how to anticipate your customers’ service needs, the process is very similar.
It starts with identifying what questions need to be solved. This is a matter of identifying what value you want to gain from the data. Next, a hypothesis is formed, which defines where to go looking for an answer. Lastly, it’s about conducting experiments, analyzing data and developing models to find the solution. There is no guarantee that every data science project will deliver immediate value, but even failures can point you in the right direction and, with persistence, the breakthroughs will come.
The second major consideration is data collection. In general, the value that can be derived from data is directly proportional to its completeness. More data is better when developing predictive models. For example, Nuance leverages data collected across hundreds of interactions to help clients optimize proactive engagement by determining the best messaging and channel. However, we’ve found that even more value can be derived when our interaction data is combined with data collected at other stages in the customer journey. These data points can be used to anticipate customer service needs and reach out proactively at a lower cost than using live agents. Big data technology makes high volume data collection easier and more cost-effective so integrating data sources can unlock a treasure trove.
With the right vision, robust data, and proper expectations on how and when big data value is unlocked, there are huge benefits to be derived for companies that commit to being data-driven. Each industry has key problems this it trying to solve. Companies that solve those problems using data will pull away from the competition. So rather than just “keep up with Joneses,” companies must find ways to become more effective using big data.