The art of empowering your data

By Carlota Feliu, Marketing Director at Datumize

One of the many things that I’ve learned during my first 6 months working at Datumize, a data analytics company is that there is some art in the science of leveraging data.

First of all, is important to remark that gathering and analyzing data is not productive without a clear purpose and goals. That’s why we tend to talk about data analytics as a strategy, more than a tool. Especially nowadays, with the massive amount of data that we handle, we cannot approach our data challenge without a clear plan. If we just go and filter this data and transform it into insights with a beautiful and crisp data analytics tool, we can end up even more lost in the lake of insights, no matter how actionable they are, with each one of them pointing towards a different direction.

We can’t write our agenda based on what the data presents. We need to be the ones using the data, putting our data to work. And in our data analytics strategy we need to consider this three phases:

Finding the key questions. I always liked the traditional description of genius as the one who can make innovative questions, more than the one capable of finding the answers. Of course, that doesn’t mean that one needs to be a genius to generate original questions. But we need to put a bit of art into our science. We need to move one step backward and try to see the big picture, to avoid preconceptions and prejudices, to challenge ourselves, and to question our learning and knowledge. We need to push the limits of what we expect to find and think limitless and big.

Leveraging the right data. As the questions that we will raise will be new and uncommon, it is more than probable that we will need to look for new and better data to find the answers. Here is where dark data analytics solutions, such as the ones developed by Datumize, come to play. It’s important to remark that leveraging hidden data in decision-making will position us as a company with a definite competitive advantage, as our intelligence will be bigger and better, so our movements will probably be more accurate and our new strategies will be more successful. So, the benefits are clear and self-sufficient.

At this point, I would love to share with you some examples from real-life success cases, so you can understand better the potential of using new data for your intelligence purposes.

• Airlines; we helped Vueling, the leading low-cost Spanish airline, to leverage the data related to the customer requests and interactions in their booking systems so that they can understand their real demand. Using this hidden data they’ve discovered gaps in their catalog and new route opportunities.

Travel; W2M a big Tour Operator is now capturing in real-time all the events related to their distribution system and network performance, since then unknown for them. Thanks to these new insights they can timely detect issues and reduce time to resolution, and in consequence, offering a better experience to the customers connecting to their systems.

Retail; thanks to the integration and analysis of data captured in their unconsolidated POS systems, we are helping a leading retail company to enjoy new insights related to their sales performance and operational intelligence.

Industry; unlocking machine data in a process industries production plant is allowing an important Oil and Gas company from Spain, CEPSA, to leverage industrial data and connect their OT and IT worlds. The new insights provide the plant directors with powerful information related to the operational performance and the process efficiency.

Moving into action. If we suffer “paralysis by choice”, all the previous efforts wouldn't be worth it. And it’s probable that the new learnings will require our side to take bold actions, make unexpected decisions, and use different strategies. Get ready! That is the minimum we can expect when we generate innovative questions. Moreover, we can use new and unexplored data to answer them. The new insights will provide us with new goals, new strategies, and further actions. We need to move from a lab to real life quickly, put new learnings in practice as soon as possible, analyze them again, and iterate to always continue improving.

These three phases of a successful dark data analytics strategy are the basis for any business or organization. Making effective use of data is no easy task and requires leadership and determination. But above all, it involves non-conformism. We can always go one step ahead.