Business analytics: The essentials of data-driven decision-making

Having big data is not enough: Tips to turn it into a business advantage

When it comes to using data to drive business, organizations such as Google or Facebook are iconic. Although a lot can be said about their practices with regards to topics such as privacy, ownership, and governance, there’s no denying that they have been pioneering the field both in terms of technical approach as well as culture.

The people that helped build the technical substrate, and the data-driven culture upon which Facebook operates, call this combination DataOps. When they started in 2007, big data was not what it is today. All four Vs that define big data — volume, variety, velocity, and veracity — were at lower levels.

But perhaps more importantly, there was not much previous experience of working with big data and using it to drive decision making in organizations. At that time, the question was still out as to whether having all that data is useful. Today, the feeling is that the value of data has been proven, and it’s more of a question of how to get it.

The way one of the pioneers of data-driven decision making, O’Reilly’s Paco Nathan, puts it:

By putting in the work required to streamline access to data, things start happening — things like business-transforming ideas coming from likely and unlikely places. There is a well-known story about how a Facebook intern performed analyses mapping how users interact with each other, leading to a global campaign and driving product features and growth. More than feeling and stories, however, there is mounting hard evidence pointing to a simple fact: Data-driven organizations perform better. For example, according an Economist survey from 2012, organizations that rely on data more than their competitors outperform them financially.

Read also: DataOps: Changing the world one organization at a time | Facebook’s new privacy settings: Look out for these shortcuts, data delete options | Google Analytics 101: Executive’s guide to measuring business data

The first step in this journey is to acknowledge the effectiveness of data-driven decision making. Then the right infrastructure needs to be in place, and the culture of organizations also needs to shift and adjust.

It’s not that data-driven decision making is all that new. But things are different in terms of scope: Collecting data and performing analytics used to be a privilege few organizations could afford, and it mostly involved after the fact, retroactively trying to come up with explanations and solutions.

Business intelligence (ΒΙ) and descriptive analytics are the terms that have been associated with this type of tools and approaches respectively. ΒΙ is something that many executives have come to familiarize themselves with and even rely on. In the beginning, it meant having a hefty pack of printouts on their desks.

Those printouts would summarize key metrics and key performance indicators (KPIs), such as production, sales, and churn. As executives started getting used to the idea of being able to review these key figures, the practice started going deeper, and bumping into some issues. This information could be too much and yet not enough.

Imagine getting metrics for an organization with thousands of branches or employees. This would be overwhelming, and it would require a substantial amount of time and effort just to scan through, let alone digest. KPIs can help in delivering a bird-eye view of an organization, but they are not enough.