The volume of data now available to businesses continues to grow exponentially. When looking to extract valuable insights into their business’s performance, C-level executives (CxOs) must navigate the big data blank canvas. This requires a strategic approach, in which CxOs should define business objectives, prioritize data quality, leverage technology, build a data-driven culture, collaborate with data experts, and communicate insights effectively to stakeholders. By doing so, they can gain valuable insights from big data and make informed decisions that drive business success.

Big Data as the “New Oil”?

In recent years, big data has been sold as the “new oil,” a concept put forward by mathematician Clive Humby. His assertion is that like oil, data has an inherent value – but only once it is refined. However, more data usually leads to extra costs and greater confusion, and as a result, it doesn’t necessarily offer more robust insights. 

When faced with a large data set, you might run some analysis on top of it. The risk here, however, is that you might miss all kinds of biases inherent in the data. They could exist due to the capturing mechanism; for example, the data could be sampled or biased in various ways that you might not have realized are there at all. But if you go in deeper, say on a row-to-row level, and trace a transaction through the system, you will learn a lot about how data is captured and what caveats are involved in interpreting it. For example, you might not be storing data on shopping baskets that start on one device, before that same customer switches from phone to laptop. Here you have identified some exchange of data between those platforms, but can’t capture it across numerous devices. Therefore, if you had started by analyzing from the end, you would have found no cross-device sales, despite them very clearly taking place. 

Working Out What’s Relevant

Before any analysis of data, it is important to decide which pieces of information are most relevant to your decision-making. As part of this process, you may realize that you are not capturing a sufficient range of data or that there are missing sources. You can then add those necessary sources, only incurring the additional cost that is useful to your analysis. 

In general, smaller data sets are sufficient to get statistically relevant insights, which enable businesses to reach meaningful decisions about their direction. Even though huge amounts of customer data may be available to you, you don’t necessarily need information on every single interaction to determine the steps your business should take next. With so much data at our fingertips, it’s easy to forget the more human side of business: Clients can be pretty vocal about what they want, and by focusing too heavily on zeros and ones, you might miss the obvious answers right in front of you. 

Telling Your Story

Ultimately, data modeling is about storytelling. We want to present a story about data that can be used to persuade: to force action that gets our business to operate better and to save money. To achieve this, we need to be able to understand the data. Human processing space is limited, and we can only grasp so much information at a time. Start with a specific goal in mind, for example: How do we reduce the number of abandoned carts? Then try to realize a solution using the data available. 

It’s a good idea to immerse yourself first in fine-grained data before making big conclusions. This is why it’s important to alternate between the two levels of analysis: granular low-level data to assess the quality in its nuances, and then high-level data to draw broader conclusions before returning to the granular to check for errors or biases that may contradict our early conclusions. Thankfully, you no longer need to be an engineer to do this – anyone can use a data modeling tool and apply their own critical thinking to reach meaningful decisions that have far-reaching impact on the functionality and success of their business.