I waited eagerly for my daughter to turn six months old and sit independently so my husband and I could begin bringing her to a neighborhood play area near our regular coffee shop. Witnessing her play independently alongside children her own age seemed like a milestone (we’re first-time parents and geeky engineers). Finally, with upright baby in tow, we began stopping by on weekends in search of other babies able to play with rattles and balls but not yet ready for the firehouse and train sets. We’d dip the nose of her stroller in and out, randomly trying to catch what seemed like an increasingly elusive clique of babies with an entirely different coffee run.
Before long, I began asking the staff and owner of the play area the same kinds of elementary trend and cluster analysis questions my clients often ask me. Put simply, how can I find the other babies? How many babies her age come here? What times and days do six- to10-month-old babies most frequently play? I quickly realized that the owner could not answer these questions and felt too overwhelmed and ill-equipped to consider them. Yet, if answered properly, these questions could have brought new customers and more revenue. We and other parents represented a missed business opportunity.
Let’s use this case as a lesson in the six baby steps to data competence. Data at any scale – even the “small data” of a new company in its infancy – can have significant commercial and marketing implications. If contemplating numbers intimidates you, note that something similar to the Pareto Principle, or the ’80/20 rule’ applies because unexpectedly only 20% of data analytics is actually the strict number crunching. The other 80% is conceptual and perhaps more approachable: figuring out what you’d like to know and how to approach the problem; finding, properly storing and cleaning up the data; implementing change management and training staff.
Nor should a small business get intimidated by buzzwords or technical jargon. Hadoop Clusters and SQL Servers are simply tools for capturing and manipulating very large data sets, which you can grow into as your business expands. That step will be far easier when you put great data practices in place early on. And you’ll benefit from keeping your finger on the pulse of the data in your company all along, rather than waiting until you need enterprise tools that may require a data scientist to use. Let’s get there, growth is a good problem to have.
1. COLLECT: Whether you are starting up or established, a brick-and-mortar store or e-commerce website, selling products or services, there is always data to collect about customers, sales, leads, suppliers and more. Begin by considering what you’d like to know, the commercial implications of that information for your company and how easily you could acquire it. Often you’ll find that you’re already collecting some data on social media platforms, through Google analytics or at the point of sale. Sometimes you need to collect data from scratch, which isn’t necessarily hard.
To determine what you might want to collect, consider: 1. What questions do customers often ask? What services do they seek that you do not yet offer? 2.