Vida spoke at last month’s rvatech/DataSummit Conference and urged attendees to do more than just provide data analytics.
You’ve got troves of data. Are your practitioners sifting through the lakes of information, providing insights while maintaining its true essence? At this year’s rvatech/DataSummit, I challenged the audience to think differently about how we work with data. It’s time to enable our data engineers, scientists and analytic teams to do more than just provide data analytics. During my presentation, I walked the audience through the evolution of data and multiple real-world analogies that begin to reframe our role in data.
TLDW (too long, didn’t watch) or didn’t attend? Here are the top three takeaways:
Marketing: The Big Data Quandary
Marketing has driven the reality that customer behavior is one, if not the, most direct example of data driving outcomes. Today, companies strive to understand the full data profile of an individual. The more historical and current data a company can analyze, the more custom or futuristic an offer they can provide. This isn’t just for consumer behavior, though. We should take the lessons learned from marketing and apply them to the rest of the enterprise: Utilize this same behavioral data to assess and forecast everything from manufacturing, supply chain, operational capacities, etc. While always remembering that the customer is key.
Great Feature Engineering
Feature engineering remains one of the major hurdles of employing data as a business driver. The first step is to decipher what is truly relevant in a sea of data, then define those attributes. Once you know what you have and can identify it, you must begin collecting, wielding and identifying patterns. You’ll start small with experimenting and testing the patterns or insights you’re uncovering until you’ve validated to the point that you can begin making business decisions based on the data.
That’s the perfect world, though. There are massive risks in feature engineering. Risks that, if one is not aware of, can mislead data outcomes and eventually negatively impact business decisions. A myopic view of the data context will kill the elaborate algorithmic plan. What’s the solution?
Above all, ensure that your deep analytics teams have diverse context and perspectives in which to contextualize data within the data wrangling and feature engineering phases, get your SMEs engaged in the effort.
Returning Value in Outcomes
Once you’re through the complex wrangling of data, feature engineering, and onto the business use case of data, it’s time to validate. What is your business hoping to achieve? Use your data to identify the outlying drivers – what are you missing? Once you can unearth indicators, test them, validate or invalidate. In the case that you can show areas worth investigating further, project and forecast outcomes. In short, engage with your data with a perspective of innovation, what could you do with information that you did not know you could even possess?
How are you viewing data differently? Are you pushing your team or colleagues to think bigger and more holistically? Do you hear the term “data analytics” more often than you should? I’d love to hear more…drop a comment below.
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