“Advanced analytics is a business process, not code or an algorithm, that solves a problem statement,” says Chad DeJong, program manager at Factora, who argues here that we must shift our mindsets related to the collection and use of data in order to get the most out of all the information we collect. Take a look…
Smart Industry: What is an underutilized technique for turning data into a competitive advantage?
Chad: Big data remains a buzzword that everyone talks about, and I suppose that is because data volumes continue to explode. As companies collect more and more data, analytics tools need to evolve to do more. In my opinion, the tools and techniques are not keeping pace with data. Of course, that is no fault of the software itself. There are hundreds of analytical software choices out there, both commercial and open-source, which can execute any algorithms that users can code. No, the critical path here is our own vision of how to use the data.
To begin, office professionals must challenge themselves to be more thoughtful about what business problems they want to solve. Ideally, this is done before the data is architected and collected, not after. Data scientists can implement a wide array of advanced modelling techniques, but it will be misguided without solving a specific, measurable business question.
Second, far too many companies continue to stagnate in the mindset of descriptive analytics, and they haven’t moved forward to predictive or prescriptive analytics. Descriptive analytics is essentially summarizing what already happened. “Our constraint tool experienced seven changeovers this week, two more than last week, while OEE decreased to 73%.” With predictive analytics, the goal is to build models of how the data works together, so that we might predict what might happen in the future—“Our current production plan will cause nine changeovers, so we can predict OEE on our constraint tool to be 67%.” Predictive analytics takes the data you have and creates data you don't have.
Another way to utilize data in a more powerful way it to apply it to operations-research techniques, such as dynamic simulation or optimization models. Dynamic discrete-event simulation models are built to describe the combination of multiple dependent and independent events in concurrence and over time, and as such they can be very data-thirsty. These simulation models often shine in very complex environments, where point-in-time algorithms (Neural Nets, Decision Trees, etc) can fail. Connecting discrete-event models to real-time data sources is the perfect marriage.
Smart Industry: How has the world of data changed for industry/manufacturing in recent years?
Chad: The new catchphrase is “Data is the New Oil”. I’m not sure who first coined that phrase, but I’ve seen it in several major publications recently, and I tend to disagree. Oil is a finite resource, can be stored for many years, and only a handful of people can mine it. None of these are true of data. The good news is that smart manufacturing has hit the tipping point. There are now, technology-wise, many reasonable routes. Unlike ten years ago, you no longer have to invest in an intimidatingly large, complex platform. There are many choices in technology, vendors and scale.
Again, with unprecedented volumes of data, it is increasingly difficult for one person or a small group of people to sufficiently handle it. The demand is growing, as the democratization of data is a significant driver in analytics. Users demand new solutions that provide access and visibility to data. Technologies that cannot proliferate information and scale across user bases are becoming extinct.
Moving back to predictive analytics, a traditional view of mathematical model is that once a data model is built, it can be automated and you can simply watch it run and provide meaningful output for months and years to come. Data has proven to us just how quickly business drivers can change—new products, new processes, new customers, and new regulations all combine to drive the supply of new data. If you’re not constantly monitoring your tools and models for completeness and accuracy, their usefulness and application to strategic questions will soon diminish.
Smart Industry: What industries are pioneers in capitalizing on the value found in data? What industries are lagging?
Chad: This second question is very tough, as every industry segment I can think of is now using data and analytics in very profound ways. Wherever there is an immediate and direct payback for analytics, (i.e., where the commodity itself is money) is where we can find cutting edge data analytics.
So, the biggest champion of data and analytics has been finance. What we call data science today has long been used to evaluate hedge funds. The gambling industry aligns to the classic theories of probability and payoffs. Retail banking and insurance are both in the business of risk management, and as such, their lifeline depends on customer analytics and product analytics.
More recently, e-commerce and media have been very active industries. They collect, analyze, and utilize customer insights by leveraging social media content on billions of mobile devices. Net Promoter Scores (NPS) are tracked and continuously updated—the willingness among customers to recommend a company or its products/services. Anyone with an account at Amazon or Netflix understands how history and personal preferences are now used instantaneously to predict future activity.