Data Science for Everyone: Understanding the Importance of Predictive Analytics
Greg Kinsey is an industry advisor and executive consultant leading the Hitachi Vantara Manufacturing Practice in EMEA. He and his team work primarily with European industrial companies to help them envision, plan, and execute digital transformations. In his own words, he helps companies become "more lean, more green, more agile, and more productive, using digital tools as the means".
Greg has over 30 years of experience working with the manufacturing industry, mainly in the automotive, electronics, aerospace, and consumer goods sectors around the world. Minitab had the opportunity to get his thoughts on several data science topics for this blog.
How important is it to democratize analytics?
I believe it is vitally important for data analytics to be part and parcel to nearly every industrial job. Whether you are working in a factory, an office, a service center, or customer-facing role-no matter the role or industry in today's world, it's crucial to have the right data analysis at your fingertips. Data helps you make the better decisions so you can optimize the work you're doing. Data prevents mistakes from being made. Having the right data to do your job improves productivity, reduces stress, and increases job satisfaction.
How important is data literacy for everyone?
Data literacy is important, but maybe more importantly we need tools that enable us to easily use data at work, in a similar fashion to how we use data at home. The digital revolution happened first as a consumer revolution. It was all about e-commerce, social media, and personal data. Nearly everyone has digital devices in their home-and almost always in their hand or on their wrist. No matter what your hobbies are, I'm sure you use data for them. Whether you're a marathon runner, amateur chef or musician, restoring antiques, or playing any type of sport-data is involved. Everything we do in our personal lives now uses data-and that data helps us get more out of life. We haven't seen that fully shift yet in the industrial working life.
Certainly people in industry are already collecting and reporting some data, but they're not necessarily getting the right analytics that they need to perform their job better. This issue is not so much about data literacy, it's about organizations creating the right environment and having the right tools in the right places, which are easy to use. That would lead to a democratization of analytics across the workforce.
Should data science be more accessible to widen the user community? Why is that vital?
Data science needs to become a part of everything that we do. The term "data scientist" is misleading I believe anyone using basic statistics could be considered a data scientist. The rigorous use data is now increasing in nearly all professional roles. So eventually we'll all become "data scientists", as part of whatever job we do.
I was deeply involved in starting the Six Sigma movement in the 1990s. It was then that we recognized the power of giving industrial workers access to basic statistical tools. We didn't call them "data scientists", but instead we came up with the term "Black Belts". They used data science to describe processes, troubleshoot problems, and apply basic analytics to daily operations. That was the origin of how data science took root within industrial operations. Six Sigma created the foundation for digital transformation.
Today, the business world is really starting to embrace the value of data. It is proven to have a huge impact on productivity, decision making, and even reducing employee stress levels, because problems can be solved faster and easier with the right data.
Is digital a topic that lives only inside the IT department?
Not at all. I believe that digital is a topic for the entire organization. The use of data and analytics, and the associated tools, should be extended to almost every function. Too many companies keep the data isolated in the hands of a few "experts", rather than allowing everyone to access data that might be useful for them in their daily work.
We are moving to a world now where the operational managers are also owning the digital strategies for their functions. So, when you talk to somebody who heads up a maintenance department or quality department or marketing department or supply chain, they are-or should be-responsible for the digitalization of those functions.
Much of the consulting work we do as Hitachi is working with the functional leaders.
The IT department supports digitalization, but I see digital transformation really being driven from an operations point of view because that's where the users sit. The operations managers know best where to start with digitalization and often have a vision for their future operations. A lot of the best ideas come from people who work in the daily operations, so it is important to engage them in your digital design efforts.
This is quite a change from 20 years ago when new systems were implemented, and organizations had to change the way they worked to fit the IT. I can remember many companies adapting their business processes to match their requirements of rigid MES or MRP applications. Often this prevented operational innovation and optimization.
Now it's being reversed; we have a chance to create digital interfaces, algorithms, and platforms that make sense for the work processes. The agile method is focused on the problems to be solved and the required user experiences. The IT department must then support those innovations and make them fit into the overall IT infrastructure.
Should organizations prioritize investment in low code and no code solutions?
Yes, absolutely. Think about how we use our smartphones. We don't need to know how to code in order to do useful things with them. This should be the same case with the analytics tools at work. The user experience should be fast, easy, and relevant.
During the early days of IT in the business world, it was all about fancy tools that only experts can use. Remember people who were called "programmers"? Today, it's all about finding analytics tools that everyone can use. At Hitachi, we're creating solutions that don't require extensive programming or coding in advanced languages.
Why do Data-driven professionals need predictive analytics?
Predictive analytics is a real game-changer. Most data used today in industry is backwards-looking. Historical data describes what happened in the past. In some areas, real-time data is available, which lets you see what's happening right now. With today's advanced analytics, you can start to project what will happen, based on the behavior of causal factors. For example, maybe the weather impacts your operations. Or maybe a big shift in exchange rates will impact your cost structure. Or there is a rapid change in consumer preferences. You would want to collect and analyze data on these factors to understand what's happening today, and tomorrow. To start extrapolating it to project what it will look like next week, or next month, that's where predictive analytics-based on machine learning and factorial data-will enable you to see into the future.
I like to think of this as "time travel". Predictive analytics gives the ability to travel to some future date and create scenarios, asking, "what if?". What if the market really does go in a certain direction and demand really changes in a specific way? What if we speed up or slow down a process? How about simulating product design changes? What if the impact of an economic or political outcome can be considered? How will that impact the economy or the regulatory environment in which we operate? The scope can be large.
Then you can wrap all these factors together and tease out scenarios for next year, next quarter, or next month. You start to see how these different cause-and-effect relationships will play out in the future. What can we do about it? What changes or corrective actions will be needed under such a future scenario? This is where the value comes. Optimizing your future performance.
This is why it matters so much. You can get on the front foot, you can anticipate what's going to be happening so you can manage your business, your organization, and your processes better to deal with all this change coming your way. It's about being agile.
Do you believe engineers can transform into data scientists with low code, no code technology?
Absolutely! I trained as an engineer-my bachelor's degree is in mechanical engineering. I learned that engineering is about problem-solving, mathematical modelling, and applying the scientific method. Data science is part of that.
The advent of technology that reduces the need for complex programming to access and use data is driving an increased use of data science within the engineering community. But it is much broader than that. Beyond the engineering community is where the big gain comes-when skilled workers who are operating machines, driving vehicles, maintaining equipment, performing manufacturing functions-when all those people start to become data scientists, those can be the biggest gains. And let's not forget about managers, including the C-suite-they are also becoming "data scientists".
When the entire organization has better data and easy-to-use analytics tools, that enables "data science for everybody". I believe this is one of the foundational elements of the 4th industrial revolution.