Innovation is imperative in a business’s survival, and developing an innovative mindset to create and capture value is crucial, according to Surajit Basak, Director of Information Technology Europe Region, UPS.
A key speaker at Velocity, the data and analytics summit, Basak, discusses how predictive analytics will bring wonders if appropriately used and the challenges enterprise-wide companies and startups face while embarking on the data and AI journey. “Retention of talent, especially retention of a good data scientist, giving them equal opportunities to grow, is a big challenge,” he says.
Excerpts from the interview:
How data-centric and AI-based culture is influencing different industries?
A data-centric culture was already there in CRM 10 years ago, but the influence of AI has changed in recent times due to the unprecedented situation of COVID-19 and remote working. Many companies and industries are embracing both AI and data-centric.
There are two traits. One is horizontal, where certain things apply to multiple industries. And the other is vertical, where things are applicable only for verticals in specific industries.
Let’s first of all, take the manufacturing area. They invest a lot in industry 4.0 and build digital twins, which influence a plant or operational unit digitally. In the finance industry, they are influencing the front-end and back-end. In retail and wholesale banking, AI is used for fraud detection, customer-facing initiatives, and customer segmentation. In healthcare, AI is doing wonders.
AI is used in drug research. And that has helped not only during the pandemic, but it’s going to help beyond in certain areas.
Earlier, we used Score — a traditional supply chain operations reference model in the supply chain. With the influence of data, we have transformed that model into a Digital Score. And the Score now stands for sense, collaborate, optimise, and respond. All of these steps are done through data-centric and AI-based algorithms. In a nutshell, this has a lot of influence on different companies.
What challenges do companies face while embarking on the data and AI journey?
The challenges the different companies face are manifold. Most of the challenges an enterprise-wide company faces are in a couple of major distinct areas. Firstly, it will be about big data — handling the data, storing it, and filtering it for meaningful decisions.
They also face a challenge about how to build their environment. There is a lot of debate about what should be on-premise and in the cloud. That is a constant debate for these companies. The third challenge enterprise-wide companies face with their customer base is security. And security is about the data, who will have access to it, and what they will do with it. These are some of the questions that enterprise-wide companies are facing.
In startups, some of the challenges that they face are slightly different. So if a startup company is building its AI or data-driven products, the first challenge they will face is how to market that product. What is the minimum viable product in that situation?
Because they have limited funding, they have a big risk. The second challenge is the retention of talent. Of course, this applies to enterprise-wide organisations as well. Retention of talent, especially retention of a good data scientist, giving them equal opportunities to grow, is a big challenge for startups.
Are organisations investing enough in data and analytics? What is the innovation you are looking forward to?
I’ll answer this question in two parts. So the first one is about investment. Yes, companies are investing a lot in digital data and artificial intelligence. It depends upon its vision and size, but most companies have invested a lot in the last five years. We see a maturity model, which is a four-layered maturity model. The first layer is descriptive analytics, the second layer is diagnostic analytics, the third is predictive analytics, and the fourth is perspective analytics. Based on the vision and mission of the company, each company should place itself in a certain way. And then try to move up the maturity curve.
This drives their investment and long-term focus. Coming to the second part of the question, which is about innovation. I’m not looking for any specific innovation, AI algorithm, or usage. What I’m looking for is innovation in mindset.
Can predictive analytics be a real game-changer for the logistics industry?
Predictive analytics is the total layer of the model that I have just said. It’s the third model piece, followed by perspective analytics. So indeed, predictive analytics can do a lot of change in the supply chain and logistics industry, especially in the areas where different manufacturing organisations have already invested and got good results.
Those things need to come in the supply chain industry. And when we talk about the influence of predictive analytics, two to three areas could be a distinct advantage. First, how do you predict your demand? Once you can analyse that and build a model, prediction becomes easy.
And once you can predict, a logistic company that works hand-in-hand with a B2B or B2C company can utilise their warehouse, inventory, and workforce. So predicting the demand will help the company. The second is predicting the customer needs, which means which customer will buy which product at what point in time. That helps a lot on the supply chain side and the retail side. And the third area where predictive analytics can make inroads is predictive maintenance. First, it will be about how to predict the fault that will occur in your supply chain system so that you can take action. The second will be how to predict the life of your system so that you can take preventive measures. Predictive maintenance will bring wonders if appropriately used in the supply chain area.
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