Panasonic IoT strategy is all about big data analytics

Panasonic has been in the sensor and data business for decades, and it's using that experience to build a data-focused IoT business.

Panasonic IoT strategy is all about big data analytics

TechRepublic Managing Editor Bill Detwiler spoke with Panasonic's Faisal Pandit about how Panasonic has been in the sensor and data business for decades, and it's using that experience to build a data-focused IoT business. The following is an edited transcript of the interview.

Bill Detwiler: How do you see IoT evolving in the next year? Maybe you start with where we kind of are from your perspective in IoT, and what Panasonic is looking at, and what do you see coming in the next year?

Faisal Pandit: A lot of people think IoT is a new thing. IoT has gained a lot of acceptance recently, but I go back to 28 years ago when I started my first job. I was a computer science undergrad. My first job was, there's a big a machine, here's a PC, connect to this machine, and collect data, and create a pretty chart out of the data. That's some form of IoT doing something with data. We've gone from people not seeing value for that data, and hence not worrying about it. The machinery or the device being sort of an island, and people not seeing the value. We've gone from that to people seeing, "Well, there's value in this data," and a strong adoption of sensors and other types of mechanisms that would allow people to collect the data in a seamless manner. We were to that point.

Now we've gone from there to, "There's a lot of data. I'm collecting data. I don't know what to do with it." It's like data, data everywhere, not knowing what to do. I think we're at that stage wherein you have domain experts and industry experts being sought after who can come in and provide some insights on what to do with the data. My biggest concern is that if people are not able to understand what to do with data, I think IoT could lose its shine over a period of time. There's a lot of buzz around it, and what I suggest to people, and when we roll out our initiatives, I always want to make sure we narrow down the scope of the data that we collect so that it doesn't become a data overload to a point where it becomes irrelevant, and you lose track of the value of the data, and you're unable to turn that into something meaningful.

SEE: IT leader's guide to deep learning (Tech Pro Research)

Bill Detwiler: What's the starting point for that when you talk to companies, and try to help them understand why they're collecting all this data? Where do you start?

Faisal Pandit: It's all about pain points. What's the problem that you're trying to solve? Believe it or not, it may sound like an easy question, but the answers are really difficult. Because A, to get your middle management or middle-ranked individuals, to be able to speak to pain points is difficult because they see that as an admission of some guilt. Getting them out of that mode, and getting them into a comfort zone where they can openly talk about the pain points is really challenging. Because you can get a set of pain points from the top-level executives, but you need to let some level of granularity on those pain points. Without the granularity you're unable to pinpoint on the specifics and recommended a solution.

So what we have done is, for instance, in our industrial manufacturing operations, we have people who walk into manufacturing floors, we talk to executives, we talk to engineers, and we take a third-party view on what the problems are, and identify these pains, and then try to prioritize what the return would be on those pain points. Then accordingly try to identify a data set that would address this. A lot of companies have gone the other way around. They go in there, whatever the devices are, whether it's machinery, or whether it's something else, they just collect data. Then they bring experts from the outside, PhDs and others, who try to slice and dice the data.

Then they give them some specific best practices, and six months later they find out that the best practices had really no value. It didn't translate into savings of any sort. We don't get into the technology conversation at all initially. We get into, "What are we trying to solve here?" Once you get a couple of pain points addressed, and you create best practices that translate into meaningful return, then you get people more comfortable because now it's no longer about me, an individual, an engineer, who has a problem in his or her area. It's all about, "There's a value in what this data brings."

SEE: Hiring kit: Data architect (Tech Pro Research)

Bill Detwiler: Let's talk about artificial intelligence a little bit. Similar question, How can artificial intelligence maybe help with that analysis when it comes to all that data? What role does it have, and how do you see that playing out in the next year?

Faisal Pandit: I think it has a tremendous role. Again, it's not a brand new technology. AI has been there, heuristics-based thinking, heuristics-based approaches has been there for many years. But now I think it's getting a lot of relevance and value because, again, when you combine your IoT data analysis, and the machine learning algorithms and methodology, you can come up with more forward-looking and predictive guidance. But the first step to getting AI involved is defining what your pain points are, and where you want to be as an organization with respect to those pain points, or with respect to your general business practices. For instance, I'll give you an example. Again, food services. There's a big push within food services on automation, on back-end automation of the kitchen, and being able to predict demand, being able to create fresh food by tying in the demand data, and the back-end data, and that inventory data.

Artificial intelligence can play a big role in that kind of a space. But again, you got to first define your objectives. If the food service industry wants to get to a certain level, why do they want to get to the certain level? They want to get to the level because you have a new demographic that sees typical food service of a fast-food restaurant as having low quality food, so they want to change that perception. They want to improve the turnaround time. They got a lot of demand coming in. They've got all of these factors that are driving a change in their business. Now artificial intelligence can come in, help them with demand prediction algorithms, and things like that to serve those needs. The challenge that I see is when people gather these initiatives, these best practices, or business improvement initiatives, and bring engineers into a meeting room, and have them develop technology without really understanding why, and then defining where they want to go to.

But as far as the technology platforms, and the technology stacks that we have available today, we're far, far better off than where we were many years ago. We have the tools today to proceed, and move forward with some of these changes. It's a matter of making sure the businesses fully understand what their pain points are, and where they want to go to. Because if you just talk about digital transformation, everybody's talking about digital transformation, and for the lack of a better word, nobody can really spell out what that means. Because we're all going through that change, we're all evolving. I'm not suggesting we've got it all nailed, but it's an evolving process. So I think my one and only advice would be to make sure we understand why we want to do this.

Bill Detwiler: Is that a hard exercise? We were talking about the roadblocks, right, and understanding where it is, and you kind of alluded to this. How hard is it to convey that to the customers as opposed to like you said, just bringing in a bunch of engineers, and letting them design technology for technology's sake. You talked about how you communicate the message from senior leadership throughout the organization, and how you make sure that there's buy-in, and everyone understands the reason for the change. So similar question with like helping companies understand maybe if they don't know what the pain point is, how do they start?

Where do they start looking for those pain points? If it is, maybe they think that, like you said, the macro issue is, "We're seen as low quality." But the real problem is, "We're low quality because of X. You've got so much demand coming in. We don't have time to prep the food, or our supply chain is bad, and so the food isn't as fresh as it could be." Walk me maybe through that a little bit. Can you talk to that, about how you get the customers to really understand? You think your pain point's this, but really it's this.

SEE: Hardware decommissioning policy (Tech Pro Research)

Faisal Pandit: I think that there are two aspects to this. One is, "What's the level of maturity within the customer's organization?" There are some customers who have very good understanding. It's a well-oiled machine. They've got a good understanding of what issues they have, and where they want to be in their journey. Then it's a matter of us coming in and, being part of that collaboration, being a partner in helping them get to that. But then there are customers who believe there is a challenge, but they don't really know they can't pinpoint it. That's what your question is really all about. What we have done is, our relations with the customers are not being transactional. This is part of Panasonic's DNA. It goes back for many, many years. Our relationships are very deep, so we have a fairly good understanding of our customers' pain points. It's more of a trusted advisor relationship.

That's what we seek, and across all our businesses. As a result of which we have a good understanding of what their pain points are, looking from the outside. The other element is, because of how we view our relationships, we have a long-term view on these partnerships. We're not there just to deliver a product so that we can meet this month's or this quarter's numbers, but we look at relationships from a 5-10-year time period. So what we do in those cases, whenever we're able to bring, obviously maintaining confidentiality of the relationships, bring best practices from other areas, and bring them to the table, and say, "Hey, here's how people are doing. Here's what other people in the industry are doing. Here's how we're helping companies. This is how we can help you. This is where the industry is going towards."

Through those conversations that may not translate into a monetary gain for us in the short run, because our goal is not to generate that short-term revenue, but our goal is to make sure we attain that trusted advisory status with the customer, and continue to flourish our relationship. That conversation leads to a lot of things that we unearth, and in that process we're able to bring new solutions to the table, and try it as a pilot. Many times they fail because nobody knows what the end-game needs to be, but during that process we're eventually able to come to a point, we're able to define some meaningful return for the customer.

It's an ongoing process. There's really no blueprint to it, which says, "Chapter One through Chapter Three, and you're done." If you don't have a commitment to your customer's relationship from a long-term perspective, I don't think you can be a solution provider. Especially at this stage when things are evolving at such a rapid pace. Five to 10 years from now when things are really more sort of cemented, and it's clear, a lot of these solutions become commercial off-the-shelf solutions with some level of customization, yeah, then the nature of the conversation changes. But initially, early on, there has to be a commitment to understanding the customer's business, and helping them define new solutions.

Also see