Babu Kuttala, Chief Data and Analytics Officer of ABB talks about how he has used analytics to drive growth at ABB, what’s next for cloud analytics, and much more.
This episode features an interview with Babu Kuttala, Chief Data and Analytics Officer of ABB, a large multinational manufacturing company. Previously, Babu has served as an executive at companies like The Hartford, Lowe's Home Improvement, and Honeywell.
In this episode, Babu talks about how he has used analytics to drive growth at ABB, what’s next for cloud analytics, and much more.
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Steve: [00:00:00] So it was great to talk to you today about, um, I actually wanted to start off and talk to you a little bit about your background in India. And I, I have a particular interest in it because, uh, during a period, when I worked for business week as a writer, I actually went to India.
I think. Eight
Babu: [00:00:15] times in
Steve: [00:00:16] 10 years. And I was, I was writing about the rise of the Indian tech industry. And I went to about five
Babu: [00:00:23] from
Steve: [00:00:24] talk to lots of executives. And I've also met a lot of people who grew
Babu: [00:00:28] getting
Steve: [00:00:29] India. Who've come to America like you and are playing important roles in information technology, either within the companies that use it.
Or obviously we know about some of the entrepreneurs. So I wanted to start off by asking you about something about your, your education. I see that you got a BS at the university of Kalika in Carola, in chemistry and biology. Then you came to the U S and you got a master's in information technology, and I wanted to find out what led you to make that change in directions.
Babu: [00:01:03] Very good. Thanks
Steve: [00:01:03] Steve.
Babu: [00:01:04] So I born in Southern part of India, a small state called catalogue, which is the Southern tip and, you know, very
close to a Bay of Bengal and our, our BNC and indistinct part industry point of Cadillac is we are, we are the only state in this world has all war 90% literacy.
So we
Steve: [00:01:27] hold
Babu: [00:01:27] that, uh, Guinness book, world record. And our brand name for our state is God's And now back to what is why I, you know, what is the reason behind my degree and why did I come to us? So I graduated in early 1990s, uh, with, uh, bachelor's in chemistry
Steve: [00:01:52] in
Babu: [00:01:53] That
is the time the rise of computers is happening.
So many companies are shifting
Steve: [00:01:59] from
Babu: [00:02:00] mainframes to mid range. Computers, ERP systems are
Steve: [00:02:04] getting
Babu: [00:02:05] very, very popular and software is being, getting into mainstream. So hence I decided to pursue a degree in software engineering,
Steve: [00:02:16] Indian
Babu: [00:02:17] particularly I was fascinated by databases, database programming. And during my undergraduate days, Steve, I spent numerous hours in chemistry and biology labs.
So I think I got that kind of analytics mind, which drove my passion for data and analytics.
Steve: [00:02:38] Cool. And when you came out, did you start, uh, as a software coder? Or what would kind of, how did you launch your career in America?
Babu: [00:02:45] So I taught and I've been to school here. Then I started as a vendor database programmer is that this is back in mid nineties in, we are building a workflow systems of four hospitals for patient, you know, the constant treatment. That's where I started my career. The lately, you know, I moved into eCommerce and started beekeeping, a good, a database architect, and then kind of, uh, Rose my career as a, that architecture data analytics and into more a industry thought leader in that analytics
Steve: [00:03:22] Yeah. Yeah. So I see you have, you've had quite a varied career in terms of the types of companies you've worked for insurance. Then you work for a resort, a home improvement retailing, and then most recently in manufacturing. So it sounds like the narrative line that runs through all these jobs is the focus on data and databases.
Is that correct?
Babu: [00:03:44] It is. And Steve it's very simple. First is I don't like stereotype jobs. And if you look at my, you know, my career. Uh, one thing, all this industry vertical, you mentioned, you know, whether it is insurance or a retailer or in the payment or manufacturing. One thing, all these companies have is common is data.
And I like to solve the problems using data. Right? My belief is for a analytical driven company, must have a strong data management culture.
Steve: [00:04:21] Gotcha. Gotcha. And you know, so it's really interesting to
Babu: [00:04:25] The
Steve: [00:04:25] about the narrative arc of
Babu: [00:04:27] your
Steve: [00:04:28] career from the mid nineties,
Babu: [00:04:31] when
Steve: [00:04:32] you know, the era of client server computers and, and these big relational databases and things like that to where we are
and make sure that you know
Babu: [00:04:41] they can offer
Steve: [00:04:42] two or three.
Lifetimes in terms of how data
Babu: [00:04:45] has changed.
Steve: [00:04:46] Can you kind of compare and contrast how data, what the capabilities were to deep for dealing with data back then compared to now?
Babu: [00:04:57] Yeah, absolutely. So I have one of the, one of the luckiest to really leverage my industry. The transformation. Okay. What about, I mean, is for example, take the case of insurance in, in early 2000, you know, the internet is a, is becoming a mainstream.
Steve: [00:05:18] If
Babu: [00:05:18] you look at insurance companies, one of the core capability they need is data because you need to, you use your data for bill understanding the risk exposure, managing the risk, also creating insurance products.
So in that time, you know, as you know, that is the time companies, insurance companies are kind of moving out from traditional MIS and the statistical functions into more like a data warehousing world. So that's, I started from there then, and also the, you know, interestingly, I got lot of basics. I learned about good, the data management practices, the importance of importance of quality of the data.
Steve: [00:06:03] The
Babu: [00:06:03] reason I'm saying this, Steve is, if you look
Steve: [00:06:06] career from
Babu: [00:06:08] the raw material is
Steve: [00:06:10] when
Babu: [00:06:11] They're the more data they have, the better data they have. They can assess your risk exposure and make sure that, you know, they can offer the right product at the right price at the right
Steve: [00:06:24] changed
Babu: [00:06:26] So I learned that then I moved to Los is this is the second largest improvement retailer in the U S as you know, in mid 2005, but the Amazon and Walmart, most of this brick and mortar retailers want to get into omni-channel capabilities. That's the biggest transformation, how they can be have eCommerce.
We are reaching, you know, these largest shops are becoming a showcase. It's like a furniture industry and the people are buying things from, from internet. So I came right into that right time and the last really want to drive big transmission around Omni channel and they want to modernize the stores.
And the CIO was looking for somebody with a strong data management background. The reason is, again, if you look at the retail, the raw materials are the products, isn't it. So that they're not a big data savvy. So he wants somebody that kind of a data management data, culture, bad background to bring here, to drive that business transformation.
So then I was helping to building this master data solutions to see a single view of the customer and the customer buying experience around optimization, customer analytics, kind of various these kind of analytics to really support the new business transformation. Within retail later, I moved into. I don't know the industry, which is entertainment, which is the Robin.
As you know, by that time, smartphones are very, very popular, high speed internet. It's a commodity and all this. Entertainment and tourism problems. They're producing so much data through their customer interaction, guest experience, et cetera. So that employer now wanted to build a data centric model to support the gusts life cycle management, to provide a memorable everlasting customer experience.
Okay. So that's what I, so I able to get into that space and help that organization to build good, strong, analytical capabilities to really do this Gus life experience. So for example, and I, and that's where you start, you started at the beginning of using this artificial intelligence and machine learning into your business processes.
I give an example, Steve, for example, you know, when you go to. A hotel. And when you ask for the room, you give you a driver's license. So at the level, this can do a driver's license or we find out, Oh, Steve is here for his birthday. From their own words, we started offering you, the system started creating dif curating different customer experiences.
For example, when you get to the room, there is a chocolate, that's a champagne. When you turn on the TV, they say it's a happy, happy birthday. I'm glad you are here. So there you start with using analytics into your business processes to have a different experience. Okay. Then again, you know, then in last eight years, in 2000, 2012, I moved to industrial manufacturing, which is I was working for Honeywell.
Now, ABB, as you can see, IOT is getting into mainstream, basically pre the predictive maintenance and the maintenance, you know, having a Sotheby's manufacturing. So these are your two devices became very, very popular and these manufacturers want to pick a bad part and they want to digitize their product.
So that's where I started working at Honeywell. Building big data solutions know I'd want some of these capabilities. Then my, I moved to ABB four and a half years ago and the continuing the journey. So in summary, you see four different segments, four different analytical data problems. And at the same during the part of that business transformation,
Steve: [00:10:37] Yeah.
Babu: [00:10:37] so Steve, I wanted to just pull one more note to get a close out on this conversation. So in summary, you know, this, this changing my career is not accidental. It's very driven because it's aligned with the dot industrial transformation happening in the business. And my, when I select for a job, then I look for a career.
You know, these things, the three things is very important for me, business value, realization, cultural and behavioral transmission, and craft in new data ecosystem across the industry that using the, our data management knowledge. So, you know, so if you look at, in my career, I am a conformational analytic strategy leader.
So the key was making my goal was enabling business value using data and analytics platforms.
Steve: [00:11:33] Good. Good. Now I want to focus in on ABB. Now. You're the chief data and analytics officer there. I know that you're based in Nora and Cary, North Carolina, but the company is based in Zurich, Switzerland. I think it would be really great. I'm not sure all the listeners are going to be familiar with ABB and its product.
So
Babu: [00:11:55] it would be great.
Steve: [00:11:55] I think if you could describe the various dimensions of the company's business and where it's going strategically.
Babu: [00:12:04] Absolutely. So ABB is a Swiss global industrial manufacturer. Our headquarters corporate headquarters is in Zurich. Be how plants all over the world. In fact, 50% of the business is coming from America. So America is one of the largest in the region for ABB. And, um, I made a guess headquarters is based in catty, not Carolina.
So the way ABB segment that is in four business areas, electrification, which make we make circuit breakers, switches, et cetera. Then we have motions that will be make large mottoes industrial motors. Then we have industrial automation business. That's where we support like energy mines. And, uh, you know, fried ship and a ships, all of those things then be able to robotics.
There'll be make a robot to support, uh, automation in factories and hospital six. So we have this four different segments and the largest segments electrification is roughly around $12 billion. Uh, and the rest is our 6 billion, 6 billion, 3 billion. We are around $28 billion company, really a billion dollars in revenues, all 120, 110,000 employees around the globe.
So in ABB B most of the executives work
Steve: [00:13:30] from
Babu: [00:13:31] different
Steve: [00:13:32] it would be great
Babu: [00:13:33] region because we
want to embrace and promote diversity.
Steve: [00:13:39] Oh, cool. Cool. Um, now I'm just guessing that automation and robotics and robotics are two of
Babu: [00:13:45] the
Steve: [00:13:46] larger kind of growth areas
Babu: [00:13:47] for the company.
Steve: [00:13:48] Is that correct?
Babu: [00:13:49] Yep. Uh, Uh, robotics is a very big high growth area in the slow dimension. And also electrification is one of the highest growth area because of this, uh, uh, sub charging station, solar, you know,
Steve: [00:14:04] Oh. Oh, so, so all of the alternative energy being brought in is adding, there's a lot of infrastructure that goes between the source
Babu: [00:14:13] So
Steve: [00:14:14] and you're providing that,
Babu: [00:14:15] exactly. So, you know, if you look at not in ABB is in the energy creation and energy distribution and energy consumption or a power, you know, you can say power, power creation, power distribution, power consumption. So we played a big role, you know, so especially in a, we will have a lot of the, you know, like what drives did you use this in big data centers?
So all of those business are growing, but electrification is going know enormously.
Steve: [00:14:44] I got ya. I got it. Now. You've you've been there for about four years. Were you brought in as chief
Babu: [00:14:51] data
Steve: [00:14:51] and analytics officer?
Babu: [00:14:53] No.
you know
Steve: [00:14:56] But at some point, at some point you got that title and I understand that you were the first person to have that role. Right. Okay. about that. How did the company decide to have somebody in that role and how do you define it?
Babu: [00:15:12] How do you
Steve: [00:15:12] do it?
Babu: [00:15:14] Perfect. So in ABB there are, we have two types of data. Yeah. The internal business data
Steve: [00:15:24] the
Babu: [00:15:24] our external products
Steve: [00:15:26] for the company
Babu: [00:15:27] So I'll make this very simple internal data means anything we create internally to serve our customers or our make our products. Like, you know, the, the, the data coming from our, uh, uh, you know, our ERP systems or sale force or our customers contact system sucks.
The next set of data is we are technical manufacturing company. So we have software is in and senses in our products. So we collect that data to provide more enhanced the Sotheby's to our customers. It's kind of the digitizing the product. So that is me. You call it us our external data with our product related.
I am responsible for the internal business data to really use this data for see how we can grow, how we can optimize, how we can simplify exit. Okay. So I'm the, I have that role. So then, and in how the, the ABB decide to have this kind of achieved . So when I started at ABB, I'll give you a little bit,
Steve: [00:16:32] Oh okay
Babu: [00:16:33] a story around this.
When I started at ABB back in 2016, I came to lead America region. Because it has the 50% of the revenue we had made a big acquisitions happened that time. We bought Baldo Thomas and bet some of the leading brand
Steve: [00:16:50] How do you do it
Babu: [00:16:51] mottos and electrification. So we need to have a, we have a goal of having a stability and reliability and the standard system data across our region.
So while I'm doing that job in this journey, it look like a nice that one of the biggest thing we need to have is a better technical architecture modernizing our data platform. That was the kind of key for our standardization. So we immediately performed in a bond of a proof of concept using a cloud based a big data solution.
So an example, do you use like a Microsoft Azure and Cloudera exit, like to show them? Yeah, we can introduce these technologies in our region, in, in America that is kind of noted by our global CIO and the SVP of supply chain management. And they said, okay, you know what? This is great, but we need this globally.
Not just for one region. So then they moved me into take the responsibility of the global technology and innovation team there. My goal was building a enterprise data architecture using the next gen data platform. So we created a modular and adaptable that architecture framework. With the dis capabilities.
Now, you know, we face another challenge because ABB is building system systems for a long time. I'd be have a very Mitchard SDLC process. But now we are going to deal with new kinds of technologies, like cloud systems, big data solutions. These are, it's still growing in the industry, not matured yet, or not even cut.
Some of them might not even come out yet. So for that, we need to have a different methodology, like fail fast, recover, fast, easy to change, add zine. So we need to have a different methodologies to either implement or pirate the systems. So not only we did that point build out the architecture, we created a and have analytics delivery methodology using scale that design.
And bring some other things like in a using point of view, creating a proof of value and also using some design thinking principles and collecting the use of stories, et cetera. So we've built a framework to implement these new emerging technologies using a new model. So we also need to it to get and pitch ABB to how you do this using this new way.
Steve: [00:19:30] Right. Right. So you, so you were building all this up kind of. Next to the, the, the data and technology infrastructure that already existed. But the new platforms
Babu: [00:19:43] for managing
Steve: [00:19:44] new workloads, new
Babu: [00:19:45] kinds of analytics,
Steve: [00:19:46] new capabilities, and very experimental, it sounds like,
Babu: [00:19:49] Exactly. So what we did is as you know, what the biggest challenges with the, you know, when you are in the analytics space, things take a long time to building a data warehouse or it's an 18 month journey. So, but now we will be living there, uh, in a time that we don't have 18 months to wait. So that's where we introduced a concept called the proof of value.
And also more along with this proof of value. Okay. Yes. The technology is working, let use to solve this problem and prove it for business case. Okay. And, and be kind of used almost like a internal crowdsource funding. So rather than asking large, large, big project budget, we are asking small, broad, just like in the 50,000, a hundred thousand to.
Test and prove it off brew to show a value to the business. Okay. So, and in, because of doing that and we delivered some of that very, excuse me, very interesting. Analytical capabilities are just text analytics. Using Microsoft services, be used IBM Watson for cognitive analytics, um, um, afforded to really mine and analyze our customer complaints and issue resolution, et cetera.
Okay. So that process is also working. While doing this, we have a new CIO, John Dean ABB, and he is very passionate about data and analytics. So he came to me, said, okay, boom, we really need a chief data analytics officer for managing this. So this, we need to support three things. We need to support all our master data.
They need to support all our legacy BI and business, internet application. And also we need that support all this new advance analytics and the new modern data platform. So that's where, you know, I, he got promoted me into the role. Then I had the role for the last two years to driving this transformation across ABB.
Steve: [00:21:47] Yeah. Yeah. And
Babu: [00:21:48] as you
Steve: [00:21:48] said, I believe you said you focus on the internal business data, not on the ex the, the IOT stuff that's in um, the products that you sell to customers. Is that correct?
Babu: [00:21:58] That is correct. So I, because in a V we have, you know, uh, uh, as you know, we, we are around the globe and we have all our, uh, uh, you know, hundreds of applications and we need to capture all this data and really modernize and simplify. So that's a quite a bit of challenge to bring the data from all the systems to help, to do the internal process optimization.
Steve: [00:22:21] Well,
Babu: [00:22:22] this
Steve: [00:22:22] is a, I think probably a good place to bring in the snowflake connection because obviously that's one of the new modern data platforms. Um, uh, so I wanted to find out when and why did
Babu: [00:22:33] you
Steve: [00:22:33] start using the snowflake technology and how are you using it now?
Babu: [00:22:38] Okay, uh, so ABB Ben B, when I look at this, you know, when I, in my, uh, uh, role be happy, Two problems, two challenges. Let me put that one is we have several ERP systems is producing a lot of data for business processes. Okay. Then 70 to 75% of our user community still want standard BI solutions like ad hoc analysis and a business intelligence, you know, standard reporting, et cetera.
So we had a cloud platform, cloud big data platform to address the larger volume, the data, but the Hadoop platforms
Steve: [00:23:22] as you
Babu: [00:23:22] not. Optimized for doing traditional data warehouse or symbol BBI capabilities. I could drill down on reporting. You said ad hoc analysis, et cetera. That's where we got the interest in using some traditional, like cloud based data, health solution.
That's what we were starting to use. We are looking into snowflake. And we had, you know, then also we had some criteria is also, as you know, we are a globally dispersed large industrial manufacturer. And so data privacy and,
Steve: [00:23:56] Well this
Babu: [00:23:57] uh, uh, that data security is very, very important for us. So there are some strict regulations in certain countries and certain regions that, you know, you cannot move the data outside the country.
we need to find out that a architecture and technology who supports this kind of, eh, eh, distributed data platforms. So when we looked at snowflake, it kind of meet all our criteria for fastest. It is a compute and storage based. It is a multicloud, multitask and architecture, and it's predominantly building this in a, we will get to support the big 7000% of the community.
So we started the snowflake journey.
Steve: [00:24:38] Right. Right. Cool. And, and if I understood correctly, one of the advantages of it was that, um, you know, you were addressing the fact that a lot of governments around the world require. Um, certain kinds of data to be stored and kept locally and not kind of spread around, but with, with snowflake,
Babu: [00:24:59] bring all the data into
Steve: [00:25:01] right?
Babu: [00:25:02] That's excited to connect exotic connect. And so it perfectly fit with our architecture principle. So our architectural principle is logically one physically multiplayer. So, so I implemented, I have a snowflake platform running in, in, in USB gentle to capture all the data from us. Then I have a, you know, the same, same architecture, but a diff a separate physical out of the implementation in Europe, you capture all the European data.
And now I have another implementation Singapore, to really matter that Asia Pacific China, all those data sets in this three snowflake instances. One common architecture, one common data model three physical implementation, which means the integration of the using this data together is very seamless. I got the flexibility to manage this data privacy and the data security.
Steve: [00:25:59] Okay. Yeah. Very interesting. Now you mentioned the fact that you, as you're building on top of these modern platforms,
Babu: [00:26:07] use it uh uh in a smart phones to see that kind
Steve: [00:26:11] of stuff
Babu: [00:26:12] to,
Steve: [00:26:12] to really,
uh
Babu: [00:26:13] daily
Steve: [00:26:13] have proof of value concepts. So, um, can you remember what your first snowflake, a proof of value pilot project was?
Babu: [00:26:24] Yeah. So basically the, our first project, you know, um, uh, there are two parts we did. So one is in the way we did this. One is the, bring all the data
Steve: [00:26:34] into
Babu: [00:26:35] our snowflake platform. From all around all the region. So that exactly the work we are, you know, we are a near finishing EWB. We started like, you know, a hundred day plan to do all the data ingestion.
And, uh, you know, I want to bring this point is bit to the snowflake. You know, you have a zero infrastructure. Uh, an upfront cost, isn't it? You know, you don't need to have a hardware, software, engineers, technicians, nothing, because it's all computer storage debased, uh, as a platform, as a Sotheby's you are getting it from a snowflake.
So they said, okay, we will have a hundred day plan to bring all the ingest the data, but that's good. But like you said, then we said, okay, we need to have a proof of value. First. It had to also deliver. So we said we are going to have executive intelligence to support all our business leaders, to have some core metrics so they can drill down, analyze to see monitor the performance of the ABB business.
So we built a execute intelligent system first and deliver across our business leadership. So they, and they able to use
Steve: [00:27:40] it,
Babu: [00:27:41] uh, in a smart phones to see that
Steve: [00:27:43] kind
Babu: [00:27:43] of metrics and
Steve: [00:27:44] to
Babu: [00:27:45] business is performing on basis. That was our first deliverable and it was a huge success because, because of the cloud and computing and this power, it was lightning speed.
And it was very easy to use and we did is literally less than
Steve: [00:28:02] Well, very impressive.
Babu: [00:28:05] Exactly. That's a big, big, uh, uh, kind of a more excitement and that drive with an ABB. Now we know we are looking into much, much bigger business cases.
Steve: [00:28:18] Yeah, I would imagine that since the kind of the end users and the beneficiaries of this technology were executives, business executives that this probably
you
Babu: [00:28:29] know
Steve: [00:28:29] primed the pump for you to get
Babu: [00:28:31] approval,
Steve: [00:28:31] to do much more
Babu: [00:28:33] with the cloud data
Steve: [00:28:34] platform, I would think right.
Babu: [00:28:36] yes, absolutely. So now we know the, our goal is, you know, how you can move analytics from corporate to the floor. Isn't it. So like, you know, the objective is now to give more data and analytics capability to, to our, uh, uh, you know, the, the, the people are in the factory floor, the assembly line managers, the financial analyst.
So because they are very close to decision making. So now we know now we have, the platform is ready. We can drive that kind of capabilities. So that way, you know, you can have a, really a plan to president model, what our president is seeing. You know, you can immediately go and correlate that with a view in the plant.
Steve: [00:29:21] So does the cloud data platform really enable a, B ABB to do things that it either couldn't do before or couldn't do as well before?
Babu: [00:29:32] Oh, yes, absolutely. C E as you know, ABB, most of our data warehouse platforms are on boom. Okay. So there is a lot of costs we need to do buying the hardware, buying the software more importantly, in any data house. The biggest Italian is always the query performance. Okay. So there's an enormous amount of time people spent to optimizing the query by the hour, adding more memory, or then you, or you find out other like in memory solutions, you add on solutions, et cetera, you
Steve: [00:30:07] know,
Babu: [00:30:07] because the more data you have, your doubt,
Steve: [00:30:09] approval
Babu: [00:30:10] performance will be
Steve: [00:30:11] with the cloud data
Babu: [00:30:13] with the cloud or data warehouse.
You don't need to worry any of those things. You know, so in my team, I don't have any more database administrators or engineers I have is more now the business analyst with a technical knowledge, so they can spend more time on building analytical solutions to serve the customer. In, in my past life, I have a DBS.
I have a Unix that's I have a network administrators, and every time there's a problem, we need to bring all together and see, what is the, what is that? Why this, that is? Why is this score is slow. That problem completely went away.
Steve: [00:30:48] yeah,
yeah. So in a way you've automated using technology. In data analytics and data management, things like that.
Babu: [00:30:59] Yeah, absolutely. And also now repurpose our skill. That is a very, very important thing, you know, because, uh, uh, so, you know, we have a, you know, we have a very strong
Steve: [00:31:09] as well before
Babu: [00:31:10] ABB. They know a lot of ABB. Business knowledge and business knowledge, but Dave was spending a lot of this, uh, uh, writing Cory monitoring, Cory tuning.
Now they are all working on now really solving the business problems. So be able to reschedule an upskill, our sources.
Steve: [00:31:31] No, that makes a lot of sense. I imagine companies all over the world are doing that right now. The ones that are on the cloud platforms. Hey, so you mentioned before that you used, um, IBM's Watson technology. And, you know, I'm talking to a lot of people in the podcast and it's really clear that, you know, cognitive computing, AI,
Babu: [00:31:52] kinds of capabilities are
Steve: [00:31:53] whole array of things, whatever
Babu: [00:31:55] now in retail
Steve: [00:31:56] is exploding in the world of business.
So I wondered if, can you talk about, you know, how
Babu: [00:32:02] you're either using the Watson
Steve: [00:32:04] technology now or how you're using machine learning technology? What kind of, what's the array of things you're doing?
Yeah
Babu: [00:32:10] So we were using predominantly know the Watson technologies for, uh, integrating our unstructured and structured data to solve our quality issues. So, you know, we get a lot of customer complaints and issues, you know, can be sold a motor. The motor is not working, you know, something like that. So we brought that, that information, that text, that then be correlated with our quality information, which is back from the factories when the product is creating.
And then they started doing this cause and effect analysis. And then identifying what exactly the problem happened in the past, you know, and how many times, and then used to use this kind of a Watson solution where kind of recommending, okay, what is the next thing we need to do to solve this problem? So we were using that kind of a cost and effect analysis using IBM Watson.
And we are using a lot of our machine learning and our machine learning, AI based, uh, uh, sales recommendation. No, you are looking at you are a sales that, uh, uh, and now you are a, uh, uh, opportunity that, uh, uh, and you are customer complaints that, and see what the probability of a new Sotheby's we can sell to a customer.
So, I mean, the key thing here, Steve, is these things, you know, these
Steve: [00:33:28] kinds
Babu: [00:33:28] of capabilities are pretty much better than butter now in
Steve: [00:33:31] retail
Babu: [00:33:31] industry, if you look at that, Amazon is doing that. Lowe's is doing that. When it comes to this B to B, like an industrial manufacturer,
Steve: [00:33:38] you're either using the Watson technology
Babu: [00:33:40] enabling to really drive our, uh, uh, digitization or our, uh, digital value chain journey.
Steve: [00:33:46] Yeah. Yeah. You know, it's interesting. Um, Marc Andreessen, the founder of Netscape and the now big venture capitalist, he in the 1990s made the statement. Software is eating the world, but I saw that NVIDIA's CEO Jensen Wong
recently
Babu: [00:34:04] years
Steve: [00:34:05] couple years ago said AI
Babu: [00:34:07] statistical programming
Steve: [00:34:09] So that's a pretty radical
Babu: [00:34:10] isn't it
Steve: [00:34:11] Do you agree with, with, with Jensen? How is AI changing the way organizations
Babu: [00:34:17] use technology
Steve: [00:34:18] and use software?
Babu: [00:34:19] So I, you know, I, I have the similar view like Jensen, but I put it in a different way. AI is going to become integral part of your software. Okay. So think about that for a
Steve: [00:34:33] right
Babu: [00:34:34] Software is like ERP system helped tremendously in industrialization age to automate repetitive manual processes. Now we are an information age and AI is going to bring intelligence, self learning and cognitive to this business processes as part of the software.
You know, so that means, you know, the, these processes will automatically is going to decide who, who should I ship to the customer, which customer should ship first? You know, which product I need to make money. So it's not going. So the software is not going to go away, but the AI is more being big, big part of this software.
Steve: [00:35:17] Yeah.
Babu: [00:35:18] And,
Exactly. So the interesting thing, Steve, here is disability, introduce new
sets of skills and competencies and talent. Okay. So if you, if you look at that, you know, in the companies, now you might not need any more air traditional welder. You need a belt, a little bit, the knowledge and skills to operate a robotic welding.
So, you know, and ago, this machine learning and
Steve: [00:35:45] is going to eat software
Babu: [00:35:46] it's kind of a very elite elite,
Steve: [00:35:48] statement
Babu: [00:35:49] No, this is a becoming a commodity and every industry and every process needed this. So that's kinda my point of
Steve: [00:35:55] use technology
I got it. I think that's really good. Now we're speaking in the midst of the coven crisis, a global
of travel
You're in a global company, manufacturing all over the place with supply chains, distribution chains. And I would imagine
that this COVID crisis has disrupted.
All of those aspects of your global business.
So I wanted to find out how have
Babu: [00:36:19] you been using
Steve: [00:36:20] data analytics to deal with those disruptions and uncertainties?
Babu: [00:36:26] First of all, it's terrible. What is happening and nobody didn't anticipated this. So there was no kind of any model. Oh yeah. You know, BB, I've done this because we use it. Isn't it. So, so we need to find, so we are using this data predominantly to build a lot of correlation analysis, to see what, how our supply chain is going to affect and how we can route our deliveries and when we can deliver things.
So we are doing this kind of analysis. To really see how we want to plan and adapt in for the next,
Steve: [00:36:58] I
Babu: [00:36:59] the next two, three to six months. So we are doing a lot of analysis or not, you know, like we are using a lot of external data and see, okay, which factors we can open, how much orders me, how is it can be shipped some orders to other factories.
So these are the kind of analytics we are doing to support this corporate situation.
Steve: [00:37:18] Yeah. Yeah.
Babu: [00:37:20] Hey,
Steve: [00:37:20] now I want to ask you to put on your visionary cap and look out into the future. Say like the next five years or so. How do you see data and data analytics, changing business and even changing society?
Yeah
Babu: [00:37:34] So for me, the change has already happened in certain industries. I'll give you an example of, uh, take the case of travel tourism, retail. Okay. Today, no one is calling to a travel agent, booked a ticket. You go to Expedia, you apply
Steve: [00:37:49] this COVID crisis has disrupted
Babu: [00:37:52] and you're using a smartphone is doing that.
This is self service. Okay.
Steve: [00:37:57] been using
Babu: [00:37:58] you are expert the same capability in partial workspace. Okay. And then look at the, what millennials are doing. They are very data savvy that are savvy they're conversing and communicating through 140 characters, texts and visual rules. That's what they're using Instagram, Snapchat, exit par.
Okay. And that is one of the reasons how we able to contain very quickly. This. Pandemic like a Corvette because through smart phones and we are sending a lot of information, a lot of videos, et cetera, this is become, now this has become the kind of must have been in our professional professional work enrollment.
Because people expect that, okay. If I have a mortar is not working, I need to take a picture of that or take a video of that, uh, that issue. And I need to send to my customer service. I don't want the person come to my office or my plant. Nope. And so what I mean
Steve: [00:38:57] Hey
Babu: [00:38:57] is, I think there is a huge demand for data and analytics is going to change the way we are going to behave and VW are going to collaborate and they're going to interact and I will.
I agree. I will mention one more point before I, you know, summarize this. If you look at the, the evolution of. Information technology is started with infrastructure centric. Isn't it? We have, we are focused on hardware. Then it moved into more application centric, like ERP applications, you know, like SAP, you know, a client server application, et cetera, then be moved into more like a business process centric.
Like e-business e-commerce internet, you know, those kinds of things. Now we are moving to very data centric. So you will see most of the businesses are based on that data and the data centric business models. And if you summarize this in a one word, that's what everybody's talking about now, digitalization and the digital value chain.
So the data analytics become decent report the center point for most of the, any of the new businesses or existing business going forward.
Steve: [00:40:13] Yeah. I think there's, it's like, there's a huge way, but we're all writing it, you know?
Babu: [00:40:18] Absolute and it's already there. And as I said, the new millennial workforce, they are very used to it, you know? So I think there's a quite a bit of need to do to get that how fast we can get into this part of the business processes and the business transformation.
Steve: [00:40:36] well, Babu, I want to thank you so much for your time today. Your stories and insights about what you do with data and how you have you been using it and the benefits you get from it has been fascinating. And I also think that just the, the interesting perspective that you bring as, you know, a real data, heavy guy over, you know, you've, you've lived through several waves of technology and you've seen it advance and you're, you've always been kind of at the, at the, at the cutting edge in each of those waves.
So I think your insights have been. Particularly useful in particular fascinating today. So thanks again very much.
Yeah. Now, one thing I just wanted to mention is, you know, I had prepared some more questions about IOT, but that was before I understood that your real focus is on the business data. So I just didn't ask those questions and I think. Was there anything about IOT that you would want to say if I were to ask something or, or should we just leave that alone?
Babu: [00:41:42] So, I mean, I can say something about IOT, because if you look at that, you know, I not started, but you know, tune in probably 20, 21, I will start the journey of IOT data collection for ABB. The reason is, you know, we have factories, the factories has the machines, so we need to capture those IOT data to do our internal factory floor optimization.
So, but I'm waiting for finishing this first journey first, then I get into that, you know,
Steve: [00:42:15] Uh, what show shall we talk about that a little bit, or should we
to your
it alone for now? Oh, yeah. Yeah. We don't have to go into great depth because it's, you know, an area you're anticipating in the future. So let me just cue it up again with another question. You know, people have been talking about the internet of things for decades and.
Eh, you know, the, the capabilities have been there, but it
really
Babu: [00:42:41] also need
Steve: [00:42:42] right now, we haven't, we're starting to have an
Babu: [00:42:44] incredible
Steve: [00:42:45] installed base of devices, sensors, and the ability to bring in that data sometimes streaming real time. And you're in the manufacturing business. I would just imagine that this is
Babu: [00:42:57] the impact you know
Steve: [00:42:59] which
So I wanted to find out what, you know, what are your plans for using IOT data going into the future?
Babu: [00:43:07] Well, in a V half a hour, we have a new, separate, a function with an ABB called ability, which is our, uh, uh, you know, uh, uh, digital, digital platform for our customers. That's where we collect this IOT type data from our products, you know, because we have software's and IOT devices in our products. We collect this information to do asset monitoring and remote, remote servicing and, uh, you know, preventive, maintenance, predictive model
So that is a market we are going to get do more and more, especially with the Corvette. You know that this is becoming more and more because I don't think anybody wants people to come to your plant or factories or, or, or your facility to do maintenance. So these companies are expert to do these things remote.
So there's a huge demand is going to happen for industrial manufacturing for these type of IOT type capabilities. At the same time, you know, internally for internal prospective ABB or like companies like ABB, to capture the same data from our factory
Steve: [00:44:20] incredible
Babu: [00:44:21] And so that way we can also do much more flat floor optimization.
And if we can, based on our, you know, the
Steve: [00:44:29] And
Babu: [00:44:30] production demand, when we can start down the plant, how would we the impact, you
Steve: [00:44:34] know,
Babu: [00:44:34] which machine we can go for, uh, services. And how does it going to effect our, uh, our productivity? So there's a lot of analytics we can use using this IOT type of data coming from our internal machines as well.
But that's the one, the journey we are going to start in early 20, 21.
Steve: [00:44:53] Okay. Very good. Very good. Okay. I think that's a good addition. I'm glad we did that. I just wanted to ask Danica, is there anything else we should ask or do you feel, or was everything clear? Did we cover everything?
Oh, good.
Babu: [00:45:23] Thanks. Thank you. So, I mean, is this going to be Emmy? You're going, are you going to get me a copy before we publish? So that way, you know, if anything you need that I can.