Democratising Analytics: Gazing inward at Customer Zero – Part II

Image courtesy of SAS

Today, increasingly more organisations are using analytics to make informed business decisions. In fact, SAS Chief Information Officer Jay Upchurch notes that there has been an “explosion” of even more usage during the pandemic, particularly with cloud-based analytics. 

Upchurch has over 16 years of experience in leading organisations in the managed hosting, managed application, software-as-a-service, and cloud space. Having been with analytics pioneer SAS since 2019, Jay Upchurch seeks to deliver efficient and consistent operations support across all business functions, to help speed up how companies can maximise the value of data and analytics.

Frontier Enterprise sits down with Upchurch to discuss all things analytics, including his experience in onboarding it with AI and machine learning, deploying SAS within SAS, and bringing analytics to SMEs and SMBs.

SAS has been around since 1976. Can you tell us more about SAS’ journey?

That’s right, we were founded in 1976, just down the road from where I am in Chapel Hill, North Carolina. Our founder, Dr Goodnight, established the company with a couple of partners while doing graduate work at a local university. Their original task was actually related to agriculture and statistics, but they realised that its applicability was much broader than just that specific domain.

It’s been a fun ride, and we like to think of ourselves as the founder of the industry and the future of the industry as well. If you look back at our journey, we’ve made the progression from initially a language when there weren’t any others in this space, to a set of tools, to a platform, to a set of industries, and now moving into the as-a-service space as well. It’s pretty exciting, and we have been fortunate enough to have a 40-year track record, trust with the brand, and the number one position in the market for advanced analytics and AI. And we believe that we drive the most technology-driven decisions on the planet.

Could you share more on your journey of onboarding AI and machine learning into the analytics sphere?

SAS has the luxury of being kind of the founder of the industry, way before insights into data and what to do with data was cool.

You can imagine in the early years, the hardest part was getting your hands on the data and figuring out some sort of correlation.

So, a lot of the work early on was data management. How do I get it? How do I move it? How do I transform it? How do I enrich it to make it something that we could get some insights from?

The other way was all around business intelligence. Once I’ve gotten the data, how do I drop correlation and service it through some type of output that you can take action on or have insights into? Then, it moved more and more into visualization, because we needed to move the analytics up and into a space where we could get the outputs into the hands of business owners faster.

You can imagine that the advancements in visualisation, and specifically data visualisation, have been tremendous. It has also allowed us to start to move and orient different user types, as we got more and more adoption not only from the language but into a platform or set of tools.

Then along came a lot of things around machine learning, and ultimately different versions of AI.

What we’ve seen there has been tremendous because now, data is moving at such a rapid pace, and your models have to keep pace with the data or they will atrophy quickly.

The ability for you to code in and let the models and machine learn as it goes allows you to keep pace with the generation of data. Now, the industry continues to get into how to manage their models, given the rate of change, and how we put those analytics into action.

That’s where the concept of exposing APIs, and allowing your platforms to drive action in other systems is absolutely at the cusp of driving value out of the work that our teams are doing.

If you look at our most recent announcement this week on conversational AI, it takes this a step further. It says that voice and voice interaction is the ultimate user interface, so how do we allow anybody to interact with the platform and the capabilities that are there, and allow it to learn and understand, and ultimately gain and draw value back through a voice interface?

It’s been a phenomenal journey, and I don’t see it stopping anytime soon. In fact, during the pandemic, what we’ve seen is an explosion of even more usage, especially cloud-delivered analytics. That part of it has been fun and exciting, both as a Chief Information Officer inside of SAS — because we’re trying to use it ourselves — as well as in the industry, as we are providing that via our SAS cloud offer.

How have you been using SAS services within the company itself, especially during the pandemic?

Let me share more about the journey that all CIOs went through during the pandemic. 

We were all faced with a lot of uncertainty at the beginning. We all needed to rush our employees home. We needed to ensure connectivity to systems. And we needed to make sure that the workforce was highly productive even in a work-from-home environment. I think that challenged infrastructure around the world. We’re in a number of countries where sometimes that infrastructure is a bit more of a challenge, or it also challenged companies in terms of where their applications were housed, and how they were built. 

I often talk about the fact that the digital divide between applications that had transformed or had modernised – and perhaps moved in the cloud versus those that had not – was really visible and painfully so to all users during the work-from-home period of the pandemic. This highlighted the fact that if you had not transformed your applications — especially the critical ones that run the business — it became a real challenge for you to continue to operate.

On the other hand, the companies that had gone through their transformation process over years and years enjoyed a competitive advantage during the pandemic. They didn’t skip a beat, and were able to react fast because demand came at them even faster. During that time, I will say we were all held hostage at our home-offices and interacting through there, and so the demand on these systems was so much higher.

As a CIO, what I saw was a mad rush to get everybody home and connected. There was a period of uncertainty where nobody knew if budgets were going to be frozen, or how revenues were going to flow, because we weren’t sure what the economies around the world were going to do.

As more people became much more aware of the digital divide between applications, even at the board level, an infusion of investments into IT came quickly.

And so, CIOs, starting around the middle of last year through to today, are trying to figure out how to take this infusion of investment and apply it in ways to transform technology that makes the most difference and impact to our business. The CIO role today is basically one of using technology as a tool to drive the business forward, either through transforming it, automating processes, or enabling resources that might have been used in one form to be used in a different way for new offers.

Those are things that we continue to worry about as a community of CIOs, but also ultimately what I was doing inside of SAS. Then, SAS as a product comes into play again, because it’s used as a way to transform the business. How do I arm the different leaders of each division inside of SAS to make intelligent decisions about how they run their business? How do I help them see insights into data? Because every division has the same challenges as if you are your own standalone company.

Inside of our shop, we have a charter that’s in one of my teams called the Customer Zero Initiative. Customer Zero is the first customer that receives any and all technology that comes out of our CI/CD pipeline from R&D.

So we consume that, we install it, we run it, we operate it, and we test it, but not in the same way that we do regression testing or unit testing within R&D. We’re really doing it as an end user to see what the experience is like, so that we can give feedback that ultimately raises the quality of the product before it goes out the door to our customers.

Internally, there are also a number of different places where we’re using our own technology that I think are fascinating. The first one is we run one of the largest SAS Viya deployments in the world, and it runs our business. So all business decisions that we take are data driven, as you can imagine given what we do. We have all divisions using that, whether it’s for financial reporting, workforce analytics…the list goes on and on.

Another area that’s fascinating for us goes back to what I mentioned earlier on conversational AI.

We run our own help desk as a service to our ‘customers’, which in this case is our internal employees.

So putting that in place, testing it, and showing how it can work and how it can improve our services is another wonderful example of where we put SAS technology into practice.

There has been a drive to get analytics into the hands of everyone — not just IT, but also including line of business. In your estimation, how successful has that been across enterprises? 

There are a couple of things here that I think about. Firstly, there are sometimes more ideas than there is time in a day, and some of those ideas are very much aspirational. Yet, we all have things that we need to do day in and day out, just to run the business. We feel like that and – coming back around to providing a service to the business – we have to earn the right to do more and more advanced, interesting things.

For example, one of my divisions inside of the Cloud Information Services organisation is focused on data and analytics, and we provide that as a service to each division within SAS.

Our priority there is data management and transformation, and we want to make sure we get the data into a place where it can be used from all the systems around the world that we run. 

Number two is that we want to make sure that we have data analysts that can analyse and service that data in an intelligent way. 

Thirdly, we look at how we can surface that in a visual manner that’s most useful to the user, so that they can interact with it, model with it, gain insights, and decide what actions to take. 

The fourth step is more interesting and aspirational for a data scientist: looking at the art of what is possible and really exploring deeper into what’s there. So, it’s not just answering a question, but beginning to let the data guide you on your journey. That’s the way we think about how we’re providing that as a service to our divisions.

This is also how our consulting organisation thinks about engaging with our customers. It’s interesting because there are absolutely some fundamental tasks that have to happen that are IT-centric, but as a vendor of this service, software, and solution to the industry, we want to make the first couple of steps as easy as possible. That means getting our platforms into that no-code or low-code space. This is a space where you do not need to be a PhD data scientist to be able to use and interact with the data. You can start to see this with things like our integrations with Power BI via our Microsoft relationships, where you have the SAS engine under the hood, and you can surface that visualisation in any way that you’re comfortable with, in any interface.

Another way that you see it is around the use of APIs. We’re trying to create that ability where you don’t have to log into SAS as a platform to do something, but you can extract, pull, and use data wherever you want. Another example in terms of what we’re trying to do with our platform is to enable industry solutions that sit on top of that, and you’re already seeing very business-driven outcomes from platforms that are in risk, fraud, retail, or Internet of Things. Again, we’re removing the barrier to entry and the barrier for adoption into our technology. That’s going really well so far, and I would say second to cloud, the industry solutions are probably the second fastest growing part of our business.

What advice would you give to SMEs and SMBs who are taking their first steps in the analytics journey?

I think there are two parts to that answer. Firstly, around the world there is a talent famine for IT skill sets in this space, and we all bow to it. I would say that all businesses, especially SMEs, have to decide whether this is a war that they want to wage, or if they’re better off buying that service from someone. 

For that reason, we offer our SAS Cloud solution. You can buy our technology, our innovation, and consume it as a service in our managed cloud experience. It’s an installation for you where we manage the data, the uptime, the platform, and we get back with a service-level agreement. In that way, a small- or medium-sized company can come in and they can just start using the applications immediately, and so the time to value is faster. And because we have the scale, and we do this for over 550 customers around the world, who else is better equipped to run this than the people who are running the biggest cloud on the planet for SAS analytics and also the folks who actually wrote the software? It comes back to how we all – myself included as a CIO – compete for talent. That’s why I think you see so much growth in the cloud, and then specifically cloud as a service.

The second thing is that we’re aggressively working our portfolio to move beyond just the enterprise software that may run in the cloud. We’re doing much more with the as-a-service platform that you can run on larger installations, multi-tenanted, with multiple customers running on it. So, you’re basically buying time, or a slice of that environment. Our Customer Intelligence 360 and market intelligence portfolio are a great example of that, and you’ll continue to see more and more releases of that in our software, especially in those industry solutions.

Historically, going back 20 years ago, it was the big enterprises that got into this. They had the data, they had the need, and they needed to get better insights.

A poor decision at the enterprise level could cost hundreds of millions of dollars. They needed that extra level of confidence before making business decisions.

So, it was natural for them to make the investment. 

Now, we’re trying to do this for the SMB and mid-market. This isn’t necessarily because they have the same risk of costs and decisions, but much more because of business agility, where they need the ability to move and go quickly. That is really where analytics AI, machine learning, and data visualisations come into play.

So much of your work previously has been in the hosting and cloud space. What drew you to SAS, and what drew you to analytics?

Analytics is so fascinating to me because it challenges you to view the world in a different way. We believe this at SAS – and this was a story that spoke to me when I was here as an intern back in the late 90s, and when I returned again two years ago.

If you take the time to look at data, and you’re naturally curious about it, it takes you on a journey and it tells you a story. 

This is a story that you wouldn’t have seen had you not taken the time and allowed your curiosity to drive you. To me, that’s absolutely fascinating. I love that we’re changing the world with the use of data. And I love the fact that we’re both doing it in a philanthropic way with our Data for Good stories, but also in a commercial manner, with customers across all industries and around the world. That spoke to me as a personal passion. Again, I think we’re on the cusp of what’s really going to be an era of intelligence, and that intelligence is being driven through the use of SAS technology.