The Democratization of Data and Analytics (with Alteryx)

Alan Jacobson: Ten years from now, a very large percentage of companies that exist today aren't going to be around anymore. I believe analytics will be one of the key reasons why some of them are winners and some of them are not, and so there's no better time than today to start on the journey.

On Alteryx and the chief data and analytics officer role

Michael Krigsman: The Democratization of Analytics. That's Alan Jacobson, Chief Data & Analytics Officer of Alteryx.

Alan Jacobson: Alteryx is a company that is helping individuals go on their analytic journey, become more capable of performing analytics on data to get answers and drive their businesses forward, and really helping companies digitally transform, become more analytic as a company.

Michael Krigsman: You are the chief data and analytics officer at Alteryx, so tell us about that role.

Alan Jacobson: I have really three roles at Alteryx. The first role is the role that's probably most common of most chief data and analytic officers, which is helping the company use data and analytics to make better business decisions, to help our customers achieve better success. That's probably the most normative role of a leader of a data and analytics function.

The second role, our product has data science in the product so, at times, our team is helping create algorithms that go into the product itself.

The third role is helping customers go on this analytic journey. So many companies are trying to become stronger at using analytics to get outcomes. The third role is helping customers go on that journey.

On organizational data strategy

Michael Krigsman: You're working with many different organizations, many customers, and I'm sure you see a lot of patterns of how different types of companies implement analytics and work with data.

Alan Jacobson: The general theme, 99% of companies out there are investing time, effort, and resources on becoming more analytically capable. The desire to learn how to use analytics better than they are today is pretty uniform across all companies.

The problems that are being solved are obviously quite diverse. Whether you're a government agency battling a pandemic, or whether you are a private company working in oil and gas or in the automotive industry, or a bank, all of these different organizations have analytic challenges that are different.

But I would actually tell you that, even there, the typical pattern with analytics is they typically follow domain lines. So, an HR analyst, whether you're working at a manufacturer or a bank, have very similar questions. What's driving attrition? How do we find better candidates to hire? How are we doing with people development?

On the data mindset and adopting an analytics and data culture

Michael Krigsman: Why is it just so hard for many organizations to adopt the data mindset and to adopt analytics and use analytics really well and effectively?

Alan Jacobson: We all know change is hard, and so if you're an accountant that's been doing accounting the same way for a decade, you're highly successful, and you're doing well at it, why do you need to learn a new skill? Even if you recognize you probably should do it, it's still hard. Not because analytics are hard – analytics are actually not that hard – but the transformation piece is quite hard.

At an individual level, this is difficult. You take that to a department or an entire enterprise, and it just gets harder and harder.

There have been studies on transformation in business. Many transformation efforts fail, and digital transformation doesn't have a higher failure rate. It has the same failure rate as all transformation.

It just comes down to change. But you'll also need to use the principles of change management to help people go on the journey. That's equally if not more important, at the end of the day.

Michael Krigsman: The common issue then is the process change, the rethinking how we go about performing the activities of a particular department. It's not usually the technology itself.

Alan Jacobson: I would actually say it's one even simpler than that. It's not simply rethinking the processes. It's having an individual be willing to kind of invest in themselves and learn new things. In today's world, it's changing dramatically.

Look. If you were a marketer 30 years ago, you didn't need to know anything about the Internet to be a world-class marketer because there was no Internet 30 years ago. But today, you'd be pretty hard-pressed to be a marketer without knowing a thing or two about the internet. In a time span of 30 years, we went from, "You didn't need to really know anything about it," to "If you don't know something about it, you don't even have a job."

I believe the same thing is happening when it comes to the world of analytics and automation. It used to be you didn't need to know anything about these things. You could simply be in a spreadsheet doing sums and averages and that was good enough.

While we might not be in a world today where the answer is, "If you can't go beyond the spreadsheet, you're unemployed," we're not there yet. Certainly, you can be gainfully employed without these skills. I do think the day is coming.

If you've been in the working world for maybe a decade, and you're really good at what you do, you probably still have to keep learning these skills and learning tools, like Alteryx, to be able to continue to go on that journey.

That's hard, and I think career professionals that maybe are retiring today didn't have to reinvent themselves and learn new skills at anywhere near the pace that people entering the job force today are needing to learn them. I think that's obviously quite difficult, and I think employers have to figure out ways to help people go on this journey.

On making data and analytics easier to use

Michael Krigsman: The role of data has become absolutely essential for many if not all organizations. The real question is, how do we address this situation where analytics is so important, and yet there is something that's holding us back? How do we broaden the use of analytics and make it easier? If you're a business leader, what should you be doing?

Alan Jacobson: First, I think it's important to know where you are in the journey. We just launched a free assessment tool on our website where business leaders could come, and they can answer some quick, easy questions to kind of get a sense of where are they versus others in their industry or kind of overall, in general, and understand kind of where their gaps are. I do think it is important to have a sense of where you are.

That said, I think most leaders know that they need to get better as an organization in the world of analytics. It's a moving target. That their competition is going to be using data and analytics effectively against them and that, fundamentally, having these skills is really important.

I spend a fair amount of my time meeting with C-suite executives at various companies and showing them how leaders in the world are using analytics to drive their businesses forward. I think leaders can't simply (from the hilltop) ask everybody to learn how to use analytics. They too need to go on the journey and learn about it as well.

Michael Krigsman: Can you give us an example or two of organizations that are using data, different functions for example? It doesn't have to be a specific company, necessarily, but give us some insight into how data can be really effective (working with the right kind of analytics).

Alan Jacobson: Our customers are highly varied. We have many examples in the current pandemic where government agencies have used Alteryx. Some of the statistics you probably see on websites may have been run using Alteryx to try to understand where are we on the journey of fighting this pandemic.

When natural disasters occur, a hurricane hits a Caribbean Island, how do you do damage assessments using analytics versus having to send in an engineer to evaluate each structure? Imagine you have over 100,000 structures that have been damaged, to do that quickly and efficiently. Doing it analytically is a game-changer.

I'll use Irma and Maria hit the Caribbean Islands. That did have over 100,000 structures damaged. FEMA, using these analytic assessments, was able to really get people on the road to recovery years faster than what the old process would have been by sending humans to every single structure.

In the private sector, we see older businesses that had been around for a long time, like Stanley Black & Decker, who had been able to use analytics to really accelerate their business and move at a much faster pace. Big four consulting companies with over 100,000 users of Alteryx really automating huge amounts of their business, gaining massive productivity.

On data and analytics in large organizations

Michael Krigsman: When you talk about large organizations with over 100,000 users of Alteryx, that means that the analytics capability is spread very widely through the organization. Is that the goal?

Alan Jacobson: Really, it's something every knowledge worker should be able to do. Really, this isn't new. If you rewound the clock, we'll go back over 100 years ago, and you're in London.

I'll use another pandemic example. It's the Cholera outbreak. A gentleman named John Snow went to go see if he could help end this pandemic.

With some data and some analytics, looking geospatially at where people were contracting the disease, he quickly figured out that it was a pump, the Broad Street pump, a well pump where it seemed the pattern was suggesting where this disease came from.

He went to the town council and said, "We should remove the handle from the pump," and he did. It ended the outbreak.

I would say, in that example, was he a data scientist? No, he wasn't a statistician or a mathematician.

What was John Snow? He was a doctor. He knew a little bit about data and analytics. Combining those two things – a large amount of domain knowledge, maybe a smaller amount of analytics knowledge – combining those two things, he changed his talent.

What we see as the pattern is the same pattern today. You take an accountant, a marketing professional, an HR professional. You provide them with a world-class, easy-to-use tool where they can drag and drop and quickly get answers to questions using data and analytics.

They learn a little bit about math. They bring their domain knowledge. And they can change their world. They can change the business that they're in. They can make huge impacts. That's really what this is all about.

On metrics and KPIs for data analytics

Michael Krigsman: I think when we're talking about business transformation, the idea of metrics, measurements, and KPIs make it easier for people to know where they are. When it comes to data and analytics, what kinds of measurements or metrics do you recommend for evaluating success and evaluating progress?

Alan Jacobson: At the end of the day, ROI is many times what business stakeholders want to see to feel comfortable. But what I can tell you is eventually it needs to move away from that.

The reason it needs to move away from that is quite simple. If your business doesn't understand really the importance of analytics and automation in the business, you're probably going to have a more fundamental issue.

What we see with most companies is, early on, they measure ROI. They get tremendous ROI. They recognize the importance and that it does deliver ROI.

Then they focus more on the input metrics. What percentage of our workforce is enabled and able to do this stuff? If I only have a small percentage of my workforce that can do anything beyond a spreadsheet technology from 30 years ago, it's probably a pretty good indication that I'm not going to be a leader in analytics, and that's probably not going to be great for the outcome of my company.

The International Institute of Analytics has assessed many, many companies on how analytically mature they are. They've seen very direct correlation between the more analytically mature an organization is to the outcomes of the company (whether that's shareholder value creation, revenue growth, profit). All of these things correlate.

Michael Krigsman: Really, in this analytics journey, the first set of metrics are going to be around how is the organization maturing or, another way of saying it, how are we adopting analytics and the use of data across the company?

Alan Jacobson: That's right. Frequently, organizations start there from an input metric, and then the output metric is value creation.

By having many people using analytics, you would expect as an outcome of that – and the data has shown this is what happens – is that value is created, whether that's cost savings, bottom line, top line, revenue growth. One way or the other, value is created.

Michael Krigsman: When you talk about creating value, at this point you really shift into operations.

Alan Jacobson: Mm-hmm.

On operational metrics for evaluating data maturity and usage

Michael Krigsman: Can you elaborate a little more on the metrics associated with active use, with the operational aspects of maturity, and using analytics across the organization?

Alan Jacobson: It really depends on what area of the organization you're in what the measurement is going to be of the value that you're creating.

You're a tax person, and you're doing, let's say, VAT tax recovery. Maybe it's a somewhat manual process. You're manually taking receipts, and you're filing it to get your VAT tax recovery back for your company.

If you're like most companies that do that, once the VAT amount is below a certain amount, you probably don't even bother because the cost to do the recovery is higher than what you're going to recover because it's a manual process.

Imagine that that tax person learns how to automate a process like that, and they now have automated the VAT tax recovery process. Now they're going to do 100% recovery because there's no VAT too small to go recover the tax.

In a large, multinational company, you can imagine that could be millions of dollars that that automation will unlock for you that you weren't getting before because it was a manual process. You were leaving that money on the table. Just using the power of automation, that one tax person automating that one process may have unlocked millions of dollars of savings.

That's what we see happening again and again, area after area, whether it's in accounting, audit, or finance, or whether it's in HR, marketing, or a product team. People unlocking things that were manual, things that weren't using the power of analytics to find dollars on the table.

Michael Krigsman: At the end of the day, the value is connected to the business problem you're trying to solve or driving efficiency across a function. It's going to depend based on the situation, as you just described.

Alan Jacobson: That's right, and it's really these domain experts that know where the gold is buried, in a sense.

I, as a data scientist, didn't necessarily know that you could save millions of dollars on VAT recovery. But a tax person who does this every day kind of sees it.

What we find is when you teach people how to do this kind of stuff, it really unlocks a new world of opportunity for them, and they start finding money everywhere.

On data governance for analytics

Michael Krigsman: Alan, where does governance fit into this picture?

Alan Jacobson: Just putting a box of tools in the corner and saying, "Hey, grab one. Good luck to you," is probably not the best recipe for transformation and success.

We find, with companies, helping them with best practices and governance is part of how you help them go on this journey. Obviously, we've had a lot of experience with about half of the Global 2000 using Alteryx.

At the end of the day, these practices are really simple to implement. They're not hard. Bring people together and have them help create the right process.

Michael Krigsman: You've been describing governance, but another component of all of this is talent. What kind of team should we have in place in order to be effective with data and analytics?

Alan Jacobson: Certainly having a conductor at your company, somebody who is going to lead the organization on this really change management journey, is really important. The modern data science team, this is part of what a modern data science team typically does. It's not simply about the data science team building solutions but helping the organization go on this journey.

On data science and analytics training and employee upskilling

Michael Krigsman: How should companies go about training and upskilling their employees to make this transformation easier?

Alan Jacobson: There are two other steps that are, I would say, equally if not more important. If you want to transform, you usually start by having to get people aware and excited. You need to get people aware and excited that there might be a new way of doing the thing that they were doing before.

Then comes training, which is what you referred to. Again, I think most organizations realize you have to do have different kinds of training.

But then the third step is sustaining. We've all done this. We've gone to the training class and then we leave the training class and we don't put it into practice. We don't have that reinforcement helping us continue on.

One of the things we have found is, in the first one, getting people aware and excited, and in the last one, sustaining, what are the programs that you're putting in place to keep it going, building a community of practice. You have coffee hours where people can come and get some help and support as they go on the journey.

What are all the things that you're going to do to sustain and support it? Those become equally if not more important than just the training.

On creating a culture of data and analytics awareness

Michael Krigsman: You're really talking about creating a culture of data and analytics awareness and comfort, so that people put it into practice on an ongoing basis.

Alan Jacobson: That's right. I use the example of a consulting company with 100,000 people using Alteryx on a day-in and day-out basis.

Now, if you think about it, if each one of them just automated one process a month, they'll have automated over a million processes in their business by the end of the year. You can't do that with a team of 20 hotshot data scientists sitting in the corner. They're not going to be able to automate a million processes in the business.

It really is about how you weave this into the fabric of a very large percentage of your knowledge workers, if not all of your knowledge workers.

Michael Krigsman: Sure. you have your data scientists doing specific, unique things that they can do, but really the goal is to spread this data awareness and capability broadly through the company.

Alan Jacobson: That's right. Even the data scientists actually get great benefit from this.

You'll find that many data scientists will say, "I'm bogged down with data janitorial services, and they ask me to make these really simple bar charts. 'Can you build me some blue bars and some red circles and some green diamonds?'"

They come to a data scientist for that help, which is kind of crazy if you think about it. That's not why you hired a Ph.D. data scientist at your company to build that bar chart.

At the end of the day, when done right, this frees up the data scientists to work on the higher-value stuff.

On democratizing data and analytics

Michael Krigsman: Any quick tips or advice for making this initial period of time just as simple and as easy and as effective for the organization as it can be?

Alan Jacobson: One of the things we love doing is introducing companies that are just starting on the journey to companies that maybe are a few chapters ahead. We will frequently pair up leaders from different businesses in different industries so that they can learn from each other.

If you haven't started on the journey and started to accelerate it pretty quickly, there's a pretty large existential threat. Ten years from now, a very large percentage of companies that exist today aren't going to be around anymore. I believe analytics will be one of the key reasons why some of them are winners and some of them are not, and so there's no better time than today to start on the journey.

Michael Krigsman: Alan, what can chief data officers and chief analytics officers do to support the organization as a company traverses this journey?

Alan Jacobson: Chief data officers, CDAOs have a really large responsibility for helping their organizations go on this journey.

It's a relatively new profession. The first titles were chief data officers. The next title was chief analytic officer, sometimes combined, chief data and analytic officers.

Today, some companies are starting to have chief transformation officers. The job is the same job. It's really the same stuff.

We're seeing companies have tremendous impact, really transforming their business with analytics. The CDAO is really sitting in kind of the driver's seat of this and is responsible for helping to drive it.

Michael Krigsman: It's digital transformation. That means helping the organization change and evolve.

Alan Jacobson: Absolutely right.

On Alteryx and making analytics easier for users

Michael Krigsman: Alan, as we finish up, do you want to give us the quick sales pitch on Alteryx?

Alan Jacobson: I believe Alteryx is a world-class tool for helping people go on that journey. Whether you're in middle school like my kids, learning how to use Alteryx and becoming Alteryx certified, or you're a knowledge worker in accounting, HR, marketing, or some knowledge field, it's incredibly easy to do.

I urge you to just give it a try. We've got free trials that people can try it out with no risk. We'd love to sit down, meet with people, and show them how to go on this journey.

I've seen, over the course of my experience with Alteryx, that this can be incredibly impactful not only to the companies that adopt it, but really to the individuals. It can change their career trajectories because this is becoming such a critical skill that so many companies need are people that can use analytics to drive their businesses forward.

Michael Krigsman: Alan Jacobson, Chief Data & Analytics Officer of Alteryx, thank you very much for taking the time to talk with us.

Alan Jacobson: Michael, thank you so much for having me.