Not long ago, data and analytics used to be solely delegated to data scientists. Yet, as technology evolves, and business needs change — coupled with the talent crunch for data experts, companies simply cannot cling to the old ways.
For Mark Anderson, CEO of Alteryx, putting the power of data into the hands of more and more people has always been the goal.
“We’ve always talked about this notion of democratising data and analytics, not just the data scientists that work on the 20th floor that deliver the work for the business. It’s got to be all the data workers. It’s got to be all the analysts in the enterprise— and (they should) have access to easy-to-use technology to really start transforming the business,” Anderson told Frontier Enterprise in an exclusive interview, during Alteryx’s “Inspire on Tour Singapore” event.
According to the chief executive, the amount of data produced within an enterprise is causing headaches for many decision makers, hence an attitude shift towards automation.
“I think it’s been a big forcing function that companies are no longer going to tolerate manual work. They’ve realised (that) they’ve got to get control over the volumes of data that swirl around their business,” Anderson said.
Upskilling is key
With more organisations leaning towards digitising their business, Anderson admitted that 2022 is an exciting year to be in the data and analytics field.
Alteryx, which now has over 300 personnel in APAC, expects to have more than 3,000 employees globally by the end of the year.
According to the CEO, before any digital transformation can be successful, upskilling of personnel should be in the pipeline.
“First of all, you need to have senior executive support, (because) it is all about the people, and the experience that you curate for these people, to teach them these new capabilities,” Anderson said.
Secondly, upskilling should be voluntary. Anderson shared an experience by a large enterprise on how they went about it:
“They (the company) started with a six-month project, and handpicked 50 people that volunteered to take the education (for) just a week or so, and then started ideating or suggesting projects that they can automate with Alteryx. Within those six months, they built the workflows, and these workflows are basically automated processes that pull data from a number of different sources, apply analytic tools to it, and then deliver it to an output. It could be like a Tableau dashboard, or it could go into a cloud data warehouse like Snowflake, or Databricks. And those 50 people over the next six months, they got certified. By the end of the six-month period, 600 people applied for the next phase,” the CEO recalled.
“We offered them training. We brought our own folks in to do the training. And then we helped them by suggesting use cases. Within a year, over 600 people were automating workflows and automating processes that then get embedded into a more digitised business process,” Anderson added.
Making the most out of data
Aside from its Analytic Process Automation (APA) platform, Alteryx has developed several other solutions following a series of acquisitions.
Auto Insights, for example, was born after the company bought Hyper Anna, a cloud platform for AI-driven business insights in 2021.
“It really allows us to sell to a different type of user, (which is) more of an executive that wants to take a bunch of data (to the next level), because Auto Insights can actually build charts and graphs that point you in areas that may not be obvious to you, that you should dig deeper into. You might use Auto Insights to help make sense of the forecasts that are coming from all of your regions around the world, for example,” Anderson said.
Meanwhile, Alteryx’s acquisition of San Francisco-based cloud company Trifacta in 2022, helped power its Designer Cloud platform.
The company also launched its Machine Learning platform, which was specifically designed for people who use Alteryx’s software on a daily basis, rather than data scientists. Anderson likened the solution to a software wizard, which pre-suggests certain models that can be used to simplify the journey around building basic ML capabilities.
“I think the biggest problem that companies have with machine learning is the handoff, like if you have citizen data scientists that are manipulating data with Alteryx, and are doing some analytical work on it. To then embed this into a machine learning model, you have to hand it off to one of your 16 PhDs that lives in another country, or in another time zone. And often, that handoff is manual, and what comes back might take a month, and then maybe there’s something wrong, (so) you have to give it back to them. We think people want to do this in a more progressive way. That’s what we try to do with machine learning,” Anderson explained.
The future of analytics and automation
With many businesses having already made the digital shift, what promising opportunities could be down the line for analytics and automation?
The vision, Anderson said, is to make their platform more useful and accessible for a lot of people, noting that the demand for data solutions will only continue to grow.
“I think in the next five years, more progress will be made than in the history of this market, because I think the forcing functions are in front of us, like the the pandemic supply chain issues, the inflation, and even recession in some countries — those are forcing companies to really understand the data that swirls around their business,” he remarked.
“We’re working to be able to make our software provisional, from anywhere in the cloud, from any device. If we go back, say, two years, you had to have Windows to use our technology — either Windows desktop, or Windows server. We’ve been on a really focused march to reduce friction. And that’s what going to the cloud gives us, and having a cloud-based platform, which will be commercially available next year. That just gives us the ability to rinse and repeat, and add more capabilities,” the CEO continued.
Just recently, the company built an analytical app to help businesses determine gender pay equity.
“We built a simple app (where) you put the geolocation of where the person resides, how they represent themselves, you know, male or female, and you enter the proposed salary. And it will tell you, from all the data sources that we’ve connected to, whether you’re paying them below or above what the other representation would be,” Anderson revealed.
“I think that a big part of our platform going forward is we’re trying to make it easy for people, with use cases that are available and downloadable from our website. We have these things called solution blueprints that are like easy buttons and templates for customers to use, to be able to get up and running with workflows faster,” he concluded.