SaaS-based Enterprises – Their Struggle to Leverage Data and What They Can Do to Overcome It

ET Bureau: What, according to you, can help enterprises to rapidly increase their data accessibility and literacy?

Roman Stanek: Ramping up data accessibility and literacy starts with reducing data complexity and time-to-value. As data volumes grow in size and live within multiple environments, it becomes increasingly difficult to maintain, secure, and utilize data across a company.

When it comes to data, I always say, “You need to be able to trace it, or you won’t trust it.” Once a data set is transformed, most users don’t know or can’t easily understand its original intent. The worst thing enterprises can do is complicate a task for their users to o figure it out on their own.

But that’s the big problem with a lot of today’s data value chains: there’s zero visibility and transparency, with everyone building their own data tool not catered to the end-user. Enterprises need to focus entirely on transparency, so their end users can easily follow the data chain. They need to build trust, which can’t happen until enterprises translate their data into accessible business terms.

ET Bureau: How well do you think enterprises operating on a SaaS model are able to leverage data to their advantage?

Roman Stanek: Actually, many SaaS companies are biting off more than they can chew and struggle with data. In some ways, the SaaS model is at odds with what makes data-centric companies prosper: adaptability and flexibility.

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SaaS companies are experts in use-case driven development — but data is the opposite, and SaaS companies have struggled to adapt. To successfully leverage data, they need to reorient processes around the concept of a flexible, unified data value chain as opposed to single-use-case initiatives.

Look at marketing data, for example. It’s been discussed for decades, yet companies still struggle to know what’s important, how to talk about it, and what to trust. If they don’t have the flexibility or expertise to master marketing data, how can they expect to solve this problem quickly and independently?

Another problem is that most SaaS execs don’t even know this is what’s holding them back. Data is such a big black box that C-suite execs don’t want to talk about or critically examine, so they delegate it to lower-level data scientists.

A lack of strategy leads to a lack of investment, which leads to a lack of return — this creates a company-wide disconnect between what the CEO expects of its data team and what role data plays in the end-user experience.

Every enterprise company has a unique need for data analytics, but because of this “unknown unknown,” can rarely read and understand the data their SaaS vendor provides.

ET Bureau: How can enterprises successfully transform their approach for a flexible and unified data value chain, while working in a SaaS framework?

Roman Stanek: Ten years ago, the data value chain was cumbersome, rigid, and full of bottlenecks before data could be distributed within an organization. The enterprise data stack is moving towards greater simplicity and accessibility, but now companies have to contend with rising data complexity — including larger volumes, diverse environments, new regulatory frameworks, etc. And especially as formula errors continue to show up in 94% of simple data spreadsheets today, it is critical for SaaS companies to pay closer attention to the quality of their data value chains from the start.

The industry has reached an inflection point, and cloud is a pivotal driver of change. The value chain is about to be completely shaken up, and companies will be forced to scale their BI and “adapt or die” with regards to this new, cloud-dependent data value chain.

Those who survive the shakeup will be the ones who turn to flexible, customizable, controllable data solutions to drive decision making and increase transparency at every level of their organization.

They will also be the ones who leverage the cloud to monetize their data and drive customer acquisition, retention, and revenue. Lastly, these companies will be better equipped to manage compliance and governance without worrying about increased risk.

ET Bureau: How can AI and Machine Learning change the landscape of BI and analytics in the foreseeable future?

Roman Stanek: Before companies use AI and machine learning in their BI and analytics, they first need to make sure they have the right expectations. To start, they need to have a lot of data. This is critical because so many companies make the mistake of approaching AI and ML with small amounts of data when the model needs more.

AI and machine learning should also be used to run predictive analytics on repeatable patterns, not just for data exploration. Leveraging AI for exploratory purposes won’t make an impact on a company’s ability to analyze data — its real value comes in predictive analytics and improving decision making. That said, companies need to be fully committed to AI and ML. These technologies are a big investment and will be a non-factor for BI if not given sufficient resources.

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When done right and with the proper expectations, AI can sift through massive amounts of complex data and free people up to focus on other core responsibilities. It learns quickly on its own and replaces transfer learning (the last great task of data scientists, if you ask me) with reinforcement learning, where the model trains itself. Only then can enterprises enjoy machine-accurate recommendations that make true data democratization and digital transformation a reality.