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Solving the SaaS platform data problem through visualization and analytics

(Image credit: Image source: Shutterstock/ESB Professional)

SaaS has a data problem. It’s as simple (and as complicated) as that. We all know the benefits of SaaS, from bringing down timescales and costs, to providing better scalability and expertise, and how it’s driving growth across the tech sector. Gartner predicts the SaaS market will be worth $143.7 billion USD by 2022, up 79 percent in four years, demonstrating its exponential rise.

We’re also aware of the power of utilizing the data generated by SaaS to increase these benefits further. Yet poor insight and analytics functions within SaaS applications mean we’ve now reached a point where SaaS vendors and SaaS users are swimming in unharnessed data and those valuable insights are sinking to the bottom of the proverbial sea.

Seagate released the findings of a major report in 2019, which predicted worldwide data creation will grow to 163 zettabytes (ZB) by 2025, ten times the amount of data that was produced in 2017. You’ve heard the term ‘data is the new oil’, due to the powerful insights and value that can be generated from it. But, just like crude oil, it’s useless unless it’s refined.

Making sense of data is a perpetual issue for business, with potentially major ramifications. Once upon a time (not so long ago I might add), this data was largely held within an organization itself. But now, with the rapid rise of SaaS, the issue has been complicated tenfold, with large amounts of data held within the hands of multiple software vendors, large and small.

Most SaaS vendors enable their customers to export the raw data held on their servers, so it’s not an issue of accessibility here. The problem is in presenting the data in a way that enables decision-makers to dice, slice and drill down into the data and insights that meet their needs at the source, without having to move it.

And I get it, a SaaS vendor’s roadmap is a complicated beast, with conflicting priorities. There’s never enough time, resource or funding to do everything you want, as fast as you and your customers want to do it. But providing effective business intelligence (BI), reporting and analytics is now business-critical and should be a top priority/demand for every SaaS vendor and user.

The power of visualization and analytics

The age old ‘A picture is worth a thousand words’ suggests complex and sometimes multiple ideas can be conveyed by a single image. In business, I believe this is still as relevant now as it was when the saying was first coined more than 100 years ago. Humans are visual creatures, and we need information displayed properly in order to process it. Research shows that the use of color increases our attention span and recall by 82 percent, and that 70 percent less time is spent finding the right data when colors are used properly.

It has also been found that humans can remember more than 2,000 pictures with at least 90 percent accuracy, even up to several days after they had seen the original. This highlights just how valuable imagery can be during the decision-making process for businesses.

SaaS platforms need to give users the tools to self-serve their own analytics, within the platform, in a simple and visual way. Data insights should not be left only in the hands of data scientists. Who knows more about the data that will best empower decision-making than the decision-maker itself? It’s up to the SaaS platform to empower that decision-maker by presenting the data in an engaging format.

Predictions and insights

 Whilst BI, reporting and analytics help you analyze your current and past position, SaaS vendors can give users additional value by offering self-service predictive analytics modelling. Not only does the analysis give users relevant and accurate data, it also provides the context behind it to drive better decisions and improve performance. This will help users become confident in predicting outcomes based on the live data at their disposal. Remember that data can be monetized - not by selling it, but by enriching it, turning it into something meaningful and untapping insight that customers may not otherwise ever unlock. And in this respect, vendors are sitting on a goldmine (or oil well, as it were).

For customers using multiple different applications and suffering from SaaS sprawl, offering scheduled and automatic alerts and nudge insights can help add an additional layer of value and cut through all the noise to enable them to make decisions fast.

Solving the problem fast

For SaaS vendors big and small looking to level up their insights, analytics and reporting, there’s one major, perennial question they have to overcome – build vs buy/partner?

In the past, many vendors would choose to build, for fear of an external solution not meeting their specific criteria, not integrating properly with their existing systems, and eventually causing more problems than it solves. Using your own team that has intimate knowledge of your product is the safe option of course. But can you be positive that your internal team has the same expertise and problem-solving experience that a partner or vendor does, a partner that has been operating in this sector for decades?

But by partnering with an external provider past, present and future data insights can be embedded seamlessly into an application in just a few days, if not sooner. It is now possible to pull together disparate sources of data to deliver a bird’s eye view in a customizable format - all without writing a single line of code.

In the words of the O’Jays, you’ve got to ‘give the people what they want’, and by empowering SaaS users to analyze and make decisions on their data in real-time, SaaS vendors will not only keep their customers happy but continue to grow their own businesses by retaining existing customers, increasing their pipeline and monetizing new revenue streams from the pool of data. Now surely that’s a win-win?

Zandra Moore, CEO and co-founder, Panintelligence (opens in new tab)

Zandra Moore is CEO and co-founder of Panintelligence.