It’s time to embrace the power of radical simplicity.

– By Charles Miglietti, CEO and co-founder of Toucan Toco, shortlisted in the 2021 SaaS Awards.

We live in a world that’s powered by data, and virtually every business — from SMBs to multinational corporations — now uses data to drive smarter decision making across their organization. But the corporate dataverse has a dirty secret: while we’re all using data in countless ways, most organizations are doing so incredibly inefficiently. Instead of making us faster and smarter, abundant data can too often become a distraction and a headache — not a force multiplier, but rather a drag on our efficiency and productivity.

The reason is data friction — the countless little complications and annoyances that seep into our data processes, and make our decision-making slower, less reliable, more expensive, and more complicated.

The reality is that while we have amazing tools at our disposal to store, access, and use data, implementing analytics effectively is simply too damn hard. There’s friction at every step of the process, from setup to distribution. And unless they can eliminate that friction, companies will keep on struggling — spending time and money chasing ineffective solutions, learning to live with frustration and avoid using data technologies, and making less intelligent decisions.

That’s an existential problem for today’s businesses — because if your rivals are using data better than you, they’re also responding faster to customer needs, and making smarter decisions in the face of new opportunities and challenges. In the long run, data friction kills businesses. If you don’t want yours to be among the casualties, it’s time to get serious about creating truly frictionless data infrastructure.

Five kinds of data friction

Why is data friction such a big deal? Simply put, it’s because friction is everywhere. Every single decision that’s made as we deploy and use data creates scope for things to go wrong, and every step of the way businesses need to dedicate time and resources to solve problems, creating layers of complexity that make it harder and harder to bring data into our decision-making processes.

Crucially, this is true both for established businesses and those that are building from the ground up. After all, established organizations have infrastructure and processes in place that weren’t designed for their current data needs, making it harder to realize the potential of data analytics. Smaller, younger organizations, on the other hand, have limited resources and need to build everything from scratch — or outsource tasks to a patchwork of third-party providers, creating even more scope for friction to set in.

Data can be complex and difficult to manage

Here are some of the key areas where data friction can take root:

  1. Connecting to data. In order to make use of your data, your analytic tools need to be able to connect with it — but how can you actually forge that connection? Often, you’ll find your IT team needs to spend time hammering out code to enable connections to work properly. The alternative — buying third-party adaptors — can prove costly, and even then your adaptors may require tweaking and maintenance from your in-house team.
  2. Preparing the data. To use your data effectively, you need to transform it from an unsorted, undifferentiated mass of numbers into a clearly formatted data warehouse that can be accessed and interpreted at scale. How do you make that happen? You guessed it: set aside precious dev time to write a bunch of code, or shell out for expensive third-party data-cleansing tools that may or may not deliver the results you need.
  3. Designing the insights. Once you’ve accessed and prepared your data, you still need to turn raw data into usable insights. That typically means starting from scratch, using trial-and-error to figure out what charts and visualization strategies make sense, and then wasting more time tweaking chart settings, picking color schemes — and then starting over when your dataset updates or your team’s focus shifts.
  4. Distributing to users. Okay, you’ve managed to create a system for packaging data insights. But now you need to make it accessible to your users — and that means building a web version, then rebuilding it for smartphones, rebuilding again for tablets, and finally rebuilding to enable embedding across other contact points. With web and mobile standards constantly evolving, designing, distribution, and debugging across multiple platforms can rapidly become a full-time job.
  5. Managing adoption. Even if you can push your insights out to users, you still have to convince them to actually use your data in their day-to-day decision making. Unfortunately, all the friction that’s accumulated along the way makes this incredibly difficult: the Rube Goldberg point-solutions most IT and data teams put together are counterintuitive and hard to use, leading many users to call it quits, and simply go back to their old way of making decisions.

Individually, any one of these pain points would cause serious problems for an organization. Collectively, they create layers of friction that give rise to a kind of analysis paralysis. No matter how good your data is, or how much you’ve invested to move it into the cloud, unless you can conquer data friction your organization will wind up stuck in neutral.

How to create frictionless data infrastructure

So what’s the solution? Well, the answer lies in data storytelling — a powerful new approach to analytics that seeks to unlock the deeper meaning behind all the rows of numbers and sheafs of paper that make up your organization’s data assets.

Essentially, data storytelling is a layer of visualization and connectivity tools that sits between your end-users and your data, and serves to take the sting out of data analytics. With an intuitive, engaging system for accessing and using data, and visualization tools that are curated for their specific use-cases, individual users are able to quickly access and leverage the specific information and insights they need to make smarter decisions.

Data storytelling takes the sting out out of data analytics

Better yet, because data storytelling is platform agnostic and embeddable by design, users can bring data into their unique workflows, regardless of whether they’re sitting at a desk, walking the shopfloor with a tablet, or using a smartphone while out in the field. Here’s what that looks like:

  • For business analysts, data storytelling removes the need to think about analytics or waste time wrestling with numbers. Instead, it creates a powerful “answer machine” that delivers the insights that are needed to make smarter decisions more quickly. Analysts barely notice that they’re using data, because data-use itself becomes an unobtrusive part of their daily workflows, with effortless connection to datasets and streamlined analytics taking place in the background.
  • For IT and data pros, data storytelling takes the stress out of managing data infrastructure. Instead of constantly racing to keep multiple sources of data — from Salesforce to SAP — properly connected and maintained, the IT/data pro gets a single platform that solves their organization’s data insights needs without requiring complex deployment or maintenance. With zero training required and a 90% adoption rate, your specialists can stop answering user support queries and get back to delivering value for your organization.
  • For business leaders, data storytelling provides the instant, real-time insights that are needed to enable smarter and more strategic decision making. Instead of holding endless meetings to try to get the answers that are needed, or grappling with complex analytic dashboards, managers and executives can get real answers to the questions that matter to them, and full 360-degree visibility into their organization and broader market ecosystem.

The key breakthrough here is that by eliminating friction, data storytelling effectively democratizes data use across the entire organization. Instead of facing constant roadblocks and hurdles, users at all levels get easy access to data insights that “just work” and deliver exactly the information they need, in exactly the format they need it to be in.

Data friction: conclusion

All this might sound too good to be true. But that’s only the case because we’ve gotten so used to the idea that data is inherently complicated, and that analytics are necessarily a headache. That shows exactly why it’s so important that we shake things up, hit the “easy button,” and seek out new ways to make data more manageable for our organizations.

This is all the more important, of course, because organizations have already poured huge amounts of time and money into building out cloud-data infrastructure. Having made those investments, we can’t afford to let that data stagnate. We need to make it accessible to our teams, and find ways to leverage its power — and that starts with eliminating data friction.

The bottom line is that to unlock the power of data, we don’t need more complexity. We need a new way of thinking. Data storytelling is the best way for organizations to eliminate friction quickly and effectively — enabling them to deliver results, move faster, make smarter decisions, and realize the full potential of their data.