The 6 common data mistakes that could be holding your business back—and how to avoid them

Data is everywhere–driving the evolution of technology, changing the way we do business, transforming what it means to be a customer. Yet, too many businesses are still operating in a data-aware state and not truly adapting to a data-driven mentality.

According to Deloitte Insights, just 1 in 10 executives believe that their employees can actually use data to make decisions. In fact, nearly 60% of business leaders don’t believe they can even access or use their company’s data with their existing tools and resources. 

What’s getting in their way? The data is there. So why are so many companies still struggling to go from data to insight to action? 

Here are the biggest data mistakes we see companies making—plus, we’ll include some ideas about what you could be doing instead. 

Data mistake #1: betting on average-of-breed vendors

Far too often, companies are reluctant to part with their legacy BI tools. Unfortunately, hanging onto these one-size-fits-all tools can hold you back from building a modern data stack. Here’s the problem: legacy BI tools were built for desktops and single-server applications, so they usually don’t scale as well as today’s cloud-based data warehouses. 

Worse, old-school BI vendors weren’t building their tools with today’s data challenges in mind. It’s not just about the scale of the data; it’s also about the demands placed on that data by a modern data-driven business. Tools built around dashboarding, cubes, and extracts start to fail as the number and complexity of use cases begin to surge. 

What to do instead

If you haven’t already, start by shifting from multiple data silos to a unified cloud data warehouse. When your most important data is disconnected, your business users will receive fragmented insights. 

Next, carefully select best-in-breed analytics tools that will let you take advantage of the full flexibility and power of the cloud. Look for solutions that are easy to use, cloud-native, built to scale, and quick to integrate into your tech stack. The right products for the modern data stack are specifically built with user experience in mind, easily allowing you to deliver your data to business users that need it without requiring advanced tech skills. 

Data mistake #2: depending on data analysts to spoon-feed insights to business users

A study by Harvard Business Review (HBR) reported that 87% of organizations believed they would be more successful if frontline workers were empowered with data. Yet, only 20% of them had made moves to put data into the hands of those workers. 

It makes no sense that business users still have to go cap in hand to data analysts whenever they need to use data to make decisions. This is a poor use of time for both parties.

Meanwhile, more than two-thirds of data analysts say they lack adequate time to implement profit-driving ideas since they’re wasting up to 50% of their workdays maintaining dashboards and providing customized reports. 

What to do instead

Instead of business users requesting reports and static dashboards from the data analytics team, let them help themselves to the data they need. For that, you’ll need a single source of truth for your data (such as Snowflake, Databricks, or Google Big Query) coupled with a self-service analytics solution that is easy for anyone to use and navigate. That means it has: 

  • An intuitive, consumer-grade user interface that anyone can use

  • A search function that works like your typical search engine 

  • Quick reporting functions that let business users share reports through their normal communications tools 

  • Enough flexibility that users can find answers to their unique, specific questions without needing a data expert 

Data mistake #3: banking on static dashboards for crucial insights

Too many companies continue to rely on data extracts and static dashboards. Sure, they may have the necessary insights, but they’re often out of date, too hard to access at the moment of need, or too fragmented to be reliable.

A Dimensional Research report found that in companies relying on legacy BI dashboards: 

  • 86% of the data used to create insights is out of date

  • 41% of insights are using data that is two months old or older

This is creating major obstacles for companies on a quest to dominate the decade of data. Two-thirds of the organizations surveyed in the HBR report believe that improving their organization's success with BI and analytics comes down to using live data and improving data trustworthiness.

What to do instead

Make the jump from static dashboards to self-service, live analytics. Instead of waiting days for a customized report, self-service analytics empowers everyone in the organization to engage with your live cloud data. 

The other benefit of working with a self-service analytics solution like ThoughtSpot is that every dataset, chart, and insight is now interactive. If you want to know more about a data point on a graph, you can drill down to find the full story. And, because you’re querying live data, you know that the insights are based on the most up-to-date information.

Data Mistake #4: Building on top of an inflexible data foundation 

While too many organizations are relying on static dashboards, an equally large number still rely on static and inflexible data pipelines. We often refer to this as the cycle of insanity. 

Every time a new use case emerges, the business user has to ask the analyst, who must ask the data engineer to build that specific data into their flow, who must then ask the IT team to make sure the new data is validated, governed and secure before it can be modeled. The process is repetitive, frustrating, and expensive.   

What to do instead 

Create a data foundation that has structure and governance, but also the flexibility to be iterated and improved. Your data stack needs to be built with composable tools, so that each product works like a configurable component within the larger architecture. The right tools will have a scriptable interface (e.g. TML) so you can follow the same principles as software engineering: extensibility, programmability, reusability, version control, and collaboration. 

When you’re putting together your new, flexible data foundation, look for tools that let you: 

  • Connect easily to your cloud data platform

  • Model your data with SQL and dbt

  • Build data models that can adapt to new use cases

  • Launch data apps across your organization quickly 

Data mistake #5: ignoring the promise of third-party data

Don’t make the mistake of thinking that the limitations of five years ago still exist. The amount—and kinds—of data available today are far and above what was available even half a decade ago. Relying exclusively on your own proprietary data will give you a very narrow view of your business. 

You’re also probably leaving money on the table. Bringing in third-party data (whether through purchasing data sets or establishing data sharing partnerships) can create a compelling data-driven user experience for your customers. 

What to do instead

For a complete view of how your business is operating, you need to take advantage of the wealth of available third-party data. With cloud and data marketplaces from Snowflake, Databricks, and others, you can get third-party data from trusted providers, and develop the granular, actionable insights you need. 

Purchasing external data is only the beginning. Map out all the data touch points your customers go through before and after interacting with your products. Who is currently collecting that data? Can you partner with them to create a valuable data ecosystem for your customers? 

Data mistake #6: building products with lackluster UX

In today’s highly competitive product landscape, users have more options than ever before. If your product team wants to make something that stands out, they need to focus on building engaging, sticky user experiences. 

Embedding an analytics solution into your B2B and B2C apps and products can deliver new capabilities and functions that will help you differentiate your products from the competition. In fact, companies who have embedded analytics into their products anticipate that it will increase revenue, engage more users, and help with customer acquisition and activation

Even though most product leaders are convinced that embedded analytics could help them build a more compelling user experience, many are still failing to act. Nearly half (45%) of the product teams surveyed by the Product-Led Association admitted that budget constraints and concerns about time to market were preventing them from embedding analytics in their products. 

What to do instead

Don’t miss your opportunity to create a more engaging UX. By embedding analytics into your digital products, you can delight your customers with valuable data insights and a more personalized experience. Creating customized analytics experiences can boost both engagement and retention. 

And, with ThoughtSpot Everywhere, you don’t have to do it all yourself.  Our low-code solution lets product leaders and developers build new data apps powered by ThoughtSpot, or add ThoughtSpot services, like our innovative search functionality, to your SaaS offerings. 

Stop letting these data mistakes hold you back 

This is the defining decade of data. Sure, data is bigger and more complex than ever before—but the implications of that are far reaching.

Today, data is being used to drive action, disrupt the way we work, transform the customer experience and shift power into the hands of frontline workers. Now is your chance to be a data leader and leave your competition in the dust.