DATA ANALYTICS

4 Reasons No One Is Adopting Your BI Solution and What You Can Do About It

Devon Tackels

Content Marketing Manager, Sigma

Big data spending is more significant than ever as businesses look to become data-driven. 55% of companies report that their investments now exceed $50M. Yet, Only 31% of companies say they are data-driven. If your investment in analytics and business intelligence (A&BI) hasn’t panned out as you expected, you’re not alone.

Business intelligence initiatives were supposed to help all employees embrace data analytics and become data-driven. Unfortunately, for many companies, that hasn’t been the case. Employee adoption of BI tools remains low. So, what happened? And how can your company close this gap to get more ROI out of its data investment?

In this post, we’re going to explore some of the common pitfalls holding back BI adoption in the enterprise and help you identify the right way to approach your next analytics investment, all while keeping broader adoption in mind.

How most A&BI deployments play out

Before we talk challenges to BI adoption, it helps to understand what commonly occurs after a company rolls out new A&BI solutions. We believe there are usually three scenarios that unfold in companies with low BI maturity, all of which end the same way: people can’t extract the full value of their data investment. If BI adoption hasn’t gone to plan, chances are you fall into one of these three camps.

  • Situation #1 – It never had a chance. In these scenarios, analytics failed to take off. This usually occurs when employees have low data literacy rates, limited experience using analytics tools, or don’t show the enthusiasm necessary to learn how to use new software.
  • Situation #2 – It was good while it lasted. It goes like this: people started using analytics tools. Still, something went wrong — usually stemming from software complexity and employee frustration — and everyone goes back to using spreadsheet tools like Microsoft Excel.
  • Situation #3 – It wasn’t everything you were promised. People analyze data regularly, but adoption only spreads so far because users get held back by product limitations. Ultimately, they can only make basic discoveries, not the higher-quality analytical insights they require.

Source: Gartner, The Use of Augmented Analytics to Improve Analytics and BI Adoption in Low-Maturity Organizations, August 2019

Why these scenarios happen

If your BI adoption is suffering from one of these scenarios, it helps to diagnose why this occurred and how your company got here. Here are some of the most common technical and cultural hurdles that must change before organizations can close the adoption gap.

 Your data infrastructure doesn’t scale

The massive amount of data created daily in today’s business environment requires companies to meet higher analytics demand — especially at peak times like the end of the quarter or Monday mornings. This demand often means that data teams get asked to build new dashboards, create or re-run reports, and conduct ad-hoc analysis at a moment’s notice.

With traditional data infrastructures, these workloads become delayed or bottlenecked, often taking hours (sometimes even days) to complete. Frustration ensues for analysts and business users. If domain experts can’t get the answers when they need them, they move forward without data; or even worse, they take matters into their own hands and go back to spreadsheets. This workflow defeats the purpose of your BI investment. You need an infrastructure that can surface insights today, not a week from today.

 You invested in complex A&BI tools that leave business people frustrated

Frustratingly slow, complex software tools discourage the enthusiasm necessary for broad adoption — and the maximum return on data investment. To achieve more widespread A&BI adoption, business people need to jump on board. Sadly, most tools out there have UIs that aren’t familiar to businesspeople or weren’t designed with them in mind. And learning new software can be overwhelming — especially if it’s not a key objective. Carving out the time and space to become proficient can be difficult.

To explore data and uncover high-quality analytics, domain experts often need deeper technical skill sets, like SQL knowledge, to ask tough questions and go beyond dashboards. Or they need the data team to step in and lend a hand repeatedly. This skill gap creates bottlenecks and leads to “report factory hell.

Because a skill gap exists, data and business experts aren’t speaking the same language, leaving data teams to apply guesswork around business needs and processes. This further adds to the report queue since questions rarely get answered the first time, and makes data models less usable — creating a vicious feedback loop. Sure, people may have access to dashboards, but without the ability to drill down further, they cannot get their questions answered.

 You don’t have a central source of data truth

According to IDG, the average company collects data from more than 400 data sources. This massive, disjointed assemblage of data makes it challenging for business decision-makers to grasp the full scope of their organization’s data in real time, understand what’s happening across business units, and generate actionable insights. Even worse, with so many moving and continuously changing pieces, it becomes increasingly difficult to trust the data. The patchwork network of systems and tools requires business teams to gather data and piece it together to craft a narrative. This workflow isn’t scalable and usually results in inaccurate or incomplete data informing decisions.

 Your company’s data literacy remains low

Unfortunately, many employees simply don’t possess the fundamental statistics skills or analytics background necessary to surface meaningful insights. 

Even when you spend the money and time building data infrastructure, centralizing data sources, and creating dashboards, these efforts will not pay off if no one knows what to do with the information. Without the ability to ask more profound questions of the data, or possessing the knowledge required to turn it into an actionable strategy, domain experts will likely fail to adopt the tools you’ve purchased.

How you can close the A&BI adoption gap

Here are three ways you can address your adoption gap and get on a path to more widespread A&BI use in your company.

Invest in a modern cloud analytics stack

The modern cloud analytics stack has reached a point of maturity where these obstacles have become increasingly easy to address for data and IT leaders.

You start by creating a data pipeline to centralize data sources within a cloud data warehouse. Using an automated ELT (extract, load, transform) solution, you can extract and load data into a cloud data warehouse where custom transformation occurs for each unique analytics task. Unlike traditional ETL (extract, transform, load) pipelines, ELT increases analytics velocity by leveraging the near infinite compute power of the cloud to perform complex joins and calculations on-demand.

With a cloud analytics stack, you’ll streamline data pipelines and create a comprehensive, centralized source of information inside the cloud data warehouse — giving analysts and domain experts alike the ability to conduct complex analyses more easily without delays. And you can always locate accurate, fresh data with the right analytics tool to surface actionable insights that inform better decisions.

MOVE TO THE CLOUD

Read our buyer’s guide to building a modern analytics stack and learn how you can take advantage of the cloud data warehouse. 

Deploy a collaborative, easy-to-use analytics tool that Business people love

If you want business people to adopt analytics, it helps to deploy a tool built with them in mind. That’s where solutions like Sigma come in.

Sigma helps companies realize the full benefits of the cloud data warehouse and drive faster analytics adoption across every business team through a self-service approach. It takes a more collaborative approach to analytics than other solutions you may have used in the past. The data team owns the data, and the business teams uncover the insights.

Instead of constantly relying on analysts to provide insights, it puts the power in the hands of the business user through a familiar spreadsheet-like interface that doesn’t require SQL to run queries against the data warehouse. Collaboration between data and domain experts generate better data models and reusable analyses that deliver compound analytical interest over time.

Because Sigma feels like a spreadsheet, business users haven’t hesitated to dive right into Snowflake data for faster insights.

Alex Mora

Data Engineer, Clover

Read the interview

This new approach eliminates the analytical bottlenecks that naturally occur when domain experts request ad-hoc reports day-in and day-out from the data team. With Sigma, if a salesperson, marketer, or any other business person has questions, they can explore endorsed data directly in the warehouse and generate shareable visualizations, reports, and dashboards themselves.

Increase data literacy and build a culture of curiosity

Data-driven cultures don’t just emerge; they are cultivated. Technology can only take you so far. Even the easiest to use tools won’t be helpful — or adopted — if users don’t have data literacy skills. Some skills, such as critical thinking with data, data ethics, basic statistics fundamentals, and data visualization, must be learned. If your company suffers from low data literacy, you need to take action. 

Your company has to make data literacy a priority if you want broader adoption of your A&BI tool. Here are a few ways that data-driven companies address data literacy within their organization:

  • Provide in-house training with third-party experts
  • Send employees to BI software user conferences
  • Require online training courses
  • Create in-house best practices and guides
  • Set up mentoring sessions with data team members
  • Host “lunch and learns” where analytics experts are available to answer questions

SEE FOR YOURSELF

Learn how E.W. Scripps Decreased Time to Data Insights by 93% with Snowflake and Sigma.

Where to go from here

If you’re serious about closing the BI adoption gap, you’ll need to put in the work. The good news? These efforts are worth it. Forrester reports that data-driven companies grow more than 30% annually on average. With the right mindset, technology, and approach, you can join the 31% of companies that feel they have been able to build a “data-driven culture.”

If you have questions, Sigma is here to help. We’re leading the charge to make data accessible and valuable for every company. Our cloud-first approach is one that you can feel confident in investing in, and will stand the test of time.

Wanna drive higher BI adoption?

Sigma can help: Schedule a demo or start a free trial today