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4 Tech Risks of Using Outdated Business Intelligence Tools

4 Tech Risks of Using Outdated Business Intelligence Tools
4 Tech Risks of Using Outdated Business Intelligence Tools

All too often companies fall into the trap of trying to survive on outdated business intelligence (BI) tools to save money, then have a hard time justifying upgrading their BI systems because the return on investment (ROI) from data has been so low.

Leaders can understandably be hesitant to sink money into upgrading or replacing seemingly ineffective tool, even when the problem they’re seeing is of their own making.

Before long though, businesses using outdated BI tools will face these four significant risks.

Low Adoption Rates for BI Tools

In 2017, the Gartner research firm found BI and data analytics adoption “remains low” at around 30 percent of all employees. While this percentage has likely grown since the publishing of that survey, many enterprises are still struggling to raise adoption rates among their workforces.

One of the solutions Gartner offered was organizations “should deploy modern BI platforms, leveraging mobile and embedding capabilities.” The implication? Adoption rates suffer when users lack access to advanced tools capable of embedding analytics directly into users’ preferred workflows.

In other words, the business intelligence tools your organization has inevitably affects how many employees will actually use them — and how often. If you stick with clunky legacy tech, you’re risking low adoption rates and missed opportunities to extract actionable data.

Lack of Data-Driven Decision Making

Business intelligence is a means to an end.

It’s the wind powering the sailboat that is data-driven decision making.

The MIT Center for Digital Business found a connection between data-driven decision making (DDD) and performance. Specifically, companies in the top one-third of their industry in terms of DDD were, on average, five percent more productive and six percent more profitable than their competitors.

However, outdated BI tools with low adoption rates can hinder organizations from reaping the full rewards of data-driven decision making.

4 Tech Risks of Using Outdated Business Intelligence Tools
4 Tech Risks of Using Outdated Business Intelligence Tools

IT Pressure & Reporting Backlogs

Many legacy BI tools put the responsibility of working directly with data on IT specialists. Instead of allowing employees to query data themselves, as modern self-service BI platforms do, outdated systems operate with IT and data specialists as the gatekeepers to various siloed sources of information. Other teams must then work through these gatekeepers to get the reports and insights they need — often with a significant waiting period built in.

Reporting backlogs of this nature are detrimental for those waiting for the reports and those creating them. Today’s self-service analytics address this challenge for organizations by allowing users to query data directly and receive answers in seconds. This leaves IT specialists more time to work on strategic projects rather than getting caught in an endless backlog of reporting.

Data-Resistant Culture

A company’s cultural attitude toward data-driven decision making and the BI tools it uses are invariably linked. Culture helps shape employees’ approach to incorporating data into their workflows and thought processes — but then the tools an enterprise uses for BI inevitably shape culture right back.

This tendency for tools and culture to feed into each other can help or hurt an organization, depending upon whether it’s promoting a data-driven approach or resisting it.

It does little good for a CEO to tell employees the company values data if it’s still using tools that make it cumbersome for users to get insights. But it’s also ineffective to deploy the latest and greatest BI tools within a company culture that undervalues data, or one in which leaders make decisions by going with intuition rather than turning to these tools.

Using outdated BI tools comes with a certain set of risks and consequences — chief among them is the missed opportunity to use cutting-edge data insights to drive more effective decision making.

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