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Advanced Analytics
5 Reasons Why Your Company's Analytics Program Is Failing
Advanced Analytics

5 Reasons Why Your Company's Analytics Program Is Failing

by Bill Petti and Sean Williams

This post is part of an ongoing series that explores how organizations can use data and analytics to drive performance outcomes.

Nearly every company is trying to get smarter about the way data is collected and evaluated. From gaining a better understanding of customer behavior to predicting the performance of new products to optimizing their workforce, most organizations are using advanced data analysis to inform their strategies and boost performance.

But despite sizable investments in hardware, software and people, many of these organizations are not realizing the return they hoped for.

At Gallup, we've been helping organizations get the most out of their data for more than 75 years. Along the way, we've learned a number of valuable lessons, seeing firsthand how difficult it can be to turn data into insights, and then for those insights to actually effect change in an organization's behavior and performance. Performance doesn't improve solely because an organization gains access to new data. Nor does it improve simply because the analytics team uncovers a brilliant insight from the data. Moving from insight to change is not easy, and there are plenty of barriers to overcome along the way.

Here are five reasons why your company's analytics program is failing:

1. You don't identify the problem you are trying to solve. First and foremost, organizations have to start by identifying specific problems they are trying to solve with their analytics program. Many times organizations approach analytics as a technology issue, focusing on hardware and software without thinking through the problems they want to solve and how they will use the insights they gather from the data. Starting with specific problems provides focus and the ability to design the program from the outset to ensure the most relevant, actionable information will be produced.

2. You don't use the right metrics to gather insights. The most powerful metrics provide important insights into the specific factors that drive an organization's performance. These metrics are also the most predictive of business outcomes, are reliable over time and are actionable for decision-makers and front-line employees.

Organizations tend to spend a great deal of time and effort creating descriptive reports and summaries that do not actually support their decision-making with data-driven insights, causing leaders to simply look in the rearview mirror. Other times, the metrics they do develop are not easily actionable by front-line employees -- the models and metrics are correct, but people simply don't know how to act on the results. Evolving your metrics from those that simply describe what has happened to those that help you understand why something happened makes it easier to fix problems and optimize performance. Understanding what will happen through the use of predictive analytics allows organizations to anticipate problems and capitalize on the most promising opportunities in the most efficient way possible.

3. You don't have the right data and systems. For organizations to fully apply their data-driven insights, they must have the right infrastructure in place. Data needs to be useable and useful -- accurate and timely data that can be easily merged from across the organization. Moreover, organizations must design their reporting systems so that employees can see information in an easy-to-understand format. This allows employees to spend less time making sense of complex data and more time using insights from the data to solve problems.

4. You don't have the right people. Organizations need employees throughout the hierarchy who understand how to use data appropriately and appreciate its limitations -- this applies equally to your top leaders, analytic talent, decision-makers, and managers. Misusing data is potentially more dangerous than not using it at all. Organizations also need employees who can deliver on the actions and behaviors the data and models suggest are optimal -- not just leaders, managers and analysts, but front-line employees as well. A team that lacks the appropriate talent and skill required to execute in all aspects of an analytics program is one of the most common failure points that Gallup has seen organizations struggle with.

5. You don't have the right culture. Finally, organizations must have a culture in place that embraces data-driven decision-making and one that is capable of making the insights, behaviors and necessary changes a priority. Culture represents the way an organization gets things done, and often times Gallup has found significant misalignments between what an organization's analytics program says they should focus on and what is actually incentivized (both formally through pay plans and informally through expectations and mentorship). The best modeling in the world can often be stifled by an unreceptive culture.

This is only a brief description of barriers to a successful analytics program. Throughout this series, we will delve deeper into these barriers and other topics, discussing what we've seen go right and go wrong with analytics programs. We hope you'll join us.

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