What Does it Take to Implement a Data-Driven Approach?

Having data and tools is enough, right? Not exactly. A lot more goes into improving your decision-making process than data analysis alone. You need a data-driven approach.

Data, Data Everywhere: The Challenge of Data-Driven Decisions and Strategy

In the data analysis business, we often hear that being data-driven will help organizations make smarter decisions and improve their processes.

After all, everyone wants their company to make forward-looking and innovative decisions based on data and facts. Given the incredible advancements in technology over the past several years, you might think organizations are using data-driven approaches to drive performance more than ever before.

Unfortunately, many companies struggle with simply managing their data, let alone using it for business insights. Fifteen years ago, many companies struggled to gather enough data for quality data analysis and data-driven approaches. More recently, the problem has shifted to too much data.

With the increase in web- and cloud-based platforms generating new data — along with the growth in applications and capabilities such as customer relationship management (CRM) tools — many companies are almost overwhelmed with data. 

New Technologies and the Data-Driven Enterprise

A fleet of new technologies that enable better data access and processing is helping to make sense of all our new data. Tools such as Tableau, Power BI, or any number of cloud-based solutions have made data access and data analysis more readily available.

Our clients use a number of these tools, on-prem and in the cloud. Each tool has a different capability, but all deliver the ability to create reports, use dashboards and visualizations, and develop complex predictive models with artificial intelligence (AI) and machine learning (ML).

For example, data warehouses provide “one version of the truth” for integrated data across multiple functions and business needs. Data lakes deliver raw and harmonized data, allowing for direct real-time analysis of information without affecting source data. Other technologies allow for analysis of web data, images, text and other “multi-structured” data. With cloud-based modern software delivery methods, it seems that more data analysis tools on the market appear every day.

So, if organizations have data and tools, shouldn’t the goal of being “data-driven” be easier? Why are so many organizations still struggling?

What it Really Takes to Design a Data-Driven Organization

We see a lot of different challenges with our clients. In addition to simply feeling overwhelmed, understanding data, especially having a consistent understanding of data, is hard. Each new application, web feed, or Excel spreadsheet provides a different perspective of the data. Data is typically not consistently defined, and data results can be interpreted through many lenses.

For example, organizations tend to have isolated applications that define data differently, depending on the functional area. Results may make sense in one area, but in another area an executive’s gut may tell them something different. With the advent of new technologies. data tends to be located in multiple data repositories — from data lakes, to data warehouses, to transactional systems. If not done correctly, these repositories can make alignment even more complex.

Doing the work to bring everyone together to define terminology, determine and share objectives, and then act on the data is hard and often difficult to justify.

What’s more, changes in your approach to data — whether it’s implementing a new, cloud-based tool or transforming how your organization thinks about data — usually lead to changes in processes. Processes affect employees’ day-to-day lives: for example, how they do their work, how they collaborate, how they innovate and how they measure their success.

As a result, overcoming people’s aversion to change is the real barrier to becoming a data-driven organization that uses an analytical approach to decision-making and process improvement. To really change how organizations make decisions and become data-driven, we need to think about how to drive change and reward teams for making better data-driven decisions.

Before you wonder why analytics may not be making a difference for your organization or your client, do a little more investigation. Sorting through the expectations and the priorities will be key. Implementing the new technologies — whether cloud, ML, or AI — will only result in better or more innovative decisions if the business uses the insights. Driving new technology and aligning it with how the business operates will help drive a path to a smarter, data-driven future.