Six Things Standing in The Way of Data-Driven Decisions

The following is an article written by Trellance’s Chief Sales Officer, Patrick McElhenie. The article originally appeared on

The journey to becoming data-driven will look different for each credit union, but the driving intention is often the same. It’s a near-universal desire of credit unions to make well-informed decisions that create loyalty-building member service and optimize performance.

This goes for other industries, too. When surveyed, 94% of companies said data and analytics are important to the growth of their company and digital transformation.

So then, why isn’t every credit union – or company, for that matter – data-driven? Often, the same challenges get in the way – and they have little to do with technology. A credit union with all the right tools, but without the right people and processes, will still find it difficult to achieve its goals.

Here are six of the most common data challenges we see.

Data is not an organizational priority

A strong desire to achieve your goals won’t be enough if there’s isn’t buy-in from leadership. In fact, this is one of the most important factors for successfully integrating the use of data throughout the credit union.

Without credit union leadership and key decision-makers in every department acting as champions, it will be difficult to convince the rest of the organization of data’s importance – and even more difficult to inspire them to adopt new data-oriented processes that may be needed to succeed.

There isn’t a data culture

If your goal is to put data at the center of decision-making, the culture at your credit union has to reflect it. Instead of operating by consensus, observations or seniority, in a data-driven credit union, the ultimate “decider” is whether a decision makes sense based on the available data.

Most credit unions are accustomed to gathering opinions or deferring to senior leadership when it comes to making key decisions, and it’s not hard to see why. A data-driven culture doesn’t happen by itself and it takes work to get there. In order to look to data for answers, it must be readily accessible, accurate and actionable. Cultural shifts that make data central to operations and help everyone understand their roles as data stewards have to rise in importance.

There is no clear data strategy in place

Collecting as much data as possible isn’t the ultimate goal – it’s knowing which data to collect and what to do with it. A credit union must first be clear on what it wants to achieve with its data, and it must be closely aligned with overall business objectives. This forms the core of sound data strategy.

If you simply start by capturing all the data you can, you’ll soon be drowning. Begin instead with a plan. The plan can be organization-wide or focused on a single project. A project-based approach can be built upon incrementally until eventually, the entire credit union is in harmony about how data will be used.

Regardless of the approach, without an articulated data strategy, it will be impossible to identify which data is important, how it should be collected, what metrics should be measured, and how success will be defined.

There are data silos

Fragmented data is common, and happens when data is accumulated across different sources, departments and channels and can’t be integrated. Every data silo is left to define “its own truth” and there is no cohesive big picture of what’s really happening. Data silos lead to confusion. Who’s really right?

Without standardization and consistency, sharing information across the organization becomes difficult and data integrity suffers. Drawing accurate conclusions is harder. Ultimately, data silos undermine the spirit of data-driven decisions, which is to align internal teams and make determining the best course of action simpler and clearer.

Bad data is getting in the way

Data quality is a frequent nemesis for all organizations and one that must be confronted head-on. There are many reasons why a credit union experiences data quality issues – incomplete, incorrect, inconsistent, duplicate and stale data are just a few.

The cost of bad data is very real. Not only is there a bottom-line effect on revenue, but the compounding effects lead to poorer-quality data analytics, which lead to poorer-quality decisions, which ultimately come back to hurt the bottom line. It’s a circle that won’t end until the root causes of bad data are addressed. It’s not enough to fix mistakes every time you see them – find out why they are happening in the first place.

A lack of training

Most credit union staff have little or no training in data science. It’s a discipline that requires specialized skills, and it can be difficult to master for someone trying to add them onto a mountain of other responsibilities.

It’s common to see credit unions invest heavily in technology and tools that they don’t have the knowledge to use effectively. Whether data specialists are brought in-house or help is sought from outside experts, it is important to have support from technicians skilled in data management, statistical models, data analysis, visualization and data security.

Every organization will experience challenges on the data journey. Problems are inevitable, but they need not become roadblocks. By being aware of these six major pitfalls, it’s possible to quickly recognize, rectify and even prevent what stands between your credit union and a data-driven roadmap to success.

Patrick McElhenie is the Chief Sales Officer at Trellance.

Technology Blog

You now have more information at hand about your credit union than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool.

This blog will:

  • Educate subscribers about data integration and Big Data and Analytics.
  • Provide tips and best practices.
  • Provide entertainment.
  • Share ideas and expertise.

Recent Posts

Skip to content