Top Data Analytics Mistakes to Avoid

Why do some data and analytics projects fail, while others go on to produce significant business outcomes? Today, many financial institutions are actively using data analytics to turn their data into actionable and profitable insights. However, the reality is most analytics projects do not always translate into easy success and big wins.  Too often, these projects will hit a plateau, unable to deliver the pot of gold or even a positive return on investment. So why the sub-optimal results?

Since deploying a data and analytics program is a complex process, there are many pitfalls and mistakes that you can make. While this list is by no means exhaustive, below are some of the most common mistakes we’ve seen as financial institutions embark on the data analytics journey.

Running before walking; walking before crawling:

Like most major projects, realistic goals and timelines need to be adhered to for the greatest chance of success.  I call this the crawl, walk, run phase of the data analytics project. The “crawl” phase is where the organization stops living and dying by their Excel VLOOKUPs and starts to use relational databases and common reporting tools.  During the “walk” phase of analytics is where the real analytics can begin. Your staff does not need to go to ten different places to get data for a report.  Near real-time analytics can be developed.  The “run” phase of analytics is where data science and statistical models become fully realized and leveraged.  During this time the data is working in every way imaginable and the organization has the structure, the culture, and the skills to make full use of the data’s potential.

Ignoring KPIs and success measures:

When you’re just getting started, it can be tempting to focus on small wins. However, it’s important to establish metrics like new business opportunities, customer satisfaction, onboarding, marketing, etc. Raw data must be turned into actionable information for it to have any real meaning.  That is why we emphasize the importance of establishing well-defined Key Performance Indicators (KPIs). KPIs are the quantifiable measures that a financial institution uses to gauge its strategic progress. The key is to keep the success measures simple, practical and relevant to the organization.  This is what will help you turn raw data into actionable, useful pieces of information so you can continually refine your KPIs for ongoing success.

Putting technology before strategy: 

When embarking on a data analytics project, one piece of advice is to not let IT drive the program. Many fintech firms are offering best-of-breed software solutions that have features and functionality that are typically under-utilized or forgotten about in the urgency of deployment. In most cases 60-90% of the product features are often unused despite paying for 100% of the product. Clearly understand and outline the long-term strategic goals of the financial institution to identify and select technology solutions that fit your data analytics goals today and tomorrow. Otherwise, you run the risk of investing in software that doesn’t fit the financial institution’s vision, and the cost of converting later can be significant.

Overlooking data quality:

Data scientists know the importance of accurate and complete data. After all, if the data itself is unreliable, you’ll wind up making invalid conclusions based on your analysis. To avoid that pitfall, it is incumbent to spend the time and effort to diligently prepare and clean the data coming from all sources. This includes a broad range of cleansing such as: incorrect values, typos, aliases, inconsistencies, duplicate entries, outdated consumer information.  Your data need not be (nor will it ever be!) 100% clean to get started with your project.  It is, however, important to establish policies and procedures to identify and clean bad data on an ongoing basis.

Lacking data governance:

Think of data governance as a set of rules for inputting and maintaining data. It is a continuous quality control discipline that governs the overall management, usage, storage, monitoring, and protection of the financial institution’s data. Without dedicated governance processes, overtime, poor data quality will lead to inferior service delivery, reduced employee productivity, missed opportunities, and increased costs.  There are several subtopics in data governance that we have shared previously.  You can tackle data governance in a variety of different ways – just be sure not to overlook it in your analytics initiative.

Underutilizing talent:

Enlist the help of an experienced Chief Data Officer to keep everyone accountable and aligned with the overall strategic business objectives associated with your data and analytics projects.  Whether hiring a full-time executive or an outsourced expert like The Knowlton Group (who can resist a shameless self-plug!), view them as a trusted advisor and strategic partner.  Consultants and data scientists will work with your data analytics teams to pour through the data and train your staff to uncover and act on new opportunities that lead to your desired goals. These experts can advise decision-makers, monitor progress, track success and establish best practices that will position your data analytics project for success.

Underestimating the journey:

A fully-developed analytics program can take 18-to-36 months to deploy in its entirety. Deploying an advanced analytics initiative and building a data culture takes time, and many executives don’t plan for the long-term commitment.  Turning the data generated by consumers into actionable, comprehensive insights is a big task. The initial steps should include establishing short-term and long-term business goals. Once the strategy and business goals are in place, the technology infrastructure needs to have an effective and rigorous process to collect, cleanse, compute and consume the data. Staff needs to be assigned—and with the right skill-set to then use this complex data for discovery of insights and interpret results that lead to business opportunities.

What mistakes has your organization experienced while deploying your data and analytics program?

Next Steps

This list of pitfalls provides just a glimpse of the many mistakes financial institutions make when embarking on their data and analytics journey.   Before implementing any analytics solution, spend the necessary time determining how you would like data to strategically support the financial institution. Focus first on your KPIs and success measures and then explore your technological, operational, and deployment needs. With this discovery completed, you will have the foundation of what will be a working roadmap for your data and analytics journey.

Need expert help planning, designing, and implementing your data and analytics strategies and solutions? Contact The Knowlton Group today by email at brewster@knowlton-group.com to learn more!

Posted in Analytics, Banks, Credit Unions, Data analytics initiatives, Leadership, Project implementation, Strategy and tagged , , .

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