If you have reviewed the important first six steps outlined in our first article on Building an Analytics Roadmap, then you are ready for the final steps to complete the journey.
To recap, we firmly believe that analytics in the bank and credit union industry will become necessary for survival. Those who adopt early will see significant competitive advantages. However, those who adopt without careful planning, strategy, execution and commitment will see their time and money wasted due to poor data governance, inadequate technology, lack of data talent, and uncommitted or misaligned leadership. These final steps in building a solid roadmap will get you on your way to reaching your BI goals.
Step 6: Assess & upgrade technology capabilities:
Nothing can derail your data analytics project than a lack of the right hardware, software and data analytics technology. Create an assessment of your technology inventory as part of the roadmap and indicate the need for further technology capabilities that align with your overall strategy. Assess if complex infrastructure and security is already in place or if the analytics systems require significant infrastructure updates including robust security, abundant data storage, and other components of a solid infrastructure.
Step 7: Ensuring data quality:
Data quality can vary significantly depending on how it was collected, stored, cleansed, and processed. Establishing data quality checks ensures that data is complete, timely, and accurate. Implementing a comprehensive data profiling effort and data quality initiative can help identify data quality issues earlier in the implementation timeline. Working with subject matter experts from the various business units can help identify and clean up any issues that arise.
Step 8: Identify analytics success measures:
Many organizations are excited to get started building out a data warehouse and establishing an analytics program. However, most fail, at the outset, to establish measures that define success (or failure) for the analytics initiative. These can be ROI-related or measures around adoption and utilization of analytics throughout the organization.
Establishing these success measures at the start of process allows management to gauge whether or not the program is meeting expectations. If success is not being met, management and the BI team can take corrective actions to re-align the analytics program with expectations.
Step 9: Create an analytics workplace culture:
Having an analytics-ready culture is a huge indicator of future success. FIs that have taken the time to create a workplace culture that understands, visualizes, and believes in the capabilities of the data will see results. Establishing a culture in which everyone — including your leadership — understands and appreciates how data brings value to business initiatives will ensure that your processes and culture are aligned around producing profitable insights and positive business outcomes. Getting the best value from your data means you must first trust it yourself and then give employees the opportunity to learn and embrace data-driven decision making. (2)
Step 10: Create a data dictionary:
A data dictionary provides detailed information about each data element within your analytics environment. Users need to know what the data fields and metrics mean. When you have a clear list of metrics and their definitions, it helps to eliminate assumptions, hours of guesswork, errors, and confusion. Take the time to generate the data dictionary with clear, unambiguous and agreed-upon definitions. This requires collaboration of all the key players within the data analytics project.
Step 11: Provide ongoing training:
You would never implement a new LOS without training your staff on how to use it, right? Similarly, an organization won’t have success with analytics without providing proper and consistent training. Business users need to be properly trained on the data elements included in the data model, how to access information, and the various other aspects of the analytics program.
Step 12: Prioritize implementation phases:
A successful data analytics roadmap should divide the total implementation into logical phases to help reduce the risk of failure. Start with the highest priority initiative(s) and refine the initial scope to mitigate the downside risks. Set goals and milestones by breaking up the entire project into manageable phases. This approach not only helps manage development of the solution, but it also gives the analytics team time to train business users on how to leverage each subsequent phase of the implementation. Failing to do this is one of the most common reasons that BI projects fail.
Are you ready to start building your FI’s data analytics roadmap? The twelve steps covered in the past two articles may seem daunting. The Knowlton Group can help. Our expertise, years of deep experience working with FIs on assessing and implementing proven data analytics strategies and solution can work for you. Contact us today to learn how we can help assist your BI strategy or assess your current goals to make sure your capabilities are aligned.
Resources
2. 4 Strategies to Create a Data-Driven Company Culture