CU Employee CULytics Founder

Top KPIs for Successful Data Analytics Practice

Strong data analytics practice has become imperative for the success of financial institutions. There is no doubt that there is a real worth of the data that institutions possess. Data-driven insights can drive revenue growth and decreased costs in real dollars. Still, data analytics initiatives fail at a significant rate. This list outlines the top 10 keys to successful data analytics practice.



1. Start with Business Value
Any initiative must be initiated based on the value it is expected to deliver. In speaking with leaders at various financial institutions, invariably the conversation goes towards implementing the next shiny technology or hiring a future data scientist or technical expert with no apparent business opportunity identified and value defined.

Once you start with the business value, then other aspects of the practice start falling into place, including executive commitment, vision, strategy, organizational alignment, execution and value realization.

2. Executive Commitment
A recent report by KPMG found out that almost 70% of all CEOs see data analytics as one of the top 3 corporate initiatives – over the next three years. The process of introduction and adoption of a new data-driven paradigm more often than not requires changes to mindsets, behaviors, and skills. As a financial institution, it impacts on how executives and employees make decisions on a day to day basis, the expertise you look for in new hires and even the operation of the entire organization. Successful data analytics practice requires a cultural shift, and culture change requires strong executive commitment.

It is thus vital to have a strong executive commitment to iron out any organizational inertia that might derail a data-driven transformation.

3. Define a vision and strategy
This ‘vision’ is the essence of a good data-analytics strategy, for it’ll eventually lead to better strategy and blueprints.

From a data-analytics perspective, it is imperative to have a vision as to where the data insights would add value to your business. How will you reach your destination if you do not know where to go? Then having a business strategy that brings focus to help you get to the vision. This approach allows you to focus on few opportunities and ignore the noise so that you able to execute towards the vision.

4. Organization Alignment
Both the vision and the strategy need to be developed collaboratively by involving the senior leaders. These artifacts cannot be created and put in place by one person and in a vacuum. If there is no demonstrable organizational alignment, then the initiatives will fall apart as soon as there are any issues that surface during execution.

Once there is alignment at the senior leadership, one should work on getting the alignment at the middle management and rest of the organization. That is where the rubber meets the road. Having the organization believe in the vision and trust that the initiative is in the organizations best interest as well as in their best interest then it is a lot more likely to be successful.

5. Start Small
Successful analytics practices start small after thinking big. Rather than jumping on the bandwagon to solve one huge problem, they tend to focus on breaking bigger problems into smaller pieces where each piece can independently be executed and deliver a measurable outcome. This approach allows them to show a series of wins that keeps the organizations engaged and excited. They can also make appropriate adjustments along the way, as they learn about the changing business priorities or expected outcomes.

6. Be Agile
Gone are the days, where the organizations take multiple years in building a data analytics practice and then come to business seeking how value can be provided. With the rapidly changing market conditions and business landscape, it is imperative to have continuous engagement with the business functions and the ability to deliver demonstrable value along the way. Therefore, one should consider adopting the agile methodology of delivery for data analytics initiatives.

7. Operationalization is the key
The real value in data analytics practice comes when the businesses can realize meaningful value. This value can only be recognized if they are equipped to take action based on the insights that the data analysis has provided promptly. Occasionally it is seen that organizations fail to realize the value of the sophisticated analytics model that provides excellent insight because they struggle to operationalize.

Operationalization is also the area, where having an early executive commitment and organizational alignment helps. In case of execution or operational challenges, different teams come together to solve them.

8. Technology is not the master
As you execute on your data analytics journey, you will realize that technology is not the master. Jumping to a next shiny technology implementation without clear vision and strategy is probably the most common mistake any credit union usually makes when embarking on their data analytics journey. Technology, while necessary, should only be positioned as the enabler of the overall model or the framework.

9. Set processes in place
While executing on the data analytics practice, make sure that appropriate processes are in place for managing the data, governing the data and finally for taking action on the data. Setting these processes in time will help you get the results faster and more efficiently, which will result in more effective implementation.

10. It is all about the story - Measure ROI and create advocates
Measure the return on investment (ROI) for data analytics wins and turn them into stories. This will help develop advocates and momentum which is essential to keep the initiative going.

Off course any journey starts with having the right people on the team. The assumption is that the organization already has right people to help drive the trip either internally or through consulting.

Establishing a thriving data analytics practice is a journey with a lot of promises. Promise to delight the customers, promise to grow the top line, promise to eliminate the unnecessary operational cost and much more. 

Share your thoughts and comments about your journey.

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