Data analytics should not exist in a vacuum. For the member focussed framework of credit unions, decisions need to be based on meaningful data that can be put to use productively. Through business intelligence data is transformed into actions. Business intelligence includes technology, data governance, qualitative and quantitative techniques, and a fact-based decision making culture.

In this article, we discuss the approach, strategy, and roadmaps to data analytics that lets you obtain long term, sustainable success in business intelligence. Here, we have taken an example of the "Alliant Credit Union" and their perspective on the same.

1. Strategic Backdrop

• Determine long term competencies and key success factors for your organization. For example- Business intelligence insight to action, Agile operational excellence. Business intelligence should be a critical part of achieving these long term objectives because if it is not organizational capability, it will not be as meaningful an investment.
• Align Value Creation to Mission and Vision. For example, Alliant’s mission is to provide members consistently superior financial value while simplifying and enabling how people save, borrow, and pay. Their vision aligned to the same is to set the standard for simple and rewarding digital banking that inspires loyalty in smart, informed consumers.

2. BI Maturity- For your BI maturity timeline, the key is to think about how you want to progress your maturity. This maturity can be aligned with the timeline or the dimensions of the ardent science. Data analytics is a mixture of science and art, and how you define your long term capabilities with having both in mindset is crucial to your business intelligence outputs. Having experimental thinking and supportive management can prove to be beneficial while making these organizational decisions.
   
   
3. BI Investment - Sometimes, projects require additional investment to mature. While investing in business intelligence concerns like return on investment and budgetary, constraints may arise. It should be noted that the scale of investment should support the advancement of enterprise BI. Analytics can be used to divide the sectors and estimate, project profitability which can be very useful while making investment decisions.
   
   
4. BI Governance – Business Intelligence can be best governed through bodies like an Enterprise Business Intelligence Community (EBIC), which makes real-time decisions on how resources are allocated and prioritized can provide for systematic governance.
   
   
5. BI Organizational Constraints- Evaluate the optimal organizational model for BI. This includes evaluating whether; BI resources should be centralized in a center of excellence or fully decentralized where BI is owned by business units. Example- Alliant adopted a federal approach, aiming to centralize some functions while decentralizing others.
   
   
6. Project Execution Plan- Before rolling out enterprise-wide capabilities, the establishment of best practices should be enabled. This can be best achieved through third-party software.

CONCLUSION

It is important to establish a reporting authority in a data governance organization plan, along with defined roles and responsibilities of the employees of the Credit Union. A sound business intelligence initiative will help to anticipate member needs and enable proactive marketing campaigns which are the two essentials of a member-centric organization. It is important that for the long term sustainability, business intelligence should be integrated with defined business objectives.