Data organizing principles are important because they provide a framework for structuring and managing data in a consistent and efficient manner. This can lead to better data quality, easier data integration, and faster access to insights. By organizing data based on clear principles, businesses can ensure that their data is accurate, up-to-date, and easy to use. This can help organizations make informed decisions, improve operations, and drive innovation. 

As mentioned by Nishant Upadhyay, VP Data Analytics at UW Credit Union, in his highly-regarded session on Data Organizing Principles at our recent webinar. Attended by almost 50 CU leaders from across the country, his talk left a lasting impression on our CULytics Community.

Here are some of the key takeaways from the webinar: 

1. Data Strategy should be integrated with business strategy - Data analytics can provide valuable insights into business operations and customer behavior, which can be used to inform strategic decisions. When data analytics strategy is aligned with the overall business strategy, it ensures that the insights derived from data analysis are directly relevant to the organization's goals and objectives. This can help companies make better decisions, optimize operations, and identify new opportunities for growth. Additionally, a data-driven approach can help businesses stay competitive in rapidly evolving markets by providing real-time insights into market trends and customer preferences.
   

2. Architecture Principles - When organizing data, numerous design decisions must be made, such as choosing data storage technologies, data access methods, buy vs build, on-prem vs cloud, security measures and many more. Having a set of architecture principles in place can help ensure that these design decisions align with the overall principles and objectives of the organization. These principles provide guidance and establish a common understanding of the best practices and standards that must be followed in designing and organizing data. As a result, the data architecture can support the organization's goals, maintain data quality and consistency, and provide efficient and secure data management. e.g. some of the principles that Nishant shared were
   

   1. Data Quality
      

   2. Security
      

   3. Engineering Speed
      

   4. Analyst Usability
      

   5. Traditional Data modelling (Kimball, Inman) vs Big Data Design etc.
      

3. Member Centric Architecture - Member-centric data architecture is a design approach that prioritizes the needs and preferences of the member in organizing and managing data. It involves creating a data infrastructure that supports the collection, storage, analysis, and dissemination of member data to enable organizations to understand their members better and deliver personalized experiences. By adopting a member-centric data architecture, organizations can improve their member insights, enhance member engagement, and increase member satisfaction and loyalty.

   Because of a glitch we were not able to record this fantastic webinar, but here is the presentation - https://culytics.com/articles/data-organizing-principles-for-credit-unions-nishant that Nishant used.

   We look forward to seeing you at the next CULytics webinar.
   
   
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