A well-balanced data architecture helps in improving efficiency and ensures better alignment with the business priorities.
The first and second questions below could help you with finding the right balance.
First: What is balancing and What to balance?
“Balancing” isn’t about something technical or it is not only about the amount of data you have and what you are doing with it.
It is all about finding the right balance between your customer experience and technology. In another perspective, finding the right balance between your business needs and the potential challenges.
Speaking technically, it is very important to design a data architecture to examine if a business intelligence solution is feasible to implement.
Second: What to consider or what to balance?
Let me explain the points to consider for finding the right balance between.
- Democratization of analytics
- Data beyond the transactions
- Time to value
- Insightful analytics
- Problem identification
The democratization of analytics:
Democratization fundamentally means providing access to the analytics for everyone across the firm in order to improve the given performance metrics or any other metric related to the individual.
The inquisitive nature of users is making them raise a variety of data needs. Everyone needs to place their own ‘Data Model’, ‘Data science algorithm’ for different purposes and usage. Thus, operations to support them need to become more scalable and automated.
The posed challenge: Data security.
Looking at the data beyond the transactions:
Enterprises need to start looking at the data beyond the transactional level to gain more and more analytics and insights to witness a great business growth in their sector.
Upon consideration and over gathering the other data sets, organizations will get access to multi-dimensional analytics that ability to derive a variety of critical business insights that help leaders to make key decisions.
The posed challenge: Data governance (Volume and variety).
Time to value:
Quick and efficient delivery of insights from data assets adds value to any overall process. Streamlining communication across teams and breaking down barriers between departments, allows for quicker time to business value when working with your data.
Quicker the time to value, better customer satisfaction.
The posed challenge: Data processing
Post data processing, organizations will possess a lot of analytics and insights, if they fail to filter out the key, critical and impactful analytics out from the rest, that could end up an organization’s leadership in making a bad decision.
If your data is available and performant, you’ll be able to leverage analytical insights immediately (instead of waiting days or weeks for data points to sync with the system). You can leverage this information to innovate faster and deliver new features.
The posed challenge: Subject matter expertise
Identifying or finding out a problem forehand is something very crucial in any business. In terms of data architecture, one should be able to detect any possible problems if a model is designed in away.
The posed challenge: Thorough architecture analysis isn’t performed, there happens the inability to determine the required tools that aren’t in place.
Implementing a well-balanced data architecture helps in deriving a clear business outcome. i.e. the better the balance between the customer experience and the technologies used, the better the business outcome. Thus, business transformation.
To make it simple, look at your business needs while paying attention to potential challenges that may arrive while you are working to fulfill the said needs. Craft your data architecture such that those challenges won’t be a roadblock for your business growth.
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