The Value of Data Management
Data Management is about recognizing your data as an asset and one of your organization’s most valuable resources. You must thoughtfully collect, store, model, and govern your data in a way that optimizes the performance of your data-driven applications. These tasks will support the streamlining of your data lifecycle to enable a data-driven organization by moving the data closer to the point of action.
Data Collection is focused on the process of managing the data that you are collecting. At first glance, many customers will say that they want to simply collect all data and keep it forever. While this may be a noble answer, it certainly is not the most effective approach. It can lead to the much more ambitious practice of trying to boil the ocean. As a big believer in agile development practices to help drive value along the way, I always recommend that we start small. By focusing on the data that is essential to the business and adjusting retention policies accordingly, the project becomes more manageable.
Data Storage is about supporting the data lifecycle. It's also about how we should best store data to support additional processing of the data and business activities. You can determine the best method to support these processes based on several variables. It's important to note that business rules based upon data classifications should be applied to these variables. Here are a few variables that would affect the data storage strategy:
- Is the data structured or unstructured?
- What point of the data lifecycle is this data being stored?
- What, if any, additional processing of this information may need to occur?
In a simplified data process, you may have multiple storage points supporting the data throughout its lifecycle as it moves closer to the point of action. As an example, you could initially store data in a data lake, apply some data integration techniques to then store in a data warehouse. It can then be moved and further processed to be stored in a business specific data mart or analytics application. As you start to think about where and how the data will be stored, it is extremely important to factor in how the data will be used currently as well as how it could be leveraged in the future.
Data Modeling is a structured representation of the data geared towards the context in which the data will be used. This data must be structured in a way that is easy to use and understand. It must meet the needs of the business users along with supporting the associated business processes. Effective data models must be designed with a focus on the process that it’s designed to support. This is another area where agile development practices play an important role. Again, I would recommend starting small and progressively enhancing the model to make it more complete.
In summary, Data Management plays a critical role in supporting the data lifecycle and building the foundations that make streamlining the data lifecycle possible. Organizations should carefully plan and consider their Data Management strategy and how it can support the larger needs of the organization. To learn more about our process contact us and lets schedule a quick discovery session.
Written by Mike Sargo
Co-Founder & Chief Data and Analytics Officer at Data Ideology
Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.