Data Management Is The Key To Data Value
Data Management is the key to a properly managed enterprise data lifecycle that enhances the value of the data and information delivered to the business. Most data management strategies are spontaneous and not well thought-out leading to a very costly affair to the entire organization. A majority of organizations live through the pain as it is a critical component and any effort to revamp major areas of data management is very involved and an expensive proposition, but when done right will save you Millions of dollars for years to come. This article discusses some of the key considerations to the major areas in data management that will help you to arrive at an excellent data management strategy.
Most organizations have a very informal approach to Data Governance and have traditionally resided under development projects leading to a very narrow focus on decision making in data related matters. Although it seems to work, it ends up being the root cause for a lot of data related issues down stream. Implementing a more formal approach to Data Governance is a good starting point in the road to a first class data management practice. You can use the DGI data governance framework to arrive at your own program. Ensure that your data governance program is a collaboration between IT and Business. The long pole in the tent is to arrive at the data rules and definition for your program and requires the most effort in establishing your program but also when done right ensures the success of the program. A key consideration is to ensure that a formal Data Governance Office (DGO) is established with ownership and authority to get things done. Automation and digitizing the data governance process ensures a more productive and sustainable program.
Have you revisited your data architecture recently? If not, then most likely your data architecture is based on last decade best practices, which means it is expensive, error prone and inefficient. If your data architect still uses the term Operational Data Store (ODS) in your current data architecture discussions it means you have a serious problem. The last decade best practice was to isolate the operational system from reporting and business intelligence queries to prevent slowing down the operational system, which is not the case anymore due to technology innovations. The more number of hops your data takes before it becomes meaningful information the more expensive it is to maintain and manage. With advances in massively parallel data appliances and big data technologies you can drastically minimize your data replications and cost of maintenance. Although migrating to a more modern data platform seems like a colossal endeavor, with the right strategy, roadmap and technology it can be achieved in a reasonable time frame. Trying to delay or ignore your architecture is like sitting on a dormant active volcano that might erupt any time soon leading to catastrophe for the whole organization.
Evaluate Your Data Assets
Most organizations today do not believe that they are ready for analytics primarily due to the lack of trust in the quality of their data. Your data might not be as bad as you think and addressing data quality is not as complicated as it sounds. The first step is to evaluate your data assets on the key dimensions of data quality namely, completeness, validity, accuracy, consistency, integrity and timeliness. Once you have a baseline for the quality of data then prioritizing and addressing the gaps will yield enormous returns in enhancing the value from your data.
Master Data Management
Master Data Management and Reference Data is more of a dream than reality in most organizations. In the recent years several fortune 500 companies have embarked on discarded proof of concepts and failed attempts to establish master data management. The primary reason for most undertakings to fail is due to the fact of not addressing the gaps in data governance, data architecture and data quality before attempting to establish master data management. This is a case where doing it in the right order determines success and failure.
The cherry on top of the data asset pyramid is advanced analytics, it is the hardest to conquer as well as the one that sets you apart and ahead from your competition. With powerful tools and technology, it has become fairly easy to build a machine learning algorithm and analytical models in just a few hours given the right data. The challenge most businesses face today is to how to operationalize analytics and integrate it with the business process. Ensuring your analytics strategy begins with the end in mind, integrating analytics into your business process, will lead to tremendous success. With the right data management strategy you not only save millions of dollars for your organization but also open possibilities in advanced analytics that seem impossible today.