Skip to main content

Best Practices to Prepare for the Future of Data Management


When talking about the future of data management, it’s often best to start with the present and examine the basics with two questions.  What is data management and what role does data management play in day-to-day operations?

These might seem like simple questions, but the answers can be more elusive than they appear. Data management is made up of all the activities that dictate the location, aggregation, storage, sharing, organization, and analysis of data assets across your entire organization. This entails data age, ownership, security, and integrity.  Needless to say, it’s a lot – and preparing all of it for the future can feel like boiling the ocean. 

Simplifying the Data Ocean

Step one is to define your company’s data strategy. This includes aligning your business goals with your data management current-state capabilities, and it involves understanding the current state and future state of your data priorities. A Data Maturity Model (DMM), like Ippon Technologies’ Data Maturity Model, helps immensely by determining data ownership and governance and establishing data quality standards.

Recently, we analyzed the next step, which is to invest in data governance (add link to blog above). Implementing a strong data governance framework ensures that data is managed effectively across the enterprise and different lines of business. Ippon’s DMM does this effectively by working with key stakeholders to create and define data stewardship roles, data policies, data standards, and processes for the entire lifecycle of data.

Both of these steps, which fall within the role of an effective DMM, focus on:

  1. Data quality is essential for making accurate, informed decisions, and this entails building data quality metrics and regularly validating data to maintain its accuracy and reliability.
  2. Assessing and improving your data practices regularly to continuously evaluate and refine practices that help drive the adoption of new technologies and changing business requirements.
  3. Regularly review your data architecture, policies, and processes that focus on maintaining a competitive lead in the adoption of future data management solutions, policies, and practices.
  4. Leveraging automation and artificial intelligence to optimize the data management lifecycle boost efficiency and increase the adoption speed of data-driven solutions. 
  5. Compliance and security with data management practices that speak to the current and possible future of relevant regulations and industry standards. This is essential to facilitate the adoption of future solutions and maintain an updated approach with changing data protection regulations. 

When talking about the future of data management, it’s often best to start with the present and examine the basics with two questions. By undergoing this exercise, companies can go a long way in preparing for the future. 

For more information on creating the best data governance for your organization, visit or download our latest eBook "The Future of Data Management" here.

Post by Steven MacLauchlan
February 28, 2024