It is now widely known that information continuously expands and evolves in the modern, data-driven world. Companies that were previously satisfied with their outdated data systems now face challenges with their tedious and siloed nature of data. This is because such data blocks their ability to use their data's potential fully. This impasse can prompt missed opportunities, a loss of competitive advantage in the market, inefficiencies, and substantially more. Thankfully, a handy solution is available at hand -- data modernization. A strategic effort to refresh existing data infrastructure to ensure its sync with today's data-driven landscape, data modernization involves embracing new technologies and best practices to uncover the true value of data. A well-designed data modernization strategy helps with data accessibility, security, scalability, and, most importantly, readiness for analysis. This is how one can achieve actionable insights.

So, in this blog, I will discuss the basic steps for creating an effective data modernization in cloud strategy to help you put it together.

What is a Data Modernization Strategy?

A data modernization strategy combines cumulative strategic steps an organization undertakes to overhaul its current data infrastructure and procedures. This strategy, then, serves as a roadmap for moving from legacy systems to a streamlined and adaptable environment that can manage the company's requirements in terms of big data and advanced analytics.

Steps to Create a Data Modernization Strategy

  • Define clear objectives: The first step in setting up such a strategy is to clearly identify the goals and objectives of your data modernization efforts. So, ask yourself: what specific problems are you trying to solve, or perhaps even what opportunities are you looking to capture in the market? Adjusting the targets of the data modernization endeavors with the general business objectives assists you with making sure that your modernization attempts stay zeroed in on conveying genuine worth to the organization.
  • Assess the current state of data: Before you start moving away from your current data environment, it is essential to take the time to assess it. So, start with a comprehensive assessment of your current data infrastructure, processes, data quality, etc. This will help you gain a better and deeper understanding of your current data state, consequently helping to better explain the scope and trajectory of your modernization efforts.
  • Prioritize initiatives: Once you have established objectives and evaluated the current data state, it is time to prioritize your data modernization projects. For that, you will need to consider factors such as potential impact, complexity, and resource requirements. Focusing on high-impact initiatives with evident returns on investment helps ensure maximal ROI from your modernization efforts.
  • Develop a data governance framework: Developing a data governance framework means defining the exact protocols and regulations about things like data ownership, accessibility, security, etc. This structure is meant to guarantee uniform and accountable data management throughout its entire lifecycle. So, when you cultivate a robust data governance framework, you foster a culture based on data utilization.
  • Identify the right technologies: Now, on to what is decidedly among the most important steps here: choosing the right technologies that align with your data modernization objectives. The technologies could range from cloud platforms to data lakes, warehouses, analytics platforms, etc. Also, don't forget to consider factors such as cost, scalability, security, and usability, among others, while deciding.

There you have it, folks—the most fundamental steps for defining your data modernization in cloud strategy. And if you need help, qualified experts are always available.

Author's Bio: 

Kaushal Shah manages digital marketing communications for the enterprise technology services provided by Rishabh Software.