Data-management technology is readjusting to the evolving ways data is disseminated. It is crucial that businesses take advantage of the opportunities that enable more efficient and effective methods of managing streaming data with new and innovative storage hardware systems.
The last significant instance of data management innovation was witnessed in the 1980s. Businesses started to realize that they needed a permanent place to store the data used for analysis and business intelligence. For instance, Wells Fargo Bank took delivery of its first enterprise data warehouse (EDW) system in late 1983. This leading edge-system used parallel processing of relational database data, and many other businesses found it a useful technology.
But the data management technology used successfully for the past few decades is not the most efficient technology for today. Having a firm grip on the rapidly spiraling amounts of data across public and private clouds, on-premises environments calls for an innovative management approach.
It is crucial that businesses modernize data management to keep pace with the growing application and security demands of today.
Update Existing Data Management Strategy
Businesses need to begin modernizing by developing a concrete understanding of their business strategies, data analytics objectives, and data needs. They need to design a data management architecture that can merge current data management tools and systems, leverage modern models and methods, achieve their objectives, and adapt to future needs.
Well-designed modern data management architecture ensures the data management systems work effectively and efficiently, continually delivers value to the company, and is flexible enough to incorporate new capabilities and enhancements.
Data Modernization Technologies
Organizations should invest in data management and cloud computing technologies.
Investing in governance and data management technologies and processes is a meaningful way to maintain holistic control over data. Quite often, strong ownership of data elements and processes and key leadership support are overlooked in data management programs. But, business leaders need to understand that this is a crucial enabler to managing a complex environment.
Data Democratization and Accountability
Democratizing data can help enterprises achieve true data trust. It can give them greater freedom to focus on business value and transformative outcomes. Trust is another critical aspect. Organizations without trusted data, struggle to find and deliver the right data to business customers. They should develop a governance strategy to ensure that data remains current and accurate. The democratized data needs to be identified, standardized, and classified to manage the data that customers use across the organization.
Strong governance will also allow organizations to reduce data prep time, giving data scientists and other users the ability to focus their time on analysis.
Clarifying data accountability is another crucial step in reimagining data governance. Organizations need to move beyond policy and process and put the responsibility of certain insights in the hands of senior leadership.
The critical issue facing business leaders is that while digital points multiply, many remain chained to monolithic legacy systems. There needs to be a holistic approach regarding modern solution development and delivery, including agile, DevOps, and the cloud. Modern delivery can help organizations rapidly deliver value, and create a culture of continuous improvement and customer-centricity and help businesses gain an advantage over their counterparts.