“Data is the new oil,” is a famous quote of Clive Humby, a British mathematician and entrepreneur who says that data is as valuable as oil, but it must be refined and analyzed to extract value. Inventor of the world wide web (WWW), Tim Berners-Lee, identifies data as “a precious thing” that “will last longer than the systems themselves”.
Indeed, data is the most valuable, enduring asset of any organization, providing the foundation for digital transformation and strategy.
Effective data management is an essential part of today’s unpredictable business environment. Managing and understanding data better can help companies make informed and profitable business decisions.
The total volume of data that organizations across the world create, capture, and consume is forecast to reach 59 zettabytes in 2021, according to Statista. This data does not only comprise structured data in the form of documents, PDFs, and spreadsheets, it also includes tweets, videos, blog articles and more that make up unstructured data, which is essentially eclipsing the volume of structured data. Therefore, organizations not only face storage challenges but have a significant challenge in processing the wide-ranging data types.
The process of migrating siloed data to modern cloud-based databases or lakes from legacy databases is known as data modernization. It enables organizations to be agile and eliminate bottlenecks, inefficiencies, and complexities of legacy systems.
A modernized data platform helps in efficient data migration, faster ingestion, self-service discovery, near real-time analytics and more key benefits.
For any modern business focused on building and updating the data architecture to spruce up their data core, data modernization is not only important but essential.
To gain optimal value, accelerate operations and minimize capital expenditure, companies must build and manage a modern, scalable data platform. Equally, it is vital to identify and deploy frameworks of data solutions along with data governance and privacy methodologies.
Data modernization is not without challenges as it requires creating a strategy and robust methods to access, integrate, clean, store, and prepare data.
Tips For Successful Data Modernization
Data modernization is critical for any modern business to stay ahead of the curve. With that said, let us find out how companies can be successful in their data modernization efforts.
Revise Current Data Management Strategy And Architecture
It is important to have an in-depth understanding of the organization’s business goals, data requirements and data analytics objectives when a company starts modernizing.
Thereafter, a data management architecture can be designed to integrate existing data management systems and tools, while innovative methods and models can be leveraged to accomplish the organization’s immediate objectives and adapt to future needs.
A well-designed architecture will enable data modernization to be approached systematically and holistically, thereby eliminating data silos and compatibility issues. It will also deliver consistent value and be flexible to integrate new capabilities and enhancements.
Inventory And Mapping Of Data Assets
If an organization cannot identify where the data assets are and what is protecting them, it will be tough to know if the access provided is suitably limited or widely available to the internet.
It is essential for organizations to first understand what data is being collected, what is being collected and what is being sent out. This helps identify the requirements and how a modern data management technology can help simplify the company’s data and analytics operating model.
The best way to begin a meaningful transformation is to simplify the problem statement. Hybrid cloud is also an integral part of any modern data management strategy.
Data Democratization A Core Objective
Until a few years ago, organizations had one major reason to modernize their data management ecosystems—which was to manage their rapidly growing data volumes.
Today the single, overriding reason is data democratization, which is about getting the right data at the right time to the right people.
It gives organizations wide-ranging abilities such as implementing self-service analytics, deploying large data science and data engineering teams, building data exchanges and zones for collaboration with trading partners and go after more data management activities.
Another key advantage of democratizing data is it helps companies achieve data trust and affords them more freedom to concentrate on transformative business outcomes and business value.
Robust governance is another focus area for organizations, who can thereby reduce data preparation time and give data scientists and other business issues the time to focus on analysis.
Continuous investment in master governance and data management technologies is the best way to gain maximum control over organizational data.
Assuming ownership of data elements and processes, with leadership support, can often be ignored in data management programs but they are a key enabler in managing complex environments.
It is important for chief information officers (CIOs) to take stock of the legacy technologies present on-premises, the decision support system that is ageing and will be out of contract in a few months and more contribute to data modernization projects being successful.
Establishing data accountability is a basic yet crucial step in reimagining data governance. Organizations that go beyond process and policy and prioritize insights and quality measures tend to be the most successful when it comes to data modernization.
In today’s rapidly changing world, almost everything is connected and digital. In this scenario, every bit of data about customers, transactions and internal processes are business assets that can be mined to enhance customer experience and improve the product.
Among the key issues facing IT leaders is while digital points continue to increase rapidly, many remain locked to monolithic legacy systems. A holistic look at solution development and delivery that leverage Agile, DevOps, Cloud and more such approaches are essential.
It is important for organizations to be aware of the evolving data management methods and practices. It could be said that data management is one of the most demanding issues IT leaders are likely to encounter in the year 2021 and beyond. For a company’s data modernization process to be successful, their data management approach should align with their overall business strategy.