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According to Forbes, companies using big data insights experience an average increase in revenue of around 44%, recognizing customer needs that need attention and serving them at just the right time. Businesses have accepted the data claims and started collecting data, but business users find it hard to find, use, and personalize the data they want when they need it. Data and insights can transform organizational results only when meaningful data is available to all data consumers (internal and external).
Therefore, companies have recognized data as an asset that drives results, but still struggle to manage data that comes from various sources without standardization and governance. Thus, this creates silos of invaluable, unused data (called data gaps).
The approach to viewing data as a data product manager is to make it available to anyone when it is needed for a particular function. Businesses can align their goals with the help of insights from data products. In simple terms, treating data as a product means provide data that can be accessed when required by the team or customer from any diverse source within the organization. For example, a software development team requires adequate and accurate data for application development.
The concept of “data as a product” is an ideal shift from centralized data (a central warehouse or lake) to a decentralized network of domains. This approach can improve the accuracy, accessibility, and security of the data.
The practice of treating data as a product is a fundamental component of an enterprise data mesh.
Data Mesh is an agile approach to managing acquired data architecture that promotes decentralized data management and governance. It is a domain driven data architecture that requires product thinking to collect data for each domain where each data stakeholder owns their data to provide clean, valid and reliable data products. Therefore, every stakeholder or team within an organization embraces product thinking and sees data as a product where ownership lies beyond their department to make the data accessible, clean, and suitable for every consumer (anyone). that requires data for any purpose or development) within an organization. .
The conventional method of data extraction and integration adopted by many companies needed to align with business needs and use cases. The diversity of data did not match the required level of governance and quality, limiting the ability of companies to derive the necessary and sustainable value from data. Data as a product can help Realize the full potential of data as an asset.
- Accurate data: The full potential of data is achieved when it is accurate and reliable. By treating data as a product, each domain’s responsibility ensures that there is contextual awareness and required information in your managed data. It provides more accurate and understandable data to consumers as they can meet their data requirements without delay.
- Accessible and Consumable Data: By treating data as a product, the intent is to make the data accessible and consumable to the consumer. Therefore, the proper information is readily available. In this approach, each business domain is responsible for preparing its data and making it accessible to others for consumption as well. Metadata provides data context to consumers to speed discovery and access to data.
- Secure Data – While valuable data is easy to access, data governance is just as crucial. The data-as-a-product approach helps manage and extend access to data to all customers within an organization, including all domains. With data management as a product, appropriate access control: who can see, use and export each data product is involved, and track all activities performed on any data set. Enables interoperability across organizational domains with global compliance and policy enforcement required.
Businesses must have an environment that treats flooding information as a product to maintain the efficiency and quality of incoming data. To provide quality data, companies must understand the data needs of their teams and the lifecycle of that data.
Leveraging a modern data capability (data as a product) can be challenging and brings ownership within an enterprise. He product thinking about data ensures the underlying analytical and historical data for each domain is intact to safeguard the context of the data.
One such platform that I have used is called K2view’s “data product platform”, which provides the implementation, management and monitoring of data products and aligns with the contemporary concept of “data as a product”. By maintaining metadata for each data product, Data Product Studio helps data owners aggregate and manage data for all business entities (such as a customer, product, location, or any transaction) and further prepares this data. to consumers in the form of an integrated data product. With high-grade scalability, robustness, and agility, the platform maintains and serves each dataset as a microdatabase in an out-of-the-box way. This can help companies unlock the potential of data-driven insights by transforming the relationship between incoming data and their consumers.
As data is drowned in centralized data platforms, such as data lakes or warehouses, it has proven that the need for more people and tools for management is limited to achieve business results. Therefore, the data mesh architecture treats the data as a product as the new way to empower each domain to unravel information from the data. The data-as-a-product approach provides an agile way to manage data at all levels of a business and makes data more accessible to all stakeholders.
yash mehta is an internationally recognized expert in IoT, M2M and Big Data technology. He has written a series of widely recognized articles on data science, IoT, business innovation, and cognitive intelligence. He is the founder of a data insight platform called Expersight. His articles have appeared in the most authoritative publications and have been awarded as one of the most innovative and influential works in the connected technology industry by the IoT departments of IBM and Cisco.