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When data is not properly integrated or is barely inter-operable, business users or processes will seldom have the right coverage of data. Availability of data often becomes a challenge which often leads to less impactful decisions and reduces the data advantage that the organization can embrace. Read further on coverage of data and its prominence as a data dimension for Artificial Intelligence and Machine Learning use cases.
In addition, Data Democratization is a strong enabler and, in the industry, we have been sharing thoughts about this recently in Dubai. Allowing business users to source and consume relevant data for their instantaneous reporting or generation of insights, can reduce significant turnaround time in acquiring or sourcing data traditionally. Another advantage of democratization is having the data consumers appraised on new data acquired along with changes to existing data.
At the outset, organizations are also building data lakes to support business consumption needs, while also putting some data on the cloud. Is Data Architecture governed in today’s digital firms? There is a growing challenge to better govern this ever-increasing available data from the source landscape. With silos getting created in multiple data lakes or data warehouses without the right guidelines, is a challenge to manage this data. Placing a Data Fabric that makes data inter-operable, will get us past this road-block, while also making data easier to understand. Data Fabric simplifies Data Management alone, across cloud and on-premise data sources.
Then, further Simplification of the data landscape has still not happened, but the data governance function can achieve simplification by logical and physical classification of data. There is a necessity for the data creators and consumers to put forth a common understanding of data and its attributes. Governance should be enabled naturally by actively managing the common definition of business terms and their relationships.
Managing data within the right context by leveraging Semantics is the very need for most organizations. Metadata Management including vertical and horizontal “asset relationship management” and Modeling using knowledge graphs can solve for this challenge. The challenge for organizations has always been the inability to harmonize disparate data across an enterprise or one single business function, as the meaning of data has never been standardized.
Data Virtualization is taking over the common advantages of data warehousing and operational data storage. The risks associated with data loss during transfer along with failures of Change-Data-Capture is no longer the challenge to be accounted for by Data Management. Automating management of data infrastructure provides swiftness, efficiency, as well as a cost reduction for a data office.
Also, digitization is demanding an Internal Marketplace – An access to certified & cataloged data on the fly, as per needs of advanced mobile applications, hyper-personalization or other analytical requests. Anyone in the organization should be able to shop for data, within their rights, across business divisions and functions, without having to go through a mundane process of data requirement management, sourcing, privacy & security evaluation & defining pipelines.
Right Governance on data can logically and physically simplify the data landscape while re-architecting the landscape will reduce risk and optimize resources. Coupled with the right discovery platform, it now becomes much easier to consume and transform more complex data across disparate sources that can serve the needs of digitization, complex insight generation from AI and ML models.
I would like to hear your thoughts around these capabilities working together to fuel your needs!