Today’s Corporate world has new avenues in great abundance, each leading to different world of technology. There are many technological marvels advancing with unparalleled speed, but its working with data which has always created a spark in me. Observing random rows in a random database makes me question – “What makes this data valuable? Why is it protected and confidential”. Is it the source where it comes from? The way it is generated? The quality? Its relevance at this time?
The answer is a single word – “USAGE”.

All the data sitting in huge data centers is not beneficial, unless we know what to do with it. We need to ask these imperative questions again and again, so that we know what and how a data can be used for strategic, data-driven business decisions. This makes the concept of data management very critical. It can make or break any organization by forming a way for its long-term survival. The world is creating and consuming data at unprecedented rates now, like never before. To handle this every growing data, there is a dire need to harness the flow of this sheer volume of information. This means correct data management for developing remarkable insights, crush competitors, delight customers, mitigate risks and uncover new avenues for profitable growth. Organizations which understood this concept early are continuously expanding their business & moving ahead in the game of Data Analytics & Data Science.

What is Data Management?

Data management is not a one-time process but it is a continuous journey. Data management is an administrative process which includes development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets of an organization. It’s important to keep in mind that best practices of data management result in better decisions.

Data Development refers to the data-focused activities within the system development lifecycle (SDLC), including data modeling and data requirements analysis, design, implementation and maintenance of databases and data-related solution components.

Data access refers to your ability to get to and retrieve information from wherever it is stored. Using certain tools can make this step easy and efficient so more time can be spent on using the data and not just trying to find it.

Data Architecture Management refers to the development and maintenance of enterprise data architecture within the context of all enterprise architecture, and its connection with the application system solutions and projects that implement enterprise architecture.

Database Operations Management refers to the planning, control and support of the structured data assets across the data lifecycle, from creation and acquisition through archival and purge.

Data Security Management refers to the planning, implementation and control of the activities that ensure privacy and confidentiality and to prevent unauthorized and inappropriate data access, creation or change.

Reference & Master Data Management refers to Planning, implementation and control of the activities that ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. This master data is typically managed from a single location or hub.

Data Warehousing & Business Intelligence Management refers to Planning, implementation and control of processes that provide decision support data and support knowledge workers engaged in reporting, query and analysis.

Data integration means getting all this data under the same roof– and all speaking the same language. It defines all the steps to retrieve data from different sources and assemble it in a unified way.

Data governance is the practice of putting controls and best practices around an organizations data. Data Governance must be proactive and reactive to ensure that the data strategy is aligned with the business strategy.

Meta Data Management refers to providing information that enables in making sense from all the data (e.g. document, images), concepts & real-world entities. In most basic terms, meta data is the data about data or information about information.

Data Quality Management is a group of best practices which are not linear and have many dimensions like Accuracy, Completeness, Consistency, Timeliness and Auditability. Having data quality on one dimension is as good as ‘no quality’.

Data Management in Digital Economy

For corporations today, we live in a Digital Economy, where high quality data has become one of the most valuable commodities to drive growth. The web has allowed companies to collect an unprecedented amount of information on consumer buying habit and preferences. The result is that, companies can specifically target their message to customers that are most likely to buy their product or service. Best example of this is Google and Amazon where the advertisements are shown based on searches a person makes. But one thing we miss to see behind the curtains is the behemoth amount of effort that goes into management of data for each individual, applied Data Science and intelligence derived out from deep analytics. This is just phenomenal how all the data is made meaningful and relevant for the corporations. Successful implementation of data management is what will separate outliers from laggards.

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