Enterprise Data Management Market 2020 - key vendors, growth probability, and future Scenario | Accenture PLC, Informatica LLC, International Business Machines Corporation

Enterprise data management (EDM) is an organization's ability to effectively create, integrate, disseminate, and manage data for all enterprise applications, processes, and entities requiring timely and accurate data delivery. EDM comprises software applications, computing & network infrastructure, business logic and policies used to manage enterprise data flow. An organization implements EDM through a variety of processes and requires collaboration through various departments such as IT, finance, and operations. The main reason of EDM implementation in an organization is to avoid risk management and conflicts resulting from mismanagement of information and data by implementing a structured data delivery strategy from data producer to data consumer (data chain).

Increase in need for managing voluminous data generated in an organization on a daily basis boosts the market growth. The need for framing data strategy from on time qualified information to gain competitive and strategic advantage drives the enterprise data management market. Rise in use for columnar databases in data warehouses where businesses send massive amounts of data from multiple sources for business intelligence analysis drives growth of the enterprise data management industry.

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Streamlined business operations and increase in need for risk management are some of the other drivers that influence growth of the market. However, data address validation issues and data silos hinder growth of the enterprise data management market. Contrarily, enterprise data management as a tool of taking competitive advantage causes increase in adoption of enterprise data management by organizations, which creates opportunities for growth of the market. Moreover, proliferation of big data technology and data analytics is also expected to boost growth of the enterprise data management market.

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Columnar databases overcome limitations of traditional relational databases, which encourage adoption of columnar databases as the future of business intelligence. A columnar database provides access to most relevant elements, which increases speed of a query even in a database containing millions of records. However, traditional relational databases offer a complete source of data. But columnar database architecture offers easier analysis of the overall data. The way data is organized in columnar databases allows for faster results and more efficient analysis. Thus, rise in adoption of columnar databases in organizations is expected to increase demand for the enterprise data management market during the forecast period.

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Data coming from IoT systems can change over time, owing to events such as firmware upgrades. This is called data drift or schema drift. It is important that data management solution can automatically address data drift without interrupting data management process. As IoT data comes in very large volumes, performing real-time analytics requires ability to run enrichments and ingestion in sub-second latency so that data is ready to be consumed in real time, increasing demand for integration of enterprise data management solutions.

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