Data management is a process of developing and managing the whole life cycle of data generated on different platforms of an enterprise. The official definition provided by DAMA (the Data Management Association) of Data Management is “Data Resource Management is the development and execution of architectures, policies, practises, and procedures that properly manage the full data life cycle needs of an enterprise”.
Big Data is a defining term of growing volume, variety, velocity, and value of data. Organisations leverage big data to find hidden insights and apply them to serve their customers better which eventually decides business growth. Big Data is now seen as the core of enterprise growth strategies.
Data becomes more powerful when it is integrated. The integration of data, coming from different sources converts data into big data. The rapid growth of big data brings big challenges as well as immense opportunities. To grab these opportunities and convert those challenges into benefits, Data management is very necessary. In other words, for using big data effectively proper data management is compulsory. Data management often seems to be a fundamental block for big data analytics. The analysis result and building models are mostly dependent on the data quality and data management plays a crucial role.
These days businesses are frequently in contact with their customers through different mediums like social networking, emails, messages, calls, etc. and because of this, the era of big data came into the picture. The Flood of data coming from web surfing, mobile devices, sensors and internal processes is full of valuable information. All data generated through these mediums are valuable for business provided exploring its decision support in the right way. These data are capable of changing the way of doing any business. So, managing this data is really important for the growth of any business.
There are many challenges in management of big data as well. It is challenging many long-held assumptions about the way data is organized, managed, ingested, and digested. Managing big data is not always possible using traditional data management techniques used in relational database domain. In big data processing, data management includes different functions and processes of data including data storing, backup and recovery, processing and many more. All these operations make data complex and tough to handle in a proper manner. The size of data is also too big. So, due to these reasons traditional data management methods fall short to handle big data.
Big data comes in Exabyte and Zettabyte and traditional data management is not capable of handling that data. And the backup process makes these data double and triple its size. For handling big data, different tools and technologies came into industry and it is evolving into free and paid ones. Hadoop, Hive, Pig, Hbase, NoSQL are a few of them and there are many more. These tools and technologies are capable of handling big data in the proper manner.
For a healthy analysis of big data, proper data management is needed. Quality of data can offer you powerful business insights that can be game changing to proper your business.
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