Today, most of the challenges in data management stem from the high pace of business and the increasing proliferation of data. The variety, velocity and ever-increasing volume of data within companies is pushing them to seek more effective management tools to keep up. Some of the key challenges facing organizations include the following:
Growing Demand to continuously optimize IT agility and costs With the availability of data management systems in the cloud, organizations can now choose whether to retain and analyze data in on-premises environments, in the cloud, or in a hybrid combination of both. IT organizations need to monitor the level of identity between on-premises and cloud environments to maintain maximum IT agility and reduce costs.
Complexity to maintain data management performance levels. Organizations are capturing, storing and using more data all the time. To maintain peak response times at this expanding level, organizations must continually consider the type of queries the database answers and change indexes as queries change, without impacting performance.
Challenges in meeting changing data requirements. Compliance regulations are complex and in various jurisdictions, and their changes are continuous. Organizations must be able to easily review their data and identify anything that conforms to new or changed requirements. In particular, personally identifiable information (PII) must be detected, tracked and monitored to comply with increasingly stringent global privacy regulations.
Need to process and convert data easily: Collecting and identifying the data itself does not provide any value: the organization needs to process it. If it takes too much time and effort to convert the data into what is needed for analysis, that analysis will not take place. As a result, the potential value of that data is lost. Constant need to store data effectively. An organization's data scientists need a way to quickly and easily transform data from its original format into the form, format or model they need for a wide range of analyses.
Lack of knowledge of the data: Data is collected and stored from an increasing number and variety of sources, including sensors, smart devices, social networks, and home and industrial video cameras. But none of that data is useful if the organization doesn't know what data it has, where it is and how to use it. Data management solutions need scale and performance to deliver meaningful information in a timely manner.
Comments