Developing a Modern Data Management Strategy
In a typical day, an employee might access data from centrally controlled document repositories, data coming through from websites, emails, blogs, twitter feeds, plus data scattered across multiple smart devices. This collection of data from various sources and types adds to the petabytes of data that organizations need to store, manage and protect. While the sources of data are growing every day, so too are those that use and produce the data.
Organizations are challenged with not only large volumes, but also rapidly escalating varieties and sources of data. Rich’ content such as video, high resolution images and audio is rising dramatically, along with the explosion of data generated through social media and instant message streams. IDG predicts that by 2022, like these data types and sources, 93 percent of all data in the digital universe will be unstructured.
Each day organizations are faced with the unyielding challenges associated with storing massive amounts of data including the capital costs of storage and the costs associated with the administration of storage. To address these issues, most organizations just add more NAS, which can result in multiple data repositories in different geographical locations across the enterprise. These data silos are extremely inefficient, as they regularly have unused capacity and prevent organizations from finding, understanding and appropriately leveraging the data they possess, other than through the individual application that created the data. For this reason, silos make it impossible for organizations to confidently meet internal or external regulatory compliance policies and mandates associated with security and data risk. Lack of centralized data and proper data management can result in considerable costs due to loss of data or fines associated with noncompliance; both can also result in corporate risk and exposure. When situations like these arise, data quickly becomes a liability rather than an asset for organizations.
Setting aside data growth and dissemination, the majority of the employee workforce is not concerned about the management of data; they are simply concerned with having the ability to access data when and where required. With the introduction of bring your own device (BYOD) policies, mobile access poses an additional complication for organizations. If adequate data management policies are not put into place alongside an organization’s BYOD initiative, it can result in employees saving duplicate copies of large datasets remotely versus pulling it from a central repository. Duplication can quickly multiply capacity, compliance and regulatory concerns, as well as waste valuable storage space.
While BYOD and mobility initiatives through cloud services continue to rise, modern organizations need to implement data management strategies that will manage costs and corporate exposure, and provide secure data access to the right person at the right time to satisfy geographically dispersed workforces and improve business agility.
C-suite executives are increasingly taking notice of the market opportunity for big data analytics as mainstream adoption grows by leaps and bounds. Organizations that deploy data analytics are better equipped to make time sensitive decisions, monitor emerging trends, react quickly to new business opportunities, and ultimately, maximize the ROI of real-time data better than ever before. If data is being dispersed throughout the enterprise, the ability to discover or to fully leverage analytics tools to gain value and insight from data is cumbersome and inaccurate.
There Is A Better Way
As organizations continue to use traditional methods to address all of the challenges related to massive data growth, storage will continue to be seen as a cost center. However, the vast data stores could be turned into a strategic business enabler.
Innovative media independent technologies and data centric approaches, such as data defined storage, enable organizations to build data management strategies based on the value of the data, which creates an enterprise storage architecture that goes beyond saving on capital hardware costs.
A data centric approach enables the unification of data stores, creating virtualized storage pools of data, which reside on storage that is determined based on the value of data, and the performance, protection and frequency of data access during its lifecycle. Data can be automatically migrated to low cost storage pools when required. Unnecessary copies of data can be removed through deduplication and defensible disposal policies, and further optimization can be achieved through compression. A single global namespace, exposing data through multiple protocols, eliminates data silos with a single unified view of corporate data further reducing infrastructure complexity. What this boils down to is, the larger the capacities, the greater the benefits and savings.
Strategic data centric approaches focus on the value of data and employ technologies that extract full content and index metadata - the data about the data - at the time of ingestion. This rich metadata is stored in a distributed repository, called a metabase, which crosses data type and location boundaries to provide organizations with global access for on-demand enterprise search and discovery.
When there is an enterprise-wide storage layer, data governance is simplified, as all data is exposed. This allows the Chief Data Officer to work more easily and consistently with IT teams to create data retention, preservation, access and security policies across all enterprise data sets. In addition, combining unified storage pools with enterprise-wide data search and discovery, productivity is improved and operating costs and risk are reduced as legal, audit, security, and records management can find data immediately and without the aid of an IT administrator. By using this data management strategy, organizations can confidently meet compliance mandates as well as avoid corporate exposure.
The need to gain strategic value from unstructured data assets is fast becoming the number one priority for organizations. When planning an enterprise data management strategy, how to leverage analytics tools efficiently should be a top consideration.
When data is locked in silos, organizations are unable to find and include all enterprise data for use with big data analytics tools. Planning to implement a data centric data management strategy enables the distributed metadata repository to be a source for analytics tools, as it can be used to provide real-time insight, without having to migrate data from silos to a separate analytics platform. It also enhances the quality of results, because having more relevant data often produces more accurate analysis. If organizations can harness all of its data, they will attain a greater competitive advantage.
Many organizations not only face challenges with storing ever increasing quantities of data, they have problems finding and leveraging that data again. A good data management strategy is fundamental to address these key requirements empowering organizations to reap value from its unstructured data rather than viewing the data as an ongoing cost center. Employing a data centric approach provides organizations with a data management strategy that is essential in today’s competitive landscape.