This vendor-written piece has been edited by Executive Networks Media to eliminate product promotion, but readers should note it will likely favour the submitter's approach.
A recent Gartner report predicts that the number of connected 'things' will reach 25 billion units by the year 2020. The Internet of Things (IoT) holds great potential for business leaders, offering the possibility of viewing, analyzing and leveraging statistical information about unstructured, big data in an instant. According to Gartner, IoT will be the next critical focus for data/analytics services. In fact, it has already started to change the face of big data storage and analytics in 2014, and it is becoming a core investment strategy for organizations across all industries. In Asia Pacific, the IoT adoption is expected to grow to US$57.96 billion by 2020.
The IoT blends not only greater data volume, but also complexity and velocity to an already challenging data lifecycle. These new connected and embedded systems demand multiple service levels; and organizations will seek to process and analyze data in both real time and over the long term. The mountain of information these devices create is forcing companies to rethink how to securely capture, store and retrieve data to derive more value from it.
IT managers need therefore to rapidly make decisions about this ever increasing flow of data and to develop a holistic modern data management strategy. Hence, they need to consider the sources of data, the frequency of reporting, and how that data will be used, so that they can design policies to identify what to keep, where to keep it and for how long. While storage and cloud costs are decreasing, it is not happening fast enough to keep up with exponential data growth. The old fashioned strategy of "storing everything forever" is too rigid, slow and expensive. Besides, the new reality for IT managers is that every piece of data is not equal in value. To address this problem, companies are embracing modern data management strategies which include writing and executing automated policies that map back to demand from the business. These policies help make better business decisions, lower costs, evolve products and services and improve the user experience.
1) Automate the organization and retention of data based on the content
Content-aware retention leverages intelligence about data — such as type, confidentiality, when it was created and the last time it was accessed — to index and classify it. Policies can provide specific rules to automatically move relevant data to more cost-effective storage. File Analysis (FA) tools for instance, can help organizations make more informed decisions around prioritizing their unstructured data management needs for classification and information governance, providing insights on retention policies for data movement. Many FA tools also offer reporting capabilities that help define these retention policies. According to Gartner, "the value of reports in FA tools is that they can be used to determine policy and strategy in areas such as access, retention and location."
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