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    Late-Stage Regulated Data: The Ticking Time Bomb

    The volume of digital information continues to skyrocket. In all its forms, including structured, unstructured, emails and text messages, data is expected to grow by 30,000 percent around the globe by 2020. How will your organization archive and index today’s information, along with previous late-stage information, to be readily accessible by users in the decades to come?

    Even a corporate giant such as General Motors is acutely aware of these challenges. Congress is asking the company why it took 10 years to recall a defective part that has allegedly led to the deaths of 13 people. This inquiry has broader implications than simply interrogating archived manufacturing systems. It also includes accessing related unstructured operational and regulatory documents along with access to employee email accounts to determine who authorized what changes to which parts, why existing customers were not notified, and what was the chain of command and decision-making process.

    Ultimately, corporations are held responsible. What is your company’s plan to capture, organize, store, manage and protect disparate data across the enterprise to meet future customer and regulatory requirements? Smart companies are creating strategies and building solutions for intelligent archiving systems.

    Even companies operating in regulatory industries continue to store data on dispersed and deteriorating tapes. The risks associated with not properly maintaining archived data can have dire consequences. There also are problems arising from data volume, velocity and variety in the early and middle stages of the lifecycle. What should the enterprise do with late-stage data and how should it be managed?

    In this slideshow, Srini Mannava, founder and CEO of Infobelt, a leading developer of information lifecycle management solutions, takes a closer look at the data storage lifecycle and four key features that should be at the core of an intelligent archiving solution.

    Late-Stage Regulated Data: The Ticking Time Bomb - slide 1

    Click through for a closer look at the data storage lifecycle and four key features that should be at the core of an intelligent archiving solution, as identified by Srini Mannava, founder and CEO of Infobelt, a leading developer of information lifecycle management solutions.

    Late-Stage Regulated Data: The Ticking Time Bomb - slide 2

    Early and middle active data

    In the early and middle active data stages, when the enterprise is using the data to derive profits, it is managed by the best operations teams on the planet. The data is backed up regularly. It is processed. It is extracted, transformed and loaded. It is normalized, analyzed, poked and prodded. Investments are made to the infrastructure and the teams that support it. Yet, in some cases, current information lifecycle management plans do not put in place preemptive governance applications that help further reduce risk and cost. Ultimately, this oversight exposes the enterprise to significant risk.

    Late-Stage Regulated Data: The Ticking Time Bomb - slide 3

    Late-stage data

    Enterprises often don’t get to the late-stage archiving and destruction phase because they didn’t establish data governance best practices at the onset. At this phase, data is in a state of limbo. Following the active stage, data tends to lose its appeal. The enterprise is off creating or acquiring new data, adding to the explosion of total managed data, and the old data takes a back seat. But it’s still there, consuming state-of-the-art storage arrays, bogging down applications, keeping the on-call operations manager up at night and copying itself onto a tape in a warehouse somewhere. 

    Today’s risk managers, CIOs, CFOs and even CMOs have an unprecedented opportunity to address late-stage data in the context of their information lifecycle management strategies. A robust archiving platform can be created to correlate late-stage data from each phase of the information lifecycle to establish and enforce information management policies.

    Late-Stage Regulated Data: The Ticking Time Bomb - slide 4

    Here are four key features that should be at the core of an intelligent archiving solution:

    Holistic solution that manages a variety of content

    There are point solutions that manage both structured and unstructured data; however, enterprises should seek solutions that also incorporate email accounts, text messaging and other emerging data and communications forms.

    Late-Stage Regulated Data: The Ticking Time Bomb - slide 5

    Ability to create custom retention policies

    From legal holds to defensible destruction, a solution should provide pathways to manage custom retention policies related to business decisions intelligently. If and when the data is destroyed, it should be done in a defensible manner beyond forensics. 

    Late-Stage Regulated Data: The Ticking Time Bomb - slide 6

    Ability to search and extract

    Paying hourly legal fees for someone to mine through massive data sets is costly. Solutions should provide quick, searchable access to reduce these costs. They also should allow for simple and controlled extraction.

    Late-Stage Regulated Data: The Ticking Time Bomb - slide 7

    Ability to manage business intelligence

    Late-stage data is a treasure trove of untapped business intelligence. From old tape to old digital data, it has most likely been sitting on the shelf collecting dust and providing little to no value. Tapping into rich trend data currently held hostage in unused late-stage data is critical to increasing its value.

    Late-Stage Regulated Data: The Ticking Time Bomb - slide 8

    Intelligent archiving

    An intelligent archiving system that incorporates these features will not only reduce your company’s risk of an unsuccessful recall, but also provide the control needed to leverage and control its late-stage data at a reasonable cost. However, these strategies and disciplines should be adopted and implemented as early in the lifecycle as possible. While the volume, velocity and variety of data are all increasing, solutions should be squarely focused on reducing risk and increasing value across the entire information lifecycle.

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