From Big Data to Big Business

Published
  • July 26 2012, 8:49am EDT

Information governance has been a back room operation, often scorned for its overemphasis of enforcing rules, regulations and policies that limit the development of the business. In truth, many early implementations have taken the form of Big Brother.

However, with the explosion of big data, the tables have turned. Today’s information governance solutions are more actionable and adaptable, providing the fast path management foundations to enable the data-driven enterprise.

One of the major issues IT leaders face in directing their organizations on how to best embrace big data as an opportunity to become data-driven is answering the first question that comes with it: Where do we begin?

Confronted with an increasingly unmanageable mountain of data, IT professionals no longer have the option of getting out in front of big data management; it's a business imperative. Balancing business value with the right kinds of management controls that enable breakthrough applications, protect a valuable asset, and govern to ensure that corporate policies are enforced, is at the top of every CIO’s list of objectives for the next three to five years.    

The question of “Where to begin?”, and the complex management scenario that follows, are not unusual ones for seasoned CIOs. Experienced CIOs know that at least once every five years, a major transformative disruption occurs in the enterprise, and business and IT leaders struggle to adapt. It happened with the PC, client/server applications, CRM, the Internet, cloud, social and mobile. Now it’s happening with big data.  Fortunately, successful leading organizations have both the ability to embrace today’s present landscape, and the foresight to visualize where they want to be in five years.

The perspective needs to be on the journey, not the event, and the reality is this: Information governance has a maturity curve. (See Figure 1, below.)

 

The maturity curve has two major factors associated with it, as well as two major pitfalls that organizations can fall into.  The axes are the growth of management controls and business value. As organizations seek to consume big data, they have to deal with both in unison. The pitfalls, which are the one-size-fits-all repository and the load-and-go application that replicates data, are similarly placed.

Moving management controls from reactive to active, while at the same time turning data from a tactical cost item to a strategic asset of the business, is the goal of information governance. The pathway to information governance is improving organization-wide capabilities to:

  • Understanding the assets the organization has, finding them and quantifying their impact.
  • Classifying data and understanding its relationships and relevance to the business.
  • Feeding business processes with relevant data to solve problems dynamically.
  • Implementing policies to enforce regulatory or corporate mandates.  
  • Proactively identifying patterns and trends that help make better decisions. 

Not all enterprises have matured completely along this pathway. Many enterprises fall into the pitfalls of too much control (making the organization subservient to a single repository) or too much freedom in serving the business (allowing them to replicate and move information to be controlled locally without regard for other uses or governance).
The maturity curve simply defines where an organization is currently and what steps are needed to create a streamlined path to an actively controlled, data-driven enterprise that leverages its data as a strategic asset.

Underlying the philosophy behind the maturity is the philosophy of how much management controls to put in place. There are four camps of attitudes, cultures and maturity levels when grappling with the challenges of managing big data. They are:

Inactive – These organizations have already fallen in to the repository pitfall. These companies deal with big data issues as storage problems. When issues such as e-discovery and regulatory requests come up, they just push the work to third-party consultants. This approach has several failings:  it’s expensive, it’s unpredictable, it’s disruptive to employees, and even in the best case there are often information collection gaps. 

Reactive – These organizations have already fallen into the replication pitfall. These companies buy applications to solve big data problems, one problem at a time. They employ software tools in a reactive way to solve information management problems in specific departments or functional areas. This creates some level of execution and cost predictability for individual groups, like legal or finance, but doesn’t create a corporate information management system that would operate at maximum efficiency by lowering costs and risk across the board.

Proactive – These companies recognize the opportunity in employing some levels of information governance and implementing systems with an integrated set of tools to proactively clean up the big data mess before it hurts the company. The impact can be compelling, often helping lower e-discovery and regulatory costs, streamline IT infrastructure, create greater visibility into and control over data and implement proper data management policies.

Active – These companies view big data as an asset and have people, platforms, processes and technologies in place to gain insight into their data and to make more intelligent decisions. They consider this to be a core infrastructure component of the IT frameworks. These companies use big data as a competitive weapon, not just something to be governed and managed.

The technologies, products and solutions that enable organizations to embrace and adapt to various trigger points on the curve to maturity are:

  • Visualization tools that expose context of all data assets. 
  • Storage solutions that optimize and migrate to the cloud. 
  • E-discovery solutions that dynamically respond to litigation or compliance needs.
  • Information governance applications that enforce mandates. 
  • Decision-making applications that are infused with relevant data. 

Matching your Maturity and Value Requirements with an Information Governance Discipline

An in-depth assessment of your information governance maturity should always be coupled with a management strategy for growth. Figure 2 (see below) captures many of the characteristics organizations will seek during the process. 

Where Do You Fit?

Organizations need to move past talking about big data and start actively managing it. I strongly believe that there is a strategy to turn big data into big business quickly and cost-effectively. As active information governance evolves, it will include implementing an infrastructure that views data as an asset, manages it in real-time in its native state and leverages it to make key processes of the business more effective and efficient.

The industry leaders in big data have moved beyond cost, risk and storage limitations as problems and moved into creating active intelligence centers that keep their companies aware of key trends and acting confidently to create the data-driven enterprise. These industry leaders are more productive, profitable and market leading as opposed to market following.

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