I recently got published in the Special Big Data Edition of CIO Story (see page 20), where I talked about the “six” essentials for transforming into an Information-driven organization.
Information is a hot commodity. Research suggests that in the next two to three years, businesses will begin to apply monetary value to their information assets by trading or selling them. Throughout history, this notion has been referred to as “Infonomics.”
The principles of Infonomics are based on the premise that information has both potential and realized economic value, which can be quantified and should be managed as an asset. The benefits in doing so include improving the collection and use of company information, determining how much to spend on business or IT initiatives, and improving relationships with customers, employees and partners by sharing better information with them. More and more organizations realize that the trick to experiencing these benefits by better managing assets, however, is to effectively apply the organization’s existing experience in managing other assets toward managing information assets. But, in order to get to that point, executive leadership (business and IT) needs to recognize the barriers to becoming an information-driven enterprise while focusing on certain fundamental strategy essentials.
If organizations are serious about improving the value and speed of information, they must consider the following six imperatives. Doing so will drive their organization’s ability to become an info-centric enterprise:
1. Embrace the Nexus of Forces
Today’s organizations must recognize and strategically embrace the emerging nexus of social, mobile, cloud and information, where Big Data and advanced analytics serve as revolutionary ways of evolving the information management ecosystem. Information leaders must therefore look for Big Data opportunities in this interconnectedness and across the nexus shifts, and craft a vision that takes into account this “nexus ecosystem” perspective. They must target information infrastructure modernization on evolving “capabilities” incrementally, rather than on individual projects, siloed applications or generic use cases. An integral part of this vision definition is an organization’s ability to bake and internalize the principles of information governance, metadata, consistent business semantics, security, privacy and sharing.
The Future of Big Data
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2. Make Data the Fourth Dimension
A holistic transformation strategy must take into account this nexus of forces and address it not only in the light of the traditional people, process and platform pillars, but also attach a fourth dimension to this mix – the “Data” pillar. This broad strategy must define an integrated information roadmap that ties corporate goals and key business initiatives (e.g., customer experience) to Data (small and big) and advanced analytics (predictive and prescriptive). The success of such an all-inclusive strategy rests on the leadership’s ability to continually communicate and engage in a company-wide conversation about becoming a data-driven organization, and helping the people to bridge the gap, a key for enabling this kind of evolution within an organization’s landscape. It also is critical that, as part of this strategy, organizations evaluate their overall information maturity, and identify and communicate ways to address capability gaps and phased improvement targets at an enterprise level.
3. Create an Actionable Big Data & Advanced Analytics Roadmap
The integrated information roadmap also must articulate an actionable Big Data roadmap across the four dimensions (discussed in imperative #2). It should define the various stages and milestones for Big Data and Analytics’ adoption within the organization – from being “aware” and “experimenting” to moving to become “opportunistic” and “strategic,” where nirvana is the final “transformative” stage, which stands for the attainment of the elusive information-driven enterprise status. Effectively tying the 3Ps to this value-capability continuum and targeting improvements, maturity and adoption along each successive phase is the secret sauce for building and implementing a successful Big Data analytics solution.
4. Consider a Hybrid Architectural Approach with a Renewed Emphasis on Security and Governance
As part of your strategic planning, consider and implement new hybrid and alternate information architectures (such as Gartner’s Logical Data Warehouse) to address the opportunity presented by the nexus of forces. As you plan to assess and address the related platform modernization, think strategically, but execute tactically to implement Big Data solutions that are tied to the integrated information roadmap, both on a pilot and an enterprise level. Based on the architectural scenario that you choose to build, there can be a plethora of vendor technology options available to choose from. Therefore, the trick is to develop an agnostic, end-to-end solution blueprint and then overlay it with either the best-of-breed or a single vendor product stack from a technology standpoint.
Additionally, information security and governance are the two silent, but powerful strategies to protect the integrity of enterprise assets, and to get the most out of your data initiatives. A holistic architecture review must proactively take into account these critical aspects of your platform modernization, especially as you look to evaluate vendor technology platforms.
5. Go Beyond the Inertia of Business Intelligence
Info-centric enterprises must get beyond the inertia of basic business intelligence – rows and columns reporting, scorecards and dashboards – and into the world of strategic, operational and tactical data sciences, predictions and prescriptions. The ability of an organization to apply data science (statistics, operations research, machine learning, etc.) to data in order to derive insights for enhanced decision-making is a critical component of evolving into an information-driven enterprise. Data science drives advanced analytics – segmentations, clustering, correlations, associations, neural nets, optimizations, next best action, etc. – and allows an organization to build upon its existing analytical foundation for prescriptive analytics. Correlations and patterns from disparate, but linked internal and external data sources yield the greatest insights and transformative opportunities. One thing to remember is that only such holistic data views make data science and, in turn, analytics useful to an organization.
6. Embrace Emerging Skillsets and Roles
This sort of an organizational transformation is impossible without an understanding of the skills and roles required to make it happen. Driving the success of data science solutions rests on evolving the organization’s landscape to grow and nurture appropriate skills, roles and responsibilities. Here are the emerging roles for the 21st century info-centric enterprise:
Chief Data Officer, Chief Analytics Officer, Data Scientist, Information Strategist
In essence, organizations that seek to be information-centric must take a holistic, all-encompassing approach to Big Data while considering it as part and parcel of a cohesive information strategy. It also is important to acknowledge that Big Data initiatives are unique and great ways to incubate a culture of innovation and creativity.
