Data Integrity: The Cornerstone for BI in the Decision Process


When studying methods of decision making in organizations, mature professionals with an objective posture often walk away wondering how individuals, organizations, and even our species have survived this long. When studying large systemic crises, it can truly be a game changer in the sport of life, providing motivation that extends well beyond immediate personal gratification.

Structural integrity in organizations, increasingly reflected by data in computer networking, has never been more important. The decision dimension is expanding exponentially due to data volume, global interconnectedness, and increased complexity, thus requiring much richer context, well-engineered structure, far more automation, and increasingly sophisticated techniques.

At the intersection of the consumer realm, powerful new social tools are available worldwide that have proven valuable in affecting change, but blind passion is ever-present, as is self-serving activism from all manner of guild. Ideology surrounding the medium plays a disproportionate role in phase 3 of the Internet era, to include crowdsourcing, social networking, and mainstream journalism. Sentiment can be measured more precisely today, but alignment is allusive, durability questionable, and integrity rare.

Within the enterprise, managers are dealing with unprecedented change, stealthy risk, and compounding complexity driven in no small part by technology. Multi-billion dollar lapses sourced from multiple directions have become common, including a combination of dysfunctional decision processes, group/herding error, self-destructive compensation models, conflicting interests, and poorly designed enterprise architecture relative to actual need.

Specifically to enterprise software, lack of flexibility, commoditization, high maintenance costs, and difficulty in tailoring has created serious challenges for crisis prevention, innovation, differentiation, and global competitiveness. It is not surprising then, given exponential growth of data, which often manifests in poor decisions in complex environments, Business Intelligence (BI) is a top priority in organizations of all types. BI is still very much in its infancy, however, often locked in the nursery, subjecting business analysts to dependency on varying degrees of IT functionality to unlock the gate to the data store.

Given the importance of meaningful, accurate data to the mission of the analyst and future of the organization, recent track records in decision making, and challenges within the organization and IT industry, it is not surprising that analysts would turn to consultants and cloud applications seeking alternative methods, even when aware of extending the vicious cycle of data silos.

Unfortunately, while treating the fragmented symptoms of chronic enterprise maladies may provide brief episodic relief, only a holistic approach specifically designed to address the underlying root causes is capable of satisfying the future needs of high performance organizations.

The dirty dozen fault lines to look for in structural integrity of data

  1. Does your EA automatically validate the identity of the source in a credible manner? (Y/N)

  2. Is your IT security redundant, encrypted, bio protected, networked, and physical?  (Y/N)

  3. Are your data languages interoperable internally and externally? (Y/N)

  4. Is the enterprise fully integrated with customers, partners, social networking, and communications? (Y/N)

  5. Do you have a clear path for preventing future lock-in from causing unnecessary cost, complexity, and risk?  (Y/N)

  6. Are data rating systems tailored to specific needs of the individual, business unit, and organization? (Y/N)

  7. Are original work products of k-workers protected with pragmatic, automated permission settings? (Y/N)

  8. Does each knowledge worker have access to organizational data essential to their mission? (Y/N)

  9. Are compensation models driving mid to long-term goals, and well aligned with lowering systemic risk? (Y/N)

  10. Is counter party risk integrated with internal systems as well as best available external data sources? (Y/N)

  11. Does your organization have enterprise-wide, business unit, and individual data optimization tools? (Y/N)

  12. Are advanced analytics and predictive technologies plug and play? (Y/N)

If you answered yes (Y) to all of these questions, then your organization is well ahead of the pack; even if perhaps a bit lonely. If you answered no (N) to any of these questions, then your organization likely has existing fault lines in structural integrity that will need to be addressed in the near future.  The fault lines may or may not be visible even to the trained professional, until the next crisis of course, at which time it becomes challenging for management to focus on anything else.

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About Mark Montgomery
I am a technologist, serial entrepreneur, business consultant, recovered VC, and inventor with interests that are both broad and deep across multiple disciplines, including organizational management, computing, communications, economics, sociology, science and nature, among others. For the past several years I have been founder and CEO of Kyield, which offers a distributed operating system for achieving optimal yield of executable knowledge across large data networks. The patented AI system core acts to unify networks with adaptive data tailored to each entity with continuous predictive analytics designed to significantly reduce ongoing costs while accelerating productivity, and generally make life more satisfying and productive for knowledge workers and their organizations. We provide popular free white papers, use case scenarios, and other information at http://www.kyield.com .

One Response to Data Integrity: The Cornerstone for BI in the Decision Process

  1. Pingback: Too Big to Fail or Too Primitive to Succeed? « Kyield

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