Big Data Wisdom, Courtesy of Monty Python

Monty Python and the Holy Grail

One of the best parts of the hilarious 1975 King Arthur parody, Monty Python and the Holy Grail is the “Bridge of Death” scene: If a knight answered the bridge keeper’s three questions, he could safely cross the bridge; if not, he would be catapulted into… the Gorge of Eternal Peril! 

 

Unfortunately, that’s exactly what happened to most of King Arthur’s knights…

Fortunately when King Arthur was asked, “What is the airspeed velocity of an unladen swallow?” he wisely sought further details: “What do you mean – an African or European swallow?” The stunned bridge keeper said, “Uh, I don’t know that… AAAGH!” Breaking his own rule, the bridge keeper was thrown over into the gorge, freeing King Arthur to continue his quest for the Holy Grail.

Many organizations are on Holy Grail Big Data quests of their own, looking to deliver game-changing analytics, only to find themselves in a “boil-the-ocean” Big Data project that “after 24 months of building… has no real value.” Unfortunately, many organizations have rushed into hasty Hadoop implementations, fueled by a need to ‘respond’ to Big Data and ‘not fall behind.’ (1)

The correct response, of course, is to first understand essential details behind the question as King Arthur did. Jim Kaskade, CEO of tech consultancy Infochimps, recently suggested to InformationWeek a simple yet “practical and refreshing” question to ask:

Whether it’s churn, anti-money-laundering, risk analysis, lead-generation, marketing spend optimization, cross-sell, up-sell, or supply chain analysis, ask yourself, ‘How many more data elements can you add with big data that can make your analysis more statistically accurate?’

The answer to this key question will lead to additional important questions:

  • “What variety of data sources are needed to fulfill my business case – structured data, unstructured data and/or unstructured content?”
  • “How do I correlate structured and unstructured information together?”
  • “How do I integrate data and content so our users can analyze it on demand, using our existing data visualization tools?”

There is no one-size-fits-all “Holy Grail” Big Data technology out there. In reality, a successful Big Data architecture consists of multiple components to address the unique aspects of all your disparate data sources, structured and unstructured, internal and external. Keep that in mind and show the wisdom of a king by taking pause and asking a few basic business questions to stay on the right path to Big Data business success.

 

(1) Source: InformationWeek article by Doug Henschen, Vague Goals Seed Big Data Failures.

Back to the Future of Business Intelligence with H.P. Luhn

When was the term “business intelligence” first coined? You might assume it was first conceived in the late 1980’s; coinciding with the initial emergence of companies offering visual analytic software, but the term was first used decades earlier by visionary IBM technology scientist Hans-Peter Luhn in his groundbreaking 1958 research paper, A Business Intelligence System.

Hans-Peter Luhn’s life work at IBM did not include quantifiable, structured data. That realm of BI would not become practical and cost effective until the advent of mainframe systems later in the 1960’s. Rather, H.P. Luhn focused on the unstructured content of his day: documents, letters, research reports and manuals. So too, the business intelligence system Luhn envisioned focused on computer-automated document processing: auto-abstraction, auto-indexing, selective dissemination of information, and information retrieval.

It’s plain to see that Luhn was well ahead of his time, envisioning critical technology components that set the stage for knowledge management and enterprise search today. And now, Luhn’s insights are more relevant to today’s business intelligence than ever before.

For example, Luhn demonstrated a keen awareness of the roles communication and collaboration play in the effective use of business information. The system would “channel a given item of information to those who need to know it” and find co-workers “whose interests or activities coincide most closely with a given situation,” using action point profiles of each person’s interests and activities. Luhn’s BI system would therefore quickly answer three vital overarching questions: what is known, who needs to know, and who knows what.

Who knows what - 3 key questions

Now over six decades later, it is remarkable how well Luhn’s insights remain a very solid, results-oriented description of BI. What is also striking to me is how early the silos separating unstructured content and structured data emerged.

Business analytics analyst and consultant Seth Grimes, who has written and presented on Luhn’s contribution to BI, sums up this issue of structured and unstructured silos well:

[For decades,] business intelligence detoured around the estimated 80 percent of enterprise information locked inaccessibly in textual form … So BI thrived crunching numerical, RDBMS-managed data… and delivered findings via tables, charts and dashboards that focus more on numbers than on knowledge.

~ Seth Grimes, BI at 50 Turns Back to the Future

Today it is widely recognized that BI based on structured data alone is not enough. Structured data and unstructured content must be integrated and harmonized together to create a complete analytic picture capable of revealing new breakthrough business insights. Again quoting Grimes, “in the last few years, BI has headed back to the future foreseen by Luhn in 1958.”

What is known? Who needs to know? Who knows what? These three timeless, mission-critical questions Hans-Peter Luhn asked in 1958 require unified information — data and content — to fully answer. And these key questions have become easier than ever to effectively answer and act upon by leveraging new digital transformation technologies capable of integrating and presenting analytic-ready data drawn from all relevant data sources, whether structured or unstructured, internal or external.