“Intelligence” is a word much bandied about in enterprises. Frankly few enterprises use it properly to denote what constitutes authentic intelligence.
"Intelligence is nothing if not an institutionalized black market in perishable commodities" Master spy novelist John LeCarré
While the above notion is intended to refer to information gathered by spies regarding enemies, this sort of thinking has colored how enterprises view intelligence: the secret information that will solve all problems and kill all enemies. It doesn’t matter if you’re talking business intelligence or market / competitive intelligence. The apparent allure of the military and spy industries for enterprises has badly skewed how many companies view intelligence.
The kind of intelligence that truly benefits enterprises derives from classic definitions of the word: learning, understanding, applying knowledge and experience to new situations to make better decisions. For both business intelligence and market/competitive intelligence, the frequent catchphrase is “making better business decisions”. Both involve data, analysis, recommendations. Both can have strategic and tactical applications. Both benefit from qualitative as well as quantitative dimensions. And both should strive for authentic INTELLIGENCE, not just resultant collections of information.
Business Intelligence methodologies and tech solutions have been used to generate actionable views of a company, frequently rooted in business operations. Until recently, BI outputs have been primarily reports -- some might say too many non-contextual reports, many of which may be trivial, pointless, or worse, misleading. The BI world includes analytics, data mining, text mining and predictive analytics.
And sometimes BI is considered to be ‘competitive intelligence’, hence my references to market intelligence. Market/competitive intelligence and business intelligence should be complementary, but are not the same thing. In my view, a certain amount of BI output contributes ‘intelligent data’ for market intelligence initiatives.
Overall, market intelligence gleans from many ‘data’ sources from many disparate sources inside and outside the firewall, both qualitative and quantitative, while filtered by specific strategic contexts. Until recently, BI has mostly derived from practices and technology that address structured data and business processes inside the firewall. BI will now have to include unstructured or content sources in analytics, to add in missing subtleties, context and insight that cannot be pulled from structured data sources.
In addition to overt BI-style analytics, many other analytics solutions have been proliferating to help make better business decisions. Web analytics were silo’d for a while as relevant only to website usages. But web analytics now have many forms and have become very sophisticated. Data captured by web analytics is now important to many teams across the enterprise and is now becoming more integrated into other “business intelligence” endeavors.
With fast-growing social media applications, SM analytics are now emerging -- again the data generated is essential and must be integrated into the overall intelligence base of the enterprise. Content analytics have become even more important as unstructured content proliferates from many sources.
These seemingly diverse analytics cannot be confined to individual silos in enterprises. “Intelligence for the Business” that results from all analytics is needed at LOB / department levels, as well as at the über-enterprise level. And mid-market enterprises need such business intelligence capabilities as well. So all kinds of analytic tools need to become more accessible to many roles and organizational segments that have not necessarily been targets of such solutions before.
Unfortunately, keeping pace with the enormous proliferation of data, and even with the various analytical outputs, is overwhelming many enterprises. More integration between what began as silos of analytics must now happen. Enterprises have to get better at the behind-the-scenes / get-your-hands-dirty work that is essential to make realistic and effective use of all the data (structured and unstructured) pouring in from so many sources. The Semantic Web will likely contribute overall to the integration of structured and unstructured data to add to intelligence relevance.
Editor's Note: You might also be interested in: Marketing Analytics: Here's What the CMO Wants #eMetrics
Predictive Analytics – The Bold New LOB Tool
Considered a subset of BI, but also present in other categories of analytics solutions, predictive analytics may become much more in demand than traditional BI solutions, especially with the new directions of innovative predictive analytics solution vendors.
Eric Segal of Information Management on predictive analytics -- citing a marketing use case:
Predictive analytics is data mining technology that uses your customer data to build a predictive model specialized for your business. …The real trick is to find the best predictive model.
…a careful combination of predictors perform better customer prediction by considering multiple aspects of your customers and their behaviors. Predictive analytics finds the right way to combine predictors by building a model optimized according to your customer data.
Predictive analytics builds models automatically, but the overall business process to direct and integrate predictive analytics is by no means automatic -- it truly needs your marketing expertise.
Predictive analytics can be part of “traditional” BI offerings -- it’s also part of the web analytics world. Baynote provides ‘Adaptive Web’ solutions that include analytics, personalization and social search for customer-focused relevance on websites, to help optimize engagement and retention. Baynote’s Collective Intelligence Platform provides companies with predictive analytics based on the implicit patterns of customers visiting their websites. Omniture, WebTrends and other web analytics solutions also provide capabilities for predictive analytics.
Content Analytics, Content Intelligence
Enterprises have been challenged for years on the content intelligence front – it has been very difficult and very messy to extract meaning (and thus retaining context) from unstructured content to be used as “data”. The amount of enterprise content is enormous, with geometric expansion in play. For enterprises that publish content on the web, content analytics also offer the means to better connect with target markets and customers:
"Content analytics" can be seen as business intelligence (BI) for/from content, as text (rather than number) crunching that generates insights to improve business outcomes. The two practices, content analytics and BI, certainly share motivations. If you don't analyze your content/data, you may be missing opportunities and running risks.
For content publishers, analytics drives better targeted content delivery, expanded audiences, and secondary uses and new distribution channels. These outcomes add up to profit. On the flip side, they are matched by reduced risk and cost avoidance given possibilities for more complete, more accurate compliance screening, e-discovery, and storage management.
Analytics also boosts value for users. Semantic search, faceted navigation, and content annotation/enrichment create findability and improve user experience and value for users. They also let users treat content like data. Call the goal "content intelligence," enabled by "smart content." Seth Grimes on Content Analytics
“Intelligence” Hits The Wall
Stephen Few’s graphic for “BI Has Hit the Wall” is quite expressive of where BI -- and all analytics -- need to head to deliver evolving value to enterprises. Stephen Few is a leading expert in creating effective data visualizations that communicate real intelligence and don’t just function as “eye candy”:
Contained in these early definitions was the seed of an inspiring vision that caused people like me to imagine a better world, but the business intelligence industry has done little to help us achieve the vision of the people who coined the term. When Thornton May was interviewing people for his book “The New Know”, he asked a prominent venture capitalist known for his 360-degree view of the technology industry what he thought of when he heard the phrase business intelligence. His response was “big software, little analysis.”
For information to be useful, we must explore it, analyze it, communicate it, and monitor it, but the BI industry’s attempts to support these activities with few exceptions have been tragically comical. The technology-centric, engineering-oriented perspective and skill set that has allowed the industry to build an information infrastructure is not what’s needed to support data sense-making. To use the data that we’ve amassed, a human-centric, design-oriented perspective and skill set is needed.
Part 2: BI & Analysis: Better Results with Collaboration, Reliable Data) takes on further analysis of new directions for BI / analytics through new approaches like predictive analytics and bringing in collaborative processes to enhance and validate BI projects. Part 2 also looks at the need to understand how to use analytics, ask the right questions and know if the results are valid.
Disclaimer: Julie Hunt is not affiliated with any of the vendors in this article.