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Unified Business Intelligence:
Three Vs: Best Practices for a Unified Business Intelligence Infrastructure

online columnist Ronen Feldman, Ph.D.     Column published in DMReview.com
April 28, 2005
 
  By Ronen Feldman, Ph.D.

Unified business intelligence (UBI), the combined analysis of structured data and unstructured content, is a relatively new practice, but one that can yield significant ROI if approached in a focused, methodical way. The two practices - structured data analysis and unstructured content (text) analysis - have evolved along largely different tracts and drawing the two together is best approached in an incremental roll out, rather than as an enterprise-wide overhaul.

Three things you should consider in your roll-out plan are value, voice and visibility. In short, target a high-value area or issue within the enterprise, hone in on the specific voice (customer, partner, competitor, etc.) that will prove most useful in solving the issue and determine the most impactful way to deliver, or make visible, that information to a target user audience.

What follows is a set of broad guidelines to help better define the value, voice and visibility for a UBI project. Since voice and value are often easier to define than the visibility, we will start with championing value, then define how to tap the voice and finally hone in on delivering visibility.

Determining Value: Where Will Text Improve Performance?

UBI has a wide and deep value proposition, but deciding where to focus initial efforts is more straightforward than organizations realize. It comes down to selecting the most important enterprise initiatives (customer retention, product quality, competitive analysis) and identifying how these can be streamlined, enhanced or improved by integrating data available through text analytics.

Finding a champion in management is a critical part of this step. Unstructured content is the contextual glue that holds the organization together, so it is not surprising that when it comes to corporate initiatives around quality, innovation, loyalty, risk, market insight and operational insight, this content has particular value. The key is to pick one initiative, then map it to the CRM, PLM or ERP sub-applications that are most relevant and get management to buy into the success of the program.

The next step is to define what action should occur from the newly found intelligence. Business intelligence systems fall into three categories, and the decisions and actions that can result are surprisingly predictable. The three major types of BI systems are strategic, tactical and operational. Strategic BI has been the domain for information service providers that provide market insights for corporate planning. Tactical BI has been the domain of traditional BI software vendors that assist in enterprise reporting. Operational BI has largely been homegrown custom systems that facilitate monitoring and exception management by using models to manage trading, churn, fraud, etc. Quarterly planning for an executive committee, weekly reporting for a functional manager and daily alerting for an administrator all require different types of visibility.

As an example, in 2001 Dow Chemical merged with Union Carbide Corporation (UCC). One of the goals of the merger was to leverage the combined intellectual assets and resources to build a more innovative company with a powerful research and development organization. With more than 80 years of history and approximately 300,000 unstructured research and development reports describing chemical and biological technologies and applications, Dow now had tremendous research assets. Dow realized that a UBI project would yield value by creating a way to catalog, analyze and access all of the data within those assets to fuel innovation. Dow's IT team went to work analyzing all of its unstructured content, drawing structured data out of text, and integrating it into a data warehouse that scientists could analyze for future research and development. The result was a powerful new tool that Dow could use for discovery. Benefits include:

  • 100,000 new chemicals quickly added to the registry database for access by researchers
  • Project cost savings estimated at $ 9 million (compared with DOW traditional project methodology)
  • Eliminating manual searching, sorting and IP asset management increased researcher productivity
  • Data errors reduced by 10-15 percent

Tapping the Right Voice

While structured data tells us what is happening in an organization in terms of sales, production, finances and productivity, unstructured data often tells us the most about a constituency's voice: what customers think, how competitors are selling, what industry pundits think is a hot trend. By tracking, organizing and analyzing this data, businesses can capture the voice of the customer, the supplier or the industry influencer to improve business performance, customer relationship management or competitive insight, among other things.

However, "voices" are inherently complicated; identifying how, when and which voice to tap is the most critical element of a UBI implementation. Many categorization, unstructured data management and taxonomy creation projects have died from their inherent broad scope and resultant scope creep. The key is to tap the right voice in a modular, value-driven way.

In short, the steps to capturing the right voice are:

  • Identify which voice is appropriate for a particular UBI project: the customer, market or product
  • Decide what elements to extract from the voice: profiles, issues or interactions
  • Select what sources to access the voice from: internal (Word documents, memos, PowerPoints), external (e-mails, RSS feeds, Web sites) or both

Once these decisions have been made, the complexities of grammar, language, format and ownership go away and a simple pilot makes the project go/no go clear.

It's Showtime: Visibility

What use is business intelligence information if it is not delivered in a manner that is understood and easily applied by its target user audience? Delivering the information produced from text analytics requires a close look at the lens of the target user group.

In this area, there are two primary considerations:

  • Is this a corporate or business unit group?
  • Functionally what is the role of the organization?

A sales analyst in a business group will have different requirements from an engineering quality analyst in a corporate group.

Having picked the lens, finding the appropriate delivery mechanism becomes much easier. Should you deliver alerts, reports, visual analytics, dashboards or self-service retrieval? You may also want to focus delivery around functional "whys" to help user groups incorporate information into existing projects and jobs. The most common "whys" include root cause, usage factors, buying factors, competitive landscape, R&D white spaces, liability exposure and policy/practice compliance.

Bringing it All Together

There is a lot of discussion about the notion of a "next frontier" in business intelligence; a unified BI architecture that incorporates traditional structured data management and analysis with text analysis tools to reach the information hidden in unstructured content such as e-mails, Word documents, RSS feeds and Web sites. The unified BI stack is already becoming a reality within many organizations. The good news is that every organization can derive deep value from turning its digital voice data into databases for analysis. The key is to simplify and get started. Making it happen just takes focus - value, voice, visibility - to successfully integrate text analytics into the BI infrastructure.

...............................................................................

For more information on related topics visit the following related portals...
Business Intelligence and Unstructured Data.

Dr. Ronen Feldman. PhD is one of the leading minds in the field of text mining and draws on years of experience in the development of knowledge discovery systems and text mining applications. Feldman is responsible for ClearForest's technical business development, rapid prototyping, and the research and development of new products. In particular, he is in charge of the wireless segment and development of language models for new vertical domains. Feldman serves as a consultant to leading Israeli companies and serves on the program committees of AAAI, KDD, PKDD and SIGIR. He is often an invited speaker in academic and industrial conferences, and he is a senior lecturer in the Mathematics and Computer Science Department of Bar-Ilan University in Israel.

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