Business Intelligence and Analytics: Improving Your Competency

By Craig Wacaser  |  Posted 2011-03-31

Business Intelligence and Analytics: Improving Your Competency

Discover your company's best kept and most valuable asset: insight from your data. The issue is not lack of data. Most would agree there is too much data and not enough insight. According to a recent survey, 60 percent of executives say they have more information than they can use.

The key is making sense of the data, turning it into actionable insight to drive better decisions and improve business performance. According to the survey, top-performing organizations are twice as likely to leverage business analytics to drive future strategies and day-to-day operations as lower performers.

As a business leader, how effective is your company at providing you with the timely information and insight you need to make your most important business decisions?

Companies continue to spend millions on transactional applications and IT infrastructure. As a result, mountains of data are collected, often sitting underutilized in large databases. An ever increasing number of companies, however, are exploiting this untapped asset. They're gaining a competitive advantage by transforming their data into valuable information and actionable insight to answer critical business questions and improve performance.

A recent study by IBM shared that 50 percent of executives feel they don't get the information they need to make critical decisions, and three out of four executives felt more predictive information would drive better decisions. The study also concluded that two out of three companies are still in the early stages of developing their business intelligence competency.

In this article, I will outline five levels of BI that all business leaders should consider. Let's begin with single-application reporting.

Level No. 1: Single-application reporting

These are canned or ad hoc reports from transactional applications such as SAP or Oracle Financials. These reports look back in time and typically answer questions such as, "What happened?" and "How are we doing?"

Business Intelligence and Analytics: Improving Your Competency

title=Cross-Application, Enterprise Reporting} 

Level No. 2: Cross-application, enterprise reporting

Many companies continue to struggle with aggregating enterprise data due to political and technical issues. Knowledgeable technical resources can establish a viable infrastructure with enterprise data warehouses or data marts, and develop comprehensive extraction, transformation and loading (ETL), data governance and cleansing routines. Often there is reluctance among business units and functional areas to share data. This unproductive issue can only be solved by high-level management commitment and support for an enterprise-wide BI strategy.

A credible enterprise data warehouse enables management to view a Single Version of the Truth (SVOT). Keep in mind that a data warehouse used for analytics needs to be designed appropriately for this purpose.

Focus on the immediate business issues to be solved and the associated data versus trying to process all company data. Start with the business questions and problems, not the data. This will help avoid the data warehouse "death spiral" where companies attempt to do too much, too quickly.

It is not uncommon for companies to get stuck attempting to perfect enterprise data and spend years aggregating, integrating and cleansing data-and ignore solving today's critical business issues. Unfortunately this "boil the ocean" approach results in a significant loss of time, money and, ultimately, management support.

There are excellent BI tools available to leverage data warehouses that enable managers and users to view canned or run ad hoc reports across applications. Dashboards and scorecards, for example, allow management to monitor and track key performance metrics and drill down for additional detail. Exception-based reporting automatically notifies management if certain metrics exceed thresholds.

Cross tabs, pivot tables and online analytical processing (OLAP) cubes enable an in-depth view of relationships between two sets of variables (that is, sales revenue per quarter or year related to geographic location).

To their detriment, companies often stop at this stage and forego additional opportunities for advanced analysis.

Business Intelligence and Analytics: Improving Your Competency

title=Data Mining and Statistical Analysis}

Level No. 3: Data mining and statistical analysis

This level goes beyond simple queries and top-ten lists to explore relationships, trends and patterns within data. Typically a manual exercise, this generally requires a specialized resource that is both technical and business-focused, with mastery of corporate data elements. Similar to a goldminer following a vein to uncover additional ore, this hypothesis-based analysis improves insight and answers questions such as, "Why are things happening?" Answers to questions such as, "What if?" typically generate additional questions for deeper analysis and insight.

Level No. 4: Analytical insight and predictive modeling

In this evolutionary stage, companies leverage advanced modeling software and analytical tools to understand pertinent relationships among numerous data sources and multiple variables across the business. Through the power of complex computing and automation, companies are able to identify patterns, trends, segments and clusters within the data to improve insight. Whereas the human brain can process and visualize a two-axis XY chart or a three-axis, multidimensional data view (imagine the three-dimensional chessboard on Star Trek, today's computers and advanced software can process data relationships across hundreds of variables simultaneously.

This process can also leverage modeling to predict future outcomes to enable you to focus your resources more effectively. Some questions that are answered at this stage include, "What customers are most likely to defect?" and "Which prospects are most likely to respond to a certain offer?"

Level No. 5: Resource optimization

In this phase, companies optimize resources based on their unique constraints and parameters. This level answers questions such as, "What should we do now?" and "What is the best outcome that can occur given our current available resources?" and "How can we optimize staffing levels, inventories and service levels?" Nirvana!

Where is your company with regard to the five levels of BI? Where is your competency in this area? Is it tactical or strategic? Where are your resources located? Are they centralized or decentralized? Is the capability shared across business functions? Is there an established process to investigate new opportunities with significant returns?

Business Intelligence and Analytics: Improving Your Competency

title=Implementing the Five Levels of BI} 

Implementing the five levels of BI

Many companies get caught up in day-to-day tactical operations and fail to exploit the wealth in their existing data. The reason often stems from the short-term mentality of putting out today's fires and forgoing strategic, longer-term initiatives. According to the aforementioned survey, the top three roadblocks to improving BI within companies are organizational issues:

1. Lack of understanding of how to use analytics,

2. Lack of management bandwidth due to competing priorities, and

3. Lack of skills internally.

With commitment and planning, your company can gain access to this critical BI that will ultimately give you the knowledge to take your company to the next level.

The first step involves confirming your data availability, credibility and history. Some well-intentioned companies casually discard data after four to six months to save on IT storage costs. However, with inexpensive terabyte drives now commonly available and used in PCs, this is now rarely an issue of expense.

Optimally, multiple years of data should be leveraged to adequately identify and analyze seasonal patterns. However, even one year of data can produce trends and patterns before unseen.

Often, isolated islands of BI competency at different levels of evolution spring up across organizations, depending on their specific business needs and available expertise. A solid corporate capability to analyze and leverage enterprise data is becoming critical to optimizing business performance in today's competitive environment.

Business Intelligence and Analytics: Improving Your Competency

title=Business Intelligence Competency Centers}

Business Intelligence Competency Centers

Business Intelligence Competency Centers (BICC) or BI Centers of Excellence (BI COE) are sometimes located within forward-thinking IT, finance or operations groups. However, it is recommended to separate this function from front-line operations to allow for a more strategic, longer-term focus. Optimally, a centrally-resourced group should act as an information hub and support business units in their evolutionary efforts to improve their BI capabilities.

This evolutionary process need not be serial in nature. If there is a significant opportunity to leverage analytics and modeling in one part of the organization, there is no legitimate reason to wait until the rest of the organization catches up. Analyzing a credible data mart within a business unit can be very effective, without waiting for an enterprise data warehouse initiative to be completed.

Growing your BI competency through this comprehensive, five-level business strategy requires a strong commitment and vision from senior management. Consider taking advantage of this golden opportunity to improve insight to drive better decisions and improve performance.

Start today by identifying your current BI capabilities and begin developing a strategy to improve your BI and analytics competency. Investigate significant opportunities with quick turnaround to show early success and build support. Advanced BI and analytics is a fascinating evolutionary process that will ultimately transform your business.

Craig Wacaser has more than 20 years experience helping medium and large companies solve business problems and improve performance through technology-oriented business solutions. He has held business development, sales and sales management positions with IBM, SAS, Hewlett-Packard, Teradata, and D&B. Craig earned his Bachelor's degree in Marketing from Arizona State University and his MBA from the Anderson School at UCLA. He can be reached at


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