Business Intelligence Musings from the Field 

By Philip Duplisey - Senior Manager, Consulting Services - Bardess Group Ltd.  

Garbage in, garbage out (GIGO) is a phrase that has been around as long as computers have been around.  It has been recently validated for me when I spent some time onsite at clients for Bardess. What made the experience so defining was that one company understood the concept and was actively working to improve their data quality, while the other company was completely oblivious to it. The contrast made my work with both clients very interesting, and I thought I would share what I learned.

No Business Intelligence tool can substitute good business processes.

Business processes enabled by information systems directly affect the quality of the data in those information systems. If the data quality is poor, it costs the organization money, this is a hidden cost, which is not fully recognized until it's too late (bad decisions made because of bad data).  For example, if data quality is not part of a salesperson's job responsibility, then we shouldn't be surprised that we end up with customer master records that are completely hosed. Incomplete addresses, no zip-codes, misspelled customer names wreak havoc on the decision making process when trying to analyze sales performance. At the same time, the regional sales manager would have a tough time explaining why sales are down, because "the system" prevented order entry due to incompleteness of the data. Obviously there is a fine balance, but often in the past, the balance has been in favor of "ease of use" on the front end with no consideration for what kind of analysis will be required on the data later on.

Dashboards can be made to show you what you want to see.

Mark Twain popularized the saying that there were lies, damn lies and statistics. In the context of business intelligence I might rephrase that by saying there are lies, damn lies, and fancy dashboards! As a consultant using charts and pictures to tell a story, it's essential to work with a subject matter expert to ensure that the conclusions derived from the charts truly represent the meaning of the data in order to deliver real value to the client. This can be difficult in organizations where the corporate culture is to make decisions based on "experience" and then look for data to validate the conclusion already made. Making decisions based on data is a bigger challenge than what most companies are willing to admit because facing the brutal facts as Jim Collins describes it in "Good to Great" requires leadership and courage.

Bardess Group to be a Solution Provider Partner Sponsor at QlikView's North America Business Discovery Tour  

QlikView, a leader in Business Discovery -- user-driven Business Intelligence (BI), is hosting a Business Discovery tour in key cities in the U.S. and Canada to demonstrate how Business Discovery is transforming BI initiatives, from small and medium-sized businesses to the enterprise.

Bardess Group, winner of QlikView's "Move Fast Award" for Mobile Apps, will be a Solution Provider Partner Sponsor sharing how their organization can deliver value to companies by taking advantage of the QlikView Business Discovery platform.

Bardess Group will appear at the following locations:

• Costa Mesa - August 10
• St. Louis - September 8
• Hartford - September 20
• Houston - September 22 
• Raleigh - October 11
• Phoenix - October 13

CLICK HERE for more information and to register at no cost for any of these half-day events.   

Is Your Organization in Data Denial?

By Joe DeSiena - President, Consulting Services - Bardess Group Ltd.  

Moving Toward Data Quality

Data Quality is the desired state where an organization's data assets reflect the following attributes:

• Clear definition or meaning,
• Correct values,
• Understandable presentation format (as represented to a knowledgeable worker), and
• Usefulness in supporting targeted business processes.

However, regardless of the state of the organization's data assets, there must still be a balance of data, process, and systems in order to meet the company's stated business objectives, which generally focus on things like:

• Increasing revenues and margins,
• Increasing market share, and/or
• Increasing customer satisfaction.

In today's economy, companies tend to focus their investments more on packaged systems and business process optimization, rather than on Data Quality.  As a result, investment in corporate Data Quality is often overlooked - and this can very easily lead to a significant reduction in the organization's ability to effectively answer critical business questions, such as:

• Who is our customer?
• Are we missing sales opportunities?
• Is the customer's product entitled to service?
• Are inaccuracies causing customer dissatisfaction?
• What should we spare; how many; where?
• Are our service functions efficient; is our decision support timely and reliable?
• How is our product defined?
• Is our billing accurate and timely?

The inability to answer these critical business questions leads to Data Quality issues such as:

• Inconsistent or incomplete product structure and service data
• Inability to uniquely identify entitled versus non-entitled equipment
• Incomplete or non-existent configuration data on entitled products
• Duplication and redundancy

But, it gets even worse! Poor Data Quality eventually stunts operational efficiency in virtually every area of the organization, as otherwise valuable resources (e.g., personnel, dollars, time, etc.) are required to spend an inordinate - and unnecessary - amount of extra effort in:

• Searching for missing data,
• Correcting inaccurate information,
• Creating temporary, or permanent, workarounds,
• Laboring to assemble information from disparate databases, and
• Resolving data-related customer complaints.

Over time, poor Data Quality significantly decreases an organization's revenue-generating opportunities.  Lost revenue can exist as the following:

• Lost Maintenance Contract Revenue - products that should be under contract are not captured and billing revenue is understated.
• Lost T&M Revenue - Non-entitled products that should be serviced under T&M are serviced under contract.
• Lost Product Upgrade Opportunities - Inability to identify customer need for product and software upgrades.
• Incorrect Maintenance Charges - Incorrect contract pricing since product configurations cannot be accurately identified.
• Lost Customer - Lost customers and revenue due to dissatisfaction with poor asset management and cumbersome reconciliations.
• Delayed Contract Renewals - lost renewal revenue and increased admin costs due to delays in new contract initiation.
• Overlooked Cross-sell & Up-sell Opportunities - missed opportunity to sell complementary or advanced solutions die to inaccurate records.

Poor Data Quality also significantly increases operating costs and, may lead to a reduction in customer satisfaction.  Increased operating costs can exist in the following areas:

• Sales Team - more time is required to manage new opportunities and create quotes, less time is spent selling, and quoting new maintenance contracts becomes inaccurate.
• Customer Care Center - T&M billing disputes increase, the cost of contract dispute resolution is higher and there is a decreased accuracy and timeliness of invoices with increased dispute losses.
• Contract Management - the effectiveness and timeliness of renewal activity is decreased.
• Logistics - stocking locations become sub-optimized by an over/under stocking of spare parts.
• Finance - data for decision support and performance reporting becomes incomplete and/or inaccurate.
• Service Delivery - tech on-calls are double dispatched due to the wrong part, service level commitments are missed and trouble call handling is degraded.
• Product Management - the product lifecycle position is inaccurately identified and inaccurate service history affects service offering decisions.
• Services Marketing - the ability to develop pricing programs is hindered, marketing programs are not deployed effectively and there is an increased burden/time for data collection.

How to Achieve Data Quality

Arguably, the greatest battle you may face inside the organization will be to get management to the point where they agree that data quality is a goal even worth considering. To do this, every organization must have a champion to help find ways for removing barriers and changing existing perceptions. The primary focus of the champion should be on:

• Assisting in making data quality a strategic priority,
• Assuring that data quality will be used to enable business processes, and
• Find - and communicate - compelling ways to make data quality attractive.

In our own experiences, Bardess Group has assisted many organizations to achieve data quality by applying the most effective methodology for accelerating the data cleansing and control processes.

Part 4 Next - Finding Success  

July 2011 

June 2011