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Barriers To Business Intelligence


 

Business intelligence suffers from a strange dichotomy: It's among the most highly desired business technologies, a roughly $10 billion-a-year market growing at more than 10% a year, yet it has difficulty proving its worth.

The main barriers to BI adoption are: cost and complexity.

According to a 2007 InformationWeek survey of 388 business technology professionals, over 30 percent of respondents claimed that BI vendors were unable to demonstrate the benefits of BI to internal stakeholders.

Whilst most companies purchase BI software to solve a specific problem in one business unit, few are able to use the resulting data silos to explore cross-company impacts, such as how a 5% drop in market share affects manufacturing, finance, and procurement.

One of the main reasons for this lack of cross organizational capability is that no vendor excels in all areas of business intelligence. This means that customers generally have to pull together the various components.

For instance, some BI users are cutting edge in customer scoring, operational analysis, and predictive analytics. However, to gain that capability requires:

  1. Millions of dollars on data warehouses and software for moving and analyzing data - 40% of the cost involved in developing sophisticated analytics and modeling for a 30-Tbyte Teradata data warehouse comes from moving data between systems.
  2. Specialized IT talent
  3. Significant businesss time to set up and manage.

 

One Solution

Although Teradata sells analytical apps for forecasting demand, a separate application must be implemented to extract data from the data warehouse and feed it into SAS analytics, which runs models for that function. SAS is a great analytical tool but not a data warehouse, and Teradata is the best data warehouse around but not a good analytical tool.

 

Operational BI

Businesses also want tools that blur the line between data transactions and decision support, delivering operational BI applications with built-in analytics, and performance management software. Could this be the reason SAP has acquired Business Objects?

SAP CEO Henning Kagermann commented that "blending Business Objects' data crunching capabilities with SAP's industry knowledge for end-to-end business processes with embedded analytics." This statement seems contrary to SAP's promise to keep Business Objects a separate unit.

How successful that union will be depends upon the level of integration they will achieve.

Claims that SAP's previous BI tools and data warehouse have not been successful, requiring an intertweeing data warehouse between SAP and Business Objects, really don't mean much when you consider the totally different processing and data transfer capabilities of a transactional ERP/CRM system compared to an OLAP analytical application.

 

Actionable BI

Many BI vendors, such as Business Objects are working to better integrate reports into operational processes. This may include:

  • Upgrades to Crystal Reports - to produce reports from data.
  • Better support for XML and Adobe Flex - making it easier to create reports from operational data and act on that information.
  • Reporting needs to become an integral part of an operational process.

Smaller niche BI vendors such as SeeWhy Software are focussing on enabling "actionable BI." SeeWhy constantly compares incoming data with historical information and trends, flags anomalies [such as a regular customer not making a booking at the usual time] and sends alerts to customer service reps.

Oracle is investigating how to integrate its ERP applications and data warehouse with its BI portfolio, which includes Hyperion performance management tools and Siebel analytics. Oracle Real-Time Decisions, based on software from Sigma Dynamics, acts a transactional server that combines rules and predictive analytics to deliver real-time data into business processes and customer interactions.

And the innovation is not all coming from the Analytics end. Data warehouse vendors are also getting in on the game. Teradata is making its way into operational analytics with its Active Enterprise Intelligence.

The trend toward real-time analysis is gaining pace, but until the perfect solution appears, companies will still need to filter through very large data sets, such as customer segmentation analysis, to choose the right prospects for marketing campaigns. Real time is just a small part of the challenge.

Either way, businesses not moving towards using modeling and analytics will fall behind.

 

Case Study 1 - Medco

BI is a journey, and the preliminary BI program goals do not cover the navana solution. A case in point is in Healthcare - the BI goal in healthcare is to move very quickly from cost containment to prevention.

One of the uses of BI for Medco is tracking pharmaceutical transactions for signs of abuse and fraud. The next BI iteration will develop analytics to determine, forecast and predict which patients are most at risk of getting sicker. This will require:

  1. Integrated prescription, lab, medical history, and demographic data to develop a "longitudinal" view of individuals' therapies
  2. Use clustering models to look at patients across different types of therapy.

By analyzing a cluster of people with complex diabetes, Medco hopes to identify trends that can predict who among a population of patients without those complications are at risk enough to suggest an intervention.

How BI Is Used

  1. A patient previously treated for high cholesterol comes to the pharmacy for insulin.
  2. At the point of transaction, Medco could instantly run the patient's data through SAS analytical models and determine how to reclassify that patient and whether any type of intervention might be recommended.

The Technology Enablers

The increased use of Web services makes such links between operational systems and analytics models more feasible.

Real-time BI could have huge implications in customer service.

 

Case Study 2 - Overstock.com

Overstock, which distributes more than 33 million e-mails a week in marketing campaigns, segments customers in 55 different ways in the data warehouse based on their purchase histories. An Overstock.com customer e-mail initiative that has increased sales significantly uses:

How BI Is Used

  1. The online retailer scores each customer after every purchase based on how profitable he or she is to the company
  2. That information is used for its e-mail blasts and other customer interactions.
  3. If a valued customer clicks on iPod accessories, for example, Overstock might send a coupon the next day.

These simple case studies demonstrate how the strategic applicBI for greater business impact, in part by embedding analytical capabilities right into everyday operations for here-and-now decision making. Given the costs and complexity, however, few companies are that far along. The BI vendors--and you can now count SAP among them--are scrambling to get it right.

NEXT: Understanding The BI Lifecycle

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