QlikView's Disruptive Technology Allows Creative Problem Solving 

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

QlikView has been described as a "Disruptive Technology", but what exactly is a disruptive technology, and how is that affecting how we solve business problems? Wikipedia defines a disruptive technology as an innovation that disrupts an existing market. Using this definition, let's see how QlikView fits this description.

In addition, QlikView was rated first in Ease of Use, Score Keeping to KPI's and Dashboard satisfaction and Usage by Gartner Research****. And BARC determined QlikView is #1 in customer loyalty, intention to buy more licenses and query performance*****.

Clearly, when it comes to BI solutions, QlikView not only exceeds expectations, but it does so across all the many ways we care to think about business intelligence software.  

At Bardess, we regularly implement QlikView, and can personally relate to the enthusiasm represented by the survey results of our own clients. This is market impact, a part of the definition of what a disruptive technology is, but what is it about the  innovative technology that is driving this change in the BI marketplace?   What's under the covers of this technology that makes it disruptive? We believe it is the "associative" data model.

QlikView is unique in what it describes as being "associative" at the database engine level. Other vendors produce an associative like interface, but because the workings of the underlying database are still a variation of ROLAP, MOLAP or HOLAP technology, it takes time to build the interface, hence the typical 18 month deployment timelines.

QlikView has used the phrase "it works like your brain works", which is another consequence of the associative experience. When we think of a problem we may have, we seldom solve the problem in a linear fashion. One of my favorite books on problem solving (yes I know it's outdated by now) was New Think: The Use of Lateral Thinking by Edward de Bono.  In principle is a similar idea espoused in the Medici Effect by Frans Johansson, which suggests we solve problems through an indirect, diverse and creative approach and not by traditional step by step logic.

Traditional BI solutions are typically driven by IT and require linear predefined thinking in the definition of how the end user will access the data.  It takes months to change, and is essentially data centric, resulting in key insights missed in hidden data. QlikView, by contrast has all the data available in memory. Not only does the user experience the result of his selection (query), but also has access to what wasn't selected. This allows for a non-linear, what-if insight driven approach to problem solving. It's best experienced firsthand.

*Industry standard

** Aberdeen Group Survey - August 2010

***IDC Research

**** Gartner BI User Survey 2010

***** BARC: BI Survey 9, September 2010

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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:

• 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 

Finding Success 

The seven major steps that must be taken to achieve Data Quality are:

1. Acknowledge the problem, and identify the root causes
2. Determine the scope of the problem by prioritizing data importance and performing the necessary data assessments
3. Estimate the anticipated ROI, focusing on the difference between the cost of improving Data Quality vs. the cost of doing nothing
4. Establish a single owner of Data Quality with accountability (e.g., make it a senior management role, such as a Data Officer/DQ COE)
5. Create a Data Quality vision and strategy, and identify the key change drivers
6. Develop a formal Data Quality improvement program based on specific tools wherever possible, and use a value-driven approach for large projects
7. Make it a priority to move your organization up through the levels of the Data Maturity model

Achieving Data Quality is critical, but getting there is often a complex process. Data Quality requires commitments from all business functions, as well as from the top-down. Quick fixes typically do not work and generally only end up creating frustration. For many organizations, it may have taken years to create and foster a culture of data denial, and it will require rigorous processes to:

• Identify the problem 
• Recognize and accept the full extent of the potential benefits that can ultimately be realized.

For many business enterprises, the numbers speak for themselves, where the implementation of a Data Quality initiative ultimately leads to:

reductions ranging from:

• 10 - 20% of corporate budgets
• 40 - 50% of IT budget
• 40% of operating costs

and increases of:

• 15 - 20 % in revenues
• 20 - 40% in sales

The application of Data Quality provides an organization with the opportunity to capitalize on its cumulative information and knowledge assets. Knowledge that was previously unknown, or unavailable, such as cross-referenced customer buying patterns, profiles of potential buyers, or specific patterns of product/service usage may be uncovered and put into practical use for the first time. The end result can lead to anything ranging from improvements in operational efficiency, accurate sales forecasting, effective target marketing, and improved levels of customer service and support - all based on a strong foundation of Data Quality.   

When You Think of Quality Data, Think Bardess! 

August 2011  

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June 2011