Using data to boost your service delivery

What is business intelligence (BI)? What wraps around it? What makes or breaks it and what are the logical steps to having good data to get to business intelligence? And what does it have in common with the Smurfs? » by Samantha Kane

You remember the Smurfs, created by Pierre Culliford in October 1958 in Belgium and brought to the North American TV screen by Hanna-Barbera productions in the fall of 1981. Well, guess what? The term and definition of business intelligence (BI) was developed in October 1958 in an IBM Journal article by Hans Peter Luhn, titled “A Business Intelligence System.” That makes both the Smurf’s and BI 50 years old in 2008!

Luhn’s journal described business as a collection of activities carried on for different purposes, be it defence, science, commerce, technology, etc. The idea of intelligence is defined in a broad sense as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.” While the meaning has not changed that much in 50 years, all the support technologies, growth of the internet and security that surround BI have made it a multifaceted design and implementation process that requires subject matter expertise from multiple resources to deliver a quality result.

BI describes a set of concepts and methodologies which are supposed to improve business decisions by using fact-based support systems. But let us not forget BI systems are data-driven. The software should support interactive ‘slice-and-dice’ analyses, statistical data mining and visualization. Some of the more common applications tackle sales, financial, production in manufacturing and distribution, as well as many other types and sources of business data for purposes that include business performance management.

Recognized software and applications include a wide range of tools to analyze performance, projects or internal operations. Scorecarding, business activity monitoring, performance measurement, business planning, business process re-engineering and competitive analysis are all being considered by management today. Other BI technologies are used to store and analyze data, such as data mining, data farming and data warehousing.

Support challenges
For a BI technology to work effectively, an organization should have a secure computer system which can specify different levels of user access to data warehouses depending on your station within an organization. It requires sufficient data capacity and a data achieving system with data retention and a policy for data duplication firmly in place.

Consider disaster recovery and business continuity to maintain the integrity of the BI. Then there are the human-ware applications that have been on the horizon for some time. There is CRM to consider, customer information management – also known as customer centric management, document management, enterprise content management (ECM) and enterprise feedback management – recently branded by IBM but certainly not invented by them. And information lifecycle management, a sustainable storage strategy that balances the cost of storage and managing information with its business value. You are surely starting to get the picture of complexity and collaborative challenges within an organization.

Let’s not forget master data management. Master data (defined by Wikepedia as a set of data which are rarely changed, or changed in a known and constant manner – editor) has two parts to it. One is formatting; the other is the content. Both require data integrity. Master data also requires the business stakeholders to either create or assist with the development of the naming conventions, the key structure code and the triggers expected when setting up the CRM models to capture business intelligence. Without all of these, the project will never be the success it could be and, at its best, will be a very expensive contact manager.

The reason master data is so important to the success of the CRM delivery is simple. The CRM structure typically has pre-defined fields that allow specific types, characters and size of characters into those fields. These fields are what prompts the user to capture the data and assists in turning it into useful information or business intelligence. Second, the originating data typically have content labels within their structure. If you remove the label tags inadvertently, the CRM structure cannot read the data. Half the content on the line could be lost. The third important information is the language. If you do not want to work with only English, then characters, accents and symbols can be the death of your CRM system or cause total pain of ownership. You require at least extended ASCII or Unicode to make it work.

In the real world, master data typically has very limited visibility within the business community, and it is not considered a high priority. Not only is this information hidden, but many users maintain their own copy of the data. This unstructured data or knowledge relating to a work group or business unit is safely tucked away in a spreadsheet or document on their laptop. Changes to the dimensional hierarchies are processed by the application support team and are requested only when some definition can not be adjusted in the presentation sent to management. There is no shared information and no collaboration on the management of this most critical business asset.

In the ideal world, every company would embrace a robust master data management strategy for all aspects of its business. All the manual effort associated with fixing the data for presentations would be eliminated, information could be shared, information would be auditable and productivity would soar. Analysts could spend time performing analysis rather than gathering and adjusting reports. Reporting would become more reliable and consistent, resulting from the improved data quality. The SOX audit would be much less painful. And, the business community would gain more control over the maintenance of this most critical resource.

Master data is not the only step to a successful knowledgeable customer centric enterprise but I believe it is the first step towards a successful CRM delivery, moving towards enterprise content management disciplines and the reduction of duplication. With security, integrity, content and storage addressed, an organization can deliver business intelligence.

Enterprise content management (ECM) – is important as it ties many of the above mentioned structures and applications together. It allows CIOs to reduce data centres and consolidate infrastructure with fewer, but more powerful, servers and applications. With one platform, CIOs can optimize support staffing requirements with fewer people and less training, all of which results in a lower total cost of ownership. Additionally, centralization offers improved manageability and increased control over content, especially with major corporate rollups from departments and divisions. It also creates an enterprise-wide plan for standardizing your ECM platform.

Our experiences have taught us that there are methodologies and structured disciplines that must be followed in order to get to a state where you can deliver business intelligence. What’s key to remember is: data is data; information is what you do with that data, and turning it into business intelligence leads to profitability through customer-centric service.

Samantha Kane is founding partner of Kane-MacKay & Associates Ltd.; www.kanemackay.com; 1-888-890-1999