Measurable is Reliable
To arrive at measurable & reliable data, it is critical to invest in right people and right technology. I believe that with right application of fact analysis, data and can be god. Hence measurable data is the only reliable data.
Newton once rightly said – I can calculate the movement of stars, but not the madness of men. How true it is in the corporate world that still in at the CXO level there are words like ‘if’, ‘but’ or ‘maybe’. They still rely on subjective opinion. The corporate who have embarked on right business intelligence system based analytics give them a key to success. BI analysis always adds up.
Dirty data is generated when transaction applications capture data which is meaningless. Dirty data is data that is misleading, incorrect or without generalized formatting, contains spelling or punctuation errors, data that is input in a wrong field or duplicate data. Over the next two years, more than 25 percent of critical data in Fortune 1000 companies will continue to be flawed, that is, the information will be inaccurate, incomplete or duplicated, according to research and advisory firm Gartner, Inc.
Gartner research shows that poor-quality customer data leads to significant costs, such as higher customer turnover, excessive expenses from customer contact processes like mail-outs and missed sales opportunities. But companies are now discovering that data quality has a significant impact on their most strategic business initiatives, not only sales and marketing. Other back-office functions like budgeting, manufacturing and distribution are also affected.
By introducing data quality initiatives, some companies have added millions of dollars to their bottom line as they gain benefits such as increased sales, lower distribution costs and better compliance.
Data quality has many facets, including:
Existence (whether the organisation has the data)
Validity (whether the data values fall within an acceptable range or domain).
Consistency (for example, whether the same piece of data stored in multiple locations contains the same values)
Integrity (the completeness of relationships between data elements and across data sets)
Accuracy (whether the data describes the properties of the object it is meant to model)
Relevance (whether the data is the appropriate data to support the business objectives)
Jigsaw.com, an online contacts database geared toward sales professionals, takes a Wiki-style approach to data cleansing. Its 335,000 members get points for uploading their own contacts to Jigsaw and correcting others. Every record must be complete, and if Jigsaw users enter information that’s incorrect or old, they lose points. Members spend their points by buying information for people they want to reach. Jigsaw CEO Jim Fowler says an Atlanta-based technology company recently asked his firm to compare its contacts databases to Jigsaw’s and weed out the bad data. “They had 40,000 records,” he says. “Only 65 percent of them were current and 100 percent were incomplete. We’re finding that most of our corporate customers have sets of data so cruddy no one can match to them. Corporations spend millions on CRM, and it’s amazing how bad that data is.”
The real value is not the data itself, but the ability to keep up with how quickly it changes.
Posted on January 19th, 2009 by Sanjay Mehta
Filed under: Business Intelligence, Emerging Trends
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