REAL-TIME DATA QUALITY IS A CORNERSTONE OF EFFECTIVE CRM
by Stephen J. Wiehe
Customer resource management (CRM) has emerged as one of the new
millennium's fundamental technology challenges. As the reality of a
multichannel
world that includes e-commerce, direct sales, call centers and
existing systems sets in, companies are faced with designing a technology
foundation that is not only customer-focused, but customer driven. Making the
most of customer relationships means managing these disparate channels by
finding a way to create and take advantage of opportunities to effectively
touch customers to impact the bottom line.
One of the key components on the road to quality CRM is data quality. Data
quality has always been something of a thorn in the side of business
organizations everywhere, with no easy way to eliminate duplications,
inconsistent data and errors, and managing the data across various databases
and data warehouses, making it difficult to actually use crucial customer data
to set intelligent marketing strategy and make smart business decisions. (In
the largest survey ever of data warehouse users, conducted by the META group,
data quality was ranked as the #1 challenge to the success of data warehouse
initiatives.) After spending a large amount of their budgets building
sophisticated databases and developing information gathering processes to get
customer intelligence, at the end of the day businesses still had to rely on
the crystal-ball-method of deciding strategy. They weren't able to get
accurate and usable hard data out of their own databases to make such
decisions, or to analyze this data company-wide. It was too often a case of
the right hand not knowing what the left hand was doing. Recent technological
developments using advanced fuzzy logic, data pattern analysis, data
clustering algorithms and a host of other sophisticated capabilities make data
gathered throughout the organization, regardless of where it is housed, to
drive good customer relationship decisions actually work to make good customer
relationship decisions.
In the realm of the Internet these new technologies are more critical than
ever. For example, if you have a front-end registration to gather information
about who is visiting your Website, you lose some control on what is input
into your database - the customer or site visitor does it for you, leaving a
big gap in quality control. How do you know when someone forgets their
password and re-registers using a different user name? We've all done it; it
happens all the time. Without being able to identify these kinds of
redundancies, the demographic information can be skewed and you can't be sure
if your messages are the right ones and if they're being delivered to the
right audience. In Internet marketing, the tailoring of a message to a target
happens in real-time, all the time. There is no time for extracting data out
of databases to clean it; it needs to be an ongoing function of your data
gathering initiatives. This is just one of many examples of how inaccurate or
inconsistent data can is critical CRM initiative. After all, if you are
relying on your data to identify opportunities with customers, you won't find
them if the data is no good.
As you address the issue of data quality, there are six basic requirements
you should consider when looking for a data quality control tool for your CRM
efforts: real-time functionality, ease-of-use, flexibility, performance,
platform independence, and affordability.
Real-Time
The immediacy of CRM is one if its most compelling attributes. Any data
quality solution aimed at CRM must be able to constantly streamline and
correct data in real time, at the point of input. By ensuring quality at the
outset, you can be assured of accuracy in reporting.
Ease-of-use
Let's face it. One of the major reasons for the data quality problems most
companies face is that cleaning and streamlining data is a difficult, costly
and time-consuming task. And if you avoid addressing the issue, the problem
compounds like interest over time. For any data quality solution to be
useful, it must easily integrate into your existing database configuration,
and be easy to use. To that end, look for a product with an intuitive
graphical user interface and a tool that is entirely point-and-click driven.
The data quality solution also needs to run on common development platforms,
such as Windows NT/95/98, which lends itself to intuitive user interface
design, as well as its familiarity to the majority of potential users. This
eliminates the frustration that has been the trademark of mainframe-based data
quality software and other high-end (read: expensive) data quality solutions.
Flexibility
Right after ease-of-use is flexibility -- they go hand-in-hand for
a usable solution. If the product's not flexible, it won't be easy to use.
Look for a tool that provides quality results on all kinds of data, not just
names and addresses. Also, since each company has data that is very unique,
the tool should easily adapt to the uniqueness of your company's critical
data. The ideal data quality tool will have a data analysis component,
allowing you to decide with the help of the tool, how best to tackle the data
quality problems you've identified, such as data inconsistency and data
redundancy. In addition, keep your eye out for a product with a modular design
will that will allow you to add new modules, each of which will tackle
specific data quality issues as they are made available from the tool
developer. This prevents users from having to re-invest in the entire tool set
each time a new beneficial module becomes available. Since CRM, and
non mainframe-based data quality control tools, are in their infancy, there
will be new capabilities developed all the time. Don't get locked in with a
product that can't grow and adapt as your needs change.
High-Performance
High-performance means not only fast, but accurate, results. The technology
exists today that can meet this requirement - it's worth your time to
investigate which data quality tool developers use cutting-edge software
building blocks, take advantage of current workstation and server processing
power, and use the most advance database development tools available to create
high-performance data quality tools. Concepts such as fuzzy logic, advanced
scoring algorithms, data pattern analysis, and intelligent data clustering
algorithms are good indicators of products with the performance to ensure the
most accurate results.
Platform Independence Any data quality control tool you select for your
CRM efforts should easily and effortlessly plug into your existing databases.
Identify which companies offer the ability to 'snap-on' to any database via
ODBC or any native drivers provided by data quality program. This allows the
tools to be used with any database and in conjunction with other data
warehousing tools. Also, this approach allows you to change your underlying
foundational database and operating system platform without having to reinvest
in new data quality tools. The database connectivity features contribute to
ease-of-use as well, since data quality work can be performed without first
removing data from the database and converting it into text files, which many,
often costly, data quality tools require.
Affordability
Finally, it goes without saying that affordability is a key aspect of any
business intelligence solution. Data quality has traditionally lived in the
mainframe world, requiring hundreds of thousands, if not millions, of dollars,
and years of time to implement. That is simply not a viable approach in the
real-time world of the CRM. You should be able to find data quality control
tools, like those offered by DataFlux, that meet all of the above
requirements, and that are a fraction of the cost of existing solutions.
Stephen J. Wiehe is president of DataFlux Corporation, a developer of data
quality and intutitive similarity searching technology located in Research
Triangle Park, NC.
Contact Katy Dunn Verve 919-789-0091 or katyd@pipeline.com
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