The CRM-Ready Data Warehouse:
The CRM Need for Atomic Data
by William McKnight
"I need an itemized list of claims
records of every individual in Suffolk County for the past year."
The chief actuary was seeking support for his theory about
the recent upsurge in fraudulent claims activity in the Northeast. The
model was developed, and he needed a sufficient statistical sample of
claims data to test it. Fortunately, the data warehouse was not summarized
well beyond the individual claims records, which would have left the operational
system as the system of record for such detail the system whose
too small batch query windows were the reason for the data warehouse in
the first place.
Fed with empirical data, the theory can be verified, possibly
operating to screen out millions in losses each week.
"How do I get a list of our customers
who have purchased diapers from Store 123 in the past three years?"
The marketing director of a retail chain logged this request
to the data warehouse help desk. A diaper vendor wished to do a targeted
promotion which could raise single-store traffic. The fortunate reply
was the data warehouse captured the detail data in addition to the customer-week,
store-week and product-week summary levels.
If it did not, and the data was not granular enough to
suit the requirements for specific promotion targeting, another store
chain would have received the vendor's business.
"I need to examine the rate of dropped
calls along I-95 from Philadelphia to Baltimore by caller, during peak
rush hours for the last six months. We think that sector may be contributing
to our increasing customer loss rate year-to-date."
The wireless competitor suspected but couldn't prove
that transmission quality, not price discounting, was driving its
customers away. But could it be something else? Repairing a tower was
costly compared to a targeted business win-back campaign. But could they
prove a correlation?
Fortunately the data warehouse was designed for more than
analysis of invoice-ready bill details and monthly billing summaries 30
days after the fact. Dropped calls were not lumped into miscellaneous
credits.
When you need to drill into customer behavior in detail,
over long time horizons, then you're grateful the data warehouse project
budgeted for extra accessible storage and incurred the extra effort to
bring high-volume customer data at the transaction-item level into the
warehouse. This data is called atomic data the details of each transaction
at each touch point
Today, there are interesting technologies that place the
atomic data requirement square in their sights by optimizing the fact
table's storage component across multiple storage management devices.
Atomic data is the most granular data possible. Numerous
problem-oriented data marts can build on this grain to grow the enterprise's
use of the data. Rather than limiting your queries to summary data only,
atomic data provides maximum flexibility for the CRM-ready data warehouse.
The ability to build a fantastic summarization scheme into your warehouse,
and also get to the details when necessary, all begins with storing the
atomic data.
Separating Hub from Spoke
Today, there are interesting technologies that place the atomic data requirement
square in their sights by optimizing the fact table's storage component
across multiple storage management devices. Consider FileTek's StorHouse/RM
product, which manages a whole set of tiered storage products, including
RAID and robotic libraries for optical storage and tapes from small
tabletop libraries to the largest data center silos. StorHouse provides
direct relational access to all these media, while the high density and
rapid access of today's drives serves queries against the long, linear
detail tables that can occupy 75 percent or more of a CRM-ready data warehouse's
volume. StorHouse repositories act like any standard data source in conjunction
with common data warehouse tools.
The atomic details enter from an integrated detail source
the data warehouse staging area or the output from an IP mediation
job and collect in the StorHouse repository. From there, any data
mart can then create the dimensions and facts of its "spoke"
directly from the atomic detail.
Technologies such as this may not be for the vanilla warehouse
and do not replace the need for scalable architecture and processes. Their
best use will be with data warehouses that are transcending current paradigms
into the multiterabyte range of storage.
In telecommunications carriers alone, the transition from
a traditional, switched transport network to an IP-centric "next
generation" network will expand the detail data generated approximately
twentyfold according to IP-mediation leader XACCT Technologies. E-commerce
technology will allow collection of more customer behavior detail at a
lower collection price than ever before. It won't just be the Fortune
100 in this market, but the entire Global 2000 plus new entrants who don't
even exist today.
William McKnight is founder and president of McKnight Associates,
a consulting firm specializing in building data warehouse and CRM solutions.
A frequent conference speaker, McKnight has worked with numerous organizations
implementing customer intimacy programs using the data warehouse as the
foundation of the program. He can be reached at wmcknight@mcknight-associates.com.
Article copyrighted by and reprinted from
DMReview, June 2000
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