If there is an
oft-cited and “classic” example of big data use, it’s the story of an
enterprise that capitalizes on parsing and analyzing unstructured and semi-structured
data about its customers from the Internet and other data sources that formerly
went unnoticed. But the other part of big data that is “big” is
effective use of structured data that comes in from systems of record like customer
master files, order and shipping files, and even financial charts of accounts.
If you look at the historical accumulation of this system of record data, it is
“big” in the sense of the volume it presents. It remains a largely
untapped data resource that traditional corporate reporting only scratches the
surface of.

System of record

The other element
of big data that doesn’t get talked about as much as breakthrough information producing
competitive advantage, is the operational agility that effective big data
analytics can produce. It is the structured, system of record, data that
contributes the most to this agility – and its contribution is vested in the
reformation of business processes that can be tuned for better performance.

Here are some
current business process headaches that poor system of record data in companies

  • Part descriptions initially entered by the company’s engineering
    department do not reflect the nomenclature that is used in the field, so
    someone has to manually go in and change them. Meanwhile, service reps
    have a hard time determining the correct parts to use in their daily work.
  • Part and assembly revision levels are difficult to synchronize for
    an aerospace company that must maintain three different sets of part and
    assembly levels – one that is internal, one that reflects the original part
    numbers from OEMs, and one that reflects part and assembly numbers that
    military customers want assigned.
  • Corporate charts of accounts become so complicated that it’s
    difficult to meet month-end close dates.

These situations
have been “accepted” by companies. For years, they have put their
shoulders to the wheel – correcting data “on the go” and as needed in
the course of a business day. But now that the rise of big data has suddenly
made dealing with data fashionable, they are beginning to look at the cost of
poor and replicated data that manifests itself in inefficient business
processes that take too long and limit corporate agility.

Relevance from data

want to see business relevance from their data,” said Rex
Ahlstrom, Chief Strategy Officer at BackOffice Associates
, which focuses on the quality of ERP
(enterprise resource planning) data and has “Expect More from Your Data”
as its company motto.” In the past, data quality has been a focus of IT,
but now more companies want to see an alignment of the business with IT so they
can improve the quality of the data that support their business processes.”

Ahlstrom explained
how automated tools could analyze data from systems of record, determining
which data really were necessarily to support a business process, and which
were extraneous.

“What we are
getting at here is data that is not only good from a quality standpoint, but
data that is business-relevant,” said Ahlstrom. “In one case, a
mining and exploration company had over 2,500 chart of account entries, but
when they performed an analysis of their data, they found that they only needed

Ahlstrom explained
how the automated process worked.

“The tool
takes a look at all of the data elements and then performs an analysis that determines
which of these data elements are actively used by a business process. We then
use the content to build a series of reports that give individuals in the
organization the opportunity to look at the data that really supports the business

The technique enables
companies to fine-tune their business processes – and also the amount of data being
pulled into them. As extraneous data is pared from the business process (as in
the chart of accounts example), the business process becomes more agile and
quicker and easier to perform.

“We’re seeing a
change in mindset now,” said Ahlstrom, “From a time when companies performed
more passive data governance to a new time when users begin to understand the
impact of data across different business processes.”

Bottom line

How far companies
decide to take business process-oriented big data initiatives remains to be
seen. But if a retailer can improve the visibility and the processes along its
supply chain or an oil and gas company can reduce time to market for a new
product because of business process simplification brought on by data reforms –
the economics can be compelling.