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The Data Analytics BoomJohn Jordan, 11.05.10, 1:22 PM ET
In "Analytics Goes Mainstream"I explained why numbers-driven
decision making is being used so widely now. Perhaps asinteresting
as the range of its application are the many converging reasons for
the rise of interest in analytics. Here are nine,from perhaps a
multitude of others.
1. Total quality management and six-sigma programs trained a
generation of production managers to value rigorous applicationof
data. That six-sigma has been misapplied and misinterpreted there
can be little doubt, but the successes derived from a
data-driven approach to decisions are, I believe, informing
today's wider interest in statistically-sophisticated forms of
analysiswithin the enterprise.
2. Quantitative finance applied ideas from operations research,
physics, biology, supply chain management and elsewhere toproblems
of money and markets. In a bit of turnabout, many data-intensive
techniques, such as portfolio theory, are nowmigrating out of
formal finance into day-to-day management.
3. As Eric Schmidtsaid in August, we now create in two days as
much information as humanity did from the beginning ofrecorded
history until 2003. That's measuring in bits, obviously, and as
such Google's estimate is skewed by the rise ofhigh-resolution
video, but the overall point is valid: people and organizations can
create data far faster than any human being orprocess can assemble,
digest or act on it. Cellphones, seen as both sensor and
communications platforms, are a majorcontributor, as are enterprise
systems and image generation. More of the world is instrumented, in
increasingly standardizedways, than ever before: Facebook status
updates, GPS, ZigBee and other "Internet of things" efforts, and
barcodes and RFID
on more and more items merely begin a list.
4. Even as we as a species generate more data points than ever
before, Moore's Law and its corollaries (such as Kryder'sLaw of
hard disks) are creating a computational fabric which enables that
data to be processed more cost-effectively than everbefore. That
processing, of course, creates still more data, compounding the
glut.
5. After the reengineering/ERP push, the Internet boom and the
largely-failed effort to make services-oriented architectures
abusiness development theme, vendors are putting major weight
behind analytics. It sells services, hardware and software; itcan
be used in every vertical segment; it applies to every size of
business; and it connects to other macro-level phenomena:smart
grids, carbon footprints, healthcare cost containment,
e-government, marketing efficiency, lean manufacturing and so on.In
short, many vendors have good reasons to emphasize analytics in
their go-to-market efforts. Investments reinforce thecommitment:
SAP's purchase of Business Objects was its biggest acquisition
ever, while IBM, Oracle, Microsoft and Googlehave also spent
billions buying capability in this area.
6. Despite all the money spent on ERP, on data warehousing and
on "real-time" systems, most managers still cannot fully trusttheir
data. Multiple spreadsheets document the same phenomena through
different organizational lenses, data quality inenterprise systems
rarely inspires confidence, and timeliness of results can vary
widely, particularly in multinationals. I speak toexecutives across
industries who have the same lament: for all of our systems and
numbers, we often don't have a firm senseof what's going on in our
company and our markets.
7. Related to this lack of confidence in enterprise data, risk
awareness is on the rise in many sectors. Whether in
productprovenance (think of Mattel), recall management (Toyota,
Safeway or CVS), exposure to natural disasters (Allstate,
Chubb),credit and default risk (anyone), malpractice (any
hospital), counterparty risk (Goldman Sachs), disaster management
or fraud(Enron, Satyam, Societe General), events of the past decade
have sensitized executives and managers to the need forrigorous,
data-driven monitoring of complex situations.
8. Data from across domains can be correlated through such ready
identifiers as GPS location, credit reporting, cellphonenumber or
even Facebook identity. The "like" button, by itself, serves as a
massive spur to inter-organizational data analysis ofconsumer
behavior at a scale never before available to sampling-driven
marketing analytics. What happens when a "sample"population
includes 100 million individuals?
9. Visualization is improving. While the spreadsheet is
ubiquitous in every organization and will remain so, the quality
ofinformation visualization has improved over the past decade. This
may result primarily from the law of large numbers (1% of a
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