Data Density & Complexity
& Need
It’s no secret that the amount of data and
information that exists is growing rapidly
with no sign of decelerating. Companies
across industries are increasingly looking
to capture new information, better use
of current information and drive timely
action based on analytics. However, the
analysis is only as good as a company’s
ability to capture the right data and manage it, which is where many companies
are failing.
To produce quality and meaningful
analysis companies are engaging with
consulting firms to develop a strategy
around how to capture, store and secure their data. To best meet client needs
consulting firms are evolving their data
management capabilities to address more
specialized needs, including meeting the
needs of inherited data structural design
and new digital platforms and addressing regulatory needs around data quality,
hosting and isolation.
In the industry of commercial analytics software, an emphasis has emerged
on solving the challenges of analyzing
massive, complex data sets, often when
such data is in a constant state of change.
Such data sets are commonly referred to
as big data. Whereas once the problems
posed by big data were only found in the
scientific community, today big data is a
problem for many businesses that operate
transactional systems online and, as a result, amass large volumes of data quickly.
The analysis of unstructured data
types is another challenge getting atten-
tion in the industry. Unstructured data
differs from structured data in that its
format varies widely and cannot be stored
in traditional relational databases with-
out significant effort at data transforma-
tion. Sources of unstructured data, such
as email, the contents of word processor
documents, PDFs, geospatial data, etc.,
are rapidly becoming a relevant source of
business intelligence for businesses, gov-
ernments and universities. For example,
in Britain the discovery that one company
was illegally selling fraudulent doctor’s
notes in order to assist people in defraud-
ing employers and insurance companies,
is an opportunity for insurance firms to
increase the vigilance of their unstruc-
tured data analysis.
These challenges are the current in-
spiration for much of the innovation in
modern analytics information systems,
giving rise to relatively new machine
analysis concepts such as complex event
processing, full text search and analysis,
and even new ideas in presentation.
Analytics is increasingly used in edu-
cation, particularly at the district and
government office levels. However, the
complexity of student performance mea-
sures presents challenges when educa-
tors try to understand and use analytics
to discern patterns in student capability
ratings predict graduation likelihood, im-
prove chances of student success, etc. To
combat this, some analytics tools for edu-
cators adhere to an over-the-counter data
format (embedding labels, supplemental
documentation, and a help system, and
making key package/display and content
decisions) to improve educators’ under-
standing and use of the analytics being
displayed.
Another emerging challenge is the
dynamic regulatory needs. As can be
appreciated in the banking industry, future capital adequacy needs are likely to
make even smaller banks adopt internal
risk models. In such cases, cloud computing and open source can help smaller
banks to adopt risk analytics and support
branch level monitoring by applying predictive analytics.
Analytics & Dashboards
As we view it, the pyramid illustration is
the very foundation of analytics and the
organization of the data gathered via an-
alytics methodologies can be a significant
management tool once this knowledge
is placed into one of many possible
dashboards.
So what is a Business analytics, how
does it work and who benefits from the
results? Business analytics refers to the
skills, technologies, practices for continuous repetitive exploration and investigation of past business performance to
gain insight and drive business planning.
Business analytics focuses on developing new insights and understanding of
business performance based on data and
statistical methods. In contrast, business
intelligence traditionally focuses on using
a consistent set of metrics to both measure past performance and steer business
planning, which is also based on data and
statistical methods.
Business analytics makes extensive
use of statistical analysis, including explanatory and predictive modeling, and
fact-based management to drive decision
making. It is therefore closely related to
management science. Analytics may be
used as input for human decisions or may
drive fully automated decisions. Business
intelligence is querying, reporting, and
online analytical processing .
In other words, querying, reporting,
and OLAP can answer critical questions .
. . what happened, how many, how often,
where the problem is, and what actions
are needed. Business analytics can answer
questions like why is this happening, what
if these trends continue, what will happen
next (that is, predict), what is the best that
can happen (that is, optimize). CW
Dan Adams, President of the AIM
Institutre contributed content to this
article.
“Especially valuable in areas rich with
recorded information, analytics relies on
the simultaneous application of statistics,
computer programming and operations
research to quantify performance. ”
