The art of extracting strategic information

The art of extracting strategic information



TO employ a highly hackneyed expression, information is the key to success.
Today, as information technology is making an incursion in our lives,
Corporations world-wide are accumulating mountains of data. Now, all that
remains is to extract useful information from the accumulated data. This task is
anything but trivial!



Just for the sake of not leaving behind the who use the terms data and
information synonymously, it is imperative that before I delve deeply into the
above mentioned topic, the difference between the two words is grasped well by
the readers. Data consists of facts, figures and plain numbers where as
information on the other hand refers to the knowledge that can be learned by
analyzing data; data is collected whereas information is extracted; data even if
it is authentic may be misleading since the information extorted from it may be
erroneous; data collection is an iterative process, however information
retrieval is a highly specialized art, there are many online MCSE, CCNA, CompTIA,
IBM and more exams solution provider on the internet but for if you are willing
to pass the exam and become certified you can get info from  I think they
are the best in solution provider and study material on the net.



A fallacy

It is a widely believed that the more data you have, the more information you
will get. This is a fallacy. It's true that more data means more information,
but that information is hidden and to recover it is anything but trivial. This
was first realized in the 1990's when all the large corporations of the world
had mostly become IT savvy and were using high-scale efficient information
management systems to record the operations of their businesses. All of their
business units were recording sales receipts, purchase invoices, order items,
financial figures, units sold etc. electronically and the data collected by
these corporations was increasing exponentially. Indeed, the amount of raw data
was colossal, but the nature of the data generated was operational and could not
be used to make strategic decisions. This may sound absurd to those individuals
who have just come to know the abilities of relational database management
systems (RDBMS), but it's a fact that in the early nineties, managers of large
Corporations faced an unusual predicament, they had tons of data, yet they could
not extract useful information from it that could assist them in making
strategic decisions. This inability to retrieve useful information was termed as
the information crisis.



Conventional database applications and strategic information: Now, why was there
an information crisis and what is this strategic information? These issues
definitely need further elaboration. Strategic information is the knowledge
needed by manager and senior executives to formulate and implement strategies
that will provide a competitively superior fit between the organization and its
environment so as to achieve organizational goals; in short it is used to take
strategic decisions, those decisions that will affect the long-term policies of
the organization. It is used to develop business and operational strategies, set
objectives, develop goals and scrutinize results. Strategic information is
therefore, said to be the lifeline of any Corporation in today's highly
competitive ambiance. Consider the following questions usually asked by decision
makers:



What if discontinue product A and start re-distributing product B in the
Southern division, with the new tax laws implemented? How will this affect our
total revenues? How much should the sale price be reduced to increase the sales
percentage by 10 per cent in the North Eastern region? How can we retain the
present customer base after the advent of two new rivals? What will be the
impact of the sale price of our Products A, B and C, what if we change our
approach from profit maximization to sales maximization? Tell me the names of
problem regions (with respect to all possible parameters). Show the highest
margins in comparison to our competitors.



Any experienced information system's manager would have guessed it right away
that such questions cannot possibly be answered by applications which were
designed to store operational data such as recording an invoice, drawing an
amount from the bank, settling an order, managing inventory items, etc.



The basic issue is that these systems were meant to record the daily routines of
the businesses, not to provide such high level of information which itself is
dependent on certain external factors as well. However, there are many specific
shortcomings in the conventional information management applications which
thwart any attempt to retrieve long-term strategic information. Before we find
out what cons are associated with conventional system, let's define what
conventional system are.



Dumb systems

A conventional system is nothing more than a slave and answers only those
questions which you ask. It is not smart enough to point out the various
anomalies or regularities reflected in the given data. Executives need patterns;
they are looking for trends in the figures as these patterns suggest a
regularity or irregularity that is crucial in healthy decision making. Here are
some cons of conventional systems.



Disparate storage forms: The domain of information technology is undoubtedly the
fastest evolving science mankind has witnessed. An application developed 2-3
years ago will now definitely be totally obsolete according to the standards of
the applications developed today. The back-end database would have changed; the
front-end programming language would have undergone fundamental changes. The
data that has been collected during several years, although it is present is
scattered across incongruent storage mechanisms, making it virtually impossible
for anyone to analyze it as one unit. Thus, the applications developed by
companies to record day to day information have highly diversified data
structures which cannot interact with one and other to provide the much needed
high level information



Event-driven nature: These conventional database applications are essentially
event-driven. Information is available with respect to events such as sale,
order, invoices, receipts, etc., not with respect to subjects such as product,
region or revenue. A system that answers questions which begin with What if...
need to be subject oriented rather than event driven. This fundamental
characteristic of operational systems makes the task to extracting useful
information challenging, not to mention impractical.



Complexity: The transaction processing systems usually face some restrictions
vis-Ã -vis the time needed to complete a transaction. These systems have been
optimized for transaction processing, not for answering complex queries. Hence,
when such systems were grilled with composite queries, they were not able to
cope up with the transaction time restrictions.



OLTP and DSS: It was clear that in order to provide information needed for in
depth analysis, something else was needed. That something should be separate
from what the straight forward transaction processing systems does. This need
for thorough analytical information characterized the information management
systems into two parts:



Online transaction processing systems: According to textbooks, OLTP makes the
wheels of the business turn. They are needed to record all the operational
information. They deal with events and usually revolve around a single entity
such as sale receipts, cash transactions, invoices, etc.



Decision support systems: DSS watches the wheels of the business turn. They are
designed to watch how the various operations of the business are running and are
meant to answer questions related to strategic decision making. DSS extracts
data from OLTPS and various external sources and keeps in a format suitable for
responding to high level queries.



How DSS evolved

In the past the IT departments of many companies tried to fulfil the needs of
their managers regarding more information in more suitable formats, but all
their efforts have been more or less bit of a damp squib, yet they were
significant in the sense that they actually led to the development of full-scale
decision support systems.



Initially, for all the unconventional detailed reports the IT departments used
to write programs that generated ad hoc reports for each request.



Globalization ushered in a new era in which each and every department was
looking for reports generated in new formats, reports that were relative to some
external benchmark. Since, it was not feasible to write programs for every such
request the IT experts came up with special extract programs. These introduction
of these programs was made as an attempt to generate reports that were
anticipated. This was an ignorant attempt as mostly the needed information was
unexpected, but data specialists were learning to store data in such a format in
which it could be easily synthesized, that is all kinds of information could be
easily extracted. Then in the 70s came the primitive forms of DSS, which at that
time were called information centres". They were the complex and highly
generic applications which could instantaneously generate requested reports and
special analytical information. After the era of information centres, we see the
current DSSs which are under a constant stage of evolution.



Today's DSS can be operated by a manager who knows nothing about databases; they
are being designed as the essential tool required in fulfilling the compelling
need for strategic information in the present competitive landscape.



Components of DSS: According to a study conducted by the University of
California, 1-2 exabytes of unique information is produced every year worldwide
and just in case you did not know one exabyte equals 1 billion gigabytes or 1018
bytes, hence making it virtually impossible to derive useful knowledge.



To tackle an issue such as this effectively, the various functionalities of the
DSS are handled by different components and to even name all of them is perhaps
easily beyond the scope of this article. However, if we broadly analyze the DSSs,
they are all associated with two fundamental concepts: (1) data warehousing; and
(2) data mining.



The implementation of these concepts has taken decades to mature but, as we
shall se ahead their rudiments can still be easily grasped.



Data warehousing: It is the name given to the process of transformation and
storage of data in a suitable format that is best for complex analytical
purposes. Data warehouse is not an application, it is an environment, it refers
to the warehouse in which all the operational data collected by OLTPS is
residing, but prior to the storage of operational data into a data warehouse,
the raw data goes through radical renovation.



Basically, a data warehouse helps in integrating, categorizing, codifying and
arranging the data from various parts of the enterprise so that it can project a
unified view of the organization and help in strategic decision making. It can
be easily seen that this data is bears no resemblance to the nature of data we
see in traditional OLTPS, yet the data is extracted from traditional database
applications and fed to the data warehouses. This is what makes the
implementation of data warehousing such a specialized task. Data warehousing
provides the environment, in which a DSS can help make a strategic decision, but
how well that decision is taken depends upon how intelligently the data in a
data warehouse is exploited and this is precisely where the implementation of
data mining comes in.



Data mining: Nowadays, most large organizations generates more information in a
week than most people can read in a lifetime. We humans have several
limitations, we can only know that which we want to know. So, what about the
information we don't know about, the unique knowledge of our own business which
is hidden from us? What if it points to a unique buying pattern of the customers
in the South Asian region? What if that hidden information uncovers an unusual
link between your company's line of products which can be exploited to achieve
effective marketing. What if a concealed pattern is present in your
corporation's data warehouse, which can predict the number of production units
needed in the next year with respect to the future population, employment and
income statistics of the country?



Surely, no one will ever want to miss such valuable information, but it is
humanly impossible to try to discover such patterns that are buried under
mountains of data. This is where the concept of data mining steps in. Data
mining just like mineral exploration, tries to discover hidden valuables. In
both the processes, we are looking for something precious and we don't exactly
know for sure where we are going to find it, what we know that there is
something precious out there that needs to be found!



There are many algorithms used in data mining tools such as decision trees,
memory-based reasoning, neural networks, genetic algorithms, etc. All are highly
advanced applications of computer science, so I shall make no endeavours to
explain them! However, what must be known is that the serve a two-fold purpose.



1. Knowledge discovery, this is achieved by uncovering the relationships that
exists in the pre-processed data of the data warehouse.



2. Future prediction by means of pattern identification.



Statistics and data mining pursue the same aim, which is to build compact and
understandable models incorporating the relationships (dependencies) between the
description of a situation and a result (judgment) concerning this description.
The only variation being that a sophisticated data mining tool builds the
relationship model much quickly which is generally more efficient as it is
developed using algorithms which have been proven over and over again.



Applications

There are countless domains in which data warehousing solutions can be
implemented and its full potential is still not known, but the IT specialists
have learned that wherever it has been implemented properly it has produced
significant benefits.



Data warehousing solutions has helped organizations keeping a very close and
personal relationship with its customers. In DSS we refer to such applications
as Customer Relationship Management (CRM) solutions, which specifically focus on
customers needs by identifying relationships between products, customers and
their spending habits.



DSS have successfully predicted which fiscal policies will help the corporation
achieve its objectives in different external environments by analyzing past
financial data. Also, DSS profitability analysis satisfies the need for detailed
enterprise profitability reporting and analysis leading to better informed
decisions which in-turn maximize profitability.



Through DSS, organizations have attained highly professional supply/demand chain
intelligence which has significantly reduced costs at various levels in the
industrial/management operations. Similarly, many other industries such as
E-business, internet banking, targeting advertising etc. have generated more
profits after adopting DSS.



Conclusion

DSS can help any corporation perform to the best of its abilities. It's a proven
fact that through professional data warehousing solutions corporations have
reduced costs, achieved higher productivity, attained customer centricity and
improved the overall organization of their internal operations. Still, there is
a dark side of the picture too. Data warehousing is not something which any
person/organization can provide as it is a highly specialized field and because
there is no absolute solution to any decision support requirement; it varies
from organization to organization. Furthermore, the users are usually unable to
elucidate their requirements when it comes to specifying the information they
wish to seek. That's the reason why a popular DSS provider markets its products
by asserting Data warehousing solutions that work"; indicating that if not
executed correctly, DSS may also fail to achieve what was expected from it.



A true data warehousing solution is not just about computer science, it's also
about understanding the customer and foreseeing its potential requirements and
companies which have realized this, like Teradata, Oracle and IBM have
successfully provided DSS to numerous organizations. Corporations all over the
world pay such companies millions of dollars for their data orgnizational needs.
In the current knowledge economy, it is now an indisputable fact that
information is the key to organizations for gaining competitive advantage and as
more and more companies realize this, the better DSSs we will see in the future.



For those of you, who not convinced whether such theoretical concepts actually
works, refer to the study conducted by International Data Corporation (IDC)
which showed that data warehousing could provide significant and impressive
Return on Investment (ROI) numbers. The study, which included 62 participants,
demonstrated that the overall ROI on warehouse projects was 401 per cent with
payback periods of two to three years. ROI, being a standard by which any
investment is measured, suggests that data warehousing solutions is an extremely
profitable endeavor. Established benchmarks have proved that the decision
support systems are not just topics of post graduate research; they are highly
practical and as more research and development takes place in this branch of
computer science, experts predict that in the future all of us will rely heavily
on DSS's predictions!



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