For more information and orders, visit the book site for "High-Performance Data Mining and Big Data Analytics: The Story of Insight from Big Data" (http://bigdataminingbook.info ).
In a research report
on analytics by MIT and IBM (Kiron, et al. 2011) , three progressive levels of analytical
sophistication for organizations are defined as aspirational, experienced,
and transformed (See Table 1).
In transformed organizations, analytics is used at all levels, day to day, strategically.
It is considered an integral part of everything, including the culture. These organizations
have high proficiency in both data management and analytics in terms of usage, skills,
and tools. Both data management and analytics are enterprise-driven and are ingrained
in the enterprise culture. Transformed organizations have robust data and analytics
foundations and management competencies, making it possible to capture, combine,
and analyze information from disparate sources, and to disseminate it across the
organization so that individuals at all levels can consume it. In these organizations,
one finds that processes, practices, and behaviors are aligned with the fundamental
belief that business decisions at all levels should be based on data analysis.
On the other hand, aspirational
organizations do not use any sophisticated analytics beyond spreadsheets, and do
not have an integrated view of their enterprise data. They lack proficiencies on
both the data management and analytics fronts. Their culture relies more on decision-making based on guts and intuition rather than data analysis.
The experienced organizations
are somewhere in between, with initiatives that move them closer to transformed
organizations. This study shows that experienced and transformed organizations continue
to expand their analytics and information management capabilities to add more business
value and differentiate themselves, while aspirational organizations keep falling
behind. This growing gap or divide has major implications for businesses. For the
following discussion, my focus is only on experienced and transformed organizations,
since these strongly believe in the value of analytics and either practice it in
full or have a goal to get there. I consider them standard organizations in the sense that, in today’s competitive world,
they are more of the norm than exception.[1] These organizations have also
the culture, the appetite, and the desire to deal with their big data challenge,
if there ever is one.
Organization Category
|
Information Management Proficiency
|
Analytics Proficiency
|
Data Culture
|
Aspirational
|
Low
|
Low
|
Line of business driven
|
Experienced
|
Medium
|
Medium
|
Moving toward enterprise driven
|
Transformed
|
High
|
High
|
Enterprise driven
|
One main differentiator
between analytics in the traditional sense and big data analytics is that in the
latter, the collected big data may or may not be useful for the specific business
purpose intended. From the perspective of analysis, this falls into the category
of you don’t know what you don’t know. However, if any
insights are extracted, they could be enormously invaluable. Due to the maturity
of traditional data management and analysis technologies, data that is stored in
these environments is already known to be of high value. This data has been prepared
to answer known business questions. The high value justifies their storage and management
in enterprise data warehouses or data marts. With new big data, there are plenty
of opportunities to ask new business questions never asked before, and the economic
situation is favorable when investigating these questions.
Table 2 enumerates a few possible scenarios in today’s standard analytics environments (experienced
and transformed organizations) when they are faced with big data. These environments
already excel in dealing with traditional and proven analytics methods and technologies
where storage, management, and analysis of the data follow standard processes and
practices. Scenario 1 depicts the status quo in these environments—where, in the
absence of any big data, it is business as usual.
Data Scenario
|
Big Data?
|
Storage
|
Analysis
|
Business Value
|
1
|
No
|
Standard
|
Standard
|
Known
|
2
|
Yes
|
Possible
|
Nonstandard
|
Somewhat known
|
3
|
Yes
|
Possible
|
Not possible
|
Not known
|
4
|
Yes
|
Not possible
|
—
|
Not known
|
Table 2: Big data scenarios in standard analytics
environments.
However, in terms of
their existing capabilities, they face different scenarios to deal with their big
data challenges. The reader should keep in mind that the size of big data has to
be interpreted in the context of time and place of each enterprise, given its sector
and its place on the analytics evolution curve. In Scenario 2, the enterprise is
capable of storing its big data, and can also analyze it using existing nonstandard[2] big data analytics techniques.
As a result, the enterprise has some understanding of the hidden value in its big
data, and can decide how much of it needs to be stored and for how long. In Scenario
3, the organization can cope with storing its big data, but does not yet have the
capability to analyze it in any efficient way for assessing its value. The reason
for this could be technological, methodological, skill set related, or budgetary.
In Scenario 4, the enterprise at its current state is not capable of storing the
big data (hence not able to analyze it either) for similar reasons to Scenario 3.
Today, Scenarios 3 and 4[3] are still dominant for classic
enterprises. Those operating under Scenario 2 are a small minority but are ahead
of the curve compared to their peers. The curiosity of finding the potential value
in big data is why big data has become a part of these organizations’ overall data
strategies. Going forward, any enterprise data strategy that ignores big data should
be considered incomplete.
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Kiron, David, Rebecca Shockley, Nina Kruschwitz, Glenn
Finch, and Micheal Haydock. 2011. Analytics: The Widening Divide. MIT
Sloan Management Review; IBM Institute for Business Value.
[1] More than a decade ago, one could say that the reverse phenomenon was true, meaning that aspirational organizations were more of the norm.
[2] Big data analysis techniques are still in
their infancy, and I consider them nonstandard in comparison with traditional
data analytics tools and techniques (including data warehousing, BI, and data
mining) that have matured, especially in the last two decades.
[3] “Without big data, you are blind and deaf in
the middle of a freeway.”—Geoffrey Moore.
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I discuss these topics in detail in my book. Visit the book site for "High-Performance Data Mining and Big Data Analytics: The Story of Insight from Big Data" (http://bigdataminingbook.info ).
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I discuss these topics in detail in my book. Visit the book site for "High-Performance Data Mining and Big Data Analytics: The Story of Insight from Big Data" (http://bigdataminingbook.info ).
