The document explores the evolution of data analytics from its inception in the 1950s to the current and future landscape, highlighting significant advancements and shifts in capabilities across three eras: Business Intelligence, Big Data, and Analytics 3.0. It emphasizes the growing importance of analytics in decision-making processes, supported by technologies like Hadoop and machine learning, with projections indicating continued growth in the big data market. Lastly, it underscores that organizations must innovate continuously to leverage data effectively in the competitive landscape.

1. Varun Nemmani March, 2016
University of Missouri- Kansas City
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The Evolution of Data Analytics: The Past,
the Present and the Future.
Introduction:
In the business environment of the 21st
century, organizations are demanding advanced analytics
that would permit them to utilize huge volumes and diverse types of data to identify patterns and
anomalies and predict outcomes. Advanced analytics- is rapidly becoming integrated into the
decision-making processes at companies across many different industries. Businesses have come
a long way from merely understanding what has happened in the past to be able to anticipate trends
and take action that would optimize results for businesses (Olavsrud, 2014). In order to be able to
understand and fully appreciate the role and myriad applications of business analytics, one has to
understand the evolution, the humble beginnings and the future of data analytics, which the current
paper has dealt in detail.
What is Big Data Analytics?
Big Data Analytics is the process by which analysts study huge volumes of data to uncover hidden
patterns of data, correlations and other useful information that would enable businesses to make
better decisions and maximize profit. Technologies like NoSQL, Hadoop and MapReduce are used
to analyze Big Data.
Why Data Analytics?
Organizations today handle and store billions of rows of data, possibly with millions of
combinations. High performance analytics is useful to analyze that data to figure out what is crucial
for their operations. Data Analytics has been hailed as the ‘Game Changer’, because businesses
could transform the raw data into something actionable, which improved their profits. One of the
first applications of analytics were found in the field of marketing, sales and customer relationship
management. Once the firms had analyzed the data, they found plethora of information ranging
from insights into the customer’s needs to consumer behavior to understanding the demand for
products/ services (Agrawal, 2015).

2. Varun Nemmani March, 2016
University of Missouri- Kansas City
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Evolution of Analytics:
The use of data to make decisions is certainly not a new one, but the field of business analytics
was born in the mid-1950’s, with the advent of technology that could generate and capture large
amount of information and detect patterns from it faster than a human could do it manually without
the assistance of any technology (Davenport, 2013).
Analytics era 1.0:
The first era is also known as the era of ‘Business Intelligence’. Analytics 1.0 was a time of real
progress in gaining an objective, deep understanding of important business phenomena and giving
managers the fact-based comprehension to go beyond intuition when making decisions. For the
first time, data about production processes, sales, customer interactions, and more were recorded,
aggregated, and analyzed. Data sets were small enough in volume and static enough in velocity to
be segregated in warehouses for analysis. However, readying a data set for inclusion in a
warehouse was difficult. Analysts spent much of their time preparing data for analysis and
relatively little time on the analysis itself- analysis was painstaking and slow, often taking weeks
or months to perform (Davenport, 2013).
Analytics era 2.0:
Also known as the era of ‘Big Data’. The analytics 1.0 era lasted until the mid- 2000’s and as
analytics entered the 2.0 phase, the need for powerful new tools and the opportunity to profit by
providing them quickly became apparent. Companies rushed to build new capabilities and acquire
new customers. The broad recognition of the advantage a first mover could gain led to a hype but
also prompted an acceleration of new offerings.
Example: LinkedIn, created numerous data products, including People You May Know, Jobs You
May Be Interested In, Groups You May Like, Companies You May Want to Follow, Network
Updates, and Skills and Expertise and to do so, it built a strong infrastructure and hired smart,
productive data scientists.
Innovative technologies of many kinds had to be created, acquired, and mastered in this era. Big
data could not fit or be analyzed fast enough on a single server, so it was processed with Hadoop,

3. Varun Nemmani March, 2016
University of Missouri- Kansas City
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an open source software framework for fast batch data processing across parallel servers. To deal
with relatively unstructured data, companies turned to a new class of databases known as NoSQL.
Much information was stored and analyzed in public or private cloud-computing environments.
Other technologies introduced during this period include “in memory” and “in database” analytics
for fast number crunching. Machine-learning methods (semi-automated model development and
testing) were used to rapidly generate models from the fast-moving data. Black-and-white reports
gave way to colorful, complex visuals.
The competencies/ skills thus required for Analytics 2.0 were quite different from those needed
for 1.0. The next-generation quantitative analysts were called data scientists, and they possessed
both computational and analytical skills (Davenport, 2013).
Analytics era 3.0:
Like the first two eras of analytics, this one brings new challenges and opportunities, both for the
companies that want to compete on analytics and for the vendors that supply the data and tools
with which to do so (Davenport, 2013).
What is Analytics 3.0?
Analytics 3.0 marks the stage of maturity where leading organizations realize measurable business
impact from the combination of traditional analytics and big data. High-performing companies
will embed analytics directly into decision and operational processes, and take advantage of
machine-learning and other technologies to generate insights in the millions per second rather than
an “insight a week or month.” Data architectures (i.e., Hadoop) will augment the traditional
approaches removing scale barriers. Analytics truly becomes the competitive differentiator for
enterprises who capitalize on the possibilities of this new era (International institute for analytics,
2015).
Current scenario of Analytics and Future projections:
Currently, 89% of business leaders believe Big Data will revolutionize the way businesses are
operated, the same way internet did and 83% of them have pursued Big Data projects in order to

4. Varun Nemmani March, 2016
University of Missouri- Kansas City
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gain a competitive edge. Wikibon- a community of practitioners and consultants
on technology and business systems, projects the Big Data market to top $ 84 B by 2026 achieving
a Compound Annual Growth Rate of 17% for the forecast period 2011- 2016 (Columbus, 2015).
Fig.1: Big Data Market Forecast, 2011- 2026 ($ US B) (Columbus, 2015).
Current leaders of the Big Data Analytics market:
IBM and SAS are the leaders of the Big Data Predictive Analytics market according to the latest
Forrester Wave report (Forrester is one of the most influential research and advisory firms in the
world). The latest Forrester Wave is based on an analysis of 13 different big data predictive
analytics providers including Alpine Data Labs, Alteryx, Angoss Software, Dell, FICO, IBM,
KNIME.com, Microsoft, Oracle, Predixion Software, RapidMiner, SAP, and SAS. Forrester
specifically called out Microsoft Azure Learning is an impressive new entrant that shows the
potential for Microsoft to be a significant player in this market (Columbus, 2015).

5. Varun Nemmani March, 2016
University of Missouri- Kansas City
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Fig.2: Forrester Wave: Leaders of the Big Data predictive analytics market, Q2 2015 (Columbus
2015).
Trends pushing the frontiers of Data Analytics:
Current advancements in technology are paving the way for the future of analytics. i) Customers
are seeking integrated hardware and software for analytics workloads, ii) R- open source
programming Language- for computational statistics and visualization is becoming pervasive, iii)
Visual Interfaces are making Advanced Analytics more accessible to business users, iv) Data
Visualization is becoming a business requirement, v) Organizations are infusing data analytics into
all decision making activities and vi) Companies are turning to PMML- Predictive Model Markup
Language- a standard for statistical and data mining models (Olavsrud, 2014).
Future of Big Data or Analytics 3.0:
It is predicted that the i) Volumes of data will continue to grow, ii) SQL and Spark will continue
to improve the way data is analyzed, iii) Prescriptive analytics will be built in to business analytics

6. Varun Nemmani March, 2016
University of Missouri- Kansas City
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software, iv) Real-time streaming insights into data will play a major role, v) Algorithm markets
will emerge, vi) Cognitive computing and analytics will emerge as game changers, vii) More
companies will drive value and revenue from their data, viii) Businesses applying analytics will
witness $ 430 Bn in productivity benefits over their competitors not using data analytics by 2020
and ix) fast and actionable data will replace big data (Marr, 2016).
Conclusion:
Only time shall decide which of these predictions would come true. However, the big data model
was a huge step forward, but it will not provide advantage for much longer. Companies that want
to prosper in the new data economy must once again fundamentally rethink how the analysis of
data can create value for themselves and their customers. Analytics 3.0 is a direction of change
and a new model for competing on analytics (Davenport, 2013).

7. Varun Nemmani March, 2016
University of Missouri- Kansas City
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References:
 Agrawal, V. (2015). What Does the Future Look Like for Big Data Analytics? Retrieved 28
April, 2016, from http://tech.co/big-data-analytics-2015-12
 Columbus, L. (2015). Roundup of Analytics, Big Data & Business Intelligence Forecasts
and Market Estimates, 2015. Retrieved 28 April, 2016, from
http://www.forbes.com/sites/louiscolumbus/2015/05/25/roundup-of-analytics-big-data-
business-intelligence-forecasts-and-market-estimates-2015/#4c5378714869
 Davenport, T. (2013). Analytics 30. Retrieved 29 April, 2016, from
https://hbr.org/2013/12/analytics-30
 International institute for analytics. (2015). Analytics 30. Retrieved 29 April, 2016, from
http://iianalytics.com/analytics-resources/analytics-3.0
 Marr, B. (2016). 17 Predictions About The Future Of Big Data Everyone Should
Read. Retrieved 17 April, 2016, from
http://www.forbes.com/sites/bernardmarr/2016/03/15/17-predictions-about-the-future-of-
big-data-everyone-should-read/#65a10dca157c
 Olavsrud, T. (2014). 11 Market Trends in Advanced Analytics. Retrieved 26 April,
2016, from http://www.computerworld.com/article/2489750/it-management/11-market-
trends-in-advanced-analytics.html