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).

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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,

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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

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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).

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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

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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).

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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