Forum

THE IMPORTANCE OF DATA ANALYTICS

DATA ANALYTICS: THE NEXT FRONTIER FOR INNOVATION, COMPETITION, AND PRODUCTIVITY

The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey’s Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.

Big data—a growing torrent

$600 to buy a disk drive that can store all of the world’s music.

30 billion pieces of content shared on Facebook every month.

40% projected growth in global data generated per year vs. 5% growth in global IT spending.

235 terabytes data collected by the US Library of Congress by April 2011.

15 out of 17 sectors in the United States have more data stored per company than the US Library of Congress.

Big data—capturing its value

$300 billion potential annual value to US health care—more than double the total annual health care spending in Spain.

€250 billion potential annual value to Europe’s public sector administration—more than GDP of Greece.

$600 potential annual consumer surplus from using personal location data globally.

60% potential increase in retailers ‘operating margins possible with big data.

140,000–190,000 more deep analytical talent positions and 1.5 million more data-savvy managers needed to take full advantage of big data in the United States.

Big data techniques and technologies

A wide variety of techniques and technologies has been developed and adapted to aggregate, manipulate, analyze, and visualize big data. These techniques and technologies draw from several fields including statistics, computer science, applied mathematics, and economics. This means that an organization that intends to derive value from big data has to adopt a flexible, multidisciplinary approach.

 

BIG DATA TECHNOLOGIES

Big Table, Business intelligence (BI), Cassandra, Cloud computing, Data mart, Data warehouse, Distributed system, Extract, transform, and load (ETL), Google File System, Hadoop, HBase, MapReduce, Mashup, Metadata, Non-relational database, R, Relational database, Semi-structured data, SQL, Stream processing, Structured data, Visualization.

The transformative potential of big data in five domains

Health Care

Public Sector Administration

Retail

Manufacturing

Personal Location Data

Together these five represented close to about 40 percent of global GDP in 2013.

CONCLUSION

Big data creates value in several ways:

Creating transparency.

Enabling experimentation to discover needs, expose variability, and improve performance.

Segmenting populations to customize actions.

Innovating new business models, products, and services.

While the use of big data will matter across sectors, some sectors are poised for greater gains.

Big data offers very large potential to generate value globally, but some geographies could gain first

There will be a shortage of the talent organizations need to take advantage of big data several issues will have to be addressed to capture the full potential of big data

Data policies

Technology and techniques

Organizational change and talent

Access to data

Industry structure

TECHNIQUES FOR ANALYZING BIG DATA

A/B testing, Association rule learning, Classification, Cluster analysis, Crowdsourcing, Data fusion and data integration, Data mining, Genetic algorithms, Machine learning, Natural language processing (NLP), Neural networks, Network analysis, Optimization, Pattern recognition, Predictive modeling, Regression, Sentiment analysis, Signal processing, Statistics, Supervised learning, Simulation, Time series analysis.