McKinsey has issued a 146 page report this month: Game changers: Five opportunities for US growth and renewal. According to them, Big Data will add $155 to $325 Billion to the US economy by 2020, which represents 0.8 to 1.7 % of GDP.
Big-data analytics is a productivity tool. Data is captured everywhere from data transactions, medical and legal records, videos, and social technologies, as well as sensors, cameras, bar codes, and transmitters. Advances in computing and analytics can transform this data into insights that create operational efficiencies. By 2020, the wider adoption of big-data analytics could increase annual GDP in retailing and manufacturing by up to $325 billion, and save up to $285 billion in the cost of health care and government services.
Information technology and the Internet have transformed competitiveness over the last twenty years, the next twenty belong to Big Data. By improving decision making, and gathering more data companies raise their efficiency, reduce costs, and become more competitive. They also create new products and services.
McKinsey estimates the amount of data collected was 0.6 to 2.1 exabytes in 2000, between 2,000 and 2,700 exabytes last year, and could reach 40,000 exabytes by 2020.
The fall in the price of computing resources, and the advent of cloud computing allow companies to tap massive computing resources to process big data sets, which would be cost prohibitive if those resources had to be purchased. This will enable widespread adoption of Big Data analytics, making it possible for small start-ups to tap those resources. Software tools such Hadoop and NoSQL, allow processing of large unstructured data sets.
Big Data’s impact on the economy is expected to come from three sources: productivity gains and cost efficiencies, shifts in market share, and savings in government and health-care costs.
The adoption of Big Data will vary by economic sector. In consumer facing and competitive sectors such as retail, financial services, and manufacturing using data to gain a competitive advantage is essential. The public and quasi-public sectors are expected to be later adopters given lack of training, tools, incentives, and also a reluctance to share data about the public over privacy concerns.
McKinsey also says Big Data could create up to $610 billion in annual productivity gains in just four sectors. Retail and manufacturing would gain up to $325 billion, and up to $285 billion in cost savings in health care and government.
Some industry leaders (including Walmart, Kroger, and Target, and most notably Amazon in the e-commerce space) are already at the cutting edge of devising new applications for big data. For example, Amazon has taken cross-selling to a new level with sophisticated predictive algorithms that prompt customers with recommendations for related products, services, bundled promotions, and even dynamic pricing; its recommendation engine reportedly drives 30 percent of sales.”
Supply chain. Big data analytics can improve productivity in the supply chain through tighter inventory management, better sourcing, and improvements in logistics. For example, real-time inventory data combined with demand forecasting to eliminate overages and shortages.
We estimate that big data analytics could produce administrative efficiencies that generate productivity gains in government up to $95 billion annually by 2020.
McKinsey does note that some issues will need to be resolved before this vision is complete. These include privacy laws, ownership of data, security, and regulatory changes to enable more rapid adoption.