Out of 100s of ideas, McKinsey believes big data analytics is one of the top 5 catalysts that can increase US productivity and raise thee GDP in the next 7 years. However, they only looked at 4 industry sectors to conceive their outlook.
Today, McKinsey Global Institute (MGI) is presenting and discussing their new, 175 page report, “Game changers: Five opportunities for US growth and renewal” at an event hosted by the Committee for Economic Development (CED). If you aren’t familiar with the CED, it is a nonprofit, nonpartisan, business-led, public policy organization that delivers well researched analysis and reasoned solutions for our nation’s most critical issues. It was founded by prominent CEOs to develop sound economic policy. Alongside 1) big data analytics, McKinsey leaders believe 2) shale gas and oil production, 3) increased trade competitiveness in knowledge-intensive manufactured goods, 4) increased investment in infrastructure, and 5) a more effective system of talent development in schools can get our economy back into growth mode.
The reported estimates and economic models for big data’s contribution to GDP were limited to four large and heterogeneous sectors; so, it is probably that a much greater contribution could be achieved if improvements were gained across all industries. If so, bid data analytics might be the number one way to boost our economy. MGI believes widespread use of big data analytics could increase annual GDP in retail and manufacturing by up to $325 billion in 2020, and produce up to $285 billion in productivity gains in health care and government services, totaling $610 billion annually. While this report did not look at the big data impact in finance, banking, security, media, insurance, information technology, transportation, real estate, utilities, agriculture, or other industries we’ve seen considerable case studies and examples in these industries. For example, GE, a Pivotal investor, sees a way for the industrial internet to boost global GDP by $10-$15 trillion in the next 10-15 years.
What’s Behind the Big Data Trend?
There are four key elements, according to MGI. For one, we have more data now and even more tomorrow. MGI cited an IDC report about how the world’s collection of data moved from about 1-2 exabytes in 2000 to 2700 exabytes in 2012 with a projection of 40,000 exabytes in 2020. We continue to add data from out operations, partners, third parties, and customers.
Secondly, there is the cloud—a massive amount of compute power available on demand. Compute power alone has grown—MGI cites how the fastest supercomputer is 4600 times faster in 2013 that it was in 2000, and the average desktop has increased 75 times in the same period. With cloud computing, we can access an extreme level of compute power and return it after use–we only pay for what we use, a completely different economic dynamic than in the past. With it, companies can scale IT infrastructure without having to allocate cash for up-front capital and then amortize equipment over 5 years.
Open source software for unstructured data is also having an impact. The majority of our new data is unstructured—social media posts, video, audio, pictures, research articles, etc. The new tools, like Hadoop and NoSQL, are giving companies free a data bulldozer when they used to only have a pick axe that was paid for. For example, our 1000-node Hadoop cluster is available for free.
Analytical algorithms are also advancing in several areas, most importantly with machine learning. This includes areas like robots, autonomous vehicles, speech recognition, facial recognition, fraud detection, and medical diagnoses
The Big Data Analytics Contribution Breakdown
Here is how each of the four sector’s improvements broke down in the MGI report:
- For the retail sector, big data applications covered three areas—supply chain, operations, and merchandising. By creating greater performance transparency, these companies can optimize inventory, transportation, returns, labor, assortments, and more. They estimate that this sector will gain $30-55 billion in GDP through use of big data. In our previous article on 20+ big data examples, we provided links to stories about how Walmart, Sears, Kmart, and Amazon are using big data.
- Within Manufacturing, McKinsey pointed to R&D, production, and supply chain for a total productivity gain of $125-270 billion. They cited improvements to decision making for manufacturing costs, product lifecycle management, customer usage data, sensor analytics, preventative maintenance, supplier data, and demand forecasting. Previously, we also cited examples in the automotive, supply chain, logistics, and industrial engineering sectors.
- For healthcare, MGI proposed improvements in clinical operations—effectiveness, decision support, remote patient monitoring, and performance transparency. As well, they saw contributions in the areas of R&D optimization, personalized medicine, drug safety, and surveillance/response. Together, these contributed to a cost savings of $100-190 billion.
- Cost efficiencies in government services can generate $95 billion in savings through automation, performance monitoring, measurement, decision support, lower procurement costs, and improved tax collection.
12 More Examples of Big Data’s Impact Across Industries:
At Pivotal, we talk to companies every day who are using big data—from Facebook and SoundCloud to Viacom and Nokia, banks and manufacturers. We see a huge impact with data analytics across just about every industry. Here are some of the examples we have covered in the past:
- Check out the MGI event and research Tweets with hashtag #usgamechangers.
- How vaccine quality is improving through predictive models and big data
- Big data in agriculture
- Using big data for healthcare fraud, waste, and abuse
- How the television and cable business is using big data
- Big data for logistics and fleets
- Financial services use of big data
- Trading and big data
- Big data in the automotive industry
- How planes create a terabyte per flight
- Big data and genetics
- Mobile telephony and big data
You can also learn more about Pivotal’s Big Data Analytics platform, Pivotal HD and see how we scale in the cloud with RabbitMQ at Nokia, Redis at Twitter, and Spring with Hadoop.
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