A report out earlier this month on Big Data by CM Research, a financial research firm based in London, predicts that during this decade the digital data universe will grow 75-fold from 1.2 zettabytes to 90 zettabytes. (1 zettabyte = 1 trillion gigabytes).
To give you an idea how far reaching Big Data will be the report states: “ask any CEO how he expects to sell more products and cut more costs and he will tell you that Big Data features prominently in his strategy.”
A couple of charts in the report remind us how far computing has come over the last 40 odd years. The number of transistors in a chip increased from 10,000 in 1970 to 1 billion in 2010, and is on track to hit 10 billion by 2020.
The cost of a megabyte of storage has dropped from $1,500 in 1970 to just $0.0001 today.
Within the Big Data category, the firm expects analytics, IT integration and security to have the highest long-term earnings growth. The database, networks and storage sectors are forecast to have disappointing growth potential. CM Research also expects certain end-to-end players – Google and Baidu – to see strong earnings growth.
Since Big Data is still in the early stages of the development cycle, the firm’s favorite long-term stocks are the established leaders – either across the entire Big Data value chain or in niche, emerging technologies that are integral to Big Data’s success.
Below is a summary of CM Research’s 19-page Big Data (Vol. II) report, which takes a close look at which industries will be impacted and who will likely be the ultimate winners and losers.
The firm delves into the Big Data supply chain and looks at where the power really lies. They split the industry into seven sectors – analytics, database, integration, network, security, storage and end-to-end players – identifying the winners and losers in each space. They conclude that analytics, security and the end-to-end players (i.e. the vertically integrated internet companies like Google and Baidu) will reap the biggest benefits from Big Data. By contrast, the report raises doubts about how successfully the established enterprise software houses – Microsoft, IBM, SAP and Oracle – can sustain their growth profile in a data-centric world that is evolving so fast.
What is Big Data?
Big Data is the frontier of a firm’s ability to store, process and access all the data it needs to operate, make decisions, reduce risks and serve customers, according to Forrester Research. Big Data combines traditional data management technologies with new forms of data management that are better suited to modern data formats. Big Data is a combination of structured data, unstructured data and binary data: structured data can be compartmentalized into fields (e.g. age, height, gender) in a relational database; unstructured data refers to data that cannot be analyzed in a relational database (e.g. video, tweets); binary data is data that can be understood by machines.
Big Data typically has four dimensions. It can come in high volume (there is lots of it), high velocity (it needs to be analyzed quickly), high variety (it comes in many different formats) and high veracity (it can be difficult to understand).
The amount of digital data stored in the world grew relatively slowly until about 2010, at which point it began to take off exponentially. At the same time, the cost of computing has continued to fall. In addition, both computing power and digital storage can now be rented on the cloud, reducing the cost of creating businesses that produce or consume Big Data. All this has created a virtuous circle of growth in Big Data technologies.
Why is it important?
Big Data is generated from all our digital activity. Consumers leave digital footprints when they browse the web, purchase items online, use email or interact with social media. Businesses, in addition to generating similar data footprints, are also keen to gather and analyze as much Big Data as they can for their own commercial use. Chief marketing officers use it to target advertising campaigns more effectively. Chief operating officers use it to run their business processes more efficiently. Chief engineers use it to improve machine performance. Chief scientists use it for predictive analysis in research projects. And so on. Thus CM Research sees three primary uses for Big Data: digital marketing, operational efficiency and scientific research.