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December 19, 2011


Big Data Investment Theme – Fidelity Investments

by Ravi Kalakota

Fidelity Investments put out an interesting analysis on Big Data as a Macro Investment Themes for clients.  Since everyone has an underperforming investment portfolio in this current market, I reproduced the article here to generate some ideas.

Fidelity Investments

Key Takeaways

  • New types of large data sets have emerged because of advances in technology, including mobile computing, and these data are being examined to generate new revenue streams.
  • More traditional types of business data have also expanded exponentially, and companies increasingly want and need to analyze this information visually and in real time.
  • Big data will be driven by providers of Internet media platforms, data amalgamation applications, and integrated business software and hardware systems.

Investment Theme – Big Data

The concept of “big data” generally refers to two concurrent developments. First, the pace of data accumulation has accelerated as a wider array of devices collect a variety of information about more activities: website clicks, online transactions, social media posts, and even high-definition surveillance videos.

A key driver of this flood of information has been the proliferation of mobile computing devices, such as smartphones and tablets. Mobile data alone are expected to grow at a cumulative annualized rate of 92% between 2010 and 2015 (see Exhibit 1, below).

Second, businesses are looking to derive interesting insights from customer profiles and actions to help them develop products and services, as well as recommendation engines that can drive sales. Yet this new kind of information is considered “unstructured” because there is usually more text—and sometimes audio and video—than numbers, so it is not suited to traditional database models. Current relational database management systems (RDBMS) can handle only structured data, which account for about 15% of total data. Unstructured data—the larger 85% share—require other methods. 

Exhibit 1

Fortunately, technology hardware has evolved to facilitate the analysis of unstructured data. Distributed systems divide problems into multiple tasks, which can be solved by parallel computing—computers built with multiple processors that work simultaneously. The acceptance of Internet-based cloud computing has also played a role in the explosive growth of big data, as has virtualization on the application side. Such technologies provide higher performance at lower cost, while improving flexibility, reliability, and scalability.

As the volume and variety of data have expanded, the expense of gathering, analyzing, and managing data has contracted. Businesses are taking advantage of these developments to use IT in new ways. Foremost is the application of predictive analytics, which can help determine that if a, b, and c, happen, then there is a good chance that d, e, and f will also happen.

This is far from just a science-fiction scenario of machines starting to think for us.

  • Internet-based companies are investigating usage patterns to help optimize website design and content creation to boost traffic and sales.
  • Financial institutions are segmenting customers by credit card behavior and tailoring products to specific risk profiles.
  • Retailers are capturing data feeds from rewards card programs and online transactions to influence what product inventory to carry and how to price it.
  • All types of companies are acquiring and mining data from social networks to conduct real-time research on breaking trends. 

More broadly, big data technologies have the potential to fundamentally change how business is conducted.

The algorithms developed to analyze the new sources of information described above can also be applied to more traditional forms of enterprise data to optimize plant utilization and labor force deployment, for example.

Companies are just starting to scratch the surface, but they may be able to realize the potential when they master the technologies needed for rigorous examination and experimentation with big data.

Investment implications

At the same time that the amount of data has increased exponentially, the cost of analyzing data has decreased dramatically. The growing desire to perform real-time analytics—whether to test product and service innovations or to inform business decision-making—is driving the development of infrastructures and applications to handle big data.

Overall change in the technology markets has accelerated, which tends to be positive for companies offering IT services and “middleware” that connects separate software applications. This theme is also having an impact on existing players in the database, storage, and business intelligence markets. Equipment manufacturers and resellers have taken notice, and many have acquired providers of software solutions for managing and analyzing unstructured data.

Investors hoping to capitalize on this theme need to be aware that once a technology market becomes more mature, there is inevitably consolidation. The key to investing in long-term winners in the big data space is identifying companies that will have “escape velocity.” In other words, companies that reach a certain critical size ahead of their competitors will likely continue to command a dominant share of the market.

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