2 Ways to Play Big Data Analytics
As we leap toward a digitally-driven world, there is an enormous amount of data being generated. The true value of such large volumes of data sets, which are both structured and unstructured as well as diverse in source and size, cannot be understood using traditional methods. Thus, advanced analytic tools are being applied to extract meaningful information from such silos of heterogeneous data sets. The derived actionable information has the potential to improve decision making, enhance productivity, reduce costs and aid innovation, making big data analytics a critical driver for valuable business insight and a competitive differentiator that investors may want exposure to.
Digging into Big Data Analytics
Big data is a term that “describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.” But big data isn’t just sheer volume. A BBVA report highlights the 5 V’s of big data – volume, velocity, variety, veracity and value – as “the five keys to making big data a huge business.” With the growing numbers of sensors, connected devices and social media, an estimated 163 zettabytes of data will be created by 2025.
The real importance of big data is not its volume, but rather what is done with it. Big data analytics is already being applied across sectors such as healthcare, life sciences, manufacturing, banking, retail, government, oil and gas, education, and so on. A July 2019 Harvard Business Review Analytic Services survey highlights that 91% of market leaders agree that an effective data and analytics strategy will be essential for successful business transformation initiatives over the next two years. It further suggests that companies that embrace a data-driven culture experience a four-times improvement in revenue performance and better customer satisfaction.
“The very challenge created by digital disruption – too much data – has also created an unprecedented opportunity,” according to Donald Feinberg, vice president and distinguished analyst at Gartner. Worldwide revenues for big data and business analytics solutions are forecast to be $189.1 billion in 2019, and the figure is expected to reach $274.3 billion by 2022, growing at a five-year compound annual growth rate of 13.2% (2018-22) as per IDC.
Multiple studies have been conducted to see the impact of big data analytics and business intelligence (BI) on businesses. One such study, conducted by Forrester Consulting for Microsoft to calculate the Total Economic Impact, concluded that Azure Analytics with BI delivered a return on investment of 271% while increasing satisfaction by 60%.
Some of the market leaders in the segment are Microsoft (MSFT), Tableau (recently acquired by Salesforce), Salesforce (CRM), SAP, SAS, Oracle (ORCL), IBM (IBM), Qlik, ThoughtSpot, Sisense, Logi Analytics, Information Builders, Pyramid Analytics, BOARD International, GoodData, Domo (DOMO), Looker, Yellowfin and MicroStrategy (MSTR). These companies are featured on the 2019 magic quadrant (a research methodology that shows competitive positioning) by Gartner for analytics and BI platforms.
How to Invest in Big Data Analytics
One of the easiest and most effective ways to invest in the big data analytics space is via exchange-traded funds (ETFs).
Launched in 2018, the Global X Future Analytics Tech ETF (AIQ) is an ETF providing investment opportunity across the segment of big data and artificial intelligence (AI). It uses the Indxx Artificial Intelligence & Big Data Index as its underlying benchmark. The index is designed to track the performance of companies that “potentially stand to benefit the development and utilization of AI technology in their products and services, as well as in companies that provide hardware facilitating the use of AI for the analysis of big data.” The components are weighted based on their security-level market capitalization. It has $40.82 million assets under management, an expense ratio of 0.68%, and has delivered 27.41% YTD returns.
The fund is based on the belief that “big data is AI’s fuel. It is both what trains AI to become increasingly powerful and what AI systems are ultimately applied to in order to generate real-world insights. The more data AI systems can tap, the greater their intelligence and disruptive potential.” The fund has a vast portfolio of around 80 stock holdings with the top ten having an average allocation of 30%, which includes:
Next is the Goldman Sachs Motif Data-Driven World ETF (GDAT), which mirrors the performance of its underlying benchmark, the Motif Data-Driven World Index. The index seeks to “invest in companies positioned to benefit from the proliferation of data, a defining theme of the 21st century.” The index has exposure to companies in certain developed markets that “may benefit from the on-going rapid increase in electronically recorded data in the world and its impact on the lifecycle of data delivery and processing.” Launched in 2019, the fund has $10.59 million assets under management and an expense ratio of 0.50%. The concentration ratio is low at 25% toward the top ten holdings.
With data being generated at an unprecedented pace, there is a huge opportunity for businesses to gain insights to stay ahead in an increasingly data-driven ecosystem.
Data based on respective websites and fact sheets as on October 16, 2019
Disclaimer: The author has no position in any stocks mentioned. Investors should consider the above information not as a de facto recommendation, but as an idea for further consideration.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.