In our complex economic landscape, in which the industrial sector now accounts for less than a quarter of output in most developed markets, the productivity- and growth-enhancing opportunities should (and indeed do) look very different to a hundred years ago, a time dominated by industry.
As more and more of the world integrates digital technology ever-deeper into day-to-day routines, and as the barriers for data generation are rapidly lowered, we have already reached a point whereby over 90 per cent of the data ever generated has been created in the past two years, and is set to accelerate further with the world expected by 2020 to have generated 128 times the amount of information generated in 2013.
That exponential growth of data creates incredible challenges and opportunities. Much of the data is noise and has little informational content for investors to leverage in their portfolios. But a vast swathe of the information being created is highly relevant for investors, providing early and accurate signals about business activity, personal preferences, and patterns of behaviour.
From an investment perspective that creates a revolutionary opportunity to blend together the traditional human-led investment judgments about a given company’s likely performance with sophisticated computer-optimised algorithms sifting the informational wheat from the chaff.
It’s a statement of the obvious to assert that we live in an era of ‘big data’; the opportunity for investors is in leading the charge to transform ‘big data’ into ‘smart data’.
Data revolution: Why is it so important?
The “Data Revolution” is much more than a trending buzzword, and encompasses different technological innovations (such as artificial intelligence, machine learning and natural language processing) that together with the exponential growth in available data, will enable companies to achieve a sustainable competitive advantage in virtually all industries.
However, faster computers and bigger databases by themselves are not enough to effectively “digest” this massive quantity of data and as a result, data processing algorithms have evolved from simply processing to learning how to process – an approach called machine learning.
Machine learning has its roots in artificial intelligence (i.e. the theory and development of computer systems able to perform tasks normally requiring human intelligence) and it is a truly transformative innovation as it can help make predictions using complex databases in almost every environment, ultimately gaining insights that were previously hidden from plain view. .jpg)
Management teams that fail to invest in and leverage these technologies risk being overtaken by competitors and may eventually see their market share shrinking.
We believe that the broad applicability of these data-driven advancements may be central to the efforts for raising global productivity growth today.
Data revolution and its role in the asset management industry
In asset management, the ability to process data, gathering insights that were previously hidden in large quantities of unstructured data and converting them to actionable insights will be critical to success for data-driven investors.
It is essential that investment techniques advance with the availability of information, as ultimately investing has always been about maintaining an informational and analytical advantage, and active management has always been about uncovering opportunities before they are priced in by the market. We believe that embracing data-driven models could not only confer an advantage, but become the raison d'être for prospering in this constantly evolving industry.
For example, we see techniques such as natural language processing (‘NLP’, a subset of machine learning technologies that uses computers to interpret vast quantities of text from multiple sources in multiple languages, and also to analyse unstructured or not easily quantifiable data) as essential for companies seeking to remain competitive, as they allow decision makers to analyse more data, more quickly, more effectively and with progressively less resources.
NLP can also elucidate the subtle relationships not only within, but between companies, or to create opportunities by leveraging on the information asymmetries that we can find in smaller companies, off-benchmark companies and companies located in less followed emerging markets.
The analysis of credit card transactions is one of the numerous large data sets which can be useful and may provide an informational advantage in the investment process.
Credit card transaction data can gain powerful insight in forecasting revenues and profitability growth for companies selling directly to consumers and has also been found to be predictive of sales growth not only for the most current quarter but up to one year in the future.
Furthermore, as credit card data is provided on a monthly basis with only a six-day lag, while corporate earnings announcement occur quarterly with a two-and-a-half-week lag, this data can provide incremental and timely insights into profitability trends more frequently, hence conveying a potential informational advantage to the investor before it reaches the market.
Today’s flood of information is far beyond what one human can digest. Extracting actionable insights requires skilled managers to harness advanced analytics and processing technologies, and as the rate of unstructured data generated increases exponentially, we firmly believe that data-driven technologies alone could not lead to better investment decision making, but it is the pairing of human judgement with technology that will produce the best results.
Can artificial intelligence replace human intelligence in the decision-making process? There will still be the need of the “human touch” to override technologies when necessary, and it would be hard for technologies to replace human qualities such as judgement and experience.
In our view, data can convey timely and informed inputs that will be effective only with the careful stewardship of portfolio managers, as research and portfolio construction processes still require a high degree of human judgement and experience.
Both humans and machines will remain essential to the ongoing transformation of the financial services industry, and beyond.
James Ashley is head of international market strategy, Strategic Advisory Solutions at Goldman Sachs Asset Management. The views expressed above are his own and should not be taken as investment advice.