Companies using ‘digital exhaust’ to ‘nowcast’ trends

Organisations are increasingly turning to alternative data to analyse and nowcast trends and extract actionable intelligence

The Swiss banking and financial services giant UBS is tracking thousands of ships – actually more than 20,000 ships – around the world to understand the structural changes of globalisation. Bank of Japan is using alternative data (mobility data and electricity demand data) that is available in real-time to‘nowcast’ the IIP (Indices of Industrial Production) one to two months before their official release. Companies are gathering ‘digital exhaust’ from myriad sources of Alt Data(alternative data) to analyse and ‘nowcast’ trends.Nowcasting in economics is the prediction of the present, the very near future, and the very recent past state of an economic indicator.

Pandemic disrupted predictive models

From job postings, to weather, mobile traffic, patent filings, legal action or shipping movement data, organisations are increasingly turning to Alt Data to forecast trends and extract actionable intelligence, as the pandemic broke the flow of regular data. The COVID-19 crisis is an example of just how relevant alternative data can be. In a few short months, consumer purchasing habits, activities, and digital behaviour changed dramatically, making pre-existing consumer research, forecasts, and predictive models obsolete. Moreover, as organisations scrambled to understand these changing patterns, they discovered little of use in their internal data. Meanwhile, a wealth of external/alternative data could–and still can–help organisations plan and respond at a granular level.

UBS is using alternative data to support its research arm by asking a big question – is globalisation being structurally changed? – and deploying data sets to turn this into practical outcomes. UBS Evidence Lab has built proprietary models relying more on real-time information. For example, it has built a monitor of container ships, using data from maritime-traffic monitoring sites.

This data covers over 20,000 ships that it tracks by location, draft (how low it sits in the water, an indicator of cargo volumes), ship size and type, cargo manifests, and ports of call. Noting that 70% of world trade is conducted by sea, the UBS algorithm, called Modified Deadweight Tonnage, tracks oceanic traffic across the Pacific, to give analysts a clear view of activity. Trade wars and stages of COVID are reflected in the results. It also needs to be paired with other data such as tonnage, etc., to create a tapestry of datasets to extract intelligence.

Monitoring geographic footprint

Physical presence matters in markets like retail and logistics. One way to measure this is by looking at a company’s list of stores on its website. Investors can analyse each individual location to learn more about the competition they face, their geographical positioning, customer reviews, and more. A hedge fund was sued by its clients for neglecting to conduct “basic due diligence” on a Chinese forestry company in which it had invested. It alleged that the Chinese company had misstated the true value of its forestry assets. A geographic footprint analysis could have revealed the true value of these assets.

Nowcasting for dynamic planning

When the COVID-19 pandemic hit, many government, financial, and other institutions, hoping to capture the rapid economic shifts taking place around the world, turned to nowcasting for answers. Nowcasting uses extremely high frequency data/live data, say a few hours or at the best a couple of days old. For example, consumer spending can be estimated in different cities by combining data such as wages from business applications and footfall from mobility trend reports. Semiconductor Analytics provides weekly data visualisation stream developed to address the organisational need to stay abreast of the silicon cycle.

The Bank of Japan is using alternative data (mobility data and electricity demand data) that is available in real-time and can nowcast the IIP (Indices of Industrial Production) one to two months before their official release. The model employs machine learning techniques to improve the nowcasting accuracy by endogenously changing the mixing ratio of nowcast values based on traditional economic statistics (the IIP Forecast) and nowcast values based on alternative data, depending on the economic situation. The estimation results show that by applying machine learning techniques to alternative data, production activity can be nowcasted with high accuracy, including when it went through large fluctuations during the spread of the COVID-19 pandemic.

Companies gather ‘digital exhaust’

Companies across industries have begun successfully using Alt Data from a variety of sources. The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered alternative data from a variety of licensed and public data sources, many of which draw from the “digital exhaust” of a growing number of technology companies and the public web.

Investment firms have established teams that assess hundreds of these data sources and providers and then test their effectiveness in investment decisions. A broad range of data sources are used, and these inform investment decisions in a variety of ways:

• Investors actively gather job postings, company reviews posted by employees, employee-turnover data from professional networking and career websites, and patent filings to understand company strategy and predict financial performance and organisational growth.
• Analysts use aggregated transaction data from card processors and digital receipt data to understand the volume of purchases by consumers, both online and offline, and to identify which products are increasing in share. This gives them a better understanding of whether traffic is declining or growing, as well as insights into cross-shopping behaviours.

A game changer

Use of Alt Data has the potential to be game changing across a variety of business functions and sectors. To get started, organisations should establish a dedicated data-sourcing team. A key role on this team is a dedicated data scout or strategist who partners with the data analytics team and business functions to identify operational, cost, and growth improvements that could be powered by external data.

A more effective strategy involves using data marketplace and aggregation platforms that specialise in building relationships with hundreds of data sources, often in specific data domains – like consumer, real-estate, government, or company data. These relationships can allow organisations ready access to the broader data ecosystem through an intuitive search-oriented platform, allowing organisations to rapidly test dozens or even hundreds of data sets.

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