Sunday, January 29, 2023

Companies using ‘digital exhaust’ to ‘nowcast’ trends

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What is digital exhaust the companies are using in the new normal?

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 globalization. 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 to analyze 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.

The pandemic disrupted predictive models

From job postings to weather, mobile traffic, patent filings, legal action or shipping movement data, organizations are increasingly turning to Alt Data (alternative data) to forecast trends and extract actionable intelligence, as the pandemic broke the flow of the regular data. The COVID-19 crisis is an example of how relevant alternative data can be.

In a few short months, consumer purchasing habits, activities, and digital behaviour changed dramatically, making preexisting consumer research, forecasts, and predictive models obsolete. Moreover, as organizations scrambled to understand these changing patterns, they discovered little use in their internal data. Meanwhile, a wealth of external/alternative data could—and still can—help organizations plan and respond at a granular level.

UBS is using alternative data to support its research arm by asking a big question – is globalization 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 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 per cent 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 data sets 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 analyze 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’s 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, e.g., a few hours or, at 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 a weekly data visualization stream developed to address the organizational need to stay abreast of the silicon cycle.

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 now-casted 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 various 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 various 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 organizational growth.

Analysts use aggregated transaction data from card processors and digital receipt data to understand the volume of consumer purchases, 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

The use of alt data has the potential to be game-changing across a variety of business functions and sectors. To get started, organizations should establish a dedicated data-sourcing team. In our experience, 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 a data marketplace and aggregation platforms that specialize in building relationships with hundreds of data sources, often in specific data domains— for example, consumer, real estate, government, or company data. These relationships can give organizations ready access to the broader data ecosystem through an intuitive search-oriented platform, allowing organizations to test dozens or even hundreds of data sets rapidly.

Also Read: Productivity paranoia creates new tensions in workplace

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