Best Practices

Internal Data + External Data = Augmented Decision-Making

The world entered the Data Revolution in 2010. According to J.P. Morgan, the data revolution is being driven by three factors: 

  1. developments in cloud computing
  2. advances in data science
  3. rapid data creation

The Data Revolution hasn’t just seen an explosion of data within organizations. It has created thousands of external datasets for corporations to leverage. If information is power, however, why are some businesses - the laggards - still relying on internal data only? I applaud data-driven corporations that are merging internal data and external data for augmented decision-making - these are the innovators. 

It’s a topic I’m incredibly passionate about - I’ll be speaking about it at Beyond 2020 alongside Deloitte CDO Juan Tello and Snowflake VP, Data Marketplace & Customer Product Strategy Matthew Glickman on December 10, and at Eagle Alpha’s Virtual Data Conference, December 4th. 

So what exactly is  external data? And how can different departments use it?  

What exactly is external data?

External data is any data generated outside an organization. Categories of external data include social media, satellite, consumer transactions, geo-location and employment data. The image below outlines Eagle Alpha’s taxonomy of 24 external data categories. These datasets are from two primary sources: 

  1. data companies that are set up to monetise data
  2. companies that have ‘exhaust’ data to monetise

External Data Categories

How do corporations use external data? 

The use cases for external data in the corporate space are endless and often unique to each business, industry, or corporate department. For example, insights can be obtained regarding competitive intelligence, manufacturing, revenue, customers, people, mobile strategy and R&D. 

Departments which rely purely on internal data are reactionary in nature, as internal data lags. Utilizing external data to mine insights gives corporate departments the ability to detect changes to market conditions in real-time. 

Jorn Lyseggen, in his book Outside Insight, outlines how external data will change decision-making in three key ways:

  1. it adds forward-looking insights
  2. decisions happen in real-time
  3. companies measure progress and plan for the future by benchmarking against their competitors.  

The table below outlines the types of external data that can be used by corporate departments and the insights that can be gained.

Example Use Cases by Corporate Departments

Purpose

Relevant Department

Example Categories And Use Cases

Customer Insights

Marketing

  • Social media data – analyse brand perception.
  • Online search data – analysis into customer behaviour.

Market / Competitive Intelligence

Product, Sales

  • Satellite – analyse activity at a competitor manufacturing plant.
  • Web crawling – crawl competitor websites for pricing data.
  • Pricing data – trends by category
  • Employment data – utilising job listings data to find growing companies for lead generation

Product Development

Product

  • Patent data – how much should we be investing in R&D?
  • Geo-location data – where should we launch new stores?
  • Review data – consumer reviews to understand product issues

Supply Chain Management

Procurement

  • Shipping data – monitor output of supplier using HS (Harmonized Shipping) codes.
  • Credit data – track account receivables of suppliers.

Macro Environment

Board, Finance / Treasury

  • Shipping data – insights into FX by analysing shipping between countries.
  • Credit data – track credit levels by sector, state and country.
  • Employment data – track hiring/firing trends, labour supply/demand.

People Insights

Human Resources

  • Employment data – Natural Language Processing (NLP) analysis of employee review comments.
  • Employment data – analysis of employment, skills and hiring trends at competitors, workforce analytics, labour supply/demand.

Acquisitions

Board / M&A Team

  • Web traffic data – track visitors to ‘order page’ of e-commerce site.
  • Employment data – analyse sentiment of staff at target or companies with growing employment numbers.

See What Investors See

Investor Relations

  • Consumer transaction data - understand revenue predictions.
  • Pricing data – insights into price points and trends.

Join me virtually to learn more.

There’s so much potential for organizations leveraging external data. Join me at Beyond 2020 on December 10 to hear how organizations like Deloitte and Snowflake are helping companies leverage external data, and at Virtual Data Conference on December 4 to hear from former Starbucks CMO Matt Ryan about how Starbucks derives value from internal and external data. 

See you there!

 
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