The Friday Five by Jefdata - The Road to 2030: A Decade Defined by Data

1. The ‘Best’ Data is ‘Boring’ Data?

2. Data Stewardship, Data Fiduciaries, Data Governance

3. Finding Your Match

4. What the New ‘Consensus’ Really Means

5. Newer, and More, Channels to Check

QUOTE OF THE WEEK

“We're not trying to build robots that replace anybody. We're just trying to make smart people even smarter. We're really just trying to augment the human intelligence so that they can be faster and smarter.”

— Vaughn Harvey, Jefferies, Managing Director and Head of CRM & Analytics, on this week’s Invisible Forces Podcast about the rise of technology within Finance

LISTEN OF THE WEEK

Invisible Forces Podcast: A Tech Revolution for Financial Services – Jefferies/Gimlet

READ OF THE WEEK

The Rise of the Financial Machines – The Economist

This week, the Friday Five by Jefdata focuses on the Jefferies Capital Intelligence team’s review of the Jefferies Data Conference in London. Shannon Murphy, Rich Carbone and team used the event as a catalyst to analyze what is top of mind for data buyers, providers and identify what emerging themes will shape the next decade. We have reformatted Shannon’s work into the Friday Five but offer its original formatting HERE.

1. The ‘Best’ Data is…‘Boring’ Data?

• Finding unique or proprietary data sets was top of mind for many over the last few years

• But since many of these data sets lack historical data – or are at risk for changes in their source’s terms of service, data leads are going back to basics

• Sourcing governmental or publicly available data is increasingly being collected to “have” in a library – in the event the long-tenored information is useful down the road

• These data sets are incorporated with existing models and investment processes and can help build a more long-term and holistic view

• Unique” data sets – which previously dominated this conversation – have been joined by data sets “hiding in plain sight”

2. Data Stewardship, Data Fiduciaries, Data Governance

• The era of data governance is here

• Policymakers are building frameworks for the obligations of collecting, aggregating and analyzing collected data – whether from humans, or Mother Nature

• What defines ‘privacy’ now? Aggregated data? Anonymized? Scrubbed of ‘identifying markers?’ Lacking location? Does “identity” even matter when you’re searching for patterns and signals?

• What does full disclosure mean? A ten page scroll of fine print? Five well-laid out bullet points?

• Regulation is coming. Pharma, energy and financial services are mature, highly regulated industries. The data and information sector is well on its way to being in more full view of policymakers.

3. Finding Your Match

• The discussion has moved from data sourcing to creating the right combination of next generation data sets

• Mosaics. Ensembles. Combinations. Complementary networks. There are nearly endless permutations of data inputs for models and investment theses. Investors are dedicating high level resources to finding the right combinations of these tools.

• The question isn’t: what does this data tell me? The question is: does this data provide a window into things we couldn’t access before, or does it answer a specific question I have?

• Data isn’t a needle in a haystack. It’s alignment and creating multi-dimensional views, like a Rubik’s cube

4. What the New ‘Consensus’ Really Means

• It Consensus has long been a cornerstone of investors trying to predict earnings or long-term financial performance

• What who – or what, exactly – constitutes this consensus has changed

• It’s not just Wall Street analysts anymore. Third party researchers and data firms produce their own predictions and forecasts, creating a larger and more diverse group of voices “creating consensus”

• Different regulatory regimes (whether MiFID II or others), have helped created an evolving research, data and reporting landscape that has grown the number of inputs creating “consensus”

5. Newer, and More, Channels to Check

• Sophisticated investors are trying to map new data sources for each dimension of a supply chain – from air, sea, space and…the internet

• It’s not just about channel checks along a few parts of the supply chain – it’s how do you create a multi-dimensional matrix to understand your investment in real time…and over time

• Historical data, real time data, point in time data – all funnel into models for manipulation across different investment cycles

What Does this Mean?

1. Multi-dimensionality. Linear is just one way to view things.

2. High level resources focus on marrying and combining different data sets to find the right mosaic.

3. Hiding in plain sight. What data sources are free, with long historical tenors, and are able to standardize?

4. Prepare for things to be…different. New frameworks are emerging to think about obligations related to data analysis. Think about where your data comes from – and is it ever in danger of being turned off?

5. Stay focused. The data landscape changes almost faster than any other – new topics, themes and sources emerge daily.