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Can Predictive Data Revolutionize Capital Markets?

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Can Predictive Data Revolutionize Capital Markets?

Can Predictive Data Revolutionize Capital Markets?

For investors around the world, the information age presents a double-edged sword.

On one hand, all this data can be harnessed to make intelligent and timely investment decisions. However, with data growing at exponential rates, this also means that there is a lot of noise – and finding the right signal can be like looking for a needle in a haystack.

Today’s infographic from Mergalim shows how the power of predictive data analytics is growing, and using tools like AI and Bayesian inference to anticipate the outcome of events before they happen is more feasible and applicable than ever.

The Big Data Landscape

Before we get into predictive analytics, let’s look at what investors are up against in the first place.

Volume: The rate of data creation is accelerating so fast, that in 2017 there will be more data created than the previous 5,000 years combined. To put this in perspective: in the U.S. alone, approximately 2,657,000 GB of data is created every minute.

Variety: Data is not uniform, and there are many types of data. With data coming from many sources simultaneously, useful analysis can be very difficult.

Velocity: Especially in the markets, data needs to be monitored in real-time to be useful. Getting information too late could mean zero liquidity for a portfolio in some sort of crisis.

In other words, one missed data signal can cause irreparable harm to a portfolio in a situation where things go awry. Therefore, along with having a smart allocation of assets, it can be advantageous to also be one step ahead of the game to know what’s coming.

The Power of Predictive Data

Predictive analytics is defined as:

“The branch of advanced analytics used to make predictions about unknown future events. It uses techniques from data mining, statistics, modelling, machine learning, and artificial intelligence to make predictions about the future.”

This kind of predictive power is already widely used by companies like Amazon, which uses algorithms to sort through billions of data points, your buying history, and current trends to recommend to you the items that you are most likely to buy. In fact, experts estimate that 35% of Amazon’s revenue comes from this practice of anticipating exactly what you want.

Not surprisingly, Wall Street has jumped on this bandwagon too.

  • Goldman Sachs famously employs more engineers than Facebook, Twitter, or LinkedIn.
  • Citadel, a secretive hedge fund, calculates the outcomes of more than 500 “doomsday scenarios” per day to assess potential risk for the firm from geopolitical and other potential crises
  • Quantitative traders use streams of data and complex algorithms to create models of the market to find predictable patterns, and create machine-derived forecasts

But there is one possible limitation with these approaches. Markets are complex systems and need to be analyzed as such. After all, human decision makers can be irrational, events can be “triggered” by seemingly random factors, and traditional mathematical models can fall apart when markets get volatile.

A Multi-Disciplinary Approach?

One solution to this limitation may be to borrow ideas from the intelligence industry, which must anticipate irrational or “random” human actions before they happen.

As an example of this, intelligence agencies like the CIA have already been working to apply other disciplines to the techniques already widely used in predictive data:

Bayesian Inference: A formula in which the probability for the hypothesis is updated based on new data.

Behavioral Psychology: The science behind how responses to environmental stimuli shape people’s actions.

Complexity Theory: Born in the 1960s, the science around how complex systems work is now well established.

Fuzzy Cognitive Maps: A way of representing social scientific knowledge and modelling decision making in systems.

Historical Perspective: Applying knowledge of past events and subject matter experts to these other disciplinary fields.

Artificial Intelligence: Today’s deep learning now allows AI to instantly recognize and process all types of previously impenetrable data.

If predictive data analytics becomes the norm and its potential is fully realized, having data only in real-time may not be enough for many active market participants. In turn, this could set up a very different landscape than exists today.

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Can Data Centers Be Sources of Sustainable Heat?

Data centers produce a staggering amount of heat, but what if instead of treating it as waste, we could harness it instead?

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Diagram showing how waste heat from data centers could be recaptured and recycled to provide sustainable heat in residential and commercial settings.

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The following content is sponsored by HIVE Digital

Can Data Centers Be Sources of Sustainable Heat?

Data centers support the modern technologies on which we rely, but also generate incredible amounts of heat as waste. 

And since computers tend to be very sensitive to heat, operators go to great lengths (and expense) to get rid of it, even relocating to countries with lower year-round average temperatures. But what if instead of letting all that heat disappear into thin air, we could harness it instead?

In this visualization, we’ve teamed up with HIVE Digital to see how data centers are evolving to recapture and recycle that energy.

How Much Heat Does a Data Center Produce?

To get an idea how much heat we’re talking about, let’s imagine a mid-sized cryptocurrency operation with 1,000 of the most energy-efficient mining rigs on the market today, the Antminer S21 Hydro. One of these rigs needs 5,360 watts of power, which over a year adds up to 47 MWh.

Multiply that by 1,000 and you end up with over 160 billion BTU, which is enough energy to heat over 4,600 U.S. homes for a year, or if it happens to be Oscar season, enough heat to pop 463,803 metric tons of popcorn. Less if you want melted butter on it. 

How Waste Heat Recycling Works?

At a high level, waste heat is recaptured and transferred via heat exchangers to district heating networks, for example, where it can be used to provide sustainable heat. Cool air is then returned to the data center and the cycle begins again.

Liquid cooling is by far the most efficient means of recapturing and transporting heat, since water can hold roughly four times as much heat as air.

Data centers around the world are already recycling their waste heat to farm trout in Norway, heat research facilities in the U.S., and to heat swimming pools in France.

A Greener Future for Data Centers?

Waste heat recycling has so far been voluntary, led by operators looking to put their operations on a more sustainable footing, but new regulations could change that. 

Amsterdam and Haarlemmermeer in the Netherlands require all new data centers to explore recycling their waste heat. In Norway, they require it for all new data centers above 2 MW, while Denmark has taken a carrot approach, and developed tax cuts and financial incentives. And in late 2023, the EU Energy Efficiency Directive came into force, which will require data centers to recycle waste heat, or show that recovery is technically or economically infeasible. 

With Europe leading the way, could North America be very far behind?

HIVE Digital Provides Sustainable Heat

HIVE Digital is already recycling waste heat from its data center operations in Canada and Sweden. 

Their 30 MW data center in Lachute, Québec, is heating a 200,000 sq. ft. factory, while their 32 MW data center in Boden, Sweden, is heating a 90,000 sq. ft. greenhouse, helping to provide sustainably grown local produce, just one degree short of the Arctic Circle.

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Learn how HIVE Digital is helping to meet the demands of emerging technologies like AI, sustainably.

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