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Understanding Big Data

What’s big data?

Big data is the term used to describe the large volumes of data, both structured and unstructured, that inundates our professional and personal lives on a day-to-day basis.

This data is now coming from an ever increasing variety of sources and is being created at an unprecedented speed.

To give some idea of just how big big data is, it is estimated that by 2020, 1.7MB of data will be created every second for every person

To analyse and draw meaningful conclusions from the raw data requires a whole new set of tools, such as:

Artificial Intelligence

The simulation of human intelligence processes by machines

Machine Learning

A branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention

Natural language processing

The sub-field of AI that is focused on enabling computers to understand and process human languages

Data visualisation

The presentation of data in a pictorial or graphical format


Reports suggest opportunities around the use of big data are set to grow rapidly. According to IDC, the data analytics market will be worth around $203 billion by 2020

As the volume of data doubles every three years, those companies embracing technology are focusing on AI to upgrade their operations and strategies.

What has been the impact of big data across sectors?


In a rapidly changing investment world, investors are relying more and more on the fast-growing developments of big data to beat an index or find alpha. Big data could give managers an edge at a time when the lines between active and passive are already starting to blur.

A way to achieve this is through alternative data. This encapsulates everything from social media sentiment analysis and call transcripts to alternative public company data to web content.

Case study:
A fast-food chain revamp

A global fast-food chain with more than 14,000 US locations revamped its menu and remodelled store locations.

BlackRock’s Systematic Active Equities (SAE) models monitor the firm’s progress. Signals detecting foot traffic at restaurant locations began to reflect significantly rising consumer activity.

SAE’s macro models identify the firm has locations in US regions – economic activity is heating up. Algorithms monitoring the sentiment of sell-side analysts identify that they are becoming more positive. SAE start building a position in the company’s stock.

Months later, the company reports a better-than-expected quarterly earnings report, sending its stock higher.

Conclusion – The SAE model had gained a head start on the company’s prospects as a result of investment signals created by the SAE team’s investment and technology expertise.

Investment process in action