There is no doubt that our world excels at creating data, of all types and of all origins, in a context of datification, especially with the growth of connected objects.

Data is now the new oil for all companies, regardless of size.

• Although this statement needs to be nuanced, if only because data is an inexhaustible resource, it is a good illustration of the fact that a company must be able to extract the value of its internal and/or external data, or risk putting themselves in danger in a context where their competitors are doing this. 

• However, even if the data must be processed, the refining of this new black gold only makes sense because it answers a concrete business challenge. In other words, if we focus on data and not the business challenges that need addressing, how do we know we have the right data?

And it's to address these challenges that we need technologies like IoT, Big Data, and machine learning.

Internet of Things

The Internet of Things is the link between connected objects. It allows the connection of devices and the collection of data to ultimately enable its valuation.

The data generated has a wide range of uses, but is generally seen as a means of determining the health and status of objects, whether inanimate or alive.

The Internet of Things is moving into virtually every industry, opening up new opportunities and new security threats.

The opportunities include:

• Improving the customer, employee and citizen experience - creating smart spaces and places, digital workplaces and smart cities.

• Optimization of operations: In all sectors, companies anticipate failures before they occur, reduce unplanned downtime, improve the safety of workers and citizens through constant monitoring and a faster response time.

Big Data

Big Data refers to the processing of data sets that are so large that they become difficult to process with traditional database management tools.

The 3 Vs

• Volume: represents the total quantity of the data collected.

For example: transforming the terabytes of Tweets created daily into a thorough analysis on the opinions of a product.

• Velocity: refers to the speed at which data is generated, captured, and processed.

For example: analyzing in real time the large volume of events to identify potential frauds.

•  Variety: refers to the form of structured or unstructured data (text, sensor data, sound, video, files, logs).

For example: Using video feeds from surveillance cameras to manage points of interest

Sometimes veracity is also mentioned to qualify the accuracy and validity of the data.

Automatic learning

Artificial intelligence is a generic term that refers to the ability of systems to learn on their own, allowing them to respond autonomously to signals from the outside world.

As a user, we constantly resort to artificial intelligence without even noticing it. This is particularly the case when we ask Siri to do a search for us or when Amazon recommends products based on our purchase history.

Autonomous cars and automated recognition also rely on artificial intelligence.

But what is the link between all these technologies?

In our connected world, the Internet of Things is the data capture, Big Data is the fuel and artificial intelligence is the brain.

Devices connected to the Internet of Things generate large amounts of data that will all be collected.

This data is stored and processed.

Machine learning will then use these huge oceans of data to improve processes and increase system autonomy.

Where to start?

To take operations to another level, be more efficient and stand out, companies need the information generated from the data to be used to address their most pressing challenges.

From this perspective, the first step is to identify these challenges, the data required to respond to them and to evaluate the efforts and resources required.

Present and its partners can help you evaluate your options based on technologies in the market.

Feel free to reach out to start the conversation.