Top 4 trends in big data and analytics right for 2021

The digital transformation we see around us today is self-driving cars, lifelike robots, and autonomous, attractive, self-driving delivery drones.

None of this would have been possible, however, without data – the oil of the fourth industry revolution – and the analytical technology we built to allow us to interpret and understand it.

Big Data is a term used to describe technology and the practice of working with data that is not only large in size but also fast and comes in many forms. For every Elon Musk who has a self-driving car for sale, or Jeff Bezos with a cashless convenience store, Big Data is a solemn activity and an army of smart data scientists who have realized a vision.

The term Big Data itself may not be as ubiquitous as it was a few years ago, and that’s just because many of the concepts it encompasses are being incorporated into the the world around us. But just because we’ve heard about it for a while, though, doesn’t mean it’s old news. The fact is that even most organizations struggle to get value from much of the data they receive. As a business practice, it is still very much in its infancy.

So here’s my look at some of the key trends that will impact how data and analytics are used for work, play, and everything in between, this year and soon.

AI drives deeper insight and increasingly advanced automation

Artificial intelligence (AI) has been a game changer for analytics. With the large amount of structured and unstructured data created by companies and their customers, even automated manual forms of analysis cannot scratch at the surface of what is available.

The simplest way to think about AI, as it is used today, is devices – computers and software – that are capable of learning for themselves. For a simple example, let’s take a look at a problem we might use a computer to solve today. Which of our customers is most valuable to us?

If we only have traditional, unlearned computing available, we may be able to take a stable by creating a database showing us which messengers will spend the most money. But what if a new customer who costs $ 100 in their first business with us shows up? Are they more valuable than a customer who has spent $ 10 a month for the past year? To understand that we need a lot more data, such as the average life value of the customer, and personal data about the customer himself such as age, spending habits or income level may also be useful!

Interpreting, understanding, and drawing insights from all of these databases is a much more complex task. AI is useful here because it can try to interpret all the data together and predict what the life value of a customer might be based on all we know – whether we understand the connections ourselves or not. An important element of this is that it does not always come up with “right” or “wrong” answers – it gives a range of similarities and then updates its results according to how accurate the those predictions.

Rich new ways to analyze and interpret data

Data visibility is the “last mile” of the analysis process before we take action based on our findings. Traditionally, communication between devices and humans is done visually, taking place in graphs, charts, and charts that highlight key findings and help us determine what the data is about. a suggestion that needs to be made.

The problem here is that not everyone is very good at seeing a potentially valuable perspective in a mass of statistics. As it becomes increasingly important that everyone within an organization has the power to work on a data-based vision, new ways of communicating these decisions are constantly changing.

The use of human language is one area where significant progress has been made. Analytical tools that allow us to ask data questions and get answers in clear, human language greatly increase access to data and improve overall data capabilities in the organization. This area of ​​technology is called natural language processing (NLP).

Another is new technologies that allow us to gain a better visual insight and understanding of data by immersing ourselves within it. Augmented reality (XR) – a term that includes virtual reality (VR) and augmented reality (AR) is clearly seen leading innovation here. VR can be used to create new types of images that allow us to provide richer meaning from data, while AR can show us exactly how the results of data analysis affect the world in real time. . For example, a mechanic trying to diagnose a problem with a car may be able to check the engine with AR glasses and predict which parts are likely to be difficult and which may need to be replaced. Soon, we should expect to see new ways to view or communicate data, expanding access to analytics and insights.

Hybrid clouds and the edge

Cloud computing is another technological advancement that has greatly influenced the way Big Data analytics is conducted. The ability to access large data repositories and work on real-time information without the need for expensive real-time infrastructure has contributed to the rise in apps and startups that offer data-based services on application. But relying entirely on public cloud providers is not the best model for all businesses, and when you trust your entire data operations to third parties, there are inevitably concerns. on security and governance.

Many companies are now leveraging themselves towards hybrid cloud systems, where some information is hosted on Amazon Web Service, Microsoft Azure, or Google Cloud servers, and other, perhaps more personal, data. or sensitive, still inside the garden with property walls. Cloud providers are increasingly on board with this move, offering “cloud-on-property” solutions that can deliver the rich features and strength of public clouds while enabling -retain data holders their data.

Edge computing is another powerful move that will impact the impact of Big Data and analytics on our lives over the next year. This basically means devices that are built to process data where it is collected, rather than sending it to the cloud for storage and analysis. Just some data needs to be activated too quickly to reverse risk – a good example here is the data collected from sensors on autonomous vehicles. In other cases, users can be assured that they have an extra level of privacy when comments can be obtained from their devices without having to send data to any third party. For example, the Now Playing feature on Google ‘s new Android phones regularly scans the environment for music so that it tells us the names of songs that are playing in the supermarket or the movies we’re watching. This would not be possible with a cloud-based solution alone as users would reject the idea of ​​sending a steady 24/7 stream of their audio environment to Google.

Enhanced DataOps

DataOps is an approach and application that borrows from the DevOps framework that is often used in software development. While those in DevOps roles manage ongoing technology processes about service delivery, DataOps is concerned with end-to-end data streaming through an organization. In particular, this means removing barriers that limit the usability or accessibility of data and the use of third-party “as a service” data tools.

No formal training is required to operate in DataOps. The evolution of the profession makes it a great opportunity for anyone with an IT experience or interest who wants to work on the most exciting and innovative projects, which are often data projects. We also see the growing popularity of “DataOps-as-a-service” vendors, offering end-to-end management of data processes and piping on tap and pay as you go. This will continue to reduce the barriers to entry for small and start-up groups with good ideas for new data-driven services but without access to the infrastructure needed to implement them. .