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5 Best Data Analytics Trends and Predictions to Watch in 2022

5 Best Data Analytics Trends and Predictions to Watch in 2022. Over the years, data analytics has pervaded almost all sectors. Startups, SMEs, and large organizations are increasingly using analytics to cut down costs, add muscle to their marketing campaigns, enhance customer experience with personalization, and boost business overall.

Today, data analytics is faster, more accurate, even more cost-effective, and at least some of the process is fully automated thanks to new technologies like artificial intelligence and blockchain. This then begs the question – Where are data analytics trends headed in 2022? What are the predictions in data analytics trends for the new year?

Our team of data scientists has studied the patterns, and this is what they forecast for the new year (in no order of priority):

1. Cloud Analytics Will Get Even More Traction

Cloud services are gaining popularity in organizations all over the world. Cloud analytics is the key to quickly and accurately delivering the desired results to users. In fact, cloud technologies are becoming the new normal, as seen in 2021.

So why is cloud analytics considered to be the future of cloud computing? 

Here are the main reasons:

– It reduces the cost of computing 

– It provides various computing resources for data analysis 

– It requires less power 

– It is easily scalable 

– It helps in user experience 

Cloud analytics can help enterprises reduce the cost of managing their IT infrastructure. It helps them identify what data is useful and what is not useful. The real-time intelligence delivered to the company can help them improve the user experience of their application through optimizing the performance and increasing the RoI (Return on Investment).

Here are some more reasons why cloud analytics is the latest buzzword in the analytics industry. Cloud analytics build on the typical use of cloud-based applications to provide faster, more efficient, and cheaper solutions for data analysis. Another reason is that cloud analytics solve a significant problem in the data management industry that is not being solved.

Organizations around the world are struggling to manage the vast amount of data that exists in their environments. This is a lot harder than it sounds, especially for those companies who are not making enough money to go down and hire armies of data scientists or data analysts.

Cloud also offers enterprise scalability for faster time to value than traditional on-premises analytic platforms. The ability to run analytical models on fast and highly available servers across the globe, making analytics processing at the edge possible.

Cloud computing providers such as AWS, etc continuously update their infrastructure with the latest technology, and users benefit from those updates immediately without having to do any maintenance themselves.

It takes extensive amounts of IT professionals’ time to build and maintain on-premises infrastructures, time that could otherwise be spent on projects that improve the business. That’s what makes the cloud an attractive proposition today. 

2. Unstructured Data Shall Share The Podium With Structured Data 

Like cloud analytics, this one is a given. All this while, businesses, because of the race to attain as much data about their consumers and clients as possible, focused primarily on structured data. It was easy to collect and easy to compartmentalize; everything was neat and tidy. Much of the time, though, what was ignored was a valuable piece of information loosely dubbed as “unstructured data”.

Here’s what you need to know about both types of data and why businesses are shifting toward unstructured data:

Structured data is exactly what it sounds like: data that has been organized in a way that makes it easier to use. Examples of structured data can include address information, contact information, product or service descriptions, and content from websites. It is typically organized in tables and contains standard tags to identify different fields within the table. It can also be text, numbers, or other structured formats. Because of its form and shape, it’s always easier to key in structured data into databases.

Unstructured data obviously has no form at all, and it does not follow a set system of organization. Examples of unstructured data include pictures, videos, audio recordings, and even the written word. This can contain information that is inconsistent, lacks standard tags, or does not follow a specific pattern. With the exploding volume of digital data in the world today, it’s easy to forget that all data was once unstructured. 

There are many ways to build a data model for structured or unstructured data. No matter which one you choose, the goal is to make your content easy to work with and be able to extract information that can be analyzed in various ways.

Structured data is any data that’s accessible to be organized into tables, list boxes, lists, and other types of data structures. This is true for all types of data, whether it’s XML, HTML, XHTML, or CSV (comma-separated values). It’s simply data that can be entered into fields in a database. The one thing that’s different is the type of data. For example, image data and sound data are both structured, but they’re not the same.

Because a lot of data that flows in today is unstructured, it does not end up in the databases, leaving companies bereft of their benefits. But with new privacy laws coming in, and Google wanting to phase out 3rd-party cookies, businesses are now being compelled to look at more and more unstructured data. By 2022, it will be crucial to develop these skills, which require finding out about new unstructured data analytics capabilities as well as learning unstructured data management techniques.

3. Data Fabric Will Ease Data Management

It was in the middle of 2020 that Express Analytics published an article on the growing importance of data fabric in data analytics. The cloud industry is constantly evolving, but when you look at data fabric, it’s nearly impossible to believe that a concept like this was once just theoretical. 

What is a Data Fabric?

It’s the term used to describe a bridge between data centers that enables faster connectivity between networks and data centers. Data fabric is a setup that helps organizations make better use of the data they have. The benefits of a data fabric include self-service data consumption, embedded governance, and automated data integration. Organizations are able to gain faster insights from their data with a data fabric. Additionally, it reduces data inconsistency and compliance risks and improves data quality.

Data fabric describes the physical topology between servers and other hardware components that processes and stores data. The idea is that data can be moved between different components such as physical servers, virtual machines, and storage accounts. Like we said earlier, it’s a concept that’s been around for a while, and it’s still considered cutting edge.

A hybrid data fabric also exists that is used to connect two or more physical networks together. This type of data fabric provides a logical point of connectivity that is used to switch traffic between the different physical networks. 

How Data Fabric Relates to Modern Enterprise Architecture

The physical world is one of the biggest challenges with software-defined networking, and much of that difficulty comes from the need to connect switches together in a way that provides network connectivity and effectively routes traffic. 

Using a data fabric reduces the amount of management needed, and can be used to centrally manage resources and settings across multiple physical and virtual resources. 

Think about a large hypothetical fabric spanning multiple places, including the cloud, that connects all types of structured and unstructured data along with methods to access and analyze that data. Data fabrics are unlike real-world fabrics because they have no fixed shape, are scalable, and are built with flexibility that accounts for data processing, management, and storage. Teams internal and external to the enterprise can use it for a variety of analytical and operational needs.

Organizations can easily adapt their infrastructure based on changing technology needs by using data fabric. A consolidated and unified data management framework is made possible by data fabric, which makes it easy to connect various infrastructure endpoints. Organizers don’t have to worry about the precise location of data.

4. Data Mesh Provides New Architecture Alternative

Businesses today have several choices when it comes to building a data architecture for their needs. Thanks to newer technology, they now can choose, for example, between data fabrics and data meshes.

The data mesh approach for managing analytical data is based on a modern, distributed architecture. Because of data mesh, end users can access and query data wherever it lives without first having to transport it to a data lake or data warehouse. Which means, doing it right on the edge.

In the decentralized data mesh model, domain-specific teams manage, own, and serve the data as a product. With data mesh, the main goal is to eliminate problems related to data availability and accessibility at scale. A data mesh allows businesses to get access to, analyze, and operationalize insights from any data source, in any location, without involving the experts.

By definition, data meshes make data accessible, readily available, discoverable, secure, and interoperable. Having faster access to query data directly translates to faster time to value without needing to transport data.

On the surface, there seem to be similarities between data fabric and data mesh. But without the two are fundamentally different. 

Data mesh is a new software approach that, instead of central databases, makes the architecture simple using what is called point-to-point connections. This means a business saves on hardware costs. What it also means is that data is easily available, also easily found, and interoperable with the apps that need access to such data. 

Data mesh is a service that can help us to sync data across different devices. Basically, it synchronizes data between different apps, databases, and local devices that are linked using the same protocol (HTTP or HTTPS) via a push model. The service helps to synchronize different devices that are connected to the same account. This is especially useful when you want to keep your data current on all of your devices. It can be used for software updates, to keep files synchronized, or move them from one location to another. 

Implementing a single data fabric covering many data sources, a business can provide a degree of unified management for all – data scientists, data stewards, and end-users. One must remember here that the management of the storage remains distributed, not the actual storage.

To sum up, simply put, a data mesh helps distributed teams to manage data as they want, with overlapping governance guidelines. A mesh also forces management to think of a new approach to data management architectures. 

A data fabric, on the other hand, aims at building a single layer with all that distributed data.

5. Automated Machine Learning Will Take Over Tedious Tasks 

Advances in artificial intelligence technologies now mean the creation of automated machine learning (ML) software that will aid data analysts in performing an array of tasks. Many of these systems have proved to be very good at performing the tasks they were built for and in a variety of fields such as bioinformatics, imaging, or natural language processing (NLP).

Automatic machine learning, or AutoML, is for the purpose of automating the tedious, iterative process of developing models. With it, data scientists, analysts, and developers can build ML models at scale, with efficiency, and with consistent quality. One of the major reasons for the growth of this tech is that it will significantly reduce labor costs. Automation has the potential to reduce the time and effort required to build models and optimize them, saving both time and money. 

ML has been widely implemented in real-world applications. Some of the first applications of ML, including the development of rule-based learning and prediction and verification in insurance, have evolved into systems that are used in real life. ML can be applied to a wide range of areas, such as literature research and information retrieval, which can have direct financial implications. ML and AI algorithms have been used in the development of virtual assistants.

The development of ML models has increased the possibilities for complex decision-making in finance and other areas, including financial arbitrage.

With the advent of Auto ML tools, common tasks like hyper-parameter tuning, model selection, experiment design, and API support will get automated. Developers and data scientists will then be able to focus on what matters most: their models and applications, rather than wasting time and energy managing low-level infrastructure. 

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