Thanks to new technology, increased data storage, and new data science techniques, business intelligence is morphing and growing by the day. More people have access to more data and more data is public, which allows even small businesses and individuals the opportunity to extract increasingly valuable business insights from their own internal data and data from all over the web.
There’s a lot of talk around data analysis trends in artificial intelligence (AI), machine learning, and augmented analytics, but it’s important to understand a little about how these theories, tools, and techniques work, and the best ways to implement them, before simply buying into the first product or service to shows up on a Google search.
So, read on for a bit of background on what you can expect from the top data analytics trends of 2022.
According to a recent symposium by famed IT research firm, Gartner, by the end of 2024 75% of businesses and organizations will largely be operating with underlying artificial intelligence platforms – with a 500% increase in streaming data and data analysis architectures.
One of the main driving forces behind this increase will be Natural Language Processing (NLP) – the ability of machines to understand, analyze, and communicate in “natural” human language. NLP is what powers smart devices, online searches, voice-to-text programs, and more. NLP trends are moving towards simplifying how humans interact with technology, to allow a much more unaffected, simplistic communication.
NLP is what allows us to perform Google searches how we naturally speak, like, “What are data analytics trends for 2021?” We no longer have to write our queries in a language style designed for machines; machines are now designed to simply understand our language.
Advances in NLP will improve data analytics and make it easier to simply ask a computer for the information you’re seeking. For example, you’ll be able to ask your analytics programs for much more specific data. Instead of just: “Show me sales data from 2020,” you’ll be able to say, “Show me sales data on females 18-24 from the Pacific Northwest from February to April of 2020.”
Explore machine learning platforms like MonkeyLearn, which allows you to perform techniques like topic classification, to automatically organize words and text by predetermined topics and aspects, or sentiment analysis, to automatically analyze text for “opinion polarity” (positive, negative, neutral, etc.).
AI is now within everyone’s grasp – machine learning and deep learning algorithms are improving, to the point that technology can finally get a handle on big data.
Machine learning and augmented analytics are helping analytics and business intelligence applications move away from static dashboards (that need to be engaged manually, in order to extract data) toward data stories that stream constantly, in real time. This will do away with the manual work required to request the data you need, as your data analysis results will be constantly at your fingertips.
And, as new tools are designed for non-coders, they will become even more user-friendly, so that you don’t need a computer science degree to decipher your data.
Blockchain allows for data security and transparency and is open source, so any business, large or small, can take advantage of it. Although it requires a fair amount of infrastructure at the present moment, its implementations are likely to grow exponentially, making it easier to take advantage of.
Aside from the security aspect, the use of smart contracts will allow businesses to streamline by saving time and money, and cutting down on data storage. Peer-to-peer contracts can eliminate red tape and allow businesses, their clients, and contractors to focus on their immediate pursuits.
Augmented analytics is the use of AI and machine learning techniques (like Natural Language Processing and image recognition) to automatically prepare data and anticipate a user’s needs by “augmenting” data analytics within data analysis, decision modeling, and BI platforms.
Augmented analytics will make it easier for humans to interact with data analysis programs and ensure that data is always at the ready, as the data will be automatically and constantly prepared for analysis from all relevant data sources.
Even today, with the help of AI and machine learning, it’s estimated that data scientists spend almost half of their time cleaning and preparing their data for analysis. With the rise in augmented analytics, there will be less and less of a need to manually clean and prep this data. These tasks will be automated, as new analytics processes and goals are set.
Augmented analytics will streamline and improve data management, creating a constant flow of analyses, working and preparing data 24/7 and in real time.
The “X” in this situation refers to all manner of possible data types and data analytics applications. As most companies are only starting to leverage machine learning, AI, and augmented analytics in their data analysis, it’s still a bit unclear what kinds of new data they’ll be processing.
When companies begin to collect, store, and analyze new forms of data, like text, images, video, audio, and more, they will uncover new analyses use cases and analytic techniques to unlock their data and drive decision intelligence. Furthermore, as consumer technology trends change, businesses will need to keep up with their customers and follow the related data and analytics.
InformationWeek estimates that cloud services will be connected to 90% of data and analytics advancement by 2022, and cloud-based AI will have increased five-fold from 2019 to 2023, meaning cloud services and cloud data storage are simply a definite in the future of data analytics.
Furthermore, the COVID-19 pandemic has only quickened the reality of working from home becoming the norm, so employees will need to access data from all across the globe. Blockchain and other security advancements make secure cloud storage more feasible, though hackers are ever-vigilant, so we’ll also be seeing new trends in cloud security for 2021.
As mentioned above, the future of data is largely public, constantly streaming, and stored in the cloud. That means it can be accessed from anywhere and is readily available for the taking. Furthermore, as machine learning and AI platforms get smarter, they get much faster at processing, so they can manage data in real time, basically right as it is actually being created.
Data storytelling techniques will give businesses access to up-to-the-second data results to be able to react to changing markets instantaneously. Text analysis tools from MonkeyLearn, for example, allow you to follow your brand sentiment on social media, product review sites, news reports, pretty much anywhere on the internet, to understand exactly what customers are saying about your brand at any given moment, so you can promote the positive and address the negative right away.
Because people are working from home, accessing data from the cloud, and companies are hiring more and more freelancers (that will require access to their data), SaaS has become a necessity. And, thankfully, the aforementioned advances in machine learning, NLP, and data analytics have made SaaS AI accessible to everyone. SaaS platforms are flexible, easy to use, easy to onboard, and much less expensive.
MonkeyLearn’s SaaS text analysis tools, for example, can be trained to the language, needs, and criteria of any business, often in just a few steps, so you always get the most out of your data. Building your own machine learning platform just doesn’t make sense anymore. It’s too expensive, takes too long, and with employees working from home and new freelancers onboarding daily, you just wouldn’t be able to access your data efficiently
SaaS data analytics platforms perform at the same speed and accuracy of even the most expensive custom-designed, in-house platforms, so you don’t need a whole team of developers and data scientists to harness AI and machine learning any longer.
The world of data analytics is exciting in 2022. The data universe is massive, and there are countless insights to be gathered for any business.
Take advantage of all the data available to your company with SaaS text analysis tools from MonkeyLearn. They’re easy to use and easy to implement. They integrate with tools you already use, and the great thing about SaaS is you only pay for what you use.
February 16th, 2021