2021 has been the year with the most intriguing progressions in the field of BI and data analytics so far. Of course, that’s only natural since we’re diving into new and unexplored territories that at one point may have even been considered impossible. At the end of the day, the goal has always been to equip businesses with tailored solutions consisting of various technological approaches that maximize the use of data.
Well, with the tempo the industry is moving in, we should expect a lot of great things in 2022 as well. And, as per tradition, we like to discuss the top trends for the following year, and instead of keeping our observations to ourselves, we thought we’d share them with all of our dear readers.
Data Fabric and Hybrid Cloud
As a design concept, data fabric has been around for the past few years, inspiring vendors to consider some if not all of its components. If you’re unfamiliar with the term, it’s a concept that wants to ensure data producers and data consumers are connected through a dynamic and meta-driven architecture. So, what might that look like as a product, you may ask? Well, Qlik, clearly inspired by the concept, released their latest addition to Qlik Sense late this year, and it might just be the interpretation that sends this trend off to the races.
Qlik Forts lets you connect all your data regardless of its whereabouts in the Cloud. So, if you’re a business that has invested in various private and public cloud platforms, you can easily link them all up and take full advantage of ALL your data. Also, because all data used by Qlik Forts stays local to it, you can easily satisfy any location requirements and analyze the data from your branches across the globe. Without a doubt, this feature will be prominent in 2022, as we’ll likely see more companies get a taste of that sweet data-driven decision-making on a whole new level in the world of analytics.
Automation and Machine Learning
We’re getting to the point where most of the commonly used machine learning algorithms are ironed out and carry out their intended purpose each day. Well, what do people do when they get to a point where they’ve pretty much gotten the hang of something? Correct! They try to make it smaller, faster, and generally more efficient, as we can see from this survey paper.
This, of course, isn’t to say that there’s nothing left to discover or innovate on in the machine learning and automation fields, quite the opposite, in fact. There’s probably a whole sub-section that’s waiting to be discovered. But next year, we’re likely to see vendors offer platforms and tools that can be used by anyone to automate any tasks they wish, with a big emphasis on the parts “used by anyone”. We expect to see data that’s prepared and cleaner than ever in more enterprises than ever before and companies like UiPath and Power Automate are already setting up the groundwork for this approach.
Well, to achieve the scaling challenge we mentioned in the previous trend, we need to start by shifting the goal of the AI and machine learning tools. So, instead of evaluating massive amounts of data, we should stick to processing only the most vital data, or in other words, we need to move from Big Data to Small Data. The term itself has been circling the internet for a while now, and before you light the torches and bring out the pitchforks, we don’t mean that we should abandon Big Data for good since it’ll always be needed. Rather, if we’re to expect more streamlined, fast, and bandwidth-sparing innovations to occur, we’ll need to consider this “small data” paradigm somehow.
We’re already observing marks of progress towards this approach in self-driving cars, where they need fast response times to react to a potential traffic collision or even just functioning in hills and forests where bandwidth is scarce. So, we should expect to see more ideas surfacing in 2022 that rely on Small Data to function in the efficient manner we’re always chasing after. Feels like we’re really getting closer towards that AI-focused future sci-fi movies have always showcased, doesn’t it?
From SaaS to iPaaS
Last year we put software as a service (SaaS) on the fourth spot in our list. Well, we still stand by that decision, so we’ll be doing it again this year, only with a little twist. SaaS has been around for a number of years now and has helped many companies dip their toes in the Cloud (what a concept?!). However, at this point in time, it’s all “been there done that,” and even though we can observe plenty of benefits from that trend adoption, 2022 isn’t going to bring anything revolutionary in that regard.
This is where the integration platform as a service (iPaaS) comes in. Obviously, it’s not a new concept, but it also hasn’t been thrown about a lot as a buzzword either. Could 2022 be the year where we see a shift there? Well, we believe so! After all, businesses just want to avoid data loss and disjointed information between departments and platforms, so this seems like the next logical solution. We believe we’re due a big breakthrough in this area, and we’ll be keeping an eye out, and so should you.
Planning and Forecasting
Predictive analytics is on the rise again. No surprise there, since all the way back in 2020 the market for predictive and prescriptive analytics was expected to grow by 20% over the span of five years. But it’s not just that, the ready availability of the technology also plays a part, since it was limited not so long ago only to AI and ML aficionados. Well, now that we have some affordable options on the market in both BI platforms, like Qlik or Anaplan, and standalone cloud services like Amazon Forecast, you can easily integrate predictive analytics in your CRM, ERP, etc.
Just like the other approaches and technologies listed in this article, this one will benefit from 2022’s surge for efficiency and approachability. It does tie back to ML and AI still, but with that area set to explode with innovations in the upcoming years, we’re very interested to see how much planning and forecasting will be improved. One thing’s for certain, either prediction accuracy will be honed to an edge, or the approachability factor will be iterated upon, thus opening the door for many smaller businesses and startups.
So, we’re at the end of our list now, and what better way to end it than with a heads-up. In this article, we talked a lot about the technologies and trends that we can expect to boom next year. However, all that progress and innovation won’t mean much if we don’t have the knowledge to pilot and utilize it. Obviously, the technology will give us the analytics we want to see, but how will we know what we want to see without the right understanding? Furthermore, how will we know that we’re getting the most out of these technologies and our data?
Well, the answer is sitting at the sixth spot on our trends list. If companies wish to take full advantage of the advancements to come, they’ll seriously need to consider training up their staff, especially the end-users, who are often required to make data-based decisions. So, we put data literacy here at the end because there probably won’t be much action to be expected in the following year; however, even the smallest progress towards an improved level of data understanding will be a big step for the future of data analytics.