As you all know, it’s a common practice to presume what new or on-going analytics and data trends will pique the industry’s interest in the following year. Well, even though 2020 was a rough ride filled with lots of experiences and emotions, we’re almost past it. And with 2021 quickly approaching, many dedicated blogs, vendors, and news outlets want to share their opinions for the upcoming year. Since we actively follow some of the BI and consultancy leaders on the market, we read quite a few takes, like Qlik’s and Gartner’s. However, our curiosity peaked, and we went the extra mile to cross-check some of these trends with our experience and back some of them up with BI Survey wherever possible. All this research sparked an interesting discourse within B EYE, and we thought we’d share our views on the subject with you all.
Analytics and Data Trends for 2021
There are lots of trends that are being discussed right now. Some may, in fact, be very relevant for 2021, and others less so. But we thought we’d just pick the top 7 on our list and talk about them, based on our experience with various customers and toolsets. Some ended up higher on our list due to this year’s big event – the COVID crisis. Others are continuously advancing trends that are hot right now and will probably be trending for at least a few years to come. So, let’s see what 2021 has in store for us, shall we?
1. Advanced Analytics
For those who’ve been following our posts, this first one probably won’t come as a surprise. Some vendors on the market have already started implementing more advanced features and utilizing machine learning algorithms within their products. Of course, be that as it may, this trend is still in its infancy. Well, we believe that in 2021 we might start seeing a lot more vendors implement advanced analytics capabilities or upgrading the ones they currently have with more features and options. Worry not, though, since there are plenty of other resources like Python or R libraries that you can use, which can help you to implement your own advanced analytics in the meantime.
At least one thing will become apparent through all of this – the lack of advanced analytics data literacy. We suspect we have yet to begin crossing that bridge. Loads of people in the industry still don’t fully perceive how machine learning algorithms can help enhance their solutions. This is understandable when you don’t know how to correctly interpret the results and, most of all, how to trust them. At the end of the day, we’ll need to get more and more people thinking about these things and increasing their data literacy levels since technology is quickly getting ahead of the curve, and it’s becoming harder for businesses to catch up. For now, these advanced analytics are being developed in silos, but they’ll need to be released sooner or later, and we believe that the sooner, the better.
2. Data Quality
Our number two trend is actually quite popular, going so far as to top many other trend lists for 2021. It’s no secret that companies have switched from using a few predefined platforms to using a few core platforms and several other best-in-class platforms for various other activities. Naturally, this generates loads of data, all with different granularity levels. In lots of cases, the data isn’t even synced correctly, so users end up having the same clients’ data being dealt with in different ways on each platform. This, combined with other irregularities, can lead to an overload of many types of data – public, non-public, bought, generated, etc. If there is one thing we can be sure of, it’s that we’ll be seeing more and more of these problems as we advance.
We believe machine learning can provide a suitable solution here. For example, an algorithm that takes all of your collected data and makes it business-ready. This can move the needle and get us to actionable analytics faster. From our experience, data warehouses aren’t going to cut it in the future, so still thinking about fitting them in our future setup is time spent in the wrong direction. Going forward, we need to rely on smart strategies, and consequently, smart tools to make them happen.
Finishing off our top 3 is a recurring trend. We’ve seen this one before, and we’ll continue seeing it for a while, mainly because we’re far away from achieving the desired results here. Most likely, when people become as familiar with most BI tools as they are with Excel. However, for now, the three biggest vendors on the market – Qlik, Power BI, and Tableau – aren’t at a point where one is visibly better than the others. Well, the way is clear, and we already see these, and other smaller companies focus more on their tools, providing excellent self-service options.
Going back to our top trend again, future development in the self-service area will likely include machine learning augments. Currently, there are plenty of features available that will help us go through the path from raw data to analytics quicker, like insight bots and other forms of AI assistance. However, even with all of that aid, a user must still be data literate first and foremost. Many companies have already realized this, but the number is still low. There are plenty of possible solutions, though, and we hope progress here is just a matter of time.
Similar to self-service, this will continue to be a top trend since the leading vendors on the market continue to invest strongly in their SaaS capabilities. It allows users to experiment with different tools easily and not just stick with one main vendor. Furthermore, various departments are able to experiment with all of the tools and find a preferred one. So, in a single company, you could have several different products used for all kinds of things. The sheer freedom SaaS allows makes it easier to develop proofs-of-concept apps and enables employees to focus on what really matters – data logic, utilization, conclusions, etc. As you can see, it’s no wonder this trend has been focused on for such a long time.
Of course, there is always the risk that companies could end up making unintended silos by doing the same thing, just with different tools. For example, remaking a cool dashboard you already have in Qlik, in Tableau. SaaS is generally beneficial all around; just don’t let the freedom turn everything into chaos.
2021 might be the first year we start seeing a trend increase for this topic. The current toolsets just don’t allow for much collaboration going from data to dashboard, or even when analyzing. So, most users must collude outside of the toolset itself. For example, by using chat rooms or group meetings. As it stands right now, one person goes and transforms and explores the data, gets a few “Eureka” moments, and presents his findings to everyone else. This needs to change, especially now when we see a shift from office work to home office. As a small group, we could make a chat room or call and be fine, however, add a few more people, and it quickly becomes chaotic.
In any case, as the pandemic made us utilize and focus on tools that allow us to meet up and collaborate from home, it will also move this trend forward in the following years. The approach could be something similar to Google Docs, or it could be something entirely different. We don’t know yet. But we do believe it’ll be an area of focus and interest in 2021.
6. Data Discovery
This has always been an important topic, and this likely won’t change in 2021. Software vendors will continue to invest in their tools, so they can provide even easier data discovery. And the same goes for BI companies. Data discovery needs to allow users to jump between possible questions – to continually discover new insights and link them with new emerging questions. There’s still a long way to go for tools on the market to allow us this freedom, but we’re getting there.
However, one thing that isn’t possible with the standard toolset is sifting through all the available data. And again, we believe that this may require the use of machine learning algorithms. This would help us ask dashboards why a KPI is the way it is and what other unexpected interactions we could be missing.
7. Privacy and Ethics
This topic has always been present, but it’s bumping up in the trends since the 2020’s epidemic hit. Currently, we’ve got so much public data going around, and everyone’s interested in trying to analyze various important subjects like population health, patient behavior, etc. Thus, companies now are far more interested in getting data on a personal level and not the usual group of opinions acquired from surveys.
This, of course, brings privacy concerns to the table. First of all, companies need to make sure that the personalized data that they’re gathering is in line with the country’s law. Second, companies utilizing algorithms to make decisions should be careful not to let the “machine learning bias” set in. For example, a health or house insurance’s approval process should give you a score based on all factors and circumstances, not just where you live in the city or what ethnicity or religion you are.
Sounds pretty straight-forward, right? However, the complexity level of such algorithms is still not fully there yet. So far, the solution has been to provide certain data to specific algorithms and make a score based on all their outcomes, but that break down can miss key points and end up over or undershooting the actual number. We believe that even though the technology isn’t quite there yet, a potential temporary solution could be to use algorithms to notify you when you’re about to make a biased move by analyzing historical data.
Well, it turns out that our top trend – advanced analytics – could help us make progress in many of the other trending topics like data quality, collaboration, and even data discovery. As we stated, the technology might not be there yet, but it’s moving rapidly to that point. All we need to do is try to keep up with it since those who first master it will have a key advantage and will most likely help dictate the direction of data and business intelligence in the coming years. So, do you agree with our ranking? What are the top trends you’re most looking forward to developing?
Author: Stiliyan Neychev