But the future of data analytics is ripe. With the advancement of artificial intelligence software, machine learning, statistical modeling, and other data science disciplines, data analytics will be more predictive and actionable rather than retrospective.
To support this claim, we asked a variety of industry experts who work with data for their thoughts on the future of data analytics. Below are 10 trends to expect in 2020 and beyond.
Data analytics trends
From trends we can see now to ones that’ll take shape in the near future, it’s clear that data analytics is moving in a different direction than we know today.
Starting off our list of trends are marketers and salespeople relying too heavily on the insight given to them by analytic tools instead of seeking to dive deeper. Bsharah believes this will soon change.
“AI and machine learning have made an impact on just about every industry, but it’s poised to really shake up how marketers and sales teams do their day-to-day jobs. However, while the potential around AI is exciting, we still need to approach these tools with a ‘trust but verify’ mentality, as they’re still prone to error.
Marketers and sellers can’t blindly follow insights and recommendations from these tools. Instead, they need to think critically about the information they’ve received, and if something seems off, they need to dig a little further. Doing this can also improve how the algorithms perform, as they can learn from human guidance.
In addition to sniffing out wonky insights, marketers should continually look to build out their data sources. The more data the tool has to pull from, the more accurate it’s likely to be.”
2. Cleaner data architecture
Sam Underwood – VP of Business Strategy at Futurety
Speaking of having trust in your analytics, we know from the data analysis process that clean data makes for accurate analyses. Here’s what Underwood has to say about that.
“We see 2019 and 2020 as being the years when organizations that have taken the time to clean and update their underlying data architecture will begin to really leverage AI and machine learning, leaving many of their competitors behind and having to play catch up to match their newfound advantage.”
Having a good starting point for data analysis transcends industry and business size.
3. More accessible AI for small businesses
Yaniv Masjedi – Chief Marketing Officer at Nextiva
Next, Masjedi believes more small and mid-market businesses will make their way onto the AI scene to leverage more advanced analytics.
“AI is going to bring new opportunities for SMB owners and marketing managers at mid-sized companies to cost out and assess ROI across an omnichannel marketing strategy. Omnichannel marketing is clearly the way forward, but often today it feels like only enterprise companies have the resources to pay for the labor power to crunch that much data. To date, big players like Amazon have leveraged their considerable clout to dominate in this manner.
AI is going to open up cost-effective and high ROI omnichannel marketing to SMBs, too. Rather than rely on an extensive marketing team, SMB owners and marketing managers at mid-sized companies will be able to pay for SaaS solutions which capture data across all marketing channels, analyze the numbers, and prioritize spending across PPC, SEO, content marketing, radio/TV airtime, etc.”
Widely accessible advanced technologies aren’t just good for competition, it opens the door for more innovation.
Das dives further into Masjedi’s point of more approachable technologies in what he refers to as the “consumerization of data analytics.”
“The trend now will be as to how everyone, from consumers to small mom-and-shops would be leveraging analytics in their everyday lives. In fact, the impact of such a mass adoption of data analytics would fundamentally change humankind.
For example, small mom-and-pop stores would leverage sophisticated data analytics to perform historical, real-time and predictive analytics on how best to run their stores. They would see in a simplified dashboard as to how their current inventory levels are catching up to the predicted demands for the day and near future.
They would then buy the right amount of raw materials needed to run their shop at the right time – thereby fulfilling the holy grail of Just-in-Time (JIT) Supply Chain computing, which has mainly benefitted large companies like DELL till now.”
Smarter insights backed by data will no longer be exclusively available to large enterprises. Different types of data analytics will be more commonplace.
There’s no shortage of data today, and accessing big data sets will be made easier with more data democracy.
“We're seeing the public domain making more use of analytics and making data public so that citizen analysts can get involved and data is used to shape public policy. Tools that are less expensive - like add-ons for widely-used platforms like Excel - mean that smaller enterprises can harness some of the power of big data in ways that would have been much more expensive in the past.
There are also sophisticated open-source tools like R that are widely available to organizations that would not find it cost effective to buy an expensive statistics package, although they have to be able to use them.
Data Democracy means empowering a wide range of employees and citizens to use data and gain insights from it, which will bring these skills to organizations that might have been priced out of analytics ten years ago. It might not be a case of new industries using analytics as much as the case that all industries will make more use of analytics and expect that to be part of the basic skill set of everyone in the organization.”
Brown knows businesses sit atop mountains of unanalyzed data. Real-time data will only continue to add to the mix, and an analytic solution will need to match this speed.
“Real-time data analytics has the potential to transform the way that professional services organizations operate. Instead of manually cobbling together best guesses based on past events, professional services organizations can use real-time analytics to deliver insight into what is happening right now and then start to predict future performance.
This in-the-moment decision-making is especially important for managing resources, maintaining profit margins, keeping projects on track, resolving issues and, ultimately, delighting customers.”
Businesses who can harness real-time data will be able to identify pain points and come to market quicker with new products/services.
7. Proactive data analysis
Bill Bartow – VP of Global Product Management at Kronos
Going off our last trend, Bartow explains how real-time data paired with advanced technologies will contribute to more proactive data analysis.
“As organizations become more sophisticated in their use of data, artificial intelligence and machine learning are helping analytics disappear into the background. Instead of analysts creating graphs, charts, and pivot tables as a reactive exercise to uncover scheduling challenges, overtime concerns, or staffing issues, intelligent solutions take a proactive approach to data analysis, crunching the numbers behind the scenes in real-time to provide managers and executives with strategic, actionable insights and recommendations in the moment a decision must be made.”
With businesses being more agile and hungry than ever, it’s important to be as proactive as possible.
Vella sees the expansion of embedded analytics and how it will transform business intelligence from being retrospective to proactive.
“Organizations will begin to take advantage of embedded analytics on a broader scale – both internally as an extension to operational visibility and as a way to provide meaningful interactions with customers, suppliers, and partners.
Additionally, the use of embedded analytics will align with the overall convergence of technologies as more businesses leverage AI and machine learning to gain more insight from broader data sets.”
When paired with business intelligence, embedded analytics makes data analytics more approachable to decision-makers and business users.
With decades of experience in computing and data science, Wedding is well aware of how advanced technologies like AI and machine learning will continue to transform data analytics.
“Soon, the use of analytics will be so automated, that the machine learning tools will be able to quickly identify hidden patterns in data suggesting which customer will leave, which customer will default on a loan, or which customer will crash their car.
Once the human knows what will happen and when then the human will be free to figure out what to do about it. The computer will do mathematics because the computer will be good at that. The human will devise creative treatments that will fix problems or maximize profits. Analytics will have the same effect on people that computers had. It will free people up from simple tasks and allow them to be more creative and productive.”
Machine learning is still not widely adopted due to its high barrier to entry, yet, the rise of data science professions and accessible technologies will even the playing field.
10. Deconstruction of big data
Elena Vinokurtseva – Head of PR at YouScan, Social Media Listening Platform
Big datasets outside your organization undoubtedly contain valuable information, however, Vinokurtseva explains how deconstructing big data will be key for forecasting and drilling down analytics.
“Consumer data is necessary for companies to introduce new products to the market, open profitable outlets, and introduce more exciting content. Here, all data is used, including published photos and pictures in social media. For example, Starbucks uses big data to select lucrative places for new coffee houses. An Italian startup analyzed social media photos for the presence (or absence) of family restaurants in different cities of the country and selected the most unsaturated city.
If you divide the data into several parameters, you get small data, and the analysis will be quicker. Such data is needed for quick decision-making in individual business processes. For example, in marketing, they will help to draw a portrait of a buyer.”
Although, making sense of big data is still difficult due to most of it being unstructured data. As advanced technologies become more accessible, the deconstruction of big data becomes more of a reality.
From having a clean data architecture to getting acquainted with AI and machine learning, there are a lot of moving pieces when looking ahead to the future of data analytics. Fortunately, the end result is sure to be more inclusive and more actionable for businesses of all sizes and industries.
Having access to external data sources enables individuals and companies to be more informed and consequently make better business decisions. Check out this comprehensive list of open data sources that are available to the public for free.
Devin is a former Content Marketing Specialist at G2, who wrote about data, analytics, and digital marketing. Prior to G2, he helped scale early-stage startups out of Chicago's booming tech scene. Outside of work, he enjoys watching his beloved Cubs, playing baseball, and gaming. (he/him/his)