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As data analytics shapes business, the trends that shape data analytics become ever more important.
Clearly, data analytics software is now a core tool set for managing a business. Today, the constantly updated apps from Big Data companies are the every engine the runs the enterprise.
Given the importance of data analytics, it’s essential that business managers of every stripe understand the trends that are shaping it going forward.
To discuss this, I spoke with Jim Hare, research vice president at Gartner. Based on a Gartner report he authored, we discussed the five key trends shaping the evolution of data analytics.
See below: transcribed highlights of my discussion with Jim Hare.
Data Analytics Trends:
1. Augmented Analytics
Augmented analytics uses machine learning. Gartner predicts that, by 2020, citizen data scientists will surpass data scientists in the amount of advanced analysis they produce, largely due to the automation of data science tasks.
Jim Hare: Augmented analytics “Is making it easier for even business analysts to build and deploy these models, without even having to be programmers. So it’s really, really changing the landscape, both on the traditional analytic side but also these data science machine learning platforms.
“It’s helping in a couple ways, one of which is making it easier to prepare data, to find insights in the data, and then even how to communicate those insights and results.”
2. Digital Culture
“Data literacy, digital ethics, privacy, enterprise and vendor data-for-good initiatives encompass digital culture,” says the Gartner report.
Gartner predicts that, by 2023, 60% of organizations with more than 20 data scientists will require a professional code of conduct incorporating ethical use of data analytics.
Jim Hare: “One of the challenges that we see a lot of organizations are facing is: not everybody understands how data analytics really works.
“So one of the things that’s critical for organizations that want to be data-driven organizations is to ‘up-level’ the knowledge and skills of the people even on the front line, who are starting to have these analytical insights.
“But they need to understand how to communicate in a way to best use that information and also what the limitations are so they don’t get themselves in trouble.”
3. Relationship Analytics
Relationship analytics highlights the growing use of graph, location and social analytical techniques.
Jim Hare: “In some cases, [relationship analytics] is people-to-people, sometimes it’s people-to-things. But there’s a lot of information you can refer to, to deduce when you start combining multiple data sets.
“Today most of the analytic solutions you see, [they] look at these types of data in isolation. So your analyzing location, you’re looking at particular data points on a map, or social analytics, you’re maybe looking at the connectivity between individuals.
“What’s important is when you start piecing together these different types of data sources and use multiple analytic techniques together, you’re able to have a much more complete picture of whatever problem you’re trying to solve. And we really think that this notion of relationship analytics, which is the connected tissue between the data on those people, places or things. This is really going to be the next wave providing deeper insights and really helping organizations.”
4. Decision Intelligence
Decision intelligence provides a framework that brings together traditional and advanced techniques to design, model, align, execute, monitor and tune decision models.
Jim Hare: “[Decision intelligence] means that these decisions often are spanning multiple applications and even different functional groups.
“Case in point, if you talk to most organizations and ask them about customer experience, it’s very siloed. You talk to the sales organization, the marketing support, all of them have their own silos. All of them have their own view what the customer is, but no one’s really looking holistically across all of these different silos or pillars.
“So decision intelligence is really bringing that level of insights and using a combination of AI and automation to break down these barriers and really look at it holistically.”
5. Operationalizing and Scaling
More people want to engage with data, and more interactions and processes need analytics in order to automate and scale.
Jim Hare: “[Operationalization] is really couple of things. Organizations are awash in too much data and they’re trying to figure out how to manage all that data to begin with.
“But then they are also trying to figure out, ‘Well, where else can we be using the data? How do we get this information, analyze it and get it in the hands of our users?’ And this requires not just looking at individual technologies or tools but taking a fundamental approach, where you’re really creating this data foundation.
“So that you’re able to handle, absorb and bring an increasing amount of data, organize it and make it useful to those who need to analyze it. And then the second part of it is: now that I’m analyzed it, who can benefit from having those insights, and how do I contextualize that information for the different roles?”
