Influencers assess 2017 and make predictions for 2018
As the year winds down, questions tend to arise about what the big trends of the past year have been and what the year to come may hold.
With those questions in mind, we asked eight key influencers in the world of big data and analytics — Chris Penn, vice president of marketing technology at SHIFT Communications; Jim Kaskade, CEO of Janrain; IT consultant Duane Baker; Bill Jensen, CEO of The Jensen Group; William McKnight, president of McKnight Consulting Group; Ronald van Loon, director of Adversitement; Dr. Manjeet Rege, associate professor of graduate programs in software at the University of St. Thomas, and Bob E. Hayes Ph.D., founder of Business over Broadway — to take a look back at 2017 and look ahead at what's to come in 2018. Here's what they had to say.
What was the most surprising data industry trend of 2017?
Chris Penn: Of all the associated technologies with big data — machine learning, IoT, et cetera — the one that really stormed the barricades was blockchain. I expected much more focus on IoT this year, and blockchain stole the show.
Duane Baker: I have been surprised at the breadth and depth of specialized neural processors that have been released this year to accelerate data-intensive applications, as well as the widespread lack of data protection and data controls exhibited throughout government and business through extensive avoidable data breaches.
Ronald van Loon: The adaptation speed and focus on machine learning across the tech industry was beyond any expectation in 2017. Almost every software vendor within the data industry has implemented machine learning applications to automate repetitive tasks, improve productivity and better cater to individual customer needs.
Which data industry trends do you expect to dominate 2018?
Bill Jensen: How many strategic and business decisions still reflect leadership’s personal cognitive biases. Leveraging earliest AI, chatbot and machine learning applications to cut costs and increase efficiencies.
William McKnight: I expect cloud options to dominate the preferred deployment options and questions of its efficacy to be removed. We will see more data, software and processes in the cloud.
Bob E. Hayes: Because the GDPR regulations go into effect in early 2018, I expect privacy issues will dominate discussions throughout 2018.
Additionally, as the role of AI creeps into making decisions about individuals’ personal lives, we will need to consider social implications [such as] ethics surrounding AI, including establishing rules of when AI can be used and understanding how deep learning algorithms arrive at their decisions.
Finally, because of the growth of data breaches, I think that security issues will be addressed with more vigor.
Which data technologies do you expect to gain traction in 2018?
Manjeet Rege: Artificial Intelligence is quickly becoming a mainstream enterprise solution and at the same time is evolving rapidly. In 2018, we expect to see widespread adoption and integration of AI technologies into business processes for improved decision making. However, one has to remember that for overall successful deployment of enterprise AI, there needs to be a data strategy in place. Embracing AI is trendy these days, and doing that without having a larger picture in mind may lead to significant costs that may not translate into revenue.
McKnight: There are a few I think will capitalize on their momentum of the past few years and the demand for data to be an asset to the organization and well-placed in the environment. These include master data management, data virtualization and data preparation tools.
Baker: We will continue to see more specialized semiconductors produced as applications move to the edge and are used for real-time and big data applications. As data continues to grow, I think demand for more solid state technologies — flash storage, in-memory computing, in-memory database — will expand, and due to data protection concerns, immutable decentralized data management technologies based on blockchain will likely gain significant deployments.
Which data technologies do you expect to decline in importance in 2018?
Jim Kaskade: It’s obvious that 2017 was the turning point for big data technologies like Hadoop. Consider that Strata itself dropped the "+ Hadoop World" submoniker and that vendors, almost unanimously, have ceased making explicit reference to Hadoop. Just as every enterprise has an enterprise data warehouse, they will now have a data lake and the technology used is of less relevance, as is the use cases leveraging it.
van Loon: Data transformation and analytics as separate solutions are going to decline in 2018, and be replaced by fully integrated data management and data operating systems that help businesses effectively analyze, classify and manage all of their data from various sources. Integrated data management and data operating systems is a more cohesive approach to providing a better customer experience and will help organizations drive data innovation.
Penn: I expect to see a continuing decline in legacy technologies which aren't keeping up with the four Vs of data: velocity, variety, veracity and volume. Look at legacy databases and legacy architectures for what's on the out.
What will the data industry landscape look like 5 years from now?
Jensen: Hopefully, the most revolutionary shift will be in the workforce/employer relationship, where corporate resources, technology, and data are dedicated to helping each individual succeed, not just the company. We have the technology. We’re creating increasingly useful data. The unknown X-factor is the leadership will have to be a lot more workforce-centered.
Kaskade: It’s all about cloud, given the scale at which data is being produced, stored, analyzed and operationalized. For example, cloud vendors will provide enterprise-class versions of IFTTT, allowing for easier development of IoT-centric applications.
Data science notebooks and workbenches will become as common as business intelligence (BI).
Digital identity will result in renewed investments in data lineage and/or data provenance that will make efforts around master data management a thing of the past. It will become critical that the data lifecycle is well understood, meaning data's origins, and an audit of where it has moved over time, and the data governance is well understood: data privacy, access, storage and processing policies.
That's what we're hearing. What do you think? Pick a question and answer it in the comments.