Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019
This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!
As we continue to bring KDnuggets readers year-end roundups and predictions for 2019, we reach out to a number of influential industry companies for their takes, posing this question:
What were the main developments in AI, Machine Learning, Analytics & Data Science in 2018 and what key trends do you expect in 2019?
For the industry's take on what happened this year and what will happen next, we have collected insights from Domino Data Lab, dotData, Figure Eight, GoodData, KNIME, MapR, MathWorks, OpenText, ParallelM, Salesforce, Splice Machine, Splunk, and Zoomdata.
Key themes singled out by these experts include the changing analytics landscape, how data science will continue to influence business, and the emerging technologies that will be leveraged to do so.
Be sure to check out collected opinions we shared last week when we asked a group of experts the related question, "What were the main developments in Data Science and Analytics in 2018 and what key trends do you expect in 2019?"
Josh Poduska is Chief Data Scientist at Domino Data Lab.
AI: from hype to business impact in 2019. The honeymoon is officially over for Artificial Intelligence. 2019 will be the year that AI will become an organizational reality, rather than experimentation, tinkering, and doubt.
Forget Google's AI call center agent. Data science's biggest impact will be in the spots you don't think about... in less "sexy" parts of the business, like faster servicing customers a technical support calls, optimized inventory, smarter product shelving, reducing wasted time on purchases, and more.
The consumer's understanding around AI will shift dramatically. We will no longer associate AI will futuristic robots and self-driving cars, but rather productivity tools and predictions to help everyday, menial tasks.
Dr. Ryohei Fujimaki is CEO and founder of dotData, the first company focused on delivering end-to-end data science automation and operationalization for the enterprise.
The pressure to achieve greater ROI from AI and ML initiatives will push more business leaders to seek innovative solution. While substantial investments are being made into data science across many industries, the scarcity of data science skills and resources limits the advancement of AI and ML projects within organizations. In addition, one data science team is only able to execute several projects a year given the iterative nature of the process and the manual work that goes into data preparation and feature engineering. In 2019, data science automation platforms will capture much of the mind share. Data science automation will cover much wider areas than machine learning automation, including data preparation, feature engineering, machine learning and the production of data science pipelines. These platforms will accelerate data science, execute more business initiatives whilst maintaining the current investments and resources.
Data science tasks will become a 5-minute operation and deliver business values in days. Gone are the days of waiting several months for a data science project. In 2019, we are going to see a transformation in how businesses implement and optimize their AI and machine learning initiatives. New data science automation platforms offer a single, seamless platform that enables companies to accelerate, democratize, and operationalize the entire data science process – from raw data through feature engineering to machine learning – eliminating the most time-consuming and labor- and skill-intensive tasks of data science. As a result, what once took months to complete, now will take only days, significantly accelerating time to value for AI and machine learning initiatives.
Dale Brown is VP of Business Development at Figure Eight.
- AI Platform Vendors Will Create More Tools To Enable Non-Data Scientist/Developers to Build AI Faster - “Alongside the increase in demand for AI within companies, we’ve also seen a continued shortage of trained data scientists. To increase the adoption of AI, AI platforms will need to empower traditional developers with tools to enable them to create machine learning models faster, as well as ensure they have an integrated platform that will allow developers to annotate and label the data needed to improve the accuracy of their models.”
- Enterprise Level AI Integrations and Consolidations will Occur - “The increased adoption of AI will make it necessary for enterprise level companies to either accelerate their development of AI related tools or acquire and integrate them into their platform - faster than their competitors.”
Kevin Smith is Vice President of Product Marketing at GoodData.
The demand for the data scientist will take a sharp turn. The data scientist, once the sexiest job of the 21st century, will become very different from what we know today. As analytics are pushed to the end user, self-service becomes routine, and data prep tools become more powerful, the data scientist will transform into more of a consultant than a data sourcing and preparation expert. They will be charged with helping the business make sense of data, understanding how to interpret results, and what courses of action might be warranted. It’s a higher value role for the data scientist and ultimately, a better use of their skills.
Michael Berthold is CEO of KNIME.
Two trends that I see continue are Automation and Interpretability. The former will continue to hype a bit throughout 2019 but then face the problem that only fairly well-defined types of data science problems lend themselves to full automation. Much more powerful are environments where the data scientist can mix automation with interaction, truly allowing them to deploy data science to others without having to outsource all of it to self-declared experts. Interpretability will turn into a larger problem for deep learning for all types of data where understanding (or controlling) the underlying decision is important. We will never accept "human failure" from AI for safety critical decisions.
Jack Norris is Senior Vice President, Data and Applications, of MapR.
2019 is the year containers and AI meet in the mainstream - NVIDIA announced open source Rapids at the end of this year. Harbinger of how the focus on operationalizing AI, better sharing across data scientists and distributing processing across locations will drive containerized. Another rising technology that drives this prediction is Kubeflow which will complement containers and distributed analytics.
Seth DeLand is Product Marketing Manager of Data Analytics at MathWorks.
Machine learning will be integrated into products and services – Companies will increasingly use machine learning algorithms to enable products and services to “learn” from data and improve performance. Machine learning is already present in some areas: image processing and computer vision for facial recognition, price and load forecasting for energy production, predicting failures in industrial equipment, and more. In the coming year, it can be expected that machine learning will be increasingly present as more companies are inspired to integrate machine learning algorithms into their products and services by using scalable software tools, including MATLAB.
Companies will use domain experts to close data science skills gap – Many companies struggle to find data science expertise, and businesses are equipping incumbent engineers and scientists with scalable tools, like MATLAB, to enable them to perform data science. Because these engineers and scientists have existing process and business knowledge, they will be well-situated to apply data science techniques, assess the results, and determine the best way to integrate models with business systems.
Zachary Jarvinen is Head of Technology Strategy, AI and Analytics, OpenText.
The long-promised enterprise AI transformation is poised to begin in earnest in 2019. Most enterprises have reached a point of digital maturity, ensuring access to quality data at scale. With mature data sets, AI providers can offer lower cost, easier to use AI tools for specific business use cases. The effect of enterprise AI at scale will be significant. Gartner expects the business value of AI to hit nearly $3.9 trillion in by 2022. Consumers also stand to benefit in almost every sector. They’ll see more innovative products and services, smarter homes, factories and cities, improved health, and a higher quality of life.
AI will augment – not replace – the workforce. AI applications will be transformational, improving efficiency and performance, generating huge cost savings, and giving rise to more innovative products and services. However, the future of work will involve humans and AI. The most innovative companies have already started planning how to best make this symbiotic future a reality. In the shorter term – this means the gap in skilled workers in data science will persists and demand will remain very high. Educational opportunities will expand to help address the need. In the long term – as we develop the skills and technology to unlock AI – society stands to benefit enormously from AI-augmented workers, homes, vehicles, power grids, factories, cities, more.
Sivan Metzger is CEO of ParallelM, a rapidly growing company in the machine learning operationalization (MLOps) space.
Additional areas of business will be brought in to share the responsibilities of delivering machine learning. As the pressure for companies to differentiate themselves from their competitors and frustration among business leaders increases, other functions will be pulled in to help bring machine learning initiatives to fruition. Those functions include operations and business analysts, who can help pick up the responsibility from data scientists once they are done building machine learning models.
Ketan Karkhanis is SVP and GM Analytics at Salesforce.
AI-augmented analytics will be mainstream - 2019 will be the year when AI-led analytics (known as Automated Discovery) will become mainstream. Human brains are not wired to evaluate millions of data combinations at sub second speeds, but machine learning is literally built for this problem and the perfect solution. Business leaders and data analysts are better understanding that AI is not going to replace jobs, but augment them, and I expect that in the next year, the majority of data analysts will have the power of data science at their finger tips without the need to write code.
Monte Zweben is CEO of Splice Machine, the intelligent data platform for real-time applications.
- New customer growth for Hadoop will dwindle and Hadoop clusters will slow in growth.
- Massive growth on cloud-based SQL data platforms
- Machine Learning will enter an operational phase, getting out of backroom experiments to move into the fabric of real-time, mission-critical, enterprise applications
- Oracle defections to scale-out SQL platforms will reach a point where the Company will disclose a risk factor in a quarterly disclosure
Andi Mann is Chief Technology Advocate at Splunk.
- 2019 is the year machine learning becomes fully realized in the workplace -- an increase in “AIaaS (AI as a Service)” offerings will flood the market in 2019 giving businesses more options for finding answers.
- There will continue to be a rise in engineering hires through 2019, as more organizations look for engineers to help manage siloed tool integrations.
- A rise in “tools for tools” that focus on better enabling IT operations.
The final prediction comes from the executives at Zoomdata.
Growth of Data Visualization (due to increasingly structured data) - In 2019 business will finally embrace data visualization and see its promise. We’ll have no choice – data growth is exponential and increasingly structured because of IoT (see chart). Like how consumers today can view their home’s energy consumption and look at comparisons to neighbors’ energy use, we’ll start to see this bleed (finally!) into the supply chain, as example. Automated analytics will find something interesting, create a visual representation of that item – then it shows it to a human so action can be taken. Data will be pulled from the whole pie, allowing people running the enterprise to see the forest for the trees like never before.
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