Time to get back to work. Buckle up and get ready for another thrilling year of ups, downs, twists, turns, and tunnels. In this article, I will share top analytics industry trends, predictions and tips on what to learn to be ready for this wild ride in the roller coaster of digital transformation.
Top Tech Trends to Watch in 2018
As almost every business evolves into a digital business, cybersecurity, cloud, automation, artificial intelligence, massive scale and fast internet connectivity become next era critical success factors. Just as I mentioned last year, my big bet is that big tech will dominate the analytics industry. I expect that trend to continue in deregulated markets that favor a massive few forces…the most powerful forces in the world today. In 2018, I believe that we will see more niche analytics firms struggle, exit the market, go private or get acquired in further industry consolidation.
We will see further analytics industry consolidation in 2018
Here are my other top 10 predicted hot tech topics to keep an eye on this coming year.
- Quantum computing starts to get industry attention
- Cloud, hybrid computing, and migrations keep technology pros busy this year
- Flexible, serverless, on demand analytics architectures with microservices technologies such as Lamba continue to pop up in pilot projects
- Artificial intelligence and use of cognitive capabilities such as deep learning, natural language, speech interaction along with “bots” continues to expand
- Augmented analytics automation becomes the shiny new thing for analytics pros
- Immersive experiences with virtual or augmented reality begin to emerge in education, entertainment, financial services and science industries
- Internet of Things (IoT) gets smarter with automated embedded artificial intelligence
- Compliance, data privacy, security, governance and related data management anxiety increases as the EU GDPR deadline nears
- Cybersecurity and blockchain continue to grab the most headlines
- Upskilling and reskilling planning becomes a higher priority
Top Trends for Analytics & Data Science Pros
Last month I was honored to be included in KD Nuggets and Microstrategy’s thought leader industry predictions. Here are excerpts from my predictions along with links to read what the other influencers forecast.
In KD Nuggets overview called Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018, I shared the following reflections and outlooks. When I look back at 2017, I will remember it fondly as the year when intelligent analytics platforms emerged. From analytics bots to automated machine learning, there has been a plethora of sophisticated, intelligent automation capabilities coming to life across every aspect of data science.
A plethora of sophisticated, intelligent automation capabilities is coming to life
Data integration and data preparation platforms have become smart enough to plug-and-play data sources, self-repair when errors occur in data pipelines, and even self-manage maintenance or data quality tasks based on knowledge learned from human interactions. Augmented analytics offerings have started to deliver on the promise of democratizing machine learning. Lastly, automated machine learning platforms with pre-packaged best-practice algorithm design blueprints and partially automated feature engineering capabilities have rapidly become game-changers in the digital era analytics arsenal.
Next year I expect to see automated artificial intelligence seamlessly unified into more analytics and decision-making processes. As organizations adapt, I anticipate numerous concerns to be raised around knowing how automated decisions are made and learning how to responsibly guide these systems in our imperfect world. Looming EU General Data Protection Regulation compliance deadlines will further elevate our need to open up analytics black boxes, ensure proper use, and dutifully govern personal data.
In Microstrategy’s Enterprise Analytics Trends to Watch in 2018 ebook, I discussed the emergence of augmented analytics. In 2018 and beyond, I expect augmented analytics approaches to alter the analytics landscape and the nature of analytics roles. Search, natural language, and intelligent analytics automation innovations powered by artificial intelligence are beginning to vastly transform the human-computer experience democratizing the power of analytics and data science. Augmented analytics automation technologies will shake up the landscape once again.
Augmented analytics automation will shake up the analytics landscape
Augmented analytics combines the beauty of the human mind with automation and artificial intelligence. Augmented analytics approaches are smart, forward thinking and actionable. In addition to providing historical reports and dashboards, augmented analytics automates predictive and prescriptive actionable guidance. Early adopters boast unmatched speed to insight and enhanced competitive advantage. Analytics vendors that have augmented analytics technologies should enjoy a stellar 2018.
When given unbiased, properly prepared data, augmented analytics delivers amazing results. Since automated analytics relies on statistical techniques, inaccurate, biased or poor-quality data that doesn’t sufficiently represent business processes delivers low quality results. Thus, I expect more discussion around the art of preparing your data for automated analysis and scrutiny of automated decision making.
Flexible, On-Demand Analytics Architecture
To amalgamate constantly changing, different data source realms, data catalogs, search, data virtualization, data pipeline orchestration, and hybrid analytics technologies have become key assets in a digital era cloud analytics arsenal. These solutions bring much needed order to modern data chaos.
Traditional data warehousing and analytics architecture is also switching from rigid, legacy data systems to nimble, flexible, on-demand cloud service designs that can get the most out of big data and analytics for the least recurring cost. The wide array of analytics cloud services, and, compute and serverless technologies, including but not limited to various database types, data lakes, Internet of Things (IoT), Lambda, streams, ingestion hubs, and microservices has become absolutely overwhelming to understand, piece together and estimate usage-based pricing. To design and migrate analytics architecture to the cloud, new cloud data architect and data pipeline engineer roles have emerged. What happens to those roles after migration? Well, that might be a prediction topic for next year!
Changing Nature of Analytics Roles
Digital technologies will change the nature of many analytics ecosystem roles. I shared my thoughts on that topic last month in an InformationWeek article, “How Innovation is Changing the Analytics Landscape”. I see it already happening in the top technology and analytics consulting firms.
I know there is skepticism when it comes to automation. My advice – don’t ignore, fear or resist it. Embrace this wonderful opportunity to get in early, learn how to properly use these innovations, understand shortcomings, and apply strengths to become an analytics superhero delivering extraordinary value from data.
If you are in an IT support role today, you will want to upskill and reskill as soon as possible. In India, automation and cloud shifts have already impacted technology pros with over 56,000 layoffs in 2017. In the United States, the information technology sector was decreasing recently and had mass layoffs throughout the year. I know more cloud savvy, technology and analytics pros looking for a role now than I did in the epic market crash of 2008. It is surprising given the hype around our booming economy and media reports of STEM skill shortages.
My top tip this year is to be ready for the nature of work as you know it today to change.
Be ready for the nature of work as you know it today to change
As more companies move to the cloud and routine tasks are automated, different roles and skills will be in demand. New roles will be created that fuse modern technology, critical thinking, creativity, negotiation and strong interpersonal skills. Everyone needs to be a master of data in the future. It will be assumed that you can work with digital technology. Complex problem solving and soft skills will become increasingly more important.
To set yourself apart, create a training plan now. The World Economic Forum “The Future of Jobs” report and the McKinsey Global Institute’s latest report, “Jobs lost, jobs gained: Workforce transitions in a time of automation“, study are two good reads to better understand what types of skills will be in demand to prepare you for the expected job market changes.