It wouldn’t be January without a smorgasbord of new-year predictions, and we’ve got some intriguing ones for you. From AI ubiquity, vendor consolidation, and breakthrough results in manufacturing analytics to new cloud options and more, the next 12 months will be eventful indeed. So before the year gets any older, let’s peer into our 2019 crystal ball:
1. Manufacturers Emerge from Pilot Purgatory and go for Enterprise Scale
In manufacturing, the typical approach to evaluating new technology involves implementing a pilot at a single facility. All well and good, until you realize that the pilot doesn’t scale to other plants with different equipment, processes, and needs. Frustrated by a lack of progress, manufacturers will move instead to build broadly applicable baseline capabilities — for data capture, change management, connectivity, and security — and implement them across several plants. This new approach will enable solutions to be quickly implemented and iterated across the enterprise. In short, 2019 will see more manufacturers making “smart company” investments rather than just “smart factory” investments. Now that’s what we call progress!
2. Major Consolidation of Digital Transformation Vendors
Some of us will recall the trajectory of ERP technology: the appearance of small, random DIY projects in the ‘70s, and their eclipse and replacement in the ‘80s by large, dominant players like Oracle and SAP. 2019 will see a similar natural evolution of the Digital Transformation landscape. Look for significant consolidation in areas such as IoT platforms, manufacturing analytics, and predictive maintenance tools.
3. AI: Out of the Lab and into the Mainstream
The last few years have been all about experimental AI-driven proofs-of-concept and pilots. The technology has now matured to the point where elements of AI and machine learning are increasingly being incorporated into operational business-critical systems. The net effect is that the distinction between AI and software, in general, will blur during the coming year.
4. Digital will Disappear
Whoa, we mean the term, not the technology. Is there any transformation today that isn’t digital? Digital is the future in a nutshell — which is why the term is becoming superfluous. Remember the Internet boom of the early 2000s, when “e” and “dotcom” were added to everything? Now, in an age where every mom-and-pop store has a website, these appendages are hardly necessary anymore. Case in point: Salesforce, the company that practically invented SaaS, axed “.com” several years ago.
5. Cloud-to-Edge gets Real
If 2018 was the year of multi-cloud (and it was), 2019 will see the model expand into even newer terrain. Or should we say “older,” as in on-premises components at the edge? Manufacturers want cloud scale for Big Data storage, management, and analytics, but many need to retain local control of some hardware, software, and sensitive information. In some industries, regulations make this mandatory. The net is a “best-of-both-worlds” prescription that cloud vendors are scurrying to fill: every major cloud provider has announced the delivery of edge computing functionality during the new year.
6. Manufacturing Analytics begin to Impact the Business Bottom Line (and it’s about time, if we may say so)
For a while now, the elements of a game-changing equation have been incubating: an explosion of IoT-generated shop floor data, a steady maturation of AI and machine-learning techniques, the advent of operational digital twin technology (hint, hint), and highly secure cloud services for industrial-strength number crunching and storage. All these elements have recently converged, and the whole is far greater than the sum of the parts. In 2019, we’re talking about nothing less than a quantum leap in what analytics can do for plant profitability: dramatic improvements in manufacturing productivity, efficiency, and optimization. Watch this space in the coming months for testimonial proof.
7. Demand Spikes for Data Engineering, a Cousin of Data Science
Till recently, the focus has been on people adept at troubleshooting and solving a specific problem with a chain of tools. With the mainstreaming of AI and machine learning, the emphasis is shifting toward a platform-driven approach that enables data professionals in new ways. An additional skill set is required: the ability to develop automated systems for blending continuous and diverse data streams, and readying them for modeling against numerous use cases rather than a one-off report or single problem. In short, the need for data engineers will rapidly match that for data scientists in 2019.
You heard it here first
Predictions don’t come with guarantees, but we offer these with confidence. Given the ever-quickening pace of business and technology innovation, a lot can and will happen in 12 months. As the calendar unfolds, we’ll monitor progress and follow up with an end-of-year review in December.
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