In this fast-paced era of ever-changing technology trends, the race for better and accelerated processes and content automation is continually leading the pack. Positions like Data Scientist and AI Subject Matter Expert have taken a giant leap as some of the leading careers in technology to help evolve what enterprises around the world both want and need. Let’s take a look at some of the top tech trends in which we see our customers and partners pinpointing as essential strategies in 2020. 

#1: Cloud-only and SaaS, I/PaaS

According to Flexera’s 2020 State of Tech Spend Report, cloud spend (including SaaS and I/PaaS) has finally surpassed on-premises software spend. More than 80% of the study respondents surveyed report they plan to increase SaaS and I/PaaS spend next year. Yet, the reluctance expressed by many security-conscious organizations for storing, processing or routing documents and data in and through the cloud remains. 

Companies of all sizes have embraced mission-critical business tools like CRM and ERP in the cloud as SaaS solutions. Look at the wild success of applications like Salesforce and NetSuite, respectively. Why not extend the same principles to documents and data? Flexera found that the top IT initiatives for enterprise organizations are digital transformation, cybersecurity and the shift to the cloud. Given this focus and the increased tech spend, companies will see industries, like Financial Services, that previously refrained from cloud usage, start to dip their toes in the waters of the cyber sky.

#2: A continuation of departmental-only rollouts of RPA systems

Recently, a consultancy partner expressed some dissatisfaction with using robotic processing automation (RPA) tools. Specifically, they were taking a step back from leading with RPA as a tool for digital transformation initiatives. The sentiment was that RPA on its own doesn’t actually overhaul business processes or address issues stemming from inefficient workflows. It simply makes slow processes faster. And many of the analysts agree, as is evident in this rant article from one particularly dissatisfied industry expert or and this disappointed recap of the state of RPA.

The inherent flaw in the methodology of RPA vendors is the almost exclusive focus on automation rather than true transformation. Boiled down to the most basic level, RPA companies license their software by bot count. The more processes, the more bots, the greater the purchase price. It is not in an RPA vendor’s best interest to solve a problem or perform the due diligence required to identify useless or superfluous processes. And with that myopic gaze, enterprise success will have a limit

The consultancies and RPA-partners that work with their clients for true process-evaluation and deep analysis of business workflow will have the greatest success and highest likelihood of true digital transformation.

#3: A surge in machine learning-powered point solutions

This year we witnessed a handful of veteran technology companies and emergent startups announce purportedly machine learning (ML) capture platforms with varying degrees of success and follow through. Chasing the elusive market share of this unavoidable

Having the framework in place to leverage ML algorithms to automatically identify and extract data from unstructured content isn’t the challenge. The hurdles to surmount are in-house (or outsourced) industry expertise and access to a large enough sample set of data to deliver an ML model that can automate the identification and extraction of data from go-live. Customers don’t want to invest the time and effort into providing that knowledge and content in addition to paying for access or a license to use the solution. A common refrain, voiced by the discontented consumer is, “You’re telling me it’s going to take a year of processing documents before I actually see the results?” If you’re promoting an ML-only application without a specific use case framework in place, that will likely be the case.

In 2020, we predict a greater focus on point solutions for industry or department-specific capture use cases. Successful startups with a small customer base will develop niche solutions that tackle documents and data within a specific and narrow industry. Larger software vendors with a more robust network will be able to expand their ML-driven capture offering to a greater number of use cases with a shorter ramp-up timeline. To survive and thrive in this ML-mad climate, specialization in the form of point solutions will be critical.

#4: Conflux of Big Data and machine learning: Context is king. 

Big Data has been trending in the technology industry for the better part of a decade. Experts and analysts alike predicted that Big Data would solve all problems in the world through its intrinsic value, realized by predictive analytics. The focus on Big Data predated machine learning as an ideal solution by a few years, but the mania of ML and its potential to automate workflows and provide insights matches that of Big Data.

However, the value of Big Data and the illuminations it can provide is only as good as the source of the data, its completeness and its cleanliness. With a primary focus on structured data (and the process of cleansing it), most data scientists only interact with a fraction of the organizational data available to them. This means they have an incomplete picture of the information they’re working to manipulate. Similarly, the ML model created is only as good as the samples used and human input provided to build it

So what do we get in the Vesica Pisces when the circles of Big Data and machine learning overlap? The answer is Context. It is the realization of all that potential and hype when Big Data is inclusive of unstructured content and ML models are created by industry experts and designed to alleviate informational problems. 2020 is the year when Context will reign King.