Not that you need a reminder of how quickly everything can change, but everything’s going to change in the next 5-10 years.
The Future of Jobs Report 2020 highlights all the expected changes coming to the workforce over the next decade, and it’s safe to say a lot of people will be starting a new career path within the next few years. As workplaces digitize their efforts, automation will wipe out close to 85 million jobs. Most of these will be positions with a high concentration of repetitive work.
In the meantime, 97 million new jobs will take their place.
That means a lot of change for organizations around the world, especially in the field of data analytics and science. Not only will they need to bring on new jobs, they’ll also have to bring on new technologies to address the gaps.
What follows is an excerpt from our whitepaper, which addresses how to create the ideal digital workplace, and addresses the top technological, skills, and knowledge changes companies need to make to remain competitive in the future.
The technologies companies are using to digitize their workforces
Named as one of Gartner’s Top Strategic Technology Trends for 2021, hyperautomation automates as many tasks as possible. It includes a combination of
robotic process automation (RPA), AI, and ML to do the job. Its goal isn’t to replace people but augment them, ultimately amplifying how much work people can automate.
Artificial Intelligence (AI)
AI includes the ability to decipher unstructured text, translate handwritten scribbles, convert images to text, and organize all of it into useable data sets. Newer technologies are diving into the Internet of Behaviors (IoB), which captures data from many of the smart devices we use including location, heart rate, emotion, and more. It’s allowing people to understand more about their customers, and employees, and how to best serve them.
Machine Learning (ML)
Adding ML capabilities to a company’s analytic processes can help you empower teams to select the best predictive and prescriptive models to use when analyzing data. Some will even help train employees how to build them. ML capabilities that require no coding or low coding experience make it easy for an entire workforce to learn and adopt, reskill, upskill, and contribute.
Listed as the top skill needed for transitioning into a new role, and companies are putting data science at the top of their lists. Although data science positions used to require expertise in at least one coding language, new positions will require a new set of skills. Forbes says data scientists need to have a strong analytics aptitude, be curious as a cat, hypothesis-driven, motivated by impact, and a structured problem solver. Modern no-code, low-code platforms make data science accessible to people with this skillset, require very little training, and shorten learning curves, enabling people to use pre-written algorithms to perform analysis.
To understand how efficiently their processes are running, companies employ process mining. This technology scrapes data from event logs and helps companies determine how their processes are running and identify any gaps, trends, or inefficiencies. The data sets produced from process mining can include logs with millions of lines of code, requiring resource-intensive processing. When used with data science and analytics, the insight gained from them can lead to noticeable increases in revenue and savings. Process mining tools and programs will most likely be implemented by IT departments to empower the workforce.
Robotic Process Automation (RPA)
Companies are using RPA to automate repetitive tasks. To gain the most from RPA tools, though, companies need to implement the right strategy. Since any investment in RPA requires up-front work to set up and run effectively, companies need to determine their long-term goals and strategies before deployment. RPA is also limited in what it can automate but works great with analytic platforms to help drive real change through analytics.
Multi-Cloud and Cloud Storage
With the automation of processes and the rapid expansion of data collection, companies are turning to cloud storage and multi-cloud infrastructures to help house all the data and software that’s used to analyze it. Like their automation counterparts, cloud storage, including servers, will need to integrate seamlessly with the rest of the automation ecosystem to ensure a high organizational adoption rate of the other tools. This strategy will fall squarely on the shoulders of CIOs and IT leaders.
No-Code, Low-Code Platforms
With demand for data and analytics growing, the ability for companies to keep up is dependent on no-code, low-code platforms. These platforms give people the ability to incorporate data science to analyze data without needing to know how to code or, at the very most, only needing to know basic coding languages to refine automated analytic processes. With data science, AI, and ML already at the top of skills required or desired by hiring companies, these platforms give companies the ability to retain, reskills, and upskill current talent plus attract new talent and high performers.
To explore as many potential outcomes as possible, companies are leaning on analytic automation to produce results. This makes it easier for companies to hire people with analytical minds capable of asking the right questions and train them to use the tools at their disposal within a company. Although automation will lead to the reduction of jobs held by people who perform repetitive tasks to turn data into insight, many of them will be replaced by positions requiring more creative and analytical thinking skills, which will also lead to more engagement.
None of these changes are easy to make for organizations or people, but there is a way to make the transition, help employees reskill/upskill, and prepare.