Data science is one of the hottest areas of growth within companies of all industries. Companies all over the world are scrambling for data science talent and the number of educational programs related to data science inside and outside universities continues to grow to keep up with the demand.
As we look to 2022, this is a trend that we can expect to continue. Data has become an important force behind how companies drive value for their clients, but they can only use data effectively if they can utilize it to innovate and derive insights using advanced analytics.
With this growth, we can expect new opportunities and challenges to present themselves. Here are five predictions for how we can expect data science to evolve over the next year:
Data Science Process Automation
Many data scientists who spend years learning about modeling, statistical analysis and machine learning will report that they are spending the bulk of their time on other tasks like data preparation, cleaning and model deployment. We have seen a growing trend toward the automation of some of these tasks, which will give data scientists the ability to focus on core modeling activities. Software companies continue to develop innovative capabilities aimed at automating how data is loaded, cleaned and analyzed. At the same time, open source communities continue to expand their work in this space.
Machine learning (ML) operations will also continue to grow as a needed area of focus to operationalize the deployment of ML models. Increasingly, cloud providers and new software entrants are offering sophisticated automation around how models are maintained and deployed. These automation efforts will accelerate the ability to productize and maintain new data science capabilities.
Context-Specific Data Science
Data science has grown in part because there is more data available now than at any time in history. The amount of data available will only continue to grow as measurement capabilities advance and computing costs come down. In order to create value out of that data, we will need not only greater data automation, but greater numbers of people who are trained in advanced analytics. As more people move into advanced analytics, we can expect greater specialization within industries. The most valuable data scientists are those that have a deep understanding of the business context they are working in so we can expect to see analytical professionals look to become experts in the types of analytics and modeling that their industry is using most to drive their businesses forward.
Analytics on the Edge
Most modern-day electronic devices in the home have incredible computational power compared to even 10 years ago. On top of that, those devices also have the ability to connect to the internet to communicate key data. There is a growing trend to deploy advanced analytics on these devices instead of passing the data back to a central database for analysis. There are many reasons for this. First, it enables a device to have a decisioning capability in real time. Secondly, it can give the manufacturer the ability to deploy advanced analytic capabilities without a need to capture data associated with an individual, thus providing greater privacy protection. With computational capacity increasing and data security becoming more important, we can expect to see a greater push toward deploying models on edge devices.
NLP for Augmented Analytics
Can we automate the role of a data scientist? To do that, you would at least need a system that could interpret a business question, select the appropriate data for analysis, build and run a model, and then provide an output that gives quantitative reasoning for a recommended business decision. With advances in Natural Language Processing (NLP), many systems are aiming to do just that. Just as many chatbots look to interpret users’ input to identify how they can best help a customer, there are various systems available on the market that look to interpret business questions like, “What are our expected sales in Q4?” These systems use NLP to interpret a question and then associate that question with the right back-end data needed to answer it analytically. In this case, it might then run a seasonally adjusted forecast to find the expected sales for Q4 and then visually present the results to the user. Systems like this open up analytics to a much wider audience and at least provide a step forward in automating what might have required a data scientist previously.
Data Science Education Opportunities
As data proliferates through organizations, analytical skills will be needed across all departments. Product and engineering will need to use data to drive product capabilities, marketing will need data to target the right audiences for their products, and human resources will need data to identify trends in hiring and retention. Given that, it will be imperative for new employees across the organization to have some level of fluency with data and analytics. To meet that need, educational programs inside and outside of universities will need to expand opportunities to train professionals from all backgrounds on key analytical skills. As long as the job market continues to grow for data science professionals, we will expect to see more and more educational opportunities for people to develop skills in this space. The good news for those looking to get into data science for the first time is there are many free online courses available to start the journey toward a career in data science.
Looking Ahead to Accelerating Growth
If the progress of the last few years is any indication, we can expect the data science field to grow significantly over the next few years. All of the key factors that will drive innovation in this space are in place today. These include increasing amounts of data and automation, demand for jobs, and extensive educational opportunities that make it easier for more people to enter the field.
With much of the growth in data science still ahead of us, there has never been a better time to jump in and learn more about how these advanced analytics skills will shape the future.