Where Is Marketing Data Science Headed?

Marketing data science is now a significant part of marketing. Some of it directly competes with traditional marketing research and many marketing researchers may wonder what the future holds in store for it. Today, data is everywhere, and organizations can quickly access massive amounts of it. People and systems generate new data at a rate of 7.5 sextillion gigabytes per day, making it easier than ever before to collect and warehouse data. Organizations face significant challenges, however, when it comes to using it.

Data science is still a new discipline and organizations that want to leverage the power of data struggle with not only data analysis but also data team building, data privacy, information bias, and technology adoption and optimization.

Every few years, headlines proclaim the imminent death of data science. Some articles list the cause of death as obsolescence, the theory being that APIs and pre-packaged algorithms will replace data scientists. Sometimes automation kills data science. There are futurists who predict we won’t need data scientists once computers powered by artificial intelligence can do the same work. And some sources predict market saturation will be the death of data scientists. As more analysts and engineers pick up data science skills, employers won’t have to pay top-dollar for career data scientists.

However, there’s no actual evidence that data science is on its deathbed and plenty to suggest that this discipline will continue growing. There is no data science bubble. What is happening is that data science is changing. It was once a niche discipline that even those working in it could not easily define. Now it is much more segmented and easier to see how data scientists deliver value. Some data scientists handle model development. Others do analysis. Still, others adopt AI for technical implementation. Many data scientists who might once have been generalists now specialize in software engineering, deep learning, data mining, data visualization, or data architecture.

As more organizations invest in data science implementation, the discipline has become more value driven. There was a time when simply having a data scientist on staff was enough. Executives and stakeholders who didn’t truly grasp the power of information hired data scientists to position their organizations as technologically progressive. Today, organizations expect data scientists to deliver insights that drive quantifiable enhancements. Far from being the end of this discipline, it is the beginning of a new era of data science.

Data Science is the Future of Everything

Big Data is playing an increasingly substantial role in all sectors. Very few sectors are untouched by data science’s impact and influence. Data powers forecasting and business decision-making in manufacturing, marketing, energy, business management, healthcare, and many other fields and will play an even more significant role in the future as an investment in data goes up. The practical applications of data science are broad. It even impacts our personal lives. Whether in recommendation engines or diagnostic software platforms, data science powers the world. For example, data science informs treatments, diagnoses diseases, and makes medicine more accessible in healthcare. In retail sales, data science powers targeted rewards programs, enhances marketing, helps manage inventory pipelines, personalizes the customer experience, and boosts profits. Boston Consulting Group recently found that companies that implement data-driven marketing see a 20 percent increase in revenue. Data science helps organizations meet net-zero carbon emissions goals and gives scientists access to predictive models of humanity’s environmental impact. In energy production, data science lets responsive grids predict energy demand and adjust capacity in real-time.

The impact data science has on our lives is already profound, but professionals in this space have only just begun to explore what data can do. In the coming decade, data scientists and researchers will likely discover applications of data science no one has dreamed of yet.