The Rise of Five Data Science Factors the World Needs Tomorrow


We’re on the verge of entering 2020. As a result, the spending made on artificial intelligence and machine learning is set to grow to USD 57.6 billion by next year, predicts International Data Corp.

As this showcases the current scenario of the data science industry there will be several applications such as customer analysis, fraud detection, and churn prediction that will drive the rapid growth toward the rise of artificial intelligence and machine learning.

AI and ML are having a significant impact on the world of digitalization today. Having said that, several trends and factors need to be addressed to shape the future’s data science industry: –

  • Improved operationalization process

The data science model that tends to perform well in the development process does not necessarily mean it will perform great at the production system. It slows down the process causing tedious rework and fine-tuning of these models once again. Also, when these models work in a production environment the models tend to degrade as these data changes that further lead to maintenance and rework. While migrating the model toward the production environment it is seen to encounter certain challenges.

The integration and acceleration of machine learning models will need a shift that will augment rework and improve production use. The current data science world is surely seeing certain changes accelerating the need for transparency. However, an increased workload is now putting pressure on data science teams to augment processes so that the work can easily get automated. By doing this, the adoption of data science processes can be easily done by even the non-data scientists.

Data-driven organizations require highly skilled data science professionals who can effectively address these challenges and compete with the AI-driven economy today.

  • Transparency is the need for business users today

It is often seen that there’s a disconnect that takes place between machine learning models and expectations of business users. Though machine learning offers huge benefits, there will always be challenges that cause a hurdle in the process. This is one major area where it gets extremely tough to explain how these models work and how they generate results leading to a lack of trust by most business users. Thus, there must be enough transparency made in the process to gain trust of these users.

  • Dearth for data science talent

Based on a LinkedIn report released in January 2019, data scientists were listed to be the most promising job. While there’s much attention given to data science, it is also critical to address the shortage in the field. A career in data science could make you earn whopping salaries as companies are keen to hire professionals who possess in-depth knowledge in data science. Whether you’re a developer or an aspiring data scientist, you can easily know what employers are looking to hire for. Reskilling through data science certifications is an ideal choice as top employers are skeptical in hiring professionals without data science skills.

Top tech giants are even willing to pay lucrative compensation to those with the relevant skillset.

  • Data made actionable for data science

A 2019 survey made by Dimensional Research said that 96% of the respondents found data quality and data labeling to be critical that slowdown the adoption of machine learning and artificial intelligence.

Businesses can store humongous data, however, at different facades of systems, there comes a significant variation between the level of leadership and governance. Be it through a manual process or through leveraging automation, the first and foremost challenge a data science team will always be is to gather data and access it from multiple sources. As a matter of fact, today, most of the chief data officers along with chief information officers seriously need to take charge in assuring data is made actionable for data science. Such challenges that are related to security, ETL, data privacy, and data integration can drive unexpected changes in data science projects. Thus, making the data science process more efficient and faster.

  • The “time-to-value” should be accelerated

The pace at which the development process takes place could slow down the process in data science. Since the projects have the nature of being iterative, this requires an in-depth understanding of business problems as well. Data science professionals create a hypothesis, test it and come up with proper validation to identify problems and find valuable patterns to build ML models. This level consumes a lot of time with the process of trial and error approaches in order to find the right set of answers. Thus, accelerating the time-to-value should be the priority toward fulfilling the promises of machine learning.