While the big data market is expected to grow from USD 28.65 Billion in 2016 to USD 66.79 Billion by 2021, the artificial intelligence market is expected to grow from USD 21.46 Billion in 2018 to USD 190.61 Billion by 2025. Artificial intelligence and machine learning can be emphasized in the role of data scientists today. According to IBM, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 by 2020.
Companies are making incredible strides in artificial intelligence today, but there are still many unanswered questions about the development of this technology. There are a number of open questions that need to be resolved about the state of companies and when they can start to incorporate AI into their business in a revolutionary way.
With all of the hype surrounding this tech, it’s important to make sure that these questions can be resolved before companies start delivering more interactions between humans and machines in the workplace. Here are some of the most important questions that are unanswered about the nature of AI in the workplace today:
What is the difference between data science and a data project?
These are two terms that are thrown around in development quite often, and they’re not exactly an interchangeable idea. Data Projects are simply focused on making better insights and prediction to take better decision. Predictive maintenance projects and data projects in almost any industry require a large amount of data. The big problem with monitoring some of this data is that it’s often obtained from nontraditional sources like sensor data, social data, and usage data.
Data Science projects ensure that there’s no need for a natural collaboration between data analysts and data scientists and the only way to stay focused is with new predictive models that can handle all of the subjective info in a data project. In other words, Data Science projects are advanced Data Projects with advanced data from non traditional sources (clicks, social statistics, sensor data etc.)
Which types of data scientists are needed for the development of AI systems?
Data scientists have many different strengths, and it’s often difficult for individuals in enterprises to start addressing the various problems associated with the projects that they are working on. 80% of the data scientists working worldwide are currently working on big companies like Google, Facebook projects. Finding data scientists with specialties could eventually lend itself helpful for many industries.
Here are some of the profile names of different types of data scientists in general.
- Business analysts with a data focus: This group focuses on business analysis using data statistically and analytically.
- Machine learning engineers: This group consists of software developers who try to build machine learning models based on the data.
- Domain expert data scientists: This group consists of experts in a specific domain, and it works on certain features from available data to solve problems in that particular domain. They may not be an expert in machine learning or statistics, but they have a very strong approach to finding solutions to problems in their domain.
- Data visualization specialists: This group develops visualization and graphs from data. They play a major role in data visualization for better understanding of data.
- Statisticians: This group works with statistics and large data sets to build models of various kinds like distribution model, significance testing, machine learning and deep learning.
- Data engineers: This group has a very close relation with data scientists. The focus of data engineers is not data analysis instead they are mainly focused on working with data ingestion, data lakes, data extraction.
- Data science managers: This group consists of experienced data analysts that are into deployment and application of data science results.
and many more
Why are some of these data scientists leaving their jobs?
Data scientists are extremely in demand and as a result of this demand is difficult to keep them in one place for long. If a company is not willing to develop and then use new computer learning initiatives, there is a good chance the data scientists will move onto a new job prospect in the future.
The other reason which we found while reading other data scientists related article is “Doing data science and managing data science are not the same”. Yes, doing data science and managing data science are as different as being an engineer and a product manager. There might be a lot overlap between the two ( doing data science and managing data science) but both are very different.
While data scientists are mostly involved in cleaning data sets, testing algorithms, and researching new methods, they are also given leadership responsibilities to handle a project and focus on data governance, MDM, compliance, legal issues. Most of the data scientists are yet not ready to accept the lead role. This is one of the reasons why they switch jobs.
Is there a need for collaboration throughout data science work?
A collaborative model in the workplace can help to improve effectiveness and efficiency in the future. Collaboration in the workplace at lease with data can work much better when the bulk of analysis is split between several data scientists. Junior and senior resources on the data team can also be helpful as there will be individuals that have a boost in their specialization and others that can look at the data with fresh eyes from a more general perspective. Having split roles in the data team helps with collaboration and workflow. The overall analysis project can always be sped with the help of a data team.
Keep some of these top questions in mind if you are exploring new AI and data science initiatives with your company. These are just a few of the top unanswered questions that we have today that are essential to improving the development of AI as a whole. We will try to cover some of the other questions in our next version of this article.