Data science has revolutionized enterprise AI and has a high potential of upscaling if valuable insight is offered to make data-driven decisions.
Each day, organizations around the globe are on the hunt to unlock 2.5 quintillion bytes of data to derive insights and value-driven actions into their business. To accomplish this endeavor, highly-skilled science experts or data scientists are brought aboard to develop enterprise AI in the business. In the ever-growing business space, every action of a data scientist helps improve the functionality of the business.
All careers come with their fair share of obstacles or challenges and the role of a data scientist is not different. Many businesses fail to make the best use of their data scientists by placing them in the wrong roles or not providing the necessary requirements. As per LinkedIn, the top 10 skills of a data scientist today include machine learning, big data, data science, R, Python, data mining, data analysis, SQL, MatLab, and statistical modeling. Most data scientists can apply these skills plowing through their computers; however, the skills are not good enough to place them in the right roles for optimum business growth. Let’s explore the common challenges faced by data scientists today.
Preparation of Data for Smart Enterprise AI
The most important function of a data scientist is identifying and preparing the right data. According to a CrowdFlower survey, nearly 80% of data scientists spend their day cleaning, organizing, mining and collecting data from different sets. Here, the data is thoroughly checked, after which it is subjected to analysis and further work. This is a very strenuous process and 76% of data scientists consider it as one of the worst parts of their jobs. The data wrangling requires the data scientists to streamline through terabytes of data – all in different formats and codes across different platforms while maintaining a log to prevent data duplication in the system.
The best way to overcome this problem is by adopting AI-based technologies that allow data scientists to remain sharp and more potent in their functionality. Augmented learning is another versatile enterprise AI tool that helps and assists in data preparation and provides insights into the problem at hand.
Generation of Data from Multiple Sources
Organizations get data from different applications, software and tools in a wide range of formats. For data scientists, handling a vast pile of data poses a big challenge. This process requires manual data entry and compilation that is time-consuming and can lead to repetitions or incorrect decisions. The data can be most useful when it is appropriately utilized for optimum functionality in enterprise AI.
Businesses can set up smart virtual data warehouses having a centralized platform to integrate all data sources in one place. The data from the central repository can be controlled or aggravated to meet and improve the efficiency of an enterprise. This simple fix can effectively save valuable time and effort necessitated by data scientists.
Identification of Business Issues
Problem identification is an important aspect of running a stable operation. Before building data sets and analyzing data, data scientists should focus on identifying key issues pertaining to the operation of the enterprise. Instead of jumping to a mechanical approach, it is essential to get to the root of the issue before setting the data set.
Data scientists can maintain a regulated workflow before initiating any analytical processes. The workflow must take into account all business stakeholders and key parties. Special dashboard software, which offers an array of visualizations widgets, can be used to make the data more meaningful for the enterprise.
Communication of Results to Non-Technical Stakeholders
The role of a data scientist aligns with business strategy, and their fundamental goal is to improve decision-making in the organization. The biggest challenge faced by data scientists is to communicate their results or analyses with business executives. Most managers or stakeholders are unaware of tools and devices used by data scientists, so giving them the correct base idea is essential in order to implement the model through enterprise AI.
Data scientists need to adopt concepts, such as data storytelling, to put forth a powerful narrative for their analyses and visualizations of the concept.
Data Security
Rapid upscaling has made organizations move to cloud management to store their important data. Cloud storage has come under threat from cyberattacks and online spoofing, making confidential data vulnerable to the outside world. To prevent these cyberattacks, strict regulations have been enforced to safeguard data in the central repository. The new guidelines have forced data scientists to navigate past these new regulations, making their work even more complicated.
To overcome the threat to security, organizations must install advanced encryptions and machine learning security systems to safeguard the data. The systems must adhere to all safety norms and aim to prevent time-consuming audits to increase the efficiency of the operation.
Efficient Collaboration
Data scientists usually work with data engineers on the same projects for the organization. It is essential to have a good line of communication to eliminate any clashes. The organizing institution should take steps to set up good communication lines to ensure the workflows of both teams match. The company can also set up a Chief Officer to oversee if both departments are working on the same lines.
Selection of Non-Specific KPI Metrics
There is a misconception that data scientists can do most of the work alone and have ready solutions for all the problems faced by the organization. This lays a lot of pressure on data scientists, making them less productive.
It is essential for every organization to have a definite set of metrics to measure the analyses put forth by a data scientist. Additionally, they must check the implications of these metrics on the functioning of the business.
The job of a data scientist is a challenging affair due to their various tasks and requirements. However, it is one of the most demanded jobs in the market today. The problems faced by data scientists can be easily reduced to improve the productivity and functionality of enterprise AI in demanding work environments.
