The new oil for businesses is data. Since then, it has been a fundamental component of every decision. Companies increasingly rely on analytics and data to improve their brand's market position and increase sales.

Nowadays, information is more valuable than actual metals. A 2017 survey by NewVantage Partners found that 85% of companies are attempting to become data-driven and that the global market for data science platforms will increase from $19.75 billion in 2016 to $128.21 billion in 2023.

Problems Faced by Business

Data science is not an abstract concept without real-world applications. However, many firms struggle to establish a consistent data strategy and reorganize their decision-making around data. The problem is not having enough pieces of information.

We now produce 2.5 quintillion bytes of data per day, so much data that it is difficult to understand how quickly we make new data. 90% of all global data has been produced in the last few years.

The real problem is that businesses can't effectively use the data they already get to gain insightful knowledge that will help them make better decisions, manage risks, and defend against threats.

Data Science Challenges: What Are They?

Data and analytics are used in data science, an application of the scientific method, to tackle problems that are frequently complex (or numerous) and unstructured. 

The term "fishing expedition" is used in the analytics sector to describe a project that was never planned correctly in the first place and involves looking through the data for unexpected connections. Although this particular form of "data fishing" does not follow the rules of practical data science, it is nonetheless quite widespread. Therefore, defining the problem is the first thing that must be done. For further details on data fishing and its techniques, refer to the data science certification course in Bangalore, trained by industry experts.

Data analysis and statistics are not a form of witchcraft. They will not be able to resolve every problem a business faces. However, according to Seattle Data Guy, a data-driven consulting service, they are valuable tools that help firms make more accurate decisions and automate tedious work and decisions that teams must make.

The following are some of the classifications that can be used to group issues that data science can help solve:

• Analyzing large data sets for patterns: Which server in my server farm requires the most upkeep?

• Identifying abnormalities in large data sets: Is this specific combination of purchases different from what this customer has previously ordered?

• Calculating the likelihood of something happening: How likely is it that this person will choose to watch my video?

• Demonstrating the connections between several things: What is the topic of this internet article?

• Putting certain information into categories: Do you believe this image shows a mouse or a cat?

Common Data Science Challenges Faced by Data Scientists

• Preparation of Data for Smart Enterprise AI

A data scientist's top priority is finding and purging the appropriate data. According to surveys, cleaning, organizing, mining, and acquiring data take up about 80% of a data scientist's day. The data is verified twice at this level before going through more processing and analysis. 

According to the majority of data scientists (76%), one of the most tedious aspects of their work is this. Data scientists must effectively sort through terabytes of data that are kept in a wide range of formats and codes on a wide range of platforms as part of the data wrangling process, all while keeping track of changes to such data to prevent data duplication.

The ideal way to handle this situation is to adopt AI-based solutions that assist data scientists in keeping their competitive edge and improving their efficacy. Augmented learning is a customizable AI tool for the workplace that helps with data preparation and illuminates the subject at hand. Master data cleaning techniques by joining the data scientist course in Bangalore, right away.

• Generation of Data from Multiple Sources

Organizations get data from the numerous tools, software, and programs they utilize in various ways. For data scientists, managing large amounts of data is a significant challenge. This method requires manual data entry and compilation, both of which take time and have the potential to lead to mistakes or needless repetitions. When the data is used wisely for artificial business intelligence, it may be at its most valuable.

Now, businesses may create complex virtual data warehouses with centralized platforms to consolidate all of their data sources in one place. It is feasible to alter or manipulate the data kept in the central repository to meet a company's needs and boost productivity. This simple tweak may significantly decrease the time and work required of data scientists.

• Identification of Business Issues

Finding problems is an essential part of running a strong organization. Data scientists should identify enterprise-critical concerns before creating data sets and evaluating data. Instead of quickly turning to a mechanical fix, it is essential to identify the problem's root before starting the data collection.

Data scientists may have a systematic methodology in place before beginning analytical operations. The procedure must take into account all relevant parties and firm stakeholders. The enterprise's data may be made easier to grasp by using specialist dashboard software that offers a variety of visualization widgets.

• Communication of Results to Non-Technical Stakeholders

A data scientist's main goal is to improve the organization's decision-making capabilities, which is consistent with the business strategy that this role supports. Effectively conveying their findings and interpretations to corporate leaders and managers is the biggest challenge for data scientists. It is crucial to give managers and other stakeholders the correct foundational concept so they can apply the model using business AI because most of them are not familiar with the tools and technologies used by data scientists.

To do so effectively, data scientists must include ideas like "data storytelling" in their analyses and visualizations of the idea.

• Data Security

Businesses have turned to cloud management for storing their sensitive data due to the necessity to scale swiftly. Sensitive information stored in the cloud was exposed to the public due to cyberattacks and internet spoofing. Strict controls have been implemented to prevent hackers from accessing the data in the central repository. As they try to get past the new limitations imposed by the new legislation, data scientists now confront more difficulties.

Organizations must implement cutting-edge encryption techniques and machine-learning security solutions to combat the security issue. The systems must comply with all current safety rules and are made to fend off time-consuming audits in order to enhance productivity.

• Efficient Collaboration

Data scientists and data engineers frequently work together on the same projects for a business. To prevent any possible problems, keeping open lines of communication is crucial. The organization hosting the event must put forth the necessary effort to create effective communication channels to ensure that the workflows of both teams are comparable. In order to ensure that both departments are operating in the same way, the business may decide to create the post of Chief Officer.

• Selection of Non-Specific KPI Metrics

It is a prevalent misconception that data scientists can manage the majority of the work on their own and have solutions ready for every problem the organization faces. Because of this, data scientists are under a lot of stress, which lowers their productivity.

Any organization must have a set of metrics to gauge the effectiveness of a data scientist's analyses. Additionally, they are in charge of examining these indicators' impacts on how the business is run.

A data scientist's work environment is challenging due to the various tasks they must perform. Despite this, it is one of the professions currently most in demand. Data scientists' obstacles can easily be overcome and then exploited to improve the functioning and effectiveness of workplace AI in high-stress work environments.

Conclusion

Professionals may encounter various types of data science challenges as they work toward their analytics goals, many of which cause them to make slower progress. The steps we've covered in this article on approaching a new data science problem are meant to emphasize the general problem-solving mindset businesses should adopt to successfully address the issues of our current data-centric era.

A well-designed data science project will strive to make decisions in addition to predictions. Always keep this overall objective in mind whenever you consider the numerous difficulties you are facing. Furthermore, if you are a data science aspirant or a data scientist wanting to learn advanced skills, head to the data science course in Bangalore. Master the in-demand technologies and become an IBM-certified data scientist.