In today’s business arena, data scientists are deemed as someone having superhuman powers. Wading across tonnes of data and coming up with a solution for business problems is nothing less than a magic. However, not all of this is a cake walk, though it may seem to be. Data scientists also face serious challenges in day to day operations solving which needs a lot of smart thinking, decision making and sharp analytical skills.
Let us take a look at few of the challenges faced by data scientists and how it can be overcome:
(1) Identifying the problem
One of the major steps in analysing a problem and designing a solution is to first figure out the problem properly and define each aspect of it. Many times Data scientists opt for a mechanical approach and start working on data sets and tools without a clear definition of the business problem or the client requirement.
Solution: There must be a well-defined workflow before beginning the actual data analysis work. The first step in this process is to identify the problem well, designing a solution, building a checklist to tick off important steps and finally analyse the results.
(2) Access to right data
For right analysis, it is very important to lay the hands on the right kind of data. Gaining access to a variety of data in the most appropriate format is quite difficult as well as time-consuming. There could be issues ranging from hidden data, insufficient volume of data or less variety in the kind of data. Data could be spread unevenly across various lines of business so getting permission to access that data can also pose a challenge.
Solution: Data Scientists need to ace data management systems and other information integration tools such as Stream analytics software which is useful for filtering and aggregation of data. Many Data integration software also allows connection with external data sources and their seamless inclusion in the workflow.
(3) Data Cleansing
According to a study by MIT, Big Data has begun to cost companies up to 25% of possible revenue because cleansing bad data is eroding operating expenses. Working with datasets full of inconsistencies and anomalies is every data scientist’s nightmare. Dirty data leads to dirty results. Data scientists work with terabytes of data and imagine their plight when they have to spend a huge amount of time just sanitizing the data before even beginning the analysis.
Solution: Data Scientists must make of Data Governance tools for overall accuracy, consistency and formatting of data. Additionally, maintaining data quality should be everybody’s goal. Business functions across the enterprise benefit from good quality data. Bad data quality is actually an enterprise issue. There must be people employed in various departments as data quality managers.
(4) Lack of domain expertise
Data scientists just need to be good at high-end tools and mechanisms is one of the biggest misconceptions doing rounds. Data Scientists also need to have sound domain knowledge and gain subject matter expertise. One of the biggest challenges faced by data scientists is to apply domain knowledge to business solutions. Data scientists are a bridge between the IT department and the top management. Domain expertise is required to convey the needs of management to IT Department and vice versa.
Solution: Data scientists need to work on gaining insights into business, understand the problem at hand and work on analysing and modelling the solutions. Along with mastering statistical and technical tools, Data scientists also need to focus on the business requirements.
(5) Data security issues
In today’s world, data security is a big issue. Since data is extracted through a lot of interconnected channels, social media as well as other nodes, there is increased vulnerability of hacker attacks. Due to the confidentiality element of data, Data scientists are facing obstacles in data extraction, usage, building models or algorithms. The process of obtaining consent from users is causing a major delay in turnaround time and cost overruns.
Solution: For this aspect, there are no shortcuts. One has to follow the established global data protection norms. There need to be additional security checks and use of cloud platforms for data storage. Organizations also actively need to use advanced solutions that involve Machine Learning to safeguard against cybercrimes and fraudulent practices.
Conclusion: Real-world Challenges are the greatest teachers
A popular proverb says, “Rough seas make good sailors”. Instead of the theoretical aspects, data modellers need to approach their jobs with pragmatism. Data Science is not all about building models and algorithms. Analysing data sets and predicting the outcome is as much an art as a science. Without human element, the whole process of Data Science will be rendered meaningless. By facing real-world challenges, Data Scientists will eventually learn to be proactive, creative and innovative in their approach.