Nowadays data science plays a very important role in the growth of a company's business. Our world already changed into a digital world where data science becomes very significant. If an organization wanted to be successful there should be growth in its business and in this century it is possible mainly with the help of machines as well as data science.
Data science is very popular nowadays and there are many challenges faced by data science technology. There are many challenges such as problem identification, issues faced by data growth, lack of data security, sometimes it is very difficult to find out skilled data scientists and many more challenges exist. So this module lets us discuss some of the data science challenges and solutions.
as we all know a vast amount of data is produced each second from multiple data sources. various software and many mobile applications such as ERPs and CRMs are used by most companies in order to collect as well as manage all the information which is mainly related to the customers, their sales, or employees associated with a particular company. Sometimes the collected data will be unstructured or semi-structured so data consolidation will become a very complex process.
Data science is mainly used to extract useful insights from the data which is collected from different sources. So it is very difficult for each data scientist to extract and understand insights from the data which is produced from heterogeneous sources. To solve this they may take more time because, in order to filter it more time is required and it becomes a time taking process, as a result, it always ends up with errors and improper decision making.
The one main solution to this is to standardize data so that accurate analysis can be done. The other solution to handle this problem is each company uses many sources to collect data so all the data sources which is used is listed and we will find out a centralized platform. This will mainly help to integrate data that are collected from those sources. Then a quality management plan and a data strategy are created this is because data collected from different sources are dynamic.
Nowadays each and every company uses data science only to improve their business by extracting useful insights from the collected data. It also helps a company to identify the business opportunities, as well as it also improves the overall business performance. So lack of data security is the main challenge which is faced by data science. Virus attacks, theft, and attack faced by the data systems are some of the vulnerabilities. These all lead to a lack of data security. Among this information, theft is more vulnerable which means the information theft leads to the leakage of confidential data of the organization and sensitive data of the customers.
So only solution to this problem is for each and every company should strictly follow the three fundamentals of data security and they are confidentiality, integrity, and accessibility.
To solve a problem first we should identify the problem clearly. If it is not done clearly the root cause of the problem will be unclear and the solutions will be improper which will not give a solution to the problem. The business problems are not clearly identified because of many reasons such as the carelessness of data scientists, lack of skilled workers, getting into prediction before properly identifying the problem, and many more.
The solution to this problem is a proper method of strategizing a workflow. In order to create a workflow, a checklist should be created by collaborating all the information from all the departments. So this leads to proper identification of the problem.
The growth of the business completely depends on how well you are identifying the business problem and how properly you are solving the problem. In order to boost the business growth, we should identify key metrics such as we should be a very clear goal and vision, return on investment, number of production deployments, delivering actionable insights, and so on.
There are many data scientists but the problem is, that it is very difficult to find out skilled data scientists. Skilled data scientists are the ones who can handle ML and AL algorithms very smoothly and they will also have a deep understanding in all these areas. This problem can be solved with proper monitoring and selecting data scientists with more experience.
in order to get value out of data science, a proper method should be taken. The first thing is we should be able to understand the need of a company in order to improve their business. There should be proper communication between the team members which will help to make better decisions and healthy communication always build a bond between the teammates which will always help to make better decisions to improve the business. To get value out of data science proper understanding of the need of customers, the right customers should be targeted, and make the team more effective.
many tools are used by data scientists to solve many problems faced by a company to improve business. Each and every problem are different from one another and selecting the right tool will only help to solve the problem. Many tools(refer to the module of data science tools) are there, which one is appropriate that should be used then only data scientists can solve the problem. Selecting the right tool is most important. The solution to this is always to take help from experienced professionals and select the right tool.
The vast amount of data is collected from different sources and the quality of data may be different. To get a proper solution to a business problem quality of data is very important. If the data analysts select incorrect data it's too dangerous. If the input data quality is less then it affects the output badly. Data quality becomes less because of many reasons such as if errors occur at the time of data entry or the data disparity. the solution to this problem is, that monitoring should be done properly at the time of data entry and system integration can be done to solve the challenge faced by asymmetric data.