How to define a problem in business terms
Determining a business problem is not as simple as defining a business problem itself. A business problem is described as a short-term or long-term challenge that an organisation has that hinders it from implementing a strategy or reaching a goal. To identify the most crucial component of a data science project, one must have expertise, knowledge, patience, and time.
The proverb “well begun, is half done” refers to this as the most crucial stage in any data science project. Determining the right business challenge creates the way for an analytical problem that is intelligible, which in turn paves the way for the actions we will take in the solution.
While not every business problem can be solved with the help of data science, it is nevertheless very important for data scientists to verify that the data is organised in a meaningful way.
Expressing the unsaid
Most of the time, when a client seeks assistance for a real-world issue, they are not aware of the causes of the issue. As data scientists, it becomes our responsibility to navigate the unknown and find the problem’s underlying cause. It is crucial for a data scientist to understand how “B” complements “A” when a customer approaches them seeking help with problem “A.” This helps the client address problems “B” and “C,” which concludes the data scientist’s work.
“If I had asked customers what they wanted, they would have told me a faster horse,” famously remarked Henry Ford. Before you show them what they want, people don’t know what they want. A data scientist should therefore never depend just on market research. Reading between the lines of a page to read what is not yet on it is the work at hand.
The client does not have to understand how you may assist them in completing their goals. Deciphering what their clients are attempting to achieve is the data scientist’s job. It is the “Jobs to be Done” paradigm that popularised this way of thinking.
This framework’s main idea is that you should build your products around the tasks that your clients are attempting to do, not the specifications they present.
Domain Expertise
In the data science community today, how to deal with the misinformation offered by the courses that promise to “learn Data Science in a month and top the Kaggle leaderboards!” may be the most pressing question. Instead of producing qualified individuals who can be hired for Data Science positions, these techniques only produce inept “programmers” who believe that a basic Random Forest from the sklearn-learn package is the answer to every issue!
While it is true that certain core skills are more difficult to learn than others, such as coding, statistics, linear algebra, and extensive mathematics, a data scientist cannot overlook the need of having a working knowledge of business in the form of minimal feasible subject matter expertise. Any SME, on the other hand, ought to be regarded as king! In particular, if one has worked in a particular field for a long time, this could be the most valuable ability one has.
What makes domain expertise so crucial for data scientists, then?
To put it plainly, without adequate understanding of the field from which the data originates, no data scientist can fully harness the potential of any algorithm. Proficiency in the field can significantly enhance the model’s accuracy. For this reason, most data scientists have a solid understanding of the various fields in which they operate. Even though they might not be experts in every field, a competent data scientist typically concentrates on multiple domains. In the field of data science, domain knowledge is what makes good models great and terrible models good.
Real-World Data Science
Real-world data science applications have not developed quickly. Results that took hours to process can now be predicted in minutes thanks to cheaper storage and more processing power.
In the modern world, data science has practically endless uses, and these applications are growing more and more numerous by the day. Data science can be used to improve, supplement, forecast, and/or highlight our findings in any field of our choosing. Several domains where data science is increasingly being used into daily operations include
Healthcare Medical Image Analysis: To determine the ideal parameters for activities like lung texture classification, a variety of techniques and frameworks, such as MapReduce, are used in procedures including tumour detection, artery stenosis, and organ delineation. For solid texture classification, it uses wavelet analysis, content-based medical picture indexing, support vector machines (SVM), and machine learning techniques.
Drug Development: Data analytics was crucial in enabling the world’s rapid creation of the vaccine against the pandemic coronavirus. Among the main vaccination obstacles that data analytics helped to overcome are the following:
- accelerated process development
- developed robust scale-up and tech transfer methods
- manufacturing success improving manufacturing processes
Finance and Banking
Fraud detection: Possibly the most frequently mentioned application of data science in the BFSI industry, fraud detection models are adaptable and can be used to identify a variety of frauds, including transactional, credit card, and tax frauds. Banking institutions have begun to heavily invest in using data science to solve problems as a result of technological intervention and the exponential rise in the sorts of frauds in the BFSI industry.
Credit Scoring: Credit scoring is an additional use of data science that assists in determining an individual’s financial score. Financial organisations use this credit score, which is ranked from 10, to determine the amount, term, and interest rate of a loan that is approved.
Manufacturing sector
Data science is used by many industries to forecast product demand. It aids in supply chain optimisation and order delivery without the possibility of over- or underordering.
Manufacturing companies can achieve cost reductions through the application of data science, particularly in conjunction with supply chain optimisation. It can lessen the chance that parts won’t be stocked and delivered on schedule. When it comes to supply chain optimisation, data science takes into account a wide range of variables that might affect the entire process, such as market shortages, material availability, transportation costs, and weather.
Businesses can use data science to examine consumer demands and behaviour. The analysis’s findings are essential for determining which products are in the greatest demand on the market, which raises the quantity of goods sold. One very useful instrument in the value chain is sales forecasting.
Shops in Retail
In the retail sector, the term “customer analytics” is not new; businesses have been using customer data to target clients with tailored solutions for centuries. The development of data science has made it simple to manage the growing size of the consumer base. Real-time management of discounts and promotions using data science apps may aid in the sale of existing products or generate interest in new ones. Utilising data science to scan the entire social media network in order to predict which products will be in demand soon and market those precise products is another use case.
Data science is so much more than that! with several applications in the actual world. Before saturation, there is still a long way to go.
Conclusion
Building a portfolio of work is crucial if you want to work in the fields of data science and analytics. It will show you how to solve problems and help you present a convincing case during your next interview. In this post, we spoke about how developing a project is essential to learning the necessary skills to become a Data Scientist. The steps of problem definition, data collection, exploratory data analysis, model building, data visualisation, and storytelling were covered in relation to data science problems.
All of these abilities can only be acquired via practical experience gained from working on projects. So opt for a Data Science course in Indore, Delhi, Patna, Lucknow and other Indian cities.
