Businesses are attempting to develop a data science acumen to acquire a competitive advantage. Data is becoming increasingly important, especially from a business point of view, and data scientists, who collect and analyse massive amounts of organised and unstructured data, even more so.
Currently, the average annual income for a data scientist is ₹6.98 lakh. An entry-level data scientist with less than a year of experience may earn around ₹5 lakh per year. Data scientists with one to four years of experience may expect to make around ₹6.10 lakh per year.
The job of a data scientist requires a combination of computer science, statistics, and mathematics. They translate the results of data analysis, processing, and modelling, into actionable strategies for organisations. One of the simplest methods to improve data science is to standardise practices and establish greater collaborations between business people and data scientists.
Today’s businesses must embrace data-driven decision-making which entails gathering data based on objectives, evaluating patterns based on information, and utilising the results to develop plans. In summary, the procedure is: collect, extract, format, and analyse insights.
Evolving trends
Deeper integration of data science into business and a better understanding of how data science works enable people to become more effective partners in advanced data initiatives. Data science, formerly considered a risky and academic portion of IT, is now a critical component of corporate operationss.
Big data and AI tools facilitate the processing and analysis of enormous data pools for a wide range of applications such as predictive modelling, pattern recognition, anomaly detection, personalisation, conversational AI, and autonomous system. Here are some aspects of data science that businesses across industries make use of:
Pattern recognition
Pattern recognition assists merchants and e-commerce organisations in identifying trends in customer purchasing intention. Making product offerings relevant and ensuring the stability of supply chains are essential for firms that want to keep their consumers pleased and prevent them from purchasing from competitors.
Data science techniques have long been used by companies such as Amazon and Walmart to analyse buying habits and provide improved purchasing, inventory management, and marketing tactics.
Pattern recognition offers a variety of complementary data processing applications. For example, it can be helpful in stock trading, risk management, medical diagnostics, seismic analysis and natural language processing (NLP), speech recognition and computer vision.
Predictive modelling
Predictive analytics applications are employed in a range of industries, including finance, retail, manufacturing, healthcare, travel, and government. Manufacturers, for example, employ preventative maintenance systems to decrease equipment failures and boost production uptime.
Boeing and Airbus rely on preventative maintenance to enhance aircraft availability. Similarly, energy corporations are utilising predictive modelling to increase equipment dependability in conditions that are demanding, expensive, and complex to maintain.
Monitoring customer satisfaction
It has traditionally been extremely difficult to adjust products and services to the unique requirements of individuals; doing so was both time-consuming and expensive. As a result, most systems that customise services or propose things must categorise people into buckets based on their traits. While this strategy is preferable to no modification at all, it is far from ideal.
When services and products are tailored to people’s desires or interests, person and purchaser pride are typically highest. Keeping consumers happy and engaged means they are far more likely to return.
Recommendation system
Recommendation System (RS) is a method of providing suggestions for all types of content to be used by a closely-related user in a decision process. It is used to deliver personalised suggestions in the simplest style. To integrate important features — identifying a relevant aspect for a user — RS must forecast that the subject should be suggested to do so, the system must be capable of predicting usefulness.
Leveraging data
A data-driven culture requires a single, accurate, and reliable source of integration and creating a central internal resource that describes how and where to find the data. This includes content, format, and structure of the database.
It is necessary to facilitate data-driven decision-making. Encourage everyone in their organisation to ask questions. Create resources that can give them the answers they need. Encourage curiosity and make it part of the culture.
In organisations that do not use a data-driven approach, data is often spread across multiple departments and isolated at various aspects of the value chain. Because the data is clustered in separate locations, the ability to contribute to continuous optimisation is limited.
A data-driven organisation uses the insights it derives from this data to transform its business processes. The key characteristics of data-driven businesses include a focus on automation, continuous improvement, optimisation, the ability to predict internal and external changes, adaptive and critical thinking, and especially a culture that is fully inclusive of data and its potential.
(The writer is Assistant Professor, Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology. )