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Data science: who benefits from it and why?

Posted October 13, 2020
Data science

In today’s fast-paced market, businesses must make complex and robust decisions quickly and efficiently. And quite often, these decisions are influenced by information hidden in considerable volumes of complex business data. By tapping into the potential this presents, data science can provide organizations with the perfect platform to capture, analyze and evaluate this information to gain valuable insights into their customers.

Mohiuddin Khan Inamdar, director of iLabs at TELUS International agrees and believes that the technology can greatly benefit businesses looking to amplify their growth potential. He shares, “By leveraging best practices and proven technologies, we’re able to help our clients harness the power of their data to make informed business decisions and understand their customers in a more personalized way to positively impact the overall customer experience (CX) journey.”

Let’s dive in to better understand what data science is and how it can help in various industries.

What is data science?

Data science is a combination of various scientific methods, statistical techniques and theories used to analyze, refine, organize and visualize both structured and unstructured data to assist with informed decision making. It intelligently combines techniques and theories sourced from mathematics, statistics, information science, and computer science programming and visualization.

Data science process flow

To better understand data science, let’s look at the steps involved in its process flow:

Data science process flow

Data gathering: available at multiple sources and in different forms including raw, semi-structured, anonymous and documented, data needs to be gathered before any analysis can be performed.

Data cleansing: once gathered, data cleansing helps remove duplicate and irrelevant data sets, handles missing data and fixes data errors, resulting in homogeneous data sets.

Data formatting: often, sourced data is available in different formats including csv, xlsx, docx, pdf, zip, plain text (txt), json, xml, html, images, mp3 and mp4. Data formatting ensures uniformity between data and simplified backend processes meant for analysis.

Data processing: includes calculating, classifying, interpreting, organizing, transforming and validating the gathered data, which can be both manual and automated to process it into a readable format.

Data retrieval or querying: since all the collected data is stored in databases, SQL or another database language is required to query the database and retrieve the data for business operations.

Data exploring: helps in summarizing the main characteristics of the data, which involves a combination of manual methods and automated tools, such as data visualizations, charts and reports. It gives a broader picture of important trends and major points for detailed study while holding back the vital information a data set holds.

Apply algorithms and techniques: after all of the abovementioned steps, scientific methods or statistical techniques / algorithms are applied to gain in-depth knowledge and insights of the data. Some of the major algorithms and techniques performed include classification, linear regression, resampling methods, dimension reduction, and others.

Build the data model: this involves storing and retrieving data from a Relational Database Management System like SQL Server, MySQL or Oracle. With a data model in place, we can query the database and derive various reports for insights that help in improving quality and productivity. Data modeling also helps improve business intelligence as data modelers can work more closely with the business operations to gather data from multiple unstructured sources, reporting requirements and spending patterns.

Visualize and communicate: as the last step of the process, data visualization tools can be leveraged to represent the gathered data in the form of charts, graphs and maps to help make informed business decisions.

Why we need data science

Since data science enables organizations to capture and evaluate the huge volumes of data generated all over the world, it is beneficial for nearly every industry.

Information and Communication Technology (ICT): Data science with its range of scientific algorithms and mechanisms can help telcos analyze the behavior of their ever-increasing subscriber base. ICT providers can also glean valuable information in the areas of service usage, customer satisfaction, churn management, revenue assurance analysis, flexible reporting and application health status. In short, ICT providers get a 360-degree view of their subscribers.

Fintech and Financial Services: In conjunction with artificial intelligence (AI) and machine learning (ML), data science enables fintech and financial services providers to better assess risk evaluation, fraud detection, transaction records, and asset management. It also gives BFSI players a holistic view of business performance through in-depth insights to better personalize their products and services to achieve maximum customer satisfaction.

Healthcare: The rise in adoption of mHealth, eHealth and wearable technologies has resulted in a data-driven healthcare industry. Data science algorithms can comb through all the data amassed to empower healthcare providers to improve diagnostic accuracy and efficiency, optimize clinical performance, reduce hospital readmissions and lower healthcare costs.

Gaming: The industry continues to grow in popularity and with it, a significant opportunity for games companies to leverage the abundance of data to not just improve visual effects and graphics, but to also augment the gaming experience and personalize marketing strategies to increase revenue.

Retail and eCommerce: Retail and eCommerce giants are leveraging data science to visualize customer behavior and predict trends to make informed decisions. It can help retailers with predictive analytics, enabling recommendations, inventory management, price optimization, targeted promotions, personalization and fraud detection. The algorithms use the data from customer transactions and interactions from various touchpoints in the customer journey lifecycle to deliver these robust insights, enabling retailers to augment the overall customer experience.

Data science for the future

As Inamdar concludes, there are significant benefits to using data to help businesses predict the future, “by harnessing its power, data science can help industries unearth opportunities and enable them to predict and foster customer needs and ultimately, grow their business.”

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