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Why Data Science is Vital and How it Adds Value to Businesses


Data is everywhere. Businesses are generating data – both structured and unstructured–at an unprecedented speed from all types of sources: IoT sensors, social media, electronic gadgets, e-commerce sites and more. But, it’s not the copious amount of data that provides value for businesses. What really matters is how organizations aggregate and leverage data to generate meaningful insights for their business; insights that further enable them to work faster, stay agile, identify new opportunities, and gain a competitive edge like never before.

Organizations can unlock these insights with data science — a study of how to analyze the streams of structured, unstructured, internal and external data that are collected from multiple sources. Companies that can effectively use data science techniques will place themselves in a powerful position in the market, as they can forecast the future, predict trends, understand changing customer behavior, and implement sophisticated, proactive business solutions and processes.

Data Science Can Improve Business Performance

Some High-Impact Use Cases for Data Science

  • Better understand sales patterns to improve demand forecasting: Forecasting methods depend largely on available data. Most demand forecasting planning processes used by retailers and manufacturers rely heavily on historical and real-time, internal data to better identify patterns in customers preferences. Data science can help retailers and manufacturers more-effectively leverage a more comprehensive range of customer data, whether it’s transactional data or a digital footprint left behind by online visitors. By more fully utilizing data, retailers and manufacturers can derive deeper insights into buying patterns online and in brick-and-mortar stores, and make adjustments to how they market, price, and plan the sale of their products. They can also use the power of predictive analytics to anticipate future purchase trends. And, they can leverage collaborative filtering models, which make personalized recommendations for a customer based on similar customers’ behavior. Many companies, such as Netflix, Amazon, Facebook, and Spotify, are using these systems to predict a customer’s likely preferences.
  • Prevent, detect and manage fraud and mitigate risks across the enterprises: Using data science, along with other cutting-edge tools, helps organizations in the financial and government sectors, for example, step up efforts for monitoring fraudulent transactions and other organizational risks. Credit card companies are leveraging data science and on-demand metrics to identify suspicious activity and block card usage immediately when problems are detected. By better identifying fraudulent activities in real-time, credit card companies can drive down the costs associated with fraud, while improving customer satisfaction.
  • Improve student outcomes: Schools and other educational institutions now have more data than ever about their students’ progress in learning. The availability of this data is enabling educators to make better decisions about curriculum and instruction, diagnosing where and why learning gaps exist and identifying key areas where more focus is needed.

Organizations across many industries can leverage data science techniques to improve business insight and gain a competitive edge. Experienced partners such as Ness are helping companies identify these opportunities, accelerate development of data science-enabled solutions, and generate a new level of value for their businesses.