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The 21st century has been characterized by the exponential growth of disruptive technology and its impact in multiple industry sectors – from manufacturing, banking, and finance to health care and retail. This has been accompanied by a concurrent data explosion, with every industry sector now generating information in the form of structured and unstructured data that must be both collected and analyzed to derive meaningful results. Technologies like AI and machine learning have become key components of business growth and are an integral part of decision-making for their ability to perform these exact actions. Industry reports estimate that almost $30 billion is invested annually in funding AI and ML-related projects globally. Each of these projects forms part of a much larger field that has become a major necessity for companies looking for deeper insights: Data Science.
Moving Towards Actionable Insights
Data Science and data engineering have emerged as the most important areas for companies in their digital transformation journey. Fifty percent of enterprises today suffer from an inability to move from ideation and prototyping to actual production of machine learning models due to a variety of factors, foremost among which is the lack of involvement of data engineering teams. This leads to a siloing of Data Science and data engineering teams, hindering easy access to the right data and slowing the ability to build models that can scale quickly. By enabling greater coordination between these two teams, the right tools can be used to convert both structured and unstructured data into valuable insights. The advances in Data Science and data engineering technologies have also led to various innovations that have made analyzing extremely large data sets faster than ever before. For example, our team leveraged their expertise in Data Management approaches that allow for parallel processing of data sets and ensure quality data for analysis and faster data querying, which would otherwise be extremely difficult to achieve while dealing with large volumes of data.
Another aspect of Data Science that companies can benefit from is the ability to use generated insights to scale their business strategies and improve the time to completion of projects. The deployment of cloud data warehouses is one of the best strategies to aid in this endeavor, as they enable easy storage and processing of large data volumes. The patterns that emerge from data analytics processes feed directly into strategic planning, especially with the help of predictive marketing analytics. It is estimated that by the end of 2021, 83% of all workloads will be stored on the cloud. Such data modernization solutions are essential to seamlessly integrating contemporary technology powered by Data Science into the company’s everyday functioning.
Making Real-World Impact with Data Science
The impact created by Data Science and engineering solutions is plain to see when different industry sectors are examined.
Adtech: One of the most obvious areas that has benefitted from these solutions has been the Adtech space. By efficiently processing vital data such as consumer platform preferences, purchase patterns, and industry trends, companies have been able to obtain actionable insights that deeply inform their marketing strategies. Activities such as targeted advertising, real-time campaign optimization, and sales forecasting all become possible with the help of Data Science and the capabilities it provides.
Retail and CPG: Another set of industry verticals where data analytics has made a major impact is in the retail and CPG space. Both these spaces require deep customer insights to aid their business growth, an endeavor in which Data Science can help. By performing real-time analysis of the supply chain, data solutions companies can help organizations track stocks and shipments, ensuring better inventory management and ultimately a smoother retail experience. Additionally, by tracking product preferences and use patterns, organizations are able to perform demand forecasting and personalize their marketing and sales strategies. In fact, industry reports indicate a 40% increase in purchase value and a 110% increase in items purchased when retail firms implement personalized marketing strategies.
Banking: Data Science can also have a significant impact in banking. Here, Data Science is essential in streamlining portfolio management and record-keeping through solutions that process data through multiple filters while ensuring minimal delays. This processing capability is also implemented for fraud detection and prevention, thanks to the real-time insights derived from enhanced data analytics. Additionally, large data flows are regularly generated on the trade floor and approaches powered by Data Science can help streamline this data into meaningful, usable insights for better financial transactions.
Conclusion
With data being the most important asset for organizations, Data Science plays a crucial role in transforming it into actionable business insights. By catering to a wide variety of use cases across industry verticals, AI and ML are expanding frontiers for business transformation and unearthing hidden inefficiencies. Firms specializing in Data Science and data engineering help companies across industries accelerate their digital transformation journeys by translating huge volumes of data into actionable insights and faster business value.