Data science uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured before transforming them into meaningful information suitable for comprehension and to enable better and faster decision making. Identifying patterns from large amounts of data has enabled organizations to rein in costs, increase efficiencies, recognize new market opportunities and increase their competitive advantages.
As data generated by a typical organization grows, so too does the importance of data science. At the heart of it lies the data scientist, with the help of mathematics and techniques such as machine learning, cluster analysis, data mining and visualization, turn raw data into valuable business information. To be effective, the data science team must possess emotional intelligence on top of education and experience in data analytics. This is to transform and present the data insights to key decision makers and stakeholders in a way that makes sense, is significant and easy to understand.
Data can come from many channels in the current interconnected world, including but not limited to smartphones, internet of things (IOT/IIOT) devices, social media, surveys, purchases, machine data, internet searches, behavior as well as the organization’s own internal data collection. This ultimately is the first step of data science which is commonly known as data collection. By collecting such information and creating what is known as a “data lake”, these data can then be cleaned, analyzed, manipulated and studied. By applying appropriate industry recognized algorithms, meaningful insights can be garnered and be presented to the organization.
Benefits of data science are plenty in the current world and given the advancement of technology in general, more and more organizations are able to reap the benefits of data science. Research has shown that empowering stakeholders with quantifiable, data-based evidence into business decisions can lead to increased profitability, improved operational efficiency, better business performance and workflows. Specific use of data science varies from company to company, for example, sales and marketing departments can use data science to further identify and define their target audiences and provide personalized offers and campaigns. Streaming services like Netflix uses data science to serve personalized content. Logistics companies use data science to optimize delivery routes, times and modes of transport.
Data science has evolved by leaps and bounds and has proven to help countless businesses around the world improve their bottom-line and increase work efficiencies. Data science consulting is a growing sector of the consulting industry where data is entrusted to a third party with the necessary know-how and expertise to make sense of a business’s data. In doing so, a business does not need to build an internal team of data scientists or data engineers to carry out the work. Instead, organizations typically work with a third party consultant to define the problem statement and key benefits they wish to achieve through the data science project and leave the heavy lifting of data mining, data scrubbing, analyzing, algorithm tweaking and implementation to the engaged consultant.
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