Demystifying the 'Why' behind your data science implementation.

Performing business in today's over-flowing data age is not only critical but also challenging. Organizations now are generating massive amounts of data as they utilize advanced technologies to stimulate innovation and deliver improved customer experience. However, most businesses are not able to extract meaningful insights from the data they produce daily. They typically face challenges such as, siloed data, obsolete tools, skills deficiency, misaligned teams, and more. While extracting value from data has been daunting, many have taken these challenges head-on by introducing broader initiatives and integrating tools that democratize data and analytics, streamline collaboration, and speed up data processing.

That's when data science comes into picture – making the most out of data. Data science professionals are capable of extracting actionable insights out of the ocean of data. They leverage scientific methods, processes, algorithms, and systems to derive insights and turn them into major decisions for are critical for businesses' strategic practices.

Creating a Business Impact with Data

As data generation and collection grows in volume, data relevance will become even more imperative. Once the raw data is captured successfully, businesses must initiate the process of converting voluminous amounts of unrefined raw data into actionable insights. Here, data scientists play a key role. They are trained to identify and classify data that stands out in some way. They visualize patterns in data to derive meaningful information.

Besides data professionals, businesses must devise a modern data strategy and data-driven culture to take advantage of all their data and drive evocative impact for key business use cases. They must involve all the people, processes, and technology when transforming the business to become a data-first organization.

In an IBM commissioned Forrester Consulting survey, 61% of organizations increased their wide range of data and analytics initiatives such as setting up or expanding data science centers of excellence, along with improving existing products or services using data and analytics. On the other side, a majority of firms are most focused on their ability to make decisions based on data: 96% of respondents were planning, implementing, or expanding data-driven decisions across all levels and parts of their business.

Decision-makers should keep their data strategies updated to drive revenue growth. Leveraging data and advanced analytics, and even machine learning and AI can help organizations to garner more from their data. To the extent, IT teams don't just commence on technology projects but they also must ask 'Why' behind their data strategy and identify the key business impact that will be felt from the result.

Comprehensively, as data holds more value for a business, this can be problematic for decision-makers as almost 80% of all data is unstructured, and needs predictive analytical tools to reap insights. Thus, by pulling numbers and statistics through data science, enterprises can create predictive models to drive a variety of possibilities.