Home > Blogs > 5 Reasons Why Every Leadership Team Should Have a Data Scientist
Companies around the world are using data to offer customised products and services, boost operational efficiency and optimise resources. The result: increased sales, improved asset performance and augmented delivery rate. What can companies learn from data? With over 2.5 quintillion bytes of data created every day, extracting actionable insights from data drives better decision-making, data-driven discovery and innovation for competitive advantage.
However, extracting meaningful insights from humungous amounts of data is easier said than done. Applying data and analytics to fundamentals of business requires extensive domain knowledge and substantial expertise. no wonder, by 2020, new roles for data scientists, developers and data engineers will reach 70,000. In India alone there were 50,000 data analytics positions vacant in 2017.
Here are five ways data scientists can help leadership teams analyse data to boost company value:
#1 Drive better decision-making: Good decision-making is the key to success in today’s hypercompetitive markets as there can be no room for errors. Including a data scientist on the leadership team helps filter the data and formulate predictions for sound decision making. For example, Southwest Airlines uses data scientists to figure out how to keep more planes in the sky for longer, and how to fill them up more than the competitors. As a result, it is able to deliver on customer expectations while making a decent profit in an asset intensive industry.
# 2 Leverage data to influence corporate strategy: The role of CFOs is no longer solely related to financial stewardship and accountability. The new mandate is that CFOs provide meaningful strategic insights to move the company forward. Data scientists have the necessary capabilities to help CFOs perform sharper analysis and formulate its growth strategy. For instance, companies can build a culture of data relevancy to analyse pricing data for boosting supply chain management, and develop interactive applications to increase profitability and customer experience. Lenovo leverages its analytics team to meet shipping commitments, improve product-line, and provide sufficient follow up on the status order. This has helped the company provide more value to its customers.
#3 Identify new business opportunities: Once data scientists analyse business trends, product heads can use it to create competitive advantage by introducing new and relevant products and services. In addition, data scientists can help businesses discover gaps and inconsistencies in organisational processes and legacy analytical systems to create new ways of doing things and drive innovation. Zen Reach, a company that connects online and offline commerce, leveraged data scientists to mine call centre conversations for creating new business opportunities.
#4 Validate new initiatives before launch: Innovation is critical to growth. However, many new ideas fail in the market if they are not properly validated or marketed. Data scientists leverage machine learning and predictive analytics to identify key attributes of the product in the market category, discover properties of best-selling products, and create algorithms to measure product success. TFL tested and validated a responsive website that can work on any device. By leveraging data, live updates and location-based services, it helped enhance user experience and encouraged users to use its other modes of transport.
#5 Evaluate success through number crunching: Organisations are using programming languages such as Python to crunch financial data with the goal of building new products and services. By leveraging languages such as R and Python, data scientists assist leaders in simulating what-is scenarios, visualizing outcomes, and evaluating success of initiatives. Oscar Health uses Python to help its customers track doctor visits, prescription and lab work, as well as introduce new services.
As self-driven cars, personalised medicine, and intelligent robots become the new normal, it is imperative that organisations develop analytics, machine learning and deep learning capabilities to stay competitive. Data scientists on leadership teams are the lynchpin for innovation and success in the digital world.
