A we enter the era of data-intensive computing, terms such as data science, machine learning and artificial intelligence are used almost every day, often even interchangeably. In comparison to a few years ago, where the role of data scientists was limited to research and R&D purposes, today, they have become mainstream and this is creating a huge demand in the industry. And the scope of AI has evolved from simple problem solving to offering human-like reasoning capabilities. This evolution is mainly due to questions that are plaguing business leaders on a daily basis: how do we drive innovation within the company? How do we provide insightful, out-of-the-box data analytics and boost the productivity and efficiency of our workforce? How do we ensure a higher success rate of our products and services than our competitors?

What’s causing the growth of AI

Artificial intelligence is being adopted into the enterprise at a rapid pace, and adoption is likely to surge in the coming years. According to IDC, global spending on AI and cognitive technologies will hit $52.2 billion by 2021. Two factors are driving this growth: first, a vast amount of data is becoming available in more and more application areas, and second, affordable infrastructure like Cloud is enabling enterprises to store, retrieve, and share data, which was unthinkable, inaccessible, or even impossible a few years ago. The growth of IoT is only making AI more mainstream; when combined with sensors, real-time localization technologies, and near-field communication, AI is able to drive value like never before. This massive shift into digitalization is allowing businesses to unify knowledge from scientific research with vast amounts of multidisciplinary data, complete tasks based on a stipulated set of rules, and create business wisdom like never before.

Why AI and data science need each other

The massive amount of data being generated by organizations today presents a huge opportunity to identify, categorize, and unearth patterns applicable to each business.  AI, with its immense capability, can automate many tasks that Data Scientists and Data Engineers perform on a daily basis, including preparing and cleansing data, checking for correctness, identifying issues, making data available to teams, and building hundreds or thousands of variations of models. And although it can automate lower-level steps in data preparation and visualization, it cannot, by itself, truly understand what a specific set of dta means for an organization, its business and in the context of the industry it operates in. The learnings from problem-solving, deduction and pattern recognition from each area have to be conjoined with other areas, and this can be done only using Data Science. Here are two major reasons why Data Science and AI need to work together:

• Feeding the right data to get the right insights: Whether it is analyzing billions of transactions to detect fraud or forecasting consumer demand to plan for growth, since AI systems primarily perform human-like functions such as logical reasoning, and learning from experience, they have to be fed with the right computing power and data to empower them to think like humans. Hence, instead of inputting large amounts of unnecessary data into AI, data science helps extract knowledge or insights from large volumes of data and feeds only the relevant bits into AI systems. This enables businesses to find appropriate and meaningful information from huge pools of data faster and more efficiently.
• Making the right decisions from the insights unearthed: Although AI systems, through all the reasoning, provide insights into operations, business performance, customer behavior, and market trends, they also need Data Scientists to help decision-makers understand what the insights really mean. Insights, together with human judgment can enable organizations to make timely, efficient, data-driven decisions. By generating hundreds or thousands of variations of models with different prediction features, they can create iterative simulations to choose the best variation. Such a dynamic, multifaceted decision process, will make AI intelligent assistants to Data Scientists, allowing them to run more complex data simulations, make key business decisions, and respond to problems in real-time.

Match made in heaven

In the age of data overload, you need more than just a cool AI platform to drive smarter decisions; what you need is a solid team of data scientists who can successfully run teams and steer what-if scenarios before your competitors do. Considering the many benefits that AI and Data Science bring to the table, instead of only focusing on feeding systems with a consistent set of data to measure past performance and plan for the future, you need to focus on data science and use a combination of analytics and machine learning techniques to draw inferences and insights out of the massive sea of data. This will help solve higher order questions and enable you to derive far greater business value than you ever imagined. AI and Data Science – truly a match made in heaven!