The title of this column series is "AI in Action," and there has indeed been a lot of action over the past year. Judging from the 11th annual NewVantage Partners survey of senior data and analytics executives, some trends are moving in the right direction. For example, more companies are creating senior roles to focus on data and analytics. The chief data officer role has quickly become much more common over time and across more industries; in the survey, 83% of companies have appointed a CDO or chief data and analytics officer (CDAO). An increasing number of companies (69% in this year's survey) are officially incorporating analytics and AI into the CDO role, and we think that's a good idea.

The metaphorical data explosion of the current age has changed how organizations function. Today, with the right amounts of effort and investment in technology and expertise, business heads can take operational and other major decisions with greater success as they're based on cold statistics and empirical evidence instead of--as was the case in the days of yore--inadequately supported gut feelings and instinct. The application of business intelligence is also much more widespread now, with organizations in several sectors using machine learning, computer vision, NLP, IoT and other data-oriented technologies to collect, process and analyze the data collected before putting it to use in a variety of ways. An often-overlooked aspect of business intelligence is the need to incorporate a long-term data-driven culture at the workplace in a way that complex analytical terms and working mechanisms become second nature for all stakeholders in any organization. Only organizations with high data fluency can foster such a culture for the long term.

The lessons of this book are delivered in clear language and without technical jargon. I've worked with Randy Bean for almost twenty years, and I've read a lot of his writing. He prides himself on his ability to communicate about technical subjects to people with no technical backgrounds. If you are someone in a business role who has heard about such topics as big data, artificial intelligence, and digitization, and you want to know what all the fuss is about without getting lost in technical detail, you have come to the right place.

"We build models and machines in AI that are more complicated than we can understand" Only 13% of finance organizations are using artificial intelligence, analytics, and automation to transform multiple processes across their enterprises; when it comes to meeting business expectations to generate actionable insights from company data, not a single survey respondent states that their function can do so significantly; none of the executives say they have a structured way to generate predictive insights to meet businesses' changing and varied demands; no one is using AI, analytics, and automation to fundamentally reimagine their finance function [Genpact survey of 500 CFOs and senior finance executives] The Indiana Donor Network can immediately spot anomalies in its donor network and boost contributions. The machine learning-based system from Sisense Inc. can spot, for example, when a hospital that regularly produces organ donations doesn't deliver any in a particular week. After an anomaly is spotted, the organization can arrange a meeting the next day with the hospital to review protocols. Before, a problem in the system would have come to light 30 to 45 days after the fact. I want to emphasize, though, that we haven't solved language understanding yet in any satisfying way"--Sam Bowman "This is a new period in the world's history--we build models and machines in AI that are more complicated than we can understand"--Jason Yosinski, co-founder of Uber AI Labs

Such is the ideal scenario for every manager everywhere, right? Just assign that task to a trusted colleague and watch as the magic happens. Well, maybe that's a reality best expressed in a famous sci-fi TV series, but ... Science fiction often predicts the future quite accurately; and the 1990s future is, well, today! In other words, the vision espoused by Star Trek: Next Generation, in many ways can now be qualified as realistic, at least with respect to the dynamic use of data. Let's face it: Data drives the Information Economy, especially when modern technologies such as artificial intelligence and machine learning are trained on it.

There are plenty of reasons for companies to incorporate data science and machine learning into their business. It can allow you to better understand and predict customer behavior, automate repetitive manual tasks, detect errors and anomalies faster, evaluate business decisions with data instead of mere intuition, get an edge over competitors, give marketing campaigns more punch, check a box for investors, attract more talent for the organization, or brag about it at conferences. Many companies face the challenge of building up a data science team from scratch and it can be hard to figure out how to start. In 2016, I was the first hire of a new data science team, with little infrastructure or strategy in place. Over the years, there were many different challenges for us to solve and mistakes to learn from as the team got more and more mature.

With billions of devices connected to the web right now, a massive amount of data is being generated every single day. And millions of devices are predicted to join the league shortly, thus augmenting the production of such data to a higher level. Today, companies across industries are striving to leverage the data they capture from different sources to accomplish their business goals and they're constantly looking for skilled data science professionals to help them do it. In addition, the role of a data scientist is considered as the 21st century's hottest job by the HBR (Harvard Business Review). Unquestionably, a data science professional not only earns a fattier pay packet than professionals at similar levels in other fields but also experiences a huge growth prospect.

NewVantage Partners has released its 7th annual survey of senior corporate c-executives on the topics of Big Data and Artificial Intelligence (AI) business adoption. The survey was first conducted in 2012 in response to Fortune 1000 business and technology c-executives who sought to understand the potential impact of Big Data and its implications for leading companies. This year, c-executive decision-makers comprised 97.5% of the survey participants, with nearly 65 Fortune 1000 or industry leading firms among the participants. In recent years, Fortune 1000 companies have come to recognize that to compete with highly-agile data-driven competitors, mainstream firms must become more adept at leveraging their data assets – 91.6% of executives report that the pace of investment in Big Data and AI is increasing, while 87.8% report a greater urgency to invest. Yet, mainstream companies face challenges in becoming data-driven.

The key to making better business decisions is surprisingly simple: take a proactive approach to using data. Every company gathers data in one form or another, but the way a company uses its data has a lasting impact on the ability to compete, innovate, and attract talent. For many companies and their employees, data is gathered and handled reactively. They collect and use data intermittently on an as-needed basis, but it's seldom collected for historical analysis to support the creation of AI solutions. Data-driven companies believe that being proactive with their data is the ultimate differentiator.

In the recent time, there is a lot of focus on big data, data analytics, artificial intelligence and machine learning. All these four terms are different sides of the same cube and it has a huge potential across boards, which is why it is believed that data has the power to change the landscape of your business. No wonder the large companies are running in that direction. However, they do have the breadth and the resources to take the risk and rise up after failures. While small and medium companies that are keen to transform into a data-driven business need to scale up wisely and scrutinize each move minutely.