Data and analytics capabilities have advanced leaps and bounds in the last few years. And no company wants to stay away from what data can bring to the table. Business have invested large amounts of money in gathering this information and building solutions on top this data. But have they really been able to reach where they wanted to? Have companies really been able to turn data into profits?

The exchange of data between corporations is known as data monetization. It is the process of earning income or creating new revenue streams by utilizing data, which is estimated to support expansion of the global data monetization market. Direct data monetization as well as indirect data monetization is the two forms of data monetization. The sale of raw data is known as direct data monetization. Companies are making income directly from the sale of data in this scenario.

Chief Data Officers (CDO) and Chief Data Analytics Officers (CDAO) are under intense pressure to find ways to "monetize" their growing volumes of data. While some organizations seek "monetization" by trying to sell their data, as I discussed in the "4 Types of Data Monetization", more and more organizations are realizing that the most impactful, profitable, and scalable way to monetize their data is by uncovering and applying the customer, product, and operational predictive insights buried in their data to their business to drive quantifiable financial outcomes. This is a natural maturation for data and analytics organizations that corresponds to the Insights Monetization phase of the Data & Analytics Business Model Maturity (Figure 1). As discussed in "It's Insights Monetization, Not Data Monetization", there are two ways that organizations can monetize their data in the "Insights Monetization" phase: I have written and talked extensively about the internal application of insights to derive and drive value (see my "The Art of Thinking Like a Data Scientist"), and I want to use this blog to further explore the external application of insights via data products. Data Products are a category of domain-infused, AI-powered apps designed to help non-technical users manage data-intensive operations to achieve specific business outcomes.

The economic value of data for companies is challenging to conceptualize and measure directly. Many executives have the wrong perception of data monetization. To them, the only way to derive economic value from data is to sell it to other companies. As a result, they overlook the immense untapped value that it represents. Companies can monetize by improving customer experiences, reducing costs, finding new customers, and so much more from the data that is produced directly or indirectly using big data analytics and AI.

Last week, we published a guest blog by our dear Vibhav Mithal, which describes a'Trust and Ethical AI.' What began as a discussion on the topic "Business Rational for Ethical AI" prompted our Head of Community to wonder what the business justification could be for developing ethical AI systems. We all know that AI is everywhere – just think of Alexa, Netflix, YouTube, Spotify, and any other search engine you may use. It could be a part of our professional lives, homes, cities, and so on. I mean, we'll be surrounded by more than 125 billion IoT devices by 2030. As for 5G IoT devices, 5G connections will increase to 3.3 billion in 2030, with no remaining 3G connections in the market and just 120 million 2G connections by 2030.

After my last blog on data monetization, I got several queries from customers, partners and colleagues. One common questions was, if we initiate building a data lake, how can we make sure the data lake is ready for advance data monetization use cases? I write from first-hand experience with my customers at different stages of their journey on building data lake. First and foremost, the journey starts from proper governance strategies which make the data lake a "trusted data lake." Next, moving from "trusted data lake" to data monetization involves a well-defined machine learning model and data science capabilities incorporated in data lake.

A sneak-peek into a few AI trends we picked for you from Data Natives 2019 – Europe's coolest Data Science gathering. We are about to enter 2020, a new decade in which Artificial Intelligence is expected to dominate almost all aspects of our lives- the way we live, the way we communicate, how we sleep, what we do at work and more. You may say it already does- and it is true. But I assume the dominance will magnify in the coming decade and humans will become even more conscious of tech affecting their life and the fact that AI is now living with them as a part of their everyday existence. McKinsey estimates AI techniques have the potential to create between $3.5T and $5.8T in value annually across nine business functions in 19 industries.

As we end 2017, I'm tired of writing "lecturing" blogs about what organizations should be doing to master data monetization in order to power their business models and achieve digital transformation. While the objective of every organization should be to master big data and data science (artificial intelligence, machine learning, deep learning) to drive "data monetization," let's take a breath and have some fun. My recent ankle surgery afforded me the opportunity to binge watch "Game of Thrones." As I watched the impending battle between the White Walkers and humanity, I couldn't help but identify a number of lessons that we can learn from Jon Snow's battle with the leader of the White Walkers... and the power of Valyrian steel! Game of Thrones and data, not exactly two things you think are in harmony, but this is where I find myself.

Special thanks for the help on this blog to the coolest, most hip group of industry experts that I have ever met: the Pathfinders. The Pathfinders is an elite forces group of master system engineers inside of Dell EMC who tackle our customers' most difficult and inspiring challenges. I am honored to be part of that club! Suppose an autonomous vehicle learns of a more efficient route and wants sell this knowledge to other autonomous cars for a fee (using blockchain to handle machine to machine transaction). Suppose the autonomous vehicle could start to monetize itself; to self-fund its own operations and the acquisition of goods and services such as gas, repairs or vehicle upgrades (using blockchain to conduct commerce).

After my last blog on data monetization, I got several queries from customers, partners and colleagues. One common questions was, if we initiate building a data lake, how can we make sure the data lake is ready for advance data monetization use cases? I write from first-hand experience with my customers at different stages of their journey on building data lake. First and foremost, the journey starts from proper governance strategies which make the data lake a "trusted data lake." Next, moving from "trusted data lake" to data monetization involves a well-defined machine learning model and data science capabilities incorporated in data lake.