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Our strategic research and consulting deliverable are designed
to provide comprehensive information and strategic insights to
our clients enabling them to achieve business transformation goals.


The major technology disruptions to look for in 2022 & beyond

Our strategic research and consulting deliverable are designed to provide comprehensive information and strategic insights to our clients enabling them to achieve business transformation goals.


TechTalk Series

Our strategic research and consulting deliverable are designed
to provide comprehensive information and strategic insights to
our clients enabling them to achieve business transformation goals.

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Case Study Cloud-Native Solutions HCL Software is an Indian multinational ICT company. It mainly develops, sells and supports in areas of Customer Experience, Digital Solutions, Secure DevOps, Security

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Amazon’s Bezos to Face Unprecedented Protests During India Trip

Promise scholarships cover up to full college tuition for students who’ve attended New Haven Public Schools during some or all of their K-12 years, and the organization also provides scholarship recipients with advice, mentorship, career skill workshops, and connections to paid summer internships in New Haven — many of which are based at Yale.
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Big data

Monetizing the Data for Business Growth


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Data is one of those collections of facts and statistics that has gained importance for business and operations over the last few years. Businesses are employing high-end analysts to solve problems and increase revenue with the collection of data. Data monetization is gaining momentum and to get a clearer picture of the same, Shekhar Jitkar, Senior Director, Quadrant Knowledge Solutions, interacted with Mr Bill Schmarzo, popularly known as the Dean of Big data & Customer Advocate for Data Management Innovation at Dell Technologies.

The conversation started with the question if Bill could state the priority list for business leaders today who are trying to operationalize their data management or monetize their data for business growth. According to Bill, the most important question that tends to get overlooked is understanding how organizations create value and measure their value creation process in the data management, and data science space. He believes that people should focus less on the word data and more on value, that’s what is important to an organization, create value through their products for customers and stakeholders and with which criterion are they measuring their value creation process.

So, the whole idea of value creation and its measurement is a provocative conversation. In the artificial intelligence (AI) and machine learning (ML) space, if an individual does not have a holistic set of variables and key performance indicators (KPIs) to optimize, there is a risk of confirmation bias in the model. Thus, as a data scientist or individuals belonging to the AI and ML field, there is a need for conversation of value creation covering the whole gamut of operational, functional and customer value. Bill emphasizes that value creation should not only depend on revenue creation but cover the promoter’s score, employee satisfaction and overall benefit for the society and the environment as well. Organizations cannot simply now look at only one form of value creation and neglect the other as this will lead to AI models creating results that are not optimal.

Transformation of Data Management

With this practical insight, Shekhar asked Bill how growing organizations can transform data management from an IT perspective to a business-centric one to empower and unleash the economic value of data aimed toward driving both data and innovation. Bill highlighted that according to him this is the heart of any challenge for a data specialist. They must constantly work towards making data not being governed by data engineers, but to bring it down to a level that enables a user to make better-informed decisions in an imperfect world. The primary aim of all data specialists is to have stakeholders make better decisions with the given data. Bill emphasizes on the fact that as data practitioners, it is their moral responsibility to not make data sound complicated. He highlights a very crucial example for data simplification, the confusion people had around COVID vaccines. He states that even with political views aside, most of the information regarding the COVID vaccine was not either openly available to all people or it was not simplified enough for people to understand. Hence, it is imperative that as data practitioners in the data field, individuals simplify data at a level that allows common people to make informed decisions.

Aligning Business Goals

Next, Shekhar shares a fact with Bill about how aligning data strategy and technology strategy with business strategy and goals is important for any organization to leap forward and grow sustainably in the long term. So, accordingly, Shekhar asked about Bill’s insights and observation in this area, i.e., whether the leap is happening by instruction in the technology field and what can be suggested as a roadmap. Bill shares his knowledge on how recent studies done by Thomas Davenport and Randy Bean show that organizations are having a hard time being data-driven. According to statistics, over the last few years, companies have become less data-driven across various technical dimensions.

In a world where data is considered the catalyst for economic growth in the 21st century, companies are regressing. Bill thinks the reason for this backtrack is that organizations are putting an increased portion of their business strategy into talking about data but not the value it is supposed to create. The business strategy should start with what the collected data is trying to achieve i.e., the value, it is supposed to create. He stresses on the fact that the variables like KPIs and metrics that a business is measuring their success on are indicative of the culture of an organization rather than a mission statement provided by them. Directly quoting him, “We are what we measure, and we measure what we reward”. He explains that what an organization is paying its people to do is what the organization is made up of. Whether a company is using the data to drive up revenue or adding customer, employee, and environmental satisfaction to the mix as well.

Missing Link in Data Management

Related to this conversation, Shekhar asked the reason for the gap in data management and whether there is a missing link between the CEO of an organization and the data management leader. Bill states that the CDO or Chief Data Officer is the missing link, the person who is at the high management level and reporting to the CEO, working across various departments in the business to ensure how they can leverage data and analytics for the organization and become more effective. Bill terms CDOs as chief data monetization officers but the correct term should be chief data value officers according to him. Their core responsibility should be to use data across different parts of the business to drive value, and in recent times there has been a growing importance of this role. Initially, when CDOs’ roles were created, they used to report to the CIO or chief information officers, but over time their job status has come to the same footing as that of a chief information officer, chief marketing officer and even the chief financial officer of a company. The CDO now owns data as an economic asset and uses data to drive value which can be regarded as one of the profit centres of an organization.

Pain-points & Challenges in Data Management

Next, Shekhar asked Bill if he could highlight the pain points and challenges in data management and what advice would he give to organizations for addressing them. Bill stresses the fact that there exist various challenges in data management starting from the confusion to configuring exactly what data a data analyst has, to get easy access to that data. Even with the given data, moving the data around to various data repositories, data marks and data lakes house and data storage, is a challenge. Bill gives his own life example, where he has been conducting various workshops for his organization’s customers and walking them through the data management journey to make them understand what the pain-points are from business need to business outcome perspective. He has been explaining how data analytics can be used to transform a business idea into a business outcome. With all the workshops he has conducted, he has learned that most of their customers were facing challenges at the beginning of data analysis. They were unable to figure out exactly what they were trying to define with their data, and how their stakeholders would get impacted. There was even confusion in understanding how their data would be measured by their stakeholders against KIPs and other variables to get an idea of effectiveness and how it was creating value. Hence, Bill believes that there is a lot of upfront work that is directly connected to business strategy and when the business need or aspiration is clearly defined then an organization has a clearer understanding of what data is needed.  It all starts with a data perspective to understand which data is valuable otherwise all data is the same. With this perspective, Bill asserts that if he ever wants to stump a CIO, he just has to ask them what their most important data set is. According to him, the value of a data set cannot be in isolation from a business, and through his workshops he learned that most of the attendees simply wanted to define a problem they were going after before the whole data analysis journey began.

Data as an Economic Asset

Continuing with this engaging ongoing conversation, Shekhar asked Bill that many people believe that data is considered as the new oil, but Bill has mentioned multiple times in the interview how data is more like an economic asset, which will be the biggest catalyst for growth in the 21st century, so how would he elaborate on this statement.  Here Bill highlights that the statement that “data is the new oil” is the most powerful statement that he has come across since being in the industry for over forty years. He clarifies the statement that even though data is considered the same as oil in terms that data can be refined, burned, and even stored, the real fact is the statement comes from an economic perspective. Just as same as oil, which was the fuel that drove the economic power of the 20th century, data is going to be that callus which drives the economic growth in the 21st century. The reason for this economic point of view is because of the business stakeholders and the data analysts who are trying to grab their attention. The stakeholders do not want to know about data or neural networks, machine learning or even regression, they simply want the economic perspective of things and how that creates value. Thus, when data is converted to economics, which is a value-based conversation, it is an economic asset, not an IT asset or even a by-product. This will help any data analyst to walk to a CEO of an organization and point out that the amount of economic assets they possess that can be leveraged big way. Bill points out that data is shareable across organizations, and which something never runs out and can be used infinite times at zero marginal cost. There is truly no other thing on earth that has these characteristics. With the help of data, people can analyse, and create policies and products with the inclusion of AI and ML. This data does not depreciate or lose its value but rather appreciates over time, from an accounting perspective. Thus, if a CEO is told that the data, he possesses is the most powerful economic driver of the 21st century, he should be the one accountable for this data and how it is used in data analytics to create an economic asset across his organization. According to Bill, it is a powerful opportunity for all data conservationists to change the frame and no longer be the ones driving data but march up to business leaders and convince them why the data is an economic asset. This will lead to a change in the narrative on the position of data analysts in businesses and change their job roles as well. With shareable data, they would have to get rid of data silos and start moving analytics to data and accelerate a data analyst’s ability to quickly analyse data as it comes and infers it in real-time. This will lead to high economic demand for data analyst along with an increase in their job responsibility. 


Shinjini Sarkar is an Senior Content Specialist at Quadrant Knowledge Solutions.