Is your organization ready for Digital Leadership?

Amit Dugh & Ravi Dugh, May 29, 2020

Digital Transformation is not just another buzzword anymore.  This process requires an organization to have data digitized. Therefore, the digital transformation of an organization is a necessary condition for being a digital leader. As data become the new currency in the information age, algorithms become the core of business excellence. Successful businesses are those that are capable of redesigning their businesses for the new digital age. I say the new digital age because we are using data to make better decisions, and with this computing power, we are shaping the competitive landscape using data.

Further, it is not only the use of data alone but also algorithms coupled with human decision-making that will determine your success or failure in the new digital age.  At the heart of algorithms is data. All organizations have all sorts of data generated at various points in the value chain.

So, is your organization ready for digital leadership? The maturity of your organization depends on how it manages its data. Roughly, digital transformation maturity can be divided into four levels.

Let’s start by thinking about how your organization currently manages and uses data.  The answer to these four questions below will determine your AI readiness and digital maturity:

  • Do you have data in silos? You perform a fundamental analysis of your data with KPIs. Data analysts distributed across the organization but only serve specific department functions.
  • Do you integrate data across the value chain to make sense of what happened? You have the diagnostic ability to perform advanced analysis, including historical trends and KPIs across the value chain. Typically, this is a business intelligence function in an organization.
  • Do you have the ability to predict using the data? You can predict what will happen and have forward-looking indicators. You have a data science team that generates insights to drive business decisions.
  • Do you ability to make something happen? You can utilize algorithms to optimize business in real-time to drive decisions and improve KPIs.  You have an established data science team across the organization with business managers who are taking strategic decisions to stay ahead of the competition.

Most organizations will fall across various maturity levels depending on how they use data across their value-generating functions. To assess an organization’s data maturity, you have to look at the core functions, e.g., production, supply chain, marketing, sales, customer service, etc. that generate the most value for the organization and evaluate its digital readiness.  Also, not all functions in the organizations can lead to a big difference in its digital competitiveness.

Data-driven companies dominate the markets as you can see, the market capitalization of data-driven companies has been higher than they have ever been in the past.

Data-Driven Transformation_ex01_tcm-156855.jpg
Source: BCG

According to a survey by Deloitte, digital maturity translates to better financial performance.

Source: Deloitte

Innovation is at the forefront of the digital economy, and many countries compete for the digital leadership position. See below chart depicting the data maturity of companies for the top 8 countries.

Data-Maturity by Country.png
Source: BCG

Data is an essential input in this information age, and data with human interaction (decision making) is going to be the new profitability lever for the new digital age. A massive digital transformation happening in almost every industry and digital maturity will separate winners from losers.



Data Science: What is it, and how to benefit from it?

We are living in the information age, and in this age, data is the currency of the digital economy. This article is aimed at how businesses and job-seeking individuals can leverage and benefit from Data Science.

Amit Dugh & Ravi Dugh, May 13, 2020


The science of using data to drive efficiencies, create new products, and deliver business value is called Data Science. It is a multi-disciplinary field with elements of statistics, programming, and business.  Data can be turned into insights to increase efficiencies, reduce costs, identify new opportunities to gain a competitive advantage. Data scientists deploy techniques for data analysis, data visualization, data mining, and also machine learning. Data Science is about uncovering findings that are central to the strategic business value (Lo).

Source: IPHS Technologies via

Data Science for Businesses

The importance of Data Science can be easily shown by the below figure depicting the market capitalization of the Top companies in the world. Companies in Energy, Financials, and automobile sectors haven fallen in rankings. The rankings of data-driven companies are on the rise.

Source: S&P Capital IQ & BCG

Further, when we closely review the industry sectors, the data-driven transformation has impacted the sectors unevenly.

Source: BCG

According to the BCG analysis, we can see that China has the most advanced technology companies by number. However, the US is the leader when viewed from data maturity. The below chart depicts the world leader in data-driven innovations.

Source: BCG

In order for the businesses to maintain and leverage data science competitive edge, they must incease their data-driven maturity by accelerating data transformation.

Data Science Careers for Individuals

Harvard Business Review in 2012 referred to Data Scientist as “The Sexist Job of 21th Century”.  This title still holds, and the field is still undergoing a massive change. In the age of the COVID19 pandemic, the Digital economy is accelerating even faster than before. Based on the latest statistics, we can see that global social media users are have not even reached 50%. Unique mobile phone users are about 66% of the population.

Source: Hootsuite

These statistics are expected to increase based on the Hootsuite report. With more digital growth comes the data collection and the insights that can be drawn out of this data. Such insights have been described above in the business section as to how it can be leveraged for increasing revenue and reducing costs in short an effective tool to have a competitive edge over others who do not use it.

Source: Hootsuite

Now let’s look at the report by the World Economic Forum report “Jobs of Tomorrow: Mapping Opportunity in the New Economy” where it is reported that the demand for both “Digital” and “Human” factors are the critical drivers for growth in jobs of future. Careers in Data and AI professional clusters are expected to increase by about 16% from 2020 to 2022.

Source: WEF (World Economic Forum)

The below figure points out the top 10 emerging jobs and respective skills required in the Data and AI professional job cluster as described by WEF.

Source: WEF (World Economic Forum)

Now that we have seen prospective career opportunities, we need to identify the job roles, the skills required to perform the jobs, and the respective salaries that are associated with the job roles.

The job roles in data science can be roughly divided into, data analyst, data architect, statistician, database analyst, machine learning engineer, data engineer, data scientist, and data & analytics manager. The roles of a data analyst, machine learning engineer, data engineer, and data scientist are core data science roles. The following is a summary of skills (Nelson):

Source: Sam Nelsom &

Often Data Science can be interchangeably used with Analytics, but there are differences, and a lot depends on the context. Analytics is about analyzing the data, e.g., using existing dashboards. Data Science is diving into data at a granular level to find patterns. Data Science requires a complex skillset in mathematics, technology, and business.

Sources: Various &

The jobs roles can also be described based on the work being performed, or the hierarchy of the Data Science needs as per below:

Source: Monica Rogati

Machine learning (ML) engineers are the intersection of computer science and data science. They are expected to program algorithms that can be used at scale for making decisions (e.g., pattern matching). Machine learning engineers often focus on writing production-level codes to develop computer systems or software robots. ML engineers typically take inputs from data scientists to build APIs to ensure real-time smooth functioning of a technical model that requires some inputs and generates some outputs to make business decisions.

Data Science industry is fast changing. In 2020, new titles have emerged in the sector (ODSC); these include:

  • AI Product Manager
  • AI Architect
  • Information Security Analyst

It is important to note that Data Science salaries are higher than other computer occupation salaries.

Source: Glassdoor

According to BLS (Bureau of Labor Statistics), Computer and Information Research Scientists have median wages of $122k with a projected growth rate of 16% for 2018-2028.

Source: Bureau of Labor

Now, let talk about individuals with skills from business and are looking for a mid-career more. How exactly can such job-seeking individuals leverage their business skills to get data science jobs.


Individuals with domain expertise and analytical skills can go onto learning data science courses available online to build and redirect their careers into Data Science applications in their respective domains. A list of online websites offering Data Science Professional Certifications is available below.


We believe that data science has a huge potential, especially considering as many industries are going through digital transformation. The technology-driven change will continue, and as such, the creative and critical thinking skills required to solve business problems will be in high demand.



FinTech and Financial Stability

Offering innovative financial services requires data-driven applications of financial technologies. Issues of security and privacy play an integral role in maintaining financial stability.

Ravi Dugh, January 17, 2020

Financial technology (FinTech) enables the rapid innovation of financial products and services. This presents both opportunities and challenges from a financial stability perspective. The key areas from policymakers and regulators’ perspective are operational risks from third-party service providers, cyber risks, and macro-financial risks.

In my opinion, the highest priority issue is related to managing operational risks from third-party providers. Often, a quicker time-to-market may mean that not enough due diligence was done to address the risks of technology adoption. According to a survey (Gai, Qiu and Sun), a breakthrough in technologies such as big data, image processing, mobile networks, etc. has created complicated integrated systems with distinctive demands for financial services offerings. From a technical perspective, both the financial industry and regulators should consider issues across five dimensions: security and privacy, data techniques, applications, facility and equipment, and service models.

Security & Privacy
Source: Gai, Keke, Meikang Qiu and Xiaotong Sun. “A survey on FinTech.” Journal of Network and Computer Applications (2018): pp.262-273.

A summary review of the issues is presented below.

  • In FinTech applications, security and privacy are paramount; for example, in payments, money transfers could risk data of many consumers if privacy by design principles and cyber-security practices are not followed by FinTechs.
  • Data techniques are critical as many technology systems become important to provide services. For example, in P2P lending data-oriented issues become a priority as data use via data analytics and models will play a key role in as both hardware and software required to provide the services become critical to the functioning of the FinTech services.
  • FinTech applications and their management are essential as their wide adoption can change industry practices e.g. Robo-advisors that rely on machine learning and algorithms, can provide speed efficiency and cut costs to serve customers, and therefore, regulators should consider financial data governance frameworks. If third parties are concentrated, then this could increase operational risks as parties are interdependent.
  • The use of facilities and equipment will also be a high priority. Data controls in the cloud can have unpredictable vulnerabilities due to the usage of virtual machines and physical locations of servers which may come under different regulatory environments. This may also pose legal risks.
  • New FinTech service models that require high computing performance or integrations with smart city or cloud computing could also present cyber and operational risks.
Security & Privacy in FinTech
Source: Gai, Keke, Meikang Qiu and Xiaotong Sun. “A survey on FinTech.” Journal of Network and Computer Applications (2018): pp.262-273

In summary, FinTech companies will get first-mover advantage with their unique business models but incorporating cybersecurity and privacy principles will reduce the risks associated with the new business models and promote financial stability.

Works Cited

FSB. “Financial Stability Implications from FinTech.” 2017.

Gai, Keke, Meikang Qiu and Xiaotong Sun. “A survey on FinTech.” Journal of Network and Computer Applications (2018): pp.262-273.

Featured Image Credit: Photo by Aaron Sebastian on Unsplash