Data has taken center stage amid major transformations and disruptions that have reshaped several industry verticals in recent years—Viz Music, Travel and Hospitality, BFSI, Retail, Education, etc. Data is the ingredient driving the changes.

Interestingly data has value both as raw material and as a finished good!

Data monetization is gaining traction. For better understanding, data here refers to raw data, software products, applications, analytics, insights, etc.

Organizations are directly monetizing their data by offering Data-as-a-Service (DaaS), as a monetization model. Other organizations are monetizing data by building 'Insights' using the 'raw material' and offering Insights-as-a-Service (IaaS) equivalent to finished goods.

So how should an organization look at data ? 

The CIO or the CDO of an organization today has to play the role of Napoleon Bonaparte!

Napoleon aggressively pursued his life ambition of 'conquering new land,' and he was driven by an insatiable appetite, passion, and energy. Like Napoleon, a CIO/CDO should explore every opportunity to 'Conquer New Data Landscape'!

People say 'Data is the New Oil'

However, given how oil is doing globally and its future, it is time we say 'Data is the new Soil' – A fertile data soil is essential for companies to grow their solutions and revenues multi-fold and realize its vision of global leadership.
Over the last decade, we have seen a changing definition of what automotive companies consider 'valuable' to business data.

The traditional approach was around effectively managing enterprise data :

Data Landscape 1.0 (DL 1.0)

• Dominated by Transaction and ERP, SRM, PLM, etc.
• Over a period of time, all major companies have become very efficient at managing this data with proper protocols and processes.
• With the advent of the Digital Era around seven years ago, new platforms and mobile apps around Customer Experience (CX), Process Automation became dominant in organizations.

Data Landscape 2.0 (DL 2.0) = DL 1.0+Data from Digital Platforms

• As we step into the 'New-Age Digital' era, the changing pace has increased exponentially more than ever before. In the future, everything that companies do will revolve around data.
• Organizations must first become 'Data Rich,' which will drive them to become 'Revenue rich.'

The time has come for us to look at data differently. It is time we now looked at building the next generation 'Data Enriched Platform.' 

This involves assessing your current data landscape and expanding its footprint exponentially. Call this 'Data Landscape 3.0'

Data Landscape 3.0 (DL 3.0) = DL 1.0+DL 2.0+‘AI ready Data’

• The 'New-Age data platform would be a confluence of highly context-rich data from various sources, including traditional SAP+CRM+DBM, different digital platforms data, IoT data- from vehicles and factory machines, and external contextual data (like raw material prices, weather data, etc.)
• As most of our Next-Gen data is already cloud-native, by employing AI, we should look at turbocharging the value that we would derive out of this integrated data platform.

Call this your i-ABC Strategy – The highly potent cocktail of IoT, AI, Blockchain, and cloud technologies. 5G may now be added to this cocktail.

An organization that wants to survive and thrive in a 'Data-First,' 'Tech-led' future must quickly embrace these technologies and design robust business processes around them.

 

This article was contributed by our expert Venkatesh Natarajan   

 

Frequently Asked Questions Answered by Venkatesh Natarajan

Q1. What is the importance of Data Strategy in today’s business?

In today’s competitive environment, data must serve the strategic imperatives of a business—the key aspirations that define the future vision of an organization. 

A modern data strategy is a roadmap to enable data-driven decision-making and applications that help an enterprise achieve its strategic imperatives. 

An effective data strategy helps an enterprise make technology choices grounded in business priorities to get the most value from their data. 

If you find that you can’t articulate how the cost of your data systems relates to the benefits to your business, or if you can’t articulate how your technology philosophy enables your business aspirations, then your organization would certainly benefit from data strategy.

Q2. What are the key elements of a Data Strategy ?

The ten key questions to be answered while arriving at a data strategy are as follows

• What data should be collected?
• How long should data be kept?
• Where should the data be stored?
• How will data privacy and security be managed?
• From where can data be accessed?
• What data can be displayed?
• What level of detail should be retained?
• Who is responsible for the data? (governance)
• How is data integrated?
• How will data be distributed? (virtualization)

Q3. How is data monetization categorized ?

Data Monetization can be categorized into the following two types

Indirect Monetization

The tangible benefits of using data in business operations are accurately calculated and benefits demonstrated. It could be the use of RPA technology to reduce the cost of invoice processing that is very clearly evident and quantifiable.

Direct Monetization

Where an organization clearly earns revenue through the use or sale of data. Data could mean raw data, application solutions, analytics, insights, software, etc.

Example: - Automotive companies share vehicle location data with customers at a cost based on consent.

Q4. How important is it for organizations to have a solid data monetization strategy? How can organizations become future-ready in this area?

Data Monetization is still a relatively new topic for most organizations, domestically and globally.With customers becoming increasingly tech-savvy and Gen Z owners beginning to run businesses, traditional buying paradigms are changing drastically.

So, data monetization has to be a foundational strategy for all organizations if they have to successfully serve this new emerging customer base through innovative digital solutions and data platforms.

Data monetization is unique. In that it can exponentially benefit both internal functions in terms of efficiency increase and externally through customer-facing connected solutions and digital platforms.

We call the external customer-facing part as Direct-Monetization and the internal organization-facing part as Indirect Monetization. While most companies are still looking at indirect monetization, few companies have a robust direct monetization strategy.

A recent Gartner study shows that while 61% of CDOs confirm that they use data to improve internal efficiencies, only 10% have monetized data directly (using solutions, DaaS, etc.).

There is a wide range of categories/avenues under which organizations can look to build services and solutions for direct data monetization – These include :

Direct Monetization Avenues

• Subscription based Services
• New Business Models
• On-Demand Services
• Online Retail
• Commissions
• Advertising
• Data as a Service (DaaS)
• Insights as a Service (IaaS)

5G technology will aid in an exponential increase in data volume that organizations must harness, manage and monetize efficiently.

New business models driven by data would emerge (e.g., eMaaS in the Automotive Industry).

Open data platforms will also play a transformative role in creating an industry-wide data standardization approach while springing up sub-platforms for organizations to monetize data.

Q5. What foundational/structural elements should an organization focus on building to ensure effective deployment and sustained success of Data Analytics and AI?

Data discovery and data inventory process :

• Since AI is data hungry, we need to map your existing data landscape.
• Building Data Landscape 3.0 is important.
  • Build organization capability to identify AI Use cases
• We need 'Problem finders' apart from 'Problem solvers.'
• To drive AI, we need 'Data hunters'
  • Capability to sniff Intelligence when studying/looking at data.
  • Assess Data privacy & Privacy intrusion implications when designing AI systems
• The organization decides to deploy Video analytics through CCTV Camera feeds to assess employee/workers' productivity. Will this be acceptable to the Worker unions ? 
  • How do we onboard senior management in the AI journey ?
  • Build a framework for Explainable AI, Responsible AI & ethical AI as the organization expands its AI footprint.
  • Develop an 'AI Metric framework' for measuring the benefits and ROI of AI projects