Data has taken the center stage by storm in the midst of major transformations & disruptions that have reshaped several Industry verticals in the recent years, Viz Music Industry, Travel & Hospitality Industry, BFSI, Retail, Education etc. Data is the ingredient driving the changes. Interestingly Data has value both as a ’raw material’ and ‘as a ‘finished good’!! Data Monetization is gaining traction. For the sake of better understanding, Data here refers to raw Data, Software products, Applications, Analytics & Insights etc.
Expanding Organization Data Landscape
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) which is 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 & energy. A CIO/CDO should like Napoleon explore every opportunity of ‘Conquering 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 its solutions and revenues multi-fold and realize its vision of Global Leadership. Over the last decade we have seen a changing definition for organizations consider as ‘valuable to business’ data.
Evolution of the Data Landscape
The traditional approach was around effectively managing Enterprise Data. Let’s call this Data Landscape 1.0 (DL 1.0)– Dominated by Transaction & ERP, SRM, PLM etc. Over a period, all major companies have become very efficient at managing this Data with proper protocols and processes.
With the advent of Digital Era, a decade ago, new Platforms, Mobile apps around Customer Experience (CX), Process Automation, Digital Commerce Platforms etc became dominant in organizations, referred to as Data Landscape 2.0 (DL 2.0) = DL 1.0 + Data from Digital Platforms.
As we step into the ‘New-Age Digital’ era, the pace of change has exponentially increased more than ever before. Going forward, everything that companies do would revolve around Data. Organizations must first become ‘Data Rich’ which will drive them to become ‘Revenue rich’.
Time has come for us to look at Data through a different lens. It is time we now looked at building the next generation ‘Data Enriched Platform’. This involves assessing an organization’s current Data landscape and expanding its footprint exponentially. Call this as ‘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 sources of SAP+CRM+DBM, various Digital Platforms Data, IoT Data- from Connected Products, 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 Technologies including IoT, AI, Blockchain and Cloud. 5G may now be added to this cocktail. Organization that aspires to survive and thrive in a ‘Data-First’, ‘Tech-led’ future need to quickly embrace these technologies and design robust business process around them.
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 for an organization. A modern Data strategy is a roadmap to enable Data-driven decision-making and applications that helps 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 almost certainly benefit from Data strategy.
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Types of Data Monetization
Data Monetization can be categorized into the following 2 types: –
- Indirect Monetization: – Where the tangible benefits of using Data in business operations is accurately calculated & benefits demonstrated. It could be 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 use or sale of Data. Data could mean raw data, application solutions, Analytics, Insights, Software etc.
Example:- Automotive companies sharing vehicle location data with customers at a cost based on consent.
Importance of a solid Data Monetization Strategy to enable Organizations to become future ready
For most Organizations, both domestic as well as globally, Data Monetization is still a relatively new topic. With customers becoming increasingly Tech Savvy and Gen Z Owners beginning to run businesses, traditional buying paradigms are changing drastically. So, Data Monetization must 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 the way that it can exponentially benefit both internal functions in terms of efficiency increase & also externally through customer-facing connected solutions & digital platforms. We call the external customer-facing part as Direct-Monetization and the internal organization-facing part as Indirect Monetization. While majority of the companies are still looking at Indirect Monetization, very few companies have a robust direct monetization strategy.
A recent Gartner study shows that while 61% 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 & solutions for Direct Data Monetization – These include:
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5G Technology will aid in exponential increase in Data volume organizations have to harness, manage and monetize efficiently. New business models driven by Data would emerge (e.g: e-MaaS in the Automotive Industry). Open data platforms are also going to play a Transformative role in creating an industry-wide data standardization approach whilst also springing up sub-platforms for organizations to monetize data.
Essential guidance for CXOs for sustained success of Data Analytics & AI towards effective Monetization
To conclude, it is important for CXOs and technology leaders to focus on the following structural/foundational elements to ensure successful deployment and sustained success for effective data monetization:
- Data Discovery & Data Inventory Process
- Since AI is data hungry, it is important for us 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
- Organization decides to deploy Video analytics through CCTV Camera feeds to assess employee / workmen 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 a ‘AI Metric framework’ for measuring benefits & ROI of AI projects
Addressing these questions and pointers can help organizations move towards an accelerated path of data monetization, making it a part of the growth strategy and organizational priority.