A new market study published by Global Industry Analysts Inc., (GIA) reports that the global market for Data Monetization was estimated at US$1.6 Billion in the year 2020. This is projected to reach a revised size of US$4.1 Billion by 2026 growing at a CAGR of 16.7% over the analysis period. The report presents fresh perspectives on opportunities and challenges in a significantly transformed post COVID-19 marketplace.

So, you’ve heard the buzz word data monetization? You recognize the size of the opportunity but perhaps you don’t know where to start. In recent months, Alqami has experienced an influx of interest from large organisations seeking advice in preparation to commence a data strategy or refine and assist executing the strategy that is currently in place. The ultimate goal is to leverage their data as a strategic asset to optimize business processes, improve decision making, enhance the customer experience and increase revenue through data distribution. But an important point we stress to our client’s is that leveraging your data is about more than just efficiently managing data - it is about building a culture committed to realizing value from data.

In light of this, I’m going to explore a framework that we use at Alqami for data management; a key component when building a data business. For each element of the framework, we explore a number of best practice guidelines.

Becoming a data driven organization requires a solid basis in each of these.

Data Management Framework
  • At the top, a business aligned data strategy,
  • Supported by an agile and well managed data infrastructure,
  • Firmly established data governance,
  • Effective data aggregation across the organization,
  • A solid people strategy,
  • Efficiently managed data sources,
  • Well run data operations,
  • Target users for the data that is fully understood and balanced,
  • And effective core technology that supports the needs of the business and the data infrastructure, while being agile enough to keep up with the pace of change

Let's have a look at each of these in turn.

What is a Data Strategy?
A central, integrated vision that articulates how data will enable and inspire business strategy.

What does it mean to have an effective data strategy?

  • The organization’s vision for data is captured in the form of a strategy, and supports the Business Strategy by profit centre and functional teams, for instance Finance, Procurement, Marketing or Technology
  • As the Business Strategy changes, data needs, whether internal or external, change as required. Moreover, the activity of leveraging data is organized in such a way as to proactively identify and drive business opportunities and decisions
  • Leveraging data generates measurable value for the organization and is well recognized - whether internally or externally, and a business case exists to support any associated costs

An effective data strategy should effectively answer a number of key questions.

  • What is the organization’s vision for data? A data strategy lays out how data will enable specific business goals.
  • How will the organization move forward on its data journey? A data strategy clarifies how the organization will execute desired data activities.
  • How will the organization drive data adoption and use? A data strategy describes how the organization needs to change to maximize value from the desired data activities. It should include change management components that will inspire change at the individual, group, and organizational levels. For instance, education, incentives, measurement, communication plans.
  • When will the organization execute the proposed activities? A data strategy lists the sequence of steps that the organization will follow to move forward; it contains a roadmap with milestones and priorities.
  • What is the organization’s economic logic? A data strategy explicitly describes how the company will monetize its data using some combination of improving, wrapping, and selling activities.

What do we mean by Data Infrastructure?
In this context, it’s the technology dedicated to analyzing, processing, storing, sharing and promoting the consumption of data to generate value for the business.

What does it mean to have effective Data Infrastructure?

  • The Data Infrastructure responds the demands of the business with no operational impact, calling upon additional internal or external services as required.
  • Data Infrastructure is properly resourced with a team of skilled staff, underpinned by annual budgets aligned to the deliverables outlined in the scope of the Data Strategy.
  • Data is analyzed, stored and accessed using carefully selected tools to support the organization's needs, whether Business or Functional, considering the nature of the underlying data.
  • Data is treated as a valuable economic asset, the supporting infrastructure is designed to augment multiple data sets with ease, whether internal, external or both, without compromising data quality, security, performance, or regulatory requirements.

What is Data Governance?
It is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. The term is used on both a macro and micro level.

It is key to a data driven business to have a robust operating model in relation to the policies, procedures and standards as they relate to data, specifically, the controls around it, and the value it can generate. The principal participants in Data Governance are key stakeholders from across the organization.

What does it mean to have effective Data Governance?

  • Key stakeholders are part of an organized framework for overseeing the organizations approach to data.
  • A recognized operating model is in a place bringing together those key stakeholders - to discuss policies, procedures and standards as they relate to data, the controls around it, and the value it can generate.
  • Policies exist describing the approach to Data Governance, as well as how data is collected, processed, maintained, stored and used. Compliance with such policies is incorporated within the organizations existing Audit and Risk Management frameworks
  • The effectiveness of the Data Governance model must be measured, and maintained to ensure it is evolving to support the needs of the organization.

Data Aggregation
This describes any process in which information is gathered and expressed in a summary form, for purposes such as statistical analysis. As a fully data driven business, that data from across and outside the organization should be able to be combined with ease, which ultimately provides more valuable insight.

What does it mean to effectively aggregate data?

  • The existing relationships between different data sets across the organization is understood at a business level
  • The opportunities associated with aggregating data are understood and proactively supported, whether across the organization, or with external third parties
  • Data policies and procedures exist covering areas such as completeness, consistency, provenance and permissioned use, as they relate to supporting the aggregation of data
  • The organization has deployed appropriate tooling to support the effective aggregation of data, operating across and outside the organization, allowing data sets to be brought together efficiently, and in compliance with any regulatory and governance requirements
  • The opportunities to combine data sets, whether internal or external, are understood, and mapped, triggered by opportunities to unlock economic value for the organization, and in compliance with any relevant regulatory and governance requirements

At the crux of it all – the people.
In terms of a people strategy, a data driven organization needs to ensure that the skills, defined roles, culture and people frameworks are in place to ensure data is treated as an economic asset.

And what does it mean to have people support data in alignment with the rest of the business?

  • There is a people strategy in place, which is part of a broader people strategy, to support data related roles, covering factors such as role definition, learning, development, remuneration and personal objectives
  • Separate data related roles are defined at Board, Profit Centre, and Functional level, and resourced by suitably capable individuals. Examples of such roles are Chief Data Officer, Chief Information Officer, Data Strategist, Data Engineer, Data Scientist, Data Analyst, and Data Architect
  • Resourcing plans for data related roles are aligned to the data needs of the business, prioritized based on revenue generation, risk management and innovation
  • Programs are in place to support the recruitment of future data talent from entities such as recruiters, research institutes, universities and competitors
  • Career paths are established to develop top data talent as part of the broader recognition of data as a transformational asset within organizations
  • Compensation structures are benchmarked to ensure they remain competitive and attract the right skill levels
  • Staff rotation schemes are in place to ensure data talent experiences a 360-degree view of the organization’s data landscape
  • The importance of cultural role models to support the evolution of the data story is recognized, managed and rewarded

Data Sources
What we mean by this is where and how does an organization source and generate the data it uses to support the business, manage risk and innovate?

What does it mean to have effective Data Sources?

  • The importance of both internal and external Data Sources in the context of how they support existing and future revenue generation, risk management and innovation is explicitly recognized, understood and managed
  • The cost, quality, operational and efficiency profile of the organizations data sources are monitored and maintained
  • A strategy is in place to proactively manage data sources, whether internal or external, with activities prioritized by opportunities to generate additional revenue, reduce cost, manage risk and accelerate innovation, leveraging both traditional and new sources of data
  • For internal sources, operational metrics and benchmarks are in place to monitor their effectiveness
  • For external sources, an effective supplier management framework has been deployed allowing for the regular review of the terms of supplier contracts, service levels, and data quality

Data Operations
In this context it means the organizational processes which ensure data is treated in a manner which ensures its full economic value can be realized.

What does it mean to have effective Data Operations?

  • The people, processes and technology associated with data management, operate in a coordinated manner to effectively supply the right data, at the right time, and of the right quality to the end user, whether internal or external
  • Data Operations are accountable to a single individual, such as the Chief Data Officer, to ensure the highest levels of data stewardship are achieved
  • Data Operations is continuously working to understand and improve data processes, identifying improvements in how data is delivered to the end user
  • Data Operations underpins a Data Management Lifecycle, where proper resources and frameworks are in place to manage and improve the quality and stewardship of data from acquisition through to disposal
  • The organization's data eco-system is mapped, governed and effectively controlled where all data entering and leaving the organization adheres to relevant policies and procedures
  • The effectiveness of Data Operations is subject to periodic audits and reviews

Data Demand
In a data driven organization, external use cases for data generated by the organization, in the course of its business activities are understood and fully leveraged.

What does it mean to understand and leverage Data Demand?

  • The importance of both core and by-product data in the context of fulfilling use cases presented by external organizations to support non-competing activities is explicitly recognized, understood and managed
  • Data valuation frameworks exist within the organization to profile the value of data based on factors such as cost to collect and maintain, its replacement cost, the expected future value of data, the market value of the data in a data market place, or its fair price for being able to use the data to mitigate risk
  • Commercial frameworks exist to enable the licensing of data by external third parties, whether core, or by-product data
  • The technical infrastructure exists to allow the effective and efficient transfer of data to external third parties, in a manner which is compliant commercially, without any operational impact
  • Data quality metrics exist to ensure any transfer of data complies with the relevant commercial, regulatory, and governing frameworks that are place

Let’s consider Core Technology
Here we mean the Core Technology supporting the Data Infrastructure such as laptops, desktops, applications, cloud storage, network, and processing.

What does it mean to have effective Core Technology?

  • The performance of the Core Technology effectively supports the Business and Functional areas as they fully leverage the data landscape.
  • Metrics exist which are understood, monitored and managed to oversee performance of the organization's Core Technology, balancing the allocation of resources between day-to-day operations, and change related activities
  • A strategic plan exists describing the evolution of the Core Technology in the support of the data landscape, which has been endorsed by the organization and is aligned with budget allocations
  • A framework exists to allow the exploration of emerging technologies to ensure the effective evolution and adoption of new Core Technology

Finally, a key capability for a data driven organization is that data quality must be embedded as an organizational discipline. To illustrate this point, it is worth considering how data is transformed into business outcomes.

You can think about data as the foundation for a hierarchy where data is the bottom. On top of data, you have information, which is data in context. Moving up we have knowledge, which is seen as actionable information, or information with meaning, and on top level wisdom - which is knowledge with insight.

Data quality influences each step as it impacts the quality of information, the quality of decisions and eventually, business outcomes. And it is important to highlight that data quality has a direct impact on the value of a dataset. So, it’s essential that an understanding of the importance of data quality is fully appreciated throughout the data driven organization.

To summarize, this is a basic overview of our data management framework that provides some guiding principles for working to become a data driven organization. When each element is acting efficiently, you can achieve a successful and profitable data business.

For a more in depth understanding, sign up here for Alqami’s e-learning course ‘How to Monetize Your Data’.

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