Maximizing the value from data by renovating existing business models or introducing new data-driven business models has become a key concern of business leaders (see What are Data-Driven Business Models? ). Latest technological advancements such as cloud, internet of things, big data, and machine learning, have contributed to the rise of data-driven business models as an emerging phenomenon in the data sphere. Data-driven business models are characterized by data as a key resource, data processing as a key activity, or both. Novel opportunities appear for organizations to monetize their data. Established companies, who are expected to be resting on tremendous amounts of data, are particularly exposed to increasing pressure in this regard. These companies have been collecting data for decades, which could give them an advantage over start-ups with limited data resources. However, prevailing organizational structures (and especially the architecture) might become a bottleneck. Data is collected and stored in silos or hoarded in monolithic data lakes. With the rise of data-driven business models, many organizations have started to invest heavily in new technologies. Business units are introducing their own data and analytics solutions, creating more silos and, along with them, increasing complexity. The fragmented data and application landscape leaves confused business users behind, neither able to treat data as an asset nor to leverage it for value generation. The Potential of Enterprise Architecture   Introducing new data-driven business models requires deep intervention in the entire organizational structure. The current (as-is) architecture must be well understood and the desired target (to-be) architecture, which would apply the data-driven business model, must be carefully planned. This is where enterprise architecture comes in. Enterprise architecture has proven its potential in many IT-related projects and is deeply rooted in the information system discipline. It is a critical component for strategic planning, top management decision making, and project management. By providing tools such as metamodels, frameworks, and management methods, enterprise architecture supports transparency, building on an organization’s key components – from business, data, and application to the technology level. Furthermore, enterprise architecture helps businesses organize their architecture in a manner directed toward a common vision. The enterprise architecture management function supports the transition from the as-is to the to-be state through several intermediate architecture stages. Enterprise architecture has been successfully applied in the context of big data analytics and business model design, as it helps to achieve transparency across relevant social and technical elements and their interdependencies. Enterprise architecture can provide answers to stakeholder concerns based on models and tool support for the design and implementation of such socio-technical systems, where companies struggle to understand the existing data resources and capabilities. Similar to the support for business model design and big data analytics, enterprise architecture can be beneficial for data-driven business model innovation. Beyond that, enterprise architecture offers blueprints, reference models, frameworks, and assessment tools that are beneficial in making the as-is state transparent and developing the to-be state of data-driven business models. In order to justify huge investments in organizational transformation, as required for such models, enterprise architecture first and foremost supports the data-driven business model design phase. A good understanding of data assets and system components (and their interrelation), as well as their link to the ecosystem, are key for successful data-driven business model realization. Data Architecture for Data Assets  As highlighted above, data architecture is an essential component of an enterprise architecture. With the dawn of data fabric and data mesh concepts, new opportunities are arising to manage data as an asset by applying these emerging data architecture design patterns. For example, domain-oriented and decentralized data ownership and architecture enable federated data management and data sponsoring along business domains. Additionally, the increasing amount of produced metadata, while the actual data flows between the system, reveals automation opportunities for classifying and contextualizing data. The bottom line is that the enterprise architecture practice offers vast potentials in the context of data-driven business model design and realization. Many organizations face architecture concerns on their journey to data-driven business model realization. With the rise of data fabric and data mesh as emerging data architecture patterns, new opportunities are revealed for established companies to maximize value from their data (see Power to the Business Users with Data Mesh ). Assefy applies emerging data architecture concepts and federates data management and sponsoring with related domains, providing an interface between the complex and fragmented data and application landscape to business users. Coming from the right side of the information supply chain, Assefy focuses on connecting business value to data assets. Contact us and find out how you can leverage your enterprise architecture to Assefy your data.

Data have traditionally been perceived as crucial to business operations, strategic decision making, and new business development. The terms under which data have been investigated have varied in recent decades, and range from business intelligence, business analytics, and big data to big data and analytics. But while the mission of maximizing value from data may remain steady, the particulars change. Today, the biggest barrier to becoming more data-driven is company culture (see The Challenge of Becoming a Data-Driven Company ). Building a data-driven culture requires deep interventions into the organization’s capabilities. Mindshift changes as well as greater knowledge and skills are required to become truly data driven. In a data-driven organization, everyone owns data. But there are challenges to overcome to reach that goal. In the following, we describe how companies can get there. Blessing and Curse of Prevailing Structures For established companies, the prevailing organizational structures are either a blessing or a curse. On the one hand, a great data and capability pool can be leveraged to put forward new data monetization initiatives. On the other hand, the same capability pool and inflexible structures might become a bottleneck, as the required innovation level is not met. Unlike digital-native companies—such as Google, Netflix or Facebook—traditional companies find it challenging to maximize value from data. The former type of company has data value realization at its core, which paves the way for introducing data-driven business models (see What are Data-Driven Business Models? ), including sales of advertising space (Facebook) or new insurance services (Amazon and Berkshire Hathaway). In traditional companies, introducing new data monetization initiatives implies organizational transformation and capability mobilization. Deep interventions into the entire organizational structures are required. Established companies face challenges in understanding existing data resources and mobilizing capabilities from different sources. Capability mobilization comprises internal company capability-building as well as the orchestration of external capabilities for value creation and capture. Developing strategies for data monetization when there is a scarcity of highly skilled resources, such as data scientists and full-stack developers, has become a key concern of executives (see Power to the Business Users with Data Mesh ). In particular, enabling seamless collaboration between business and IT while fostering innovative working methods is a tremendous challenge. Establishing the required structural, cultural, and procedural conditions for data monetization initiatives to thrive in the context of prevailing organizational structures and scarce resources requires a gradual development of business model components and an understanding of the data from analytical and technical standpoints (see Architecture Management for Data-Driven Business Models ). Here are five practical recommendations to overcome the challenges of prevailing structures. 1. Treat Data as an Asset Data monetization initiatives require an understanding of data as an asset. Data quality and reliability are crucial to the value proposition. Data privacy and ethical constraints might have long-term impacts on the company and it is vital to assess them. For data monetization initiatives, two perspectives on data should be considered. One, data processing within data-driven business models should be described via business-model frameworks and use cases with a high abstraction level. Detailed views on the data and their processing should be provided from an information system standpoint. This includes informational entities and how they are used, created, captured, transmitted, stored, retrieved, manipulated, updated, displayed, and/or deleted by processes and activities within the data monetization initiatives. The internal perspective answers the questions: What data is required? and How is it processed for the value proposition? Two, data is an asset used as an input resource for data monetization initiatives. Regarding data as an input resource for data monetization initiatives makes their harvesting crucial. Sourcing data for monetization internally necessitates the organizational building of a data foundation for data monetization initiatives. This includes data governance and data management procedures, regulations, and policies. A reliable data foundation is essential for data monetization initiatives. The effort required in laying this foundation should not be underestimated. Establishing standardized data management and governance procedures can be a transformation in and of itself. 2. Apply a Start-up Way of Working In order to deal with the inflexibility of the existing structures and allow the data monetization initiatives to unfold, a “start-up” way of operating is recommended. If the established company takes such an approach in combination with recommendation 1, it can benefit from the wealth of data collected over decades and other resources while being able to move to new technology at the required pace. These synergies allow the established company to outperform the innovator. In terms of capability mobilization, the sourcing efforts must be broadly conducted beyond the internally available resources, especially when establishing a new business model. Role descriptions for analytical capabilities to realize data monetization initiatives should be defined independently of an internal view of prevailing resources. Matching the demand with the available capabilities should be a subsequent step. Additionally, one core team with end-to-end responsibility should be appointed. 3. Establish an Agile Mindset Capability mobilization in data monetization initiatives should be conducted with an iterative/agile mindset. In this context, an agile mindset refers to project management techniques that are applied to endeavors with blurry requirements. This interpretation of agility contrasts with traditional project management in software development, which is characterized by clear outcomes, and time and budget constraints. The teams working on data monetization initiatives need to embrace failure and learn quickly while moving forward. As the requirements are unclear, an iterative requirement selection and prioritization approach is required. Without clear outcomes and no defined requirements, big data analytics must be seen as a cost of doing business. Data monetization endeavors are long-term initiatives; traditional management techniques may not be fruitful. 4. Foster a Central Decentralization Establishing an agile and start-up way of working requires flat hierarchies and autonomous team setups. For data monetization initiatives, independent teams with end-to-end responsibility should mobilize capabilities along with the key enablers, with a central unit (value realization office) coordinating the decentralization-related work. The value realization office ensures senior management engagement with regular reports, progress tracking, and decision points. Additionally, it coordinates the allocation of financial resources for the planned endeavor. Effective financing is a crucial component of data monetization initiatives. In order to ensure sufficient funds, the procedure for data monetization initiatives must be continuously cost/effort driven. Ideally, the funding is structured in a staged approach, similar to start-up funding rounds. 5. Prioritize and Pipeline Business Models / Use Cases Data monetization initiatives confer a high risk for established companies, as they require capability building in various domains with unclear returns. Implementation requirements are indistinct, and the business impact difficult to estimate. Analytical capabilities must be established to understand the data and technical capabilities for their processing. In order to prevent falling into the “hype trap” of data monetization initiatives, it is vital to keep a value-driven mindset through the endeavor. Within the ideation phase of data monetization initiatives, a multitude of business model ideas / use cases should be developed. Each option promises returns and demands capability mobilization. Sequencing the business model ideas / use case for testing and realization is crucial. An exploratory approach involving iterations of testing, prototyping, and, ultimately, implementing, is recommended. Contact us to find out how Assefy can help your organization to become more data-driven.

Business leaders today are eager to maximize the value from data. This has become a key challenge for established companies, as it helps improve operations and decision making, enhances products and services, and ultimately leads to the development of new business models. Advancements in information technology, especially in machine learning, big data and analytics, cloud computing, and Internet of Things (IoT) technologies, have further increased the importance of data for business development and innovation. There are novel opportunities for organizations to renovate their business models with big data analytics or develop new data-driven business models. These data-driven business model innovation opportunities particularly put established companies, which are expected to be sitting on tremendous amounts of data, under increasing pressure to act. But what are data-driven business models in the first place? Getting the Terms Right  A verity of terms have been proposed to describe data-driven business models (BMs), ranging from big data business models, data-infused business models, data monetization, data capitalization, and data sales, to name a few. On the raw data level, capitalization is conducted through the sales of first-, second-, and third-party data. First-party data are collected by a company and are not freely available. Second-party data are collected in collaboration with another company. Lastly, third-party data are collected by someone else. Selling these data assets is also referred to as data sales. Deriving information from first-, second-, and third-party data can lead to value generation through insights, improved decision-making, and the performance and enhancements of customer experience and value propositions. These improvements are reflected in profit increases and cost reductions. This information’s value also grows exponentially through its combination with existing knowledge. Data value realization is either accomplished by improving the prevailing BM or through the development of new BMs with data as the key resource. The value generation and capturing processes are referred to as data monetization or data-driven business model innovation. To describe data-driven BMs, we draw on business model representation frameworks. The essential structure of any business can be represented with business modeling techniques. Several modeling frameworks, varying in their characteristics and components, have been proposed in the past. Their primary purpose is to describe how an organization creates and captures value. Business modeling has gained importance as emerging technologies threaten established businesses and their traditional BMs. In order to commercialize disruptive technologies, companies face the challenge of understanding their prevailing BM. The critical components of the business and their interrelation are represented in the models, allowing to form conclusions on technology integration. Thus, BMs can be seen as a “vehicle” for innovation, helping to understand the traditional business in order to envision a new, targeted model, providing a source of competitive advantage. The business model canvas is a commonly accepted framework for representing BMs. It proposes nine components (key partners, key activities, key resources, value proposition, customer relationships, channels, customer segmentation, cost structure, revenue streams) to convey the essentials of a business. Data-driven business models rely on data as a key resource and/or have data processing as a key activity, making data essential for the value proposition. Companies can innovate their existing BM by applying more data to reach operational efficiency and new revenue streams or by developing completely new BMs by harvesting data. Some Examples  Digital native companies have been realizing data-driven BMs ever since they have been founded. Here are a few examples: Netflix analyzes its customer data to match actors, producers, and storylines to targeted customer segments. This has allowed them to produce hits like Stranger Things, The Irishman, and House of Cards. Alibaba combined its customer data platforms and was able to achieve 20% higher conversion rates and efficiency increases with an AI-enabled customer service chatbot resolving 95% of queries. PatientsLikeMe , which provides a platform to share user experiences related to illnesses and treatments, realized a data-driven BM by selling anonymized data from their social media platform to pharmaceutical companies and companies that produce medical devices. Established companies have also started to realize data-driven BMs: The pharmaceutical distributor Tamro sells customer spending insights to drug manufacturers. Capturing sales data allows Tamro to provide pharmacies information on their sales compared to their competitors. Barclays offers a service for SME companies that provides them with information about their revenue inflows and outflows and analyzed data about their payments and transactions. Customers can also compare their own data with data from similar businesses in similar locations. Vodafone sells anonymized network data to navigator producer TomTom. Vodafone has real-time, location-based data about its customers that TomTom buys to better understand how people and vehicles move in order to optimize navigation suggestions. Challenges  Many business leaders do not consider data to be synonymous with profits. Simply assuming that optimistic data gathering will prove profitable is naïve. Instead, organizations must evolve their culture, business models, operating models, and enterprise architecture (see Architecture Management for Data-Driven Business Models ) in order to capitalize on data. Obstacles to data-driven BM design and realization are related to data quality, data privacy, culture, data literacy, and organizational transformation (see The Challenge of Becoming a Data-Driven Company ). Since May 2018, with the advent of GDPR and its strict regulations, data privacy has become increasingly important both for companies and European citizens. This law sensitizes companies to the moral and ethical responsibility of personal data usage. Data privacy and ethical constraints might have long-term impacts on a company realizing data-driven BMs. Transforming organizations into data-driven businesses and equipping companies with the required analytical and technical capabilities could prove to be the most challenging obstacle. In order for sensitive transformational interventions to be successful, however, the prevailing roles, processes, and technologies and their interplay must be properly grasped. In particular, established companies that are expected to be sitting on tremendous amounts of data are under significant pressure to act. For established companies, the prevailing organizational structures might become a blessing or a curse. On the one hand, a large data and capability pool can be leveraged to put forward a new business model. On the other hand, the same capability pool and inflexible structures might become a bottleneck, as the required innovation level is not met (see Mobilizing Capabilities of Data-Driven Culture ). Engaging business users in data conversations will be a catalyst for fostering a more data-driven culture. Assefy provides a modern data collaboration platform that connects datasets to business semantics, helping business users to find, understand, and apply data for value generating use cases. Contact us to find out more.

Companies have always collected data, and these data sources might turn out to be data treasures. However, the advantage of collecting huge amounts of data over decades comes with the disadvantage of prevailing organizational structures that hinder value realization from data. This article describes four approaches established companies can take to monetizing their data. The approaches are drawn from research into the experiences of early-adopting companies and highlight how the environment for data monetization was built. Direct or Indirect? When aiming to realize value from data, companies need to mobilize analytical as well as technical capabilities. The former is required to understand the data that are central to the data monetization initiative, and comprises human capabilities including mathematical and business analytical knowledge as well as the ability to use technology. The latter comprises technological components that are needed for data processing, as well as data infrastructure capabilities including hardware, software, and network capabilities. Companies take either direct or indirect approaches to mobilizing the required capabilities. With indirect approaches, companies start building either analytical (business-centric approach) or technical capabilities (technology-centric approach) first. These companies have the goal of realizing new data-driven business models but begin with projects that gradually develop capabilities (see What are Data-Driven Business Models? ). As an explanation for taking the indirect approach, the interviewees from our research emphasized that “improving the existing services, products, and operations is more obvious to the business, easier to grasp, and bears less risk.” Established companies have differential approach options based upon their prior actions. These prior actions may limit or enhance a company’s ability to act directly and indirectly and may drive its ability to focus on analytical or technical matters. Taking a direct approach usually implies a clear vision and the willingness to invest in data monetization initiatives immediately. Companies taking a direct approach tend to have a greater digital maturity, which allows them to grasp data monetization opportunities. The projects are typically sponsored by the chief executive officer (CEO) and financed by funds for new business opportunities. The new data-driven business model tends to be integrated into the existing organizational structure, or a new company based on a new data-driven business model is established (i.e., a start-up). Our cross-industry research with early adopters has led us to suggest the below four approaches that come out of a broader direct or indirect approach. Business-Centric Approach Beginning with analytical capabilities, companies start developing an increased understanding of their data within business units, which usually fund these initiatives. These companies have typically done some previous big data analytics groundwork that enables a business unit to analyze existing data assets. In our research, the use cases for the gradual enhancement of the traditional business model tended to be very detailed, but this allowed the potential data-driven business models to be described at a higher level. The business-centric approach delivered promising results, as the business was involved early on. A business-centric approach was followed by 26% of the cases analyzed in this research. Technology-Centric Approach Companies that start building technical capabilities have usually decided to invest in big data analytics platforms as part of their digital initiatives. For them, having the platforms in place is the first step toward the realization of value from data. These companies typically have not done any prior big data analytics groundwork. Our research found that selecting the most sophisticated solution to provide best-in-class technical capabilities was a common strategy. As it turned out for some companies, the implemented solutions were very advanced and not required for the developed use cases. One interviewee highlighted that “the investment was not justified”. A technology-centric approach was identified in 37% of the cases. Integration Approach Transforming an organization to integrate a new data-driven business model into existing structures typically requires a clear business opportunity, a common vision, and CEO sponsorship. A large-scale transformation is usually initiated to mobilize required capabilities for data monetization. The initiative typically begins with a data-driven business model design supported by external consultants infusing the ideation process with relevant industry and cross-industry data-driven business model cases. This process often results in a populated business model comprising the relevant facts of the identified business opportunity. Based on this design, a minimum viable product is initiated, presenting early tangible results. Once the minimum viable product reaches a certain maturity, it enters the implementation stage, where the developed product is scaled for commercialization. The integration approach was followed by 16% of the cases. Start-up Approach In contrast, the establishment of a data-driven business model through a new company requires a different approach. However, a clear business opportunity and CEO sponsorship are vital here as well. Having a clear understanding of the data and the monetization opportunities they afford, paired with a willingness to invest, allow new revenue streams to be harvested. This boldness leads to the decision to set up a new company. Capabilities are built from scratch. Ideally, the new subsidiary remains completely separate, both conceptually and spatially. Access to the data is usually granted through APIs. The team typically works in a start-up fashion with end-to-end responsibilities, from design to realization of the data monetization initiative. The start-up approach by 5% of the cases. Companies that fail to take one of the four approaches remain in a pending state with an unclear strategy. These companies invest in use case development within the business units and conduct software selection projects but do not take the next step toward a data monetization initiative. An unclear strategy stage was identified by 16% of the cases analyzed in this study. Decisive factors for the approach taken are the sponsor of the initiative, the funding source, and the motivation to embark on the journey. Successfully deploying data monetization initiatives requires capability building and sourcing. The cultural transformation aspects are the most challenging (see The Challenge of Becoming a Data-Driven Company ). Agile collaboration modes between business and IT are required, using lean start-up methods. Senior sponsorship is vital here, also. Having a clear understanding of the data and their capitalization opportunities, paired with a willingness to invest, allows new revenue streams to be harvested. This boldness leads to the decision to embark on a data monetization journey (see also Mobilizing Capabilities of Data-Driven Culture ). Contact us to learn how these 19 companies adopted data monetization initiatives and how Assefy can be a catalyst for your success.

Companies have been striving to become more data-driven for decades – with mixed results. Yet the rapidly accumulating evidence on the benefits of big data analytics provides legitimacy to the claim that it will revolutionize many fields. Gathering and analyzing a tremendous amount of data in real-time enables managers to improve decision-making and performance and new datasets open business opportunities that might have stayed untapped. Many studies have revealed that data-driven businesses are, on average, more productive and profitable. The potential value of data is realized in three major areas: improved decision making, enhanced products and services, and new business models (see What are Data-Driven Business Models? ). What’s more, the pandemic has further heightened the value of good data for decision making and innovation. Most companies have clearly realized the importance of data and pay lip service to accelerating their data journey. However, though much has been achieved, more remains to be done. Elaborating on the view of “data as crude oil” that needs refinement emphasizes the essential ties between value creation and value capture in the deployment of big data and analytics. While the perceived value from data depends on an organization’s strategic goals, value creation is accomplished during data refinement, which comprises data cleansing, analysis, and the reintegration of insights into the business context. However, these efforts only lead to competitive advantages and sustainable traits if the generated value is captured by “driving profitable activities.” Without mechanisms to capture the value generated from the adoption of big data, analytics, and machine learning, the prospective benefits will not be gained. Culture is Key Refining data from crude (oil) into usable resources was the major challenge for many companies. However, data refinement with data cleansing and analysis is a largely technology related topic. Today, the biggest barrier is the company culture. According to recent Harvard and MIT studies, cultural change is the most critical business imperative. Business users must be encouraged and assisted to find and understand contextualized data by applying a common language to foster data literacy. This trend toward cultural change is further accelerated by self-service big data and analytics as well as artificial intelligence, since individuals now consume information and data when and how they want to (see Power to the Business Users with Data Mesh ). It doesn’t help that the task of being data-driven keeps getting harder. Since new data assets are mostly unstructured, it has become vital to contextualize and classify data. Increasingly, companies must come to recognize and appreciate that data is a business asset that flows through an organization, cutting across traditional boundaries, often without clear ownership. Managing data throughout an enterprise with federated ownerships is thus a major concern. The complexity increases further in consideration of responsible and ethical data use. Governing and managing data while addressing data and artificial intelligence ethics and issues will become a central corporate concern for most businesses in the coming years. Engaging Business Users To exploit the strategic business potential embedded in data, companies must enable their business users to realize value from data. While data refinement is accomplished in IT/data departments and teams, value capturing from data is routed in business units. Therefore, engaging the business in data monetization and identification of data-driven business models must be prioritized. The transition from data as a purely IT-related topic to a major business concern is also observable in the emerging role of the Chief Data Officer. For decades, dealing with data was part of the duties of the Chief Information Officer. With the increasing importance of data for business value generation, however, the Chief Data Officer role has emerged, shifting the data topic closer to business and the executive level. A data-driven company has accomplished the transition of data from a siloed departmental topic to an enterprise-wide concern, shaping the decision making in all areas of the business. To change business users’ perception of data requires setting the right incentives. Indeed, promoting desired behavior and fostering the treatment of data as an asset with benefits has shown significant results (see Mobilizing Capabilities of Data-Driven Culture ). Cataloging data using agile information governance and metadata management practices helps Chief Data Officers/data leads to fuel data thinking and culture in the organization. Assefy catalyzes the engagement of business users in data value realization. Contact us to find out more.

To exploit the strategic business potential embedded in data, companies must enable their business users to realize its value. While the preparation of data is accomplished centrally, value realization is conducted across the organization in decentralized and cross-functional teams. This means that business users play a crucial role as data consumers, connecting the dots between data and business outcomes. Yet the importance of business users has been consistently neglected in the past by proposed enterprise data architecture concepts. Addressing Business Users Many organizations are investing heavily in their next generation data landscape with the hope of maximizing the value of data at scale to provide business insights, automate intelligent decisions, and ultimately monetize data. These efforts were the driving forces for the transition from data warehouses to data lakes. Today, the shortcomings of monolithic data lakes as the single source of truth with central teams for data management are becoming more and more notable: they are slow, the data is locked in, and it is becoming very expensive to maintain the data pipelines. The gap between data and business users as data consumers has been artificially closed by expert data teams. Without their intervention, business users were unable to use, understand, or apply data (see The Challenge of Becoming a Data-Driven Company ). In the meantime, the demand for new data architecture concepts is becoming harder to overlook. Other areas of IT management have adopted a more business-oriented approach decades ago. For example, operating systems are aligned to business capabilities, thus easily justifying their need and existence. Similarly, efforts in agile IT management focus on products with the end user (customer) in mind. Data-driven companies have deeply rooted the idea of data as a product in their organizational culture. Their goal is to democratize data and give power to the business departments to govern their data and to provide consumable data products to other units. All of this has given rise to the emerging concept of data mesh. What is Data Mesh? Data mesh is a concept for designing enterprise data architecture. It provides a modern approach to managing distributed architectures for analytical data. The main objective is to enable end users to easily access and query data where it lives without first transporting it to a data lake or data warehouse. Through this, data mesh fosters thinking of data as a product and offers a decentralized strategy to distribute data ownership to domain-specific teams that govern and manage data assets. Data mesh empowers data consumers and makes data available and accessible at scale, and reduces the dependency of data consumers on highly specialized data teams. It is not a single technology or solution, but rather a pattern for enterprise data architecture, building on top of prevailing data lakes and data warehouses. However, this approach of allowing a domain-driven, self-service, and decentralized management of data as a product requires a shift in data culture, thinking, and governance. The design of enterprise data architectures in accordance with data mesh should follow four principles: Data should be domain-driven, i.e., locally owned by the team responsible for collecting and/or consuming the data; Domain data should be considered as a product; Self-service data infrastructure as a platform should be prioritized, as this helps to overcome the challenge of duplicate efforts required to operate data pipelines for domains; The management of computing resources should be federated, acknowledging the reality that in a distributed environment there will be multiple interdependent data products that must interoperate. Data Assets as Data Products Most notably, the data mesh concept proposes the convergence of data and product thinking, which enables data governance and management to become more aligned to the business structure of an organization. In addition, domain-driven offering of data to products brings new opportunities for business users for self-service consumption of data products. This results in shorter timeframes for achieving business outcomes and insights. Domain teams view data assets as data products, offering them to other business units. Furthermore, data products can be offered to external entities for data monetization. The characteristics of data products include the following: discoverable, addressable, trustworthy, self-describing, interoperable, and secure. With the dawn of data fabric and data mesh concepts, new opportunities arise to connect business users and data, especially when cataloging data assets in complex architectures. It is clear to modern businesses that domain-oriented, decentralized data ownership and architecture must be leveraged. Self-service data infrastructures as a platform allow data consumers to access the full bandwidth of data and analytics products. Find out how Assefy applies data mesh concepts to empower business users to find, understand, and use more data in their daily work.

It is said that “information (data) is power,” but this is only partially correct. What power is there to be gained from information you don’t trust? We argue that power actually equals information (data) + trust. Business leaders must be able to trust and rely upon information (and by extension, data) to enhance decision-making and ultimately derive value from data. Both parts of the data + trust equation must be taken into consideration to achieve data value realization. The crucial requirement for building trust and laying the foundation for business advantage is high data quality. In our previous articles we have emphasized that data monetization requires an understanding of data as the key resource (see What are Data-Driven Business Models? and Mobilizing Capabilities of Data-Driven Culture ). We further highlighted that data quality and reliability are decisive for the value proposition. In this article we provide a deeper view on data quality and its importance for business. What is Data Quality? Historically, there has been a variety of definitions of data quality. While most definitions focus solely on the dimensions that should be considered—such as accuracy, completeness, timeliness, consistency, integrity, reliability, uniqueness, and accessibility—others take a broader view on data quality, and describe it as the fitness of data for an intended purpose. A good definition should accommodate both aspects. First, as the purpose of data use is determined by the business user (data consumer), the requirements that the data set must meet may vary. Therefore, data quality very much depends on business context and business needs. For example, while a data set may meet the requirements for a business use case related to shipping, because it contains required location information in the form of a postal address, it may not be appropriate for use cases requiring more precise information in the form of customer geographic position. Second, to be fit for its intended purpose, the data must be without errors, and data quality attributes must be defined in relation to quality dimensions for that data. Therefore, data quality dimensions—for example data accuracy, data completeness, and data consistency—can be directly related to data quality attributes. These data quality dimensions should be linked to business requirements. Data quality is data’s fitness for an intended purpose, which is assessed on its possession of certain data quality attributes set alongside data quality dimensions. Various businesses and institutions have developed methodologies for the assessment of data quality attributes alongside quality dimensions. For example, UnitedHealth Group’s Optum healthcare services subsidiary created the Data Quality Assessment Framework (DQAF), a method for assessing its data quality. The DQAF provides guidelines for measuring data quality dimensions that include completeness, timeliness, validity, consistency, and integrity. Optum has even shared details about the framework so that other organizations may use it. The International Monetary Fund (IMF), similarly, has its own assessment methodology, also known as the Data Quality Assessment Framework. The focus of the IMF’s assessment tool is the accuracy, reliability, consistency, and other data quality dimensions of the statistical data that member countries submit to the IMF. Business Impact of Bad Data The impact of bad data on business revenue is immense. A widely read MIT Sloan Management Review article from 2017 asserted that most companies lose up to 25% of their revenue due to the cost of bad data. All parts of a business suffer when there exists inaccurate, incomplete, redundant or duplicate data. Costs pile up as companies seek to accommodate bad data by correcting errors and looking for confirmation in other sources. They then have to deal with the inevitable mistakes that follow. Some examples of the economic damage that data quality problems can wreak include added expenses due to products being shipped to the wrong customer address, lost sales opportunities because of erroneous or incomplete customer records, fines for improper financial or regulatory compliance (e.g., GDPR, BCBS 239, CCAR, HIPAA), and a lack of timeliness in goods and services going to market because businesses are acting on delayed business data. From Data to Business Outcomes It is not difficult to convince business stakeholders that bad data is connected to bad decision-making and ultimately leads to bad business outcomes (garbage in, garbage out). For data monetization initiatives, poor data quality is recognized as a potential obstacle and threat. Business leaders understand that business practices do not exist in isolation and that successful business outcomes depend on having a data infrastructure informed by policies and practices that lead to the required level of data quality. However, issues can arise with regard to who has ultimate responsibility for data quality and who will fund quality assurance activities. Who is Responsible for Data Quality? Clarifying who is to fund the necessary activities for quality assurance and who ultimately “governs” the data across an enterprise can be a difficult task. Such a responsibility requires taking ownership of critical assessments of the prevailing policies and practices for data governance. A large number of quality issues arise from a lack of a cross-business-unit view on data. Therefore, the ideal is that everyone in an organization is responsible for data quality. Other data quality challenges arise when there is unstructured and semi-structured data, AI and machine learning, and issues around data privacy and protection laws. There is No Perfect Data Of course, no data will ever be absolutely perfect. However, an acceptable margin for error must be clearly identified by a business, and the potential impacts of accepted errors on the transformational impact of the resulting analytical models assessed. Contact us to find out how Assefy can accelerate your data governance journey.

In recent decades the use of digital data has become an important element of conducting business. Technological innovations in the domains of business intelligence, big data, and analytics, as well as in artificial intelligence and machine learning, have further accelerated this trend. No sphere of business will remain untouched by these developments. However, most companies face challenges in maximizing value from data. Especially when it comes to scaling the value of data initiatives, concerns arise related to making data findable, accessible, and understandable. Additionally, as concepts like data mesh have emerged, promoting the perception of data as a product and the use of a self-service model of data consumption, the reusability and interpretability of data have become vital (see also FAIR data). Aiming to address these challenges, companies might look into data catalogs and/or data marketplaces. But what exactly are these, and how do they differ from each other? What is an (Enterprise) Data Marketplace? The term “data marketplace” has been around for a couple of years. The standard data marketplace is a space in which third-party data can be bought and sold. A data marketplace that is created to serve only a company’s internal data shoppers and invited participants is referred to as an enterprise data marketplace. The data sets offered can be enriched with commercially available data from external sources. Furthermore, it is possible to make data sets available for external (commercial) exchange. What is a Data Catalog? A data catalog is an inventory of data assets, which is created through the discovery, description and organization of distributed datasets. A data catalog allows data consumers (i.e., business users) to search for data sets that they may be able to use to strengthen their businesses objectives. The more modern data catalogs, which incorporate machine learning, feature automation that makes using them very straightforward. Tasks related to data cataloging, including metadata discovery, ingestion, enrichment, and the creation of cross-references between metadata, are all automated in this type of data catalog. These newer data catalogs can act as a strong foundation for businesses’ metadata management projects. Now, What’s the Difference? While both data marketplaces and data catalogs aim to accelerate the value generated from data initiatives, the marketplace has a greater focus on exchanging data for something in return, while the catalog primarily focuses on creating and maintaining an inventory of data assets. Can both co-exist? Yes, they can. Cataloging data and having an inventory of data assets is a prerequisite for a well-run enterprise data marketplace. Depending on the company culture and use cases for data value realization / data monetization, a data catalog can be extended with marketplace functionalities to allow data exchange and a shopping experience for data consumers. The data exchange can be realized as a financial transaction or through other forms of exchange of valuable items, e.g., coins and benefits. Fostering Collaboration The purpose of both a data catalog and data marketplace is to connect the data provider and consumers by making data findable, understandable and accessible. First and foremost, the ambition should be to foster collaboration and establish a data-driven culture. A collaboration platform like Assefy applies the advantages of both concepts to cultivate data as an asset; allow data collaboration; and make data findable, accessible, and understandable. The diagram below illustrates the core components of a data collaboration platform that incorporates a data catalog for data governance and inventory and metadata management, as well as marketplace functionalities via the storefront, giving data consumers a “shopping” experience. The data pipeline describes acquisition of the data and the process of transformation from data sources to curated data sets. Furthermore, the pipeline incorporates the processes used to curate the data sets. These processes are also well documented and curated to make them reusable and promotable at the storefront. The data catalog comprises the data governance policies and practices for data ownership, decision rights, and marketplace ground rules. The data inventory holds and maintains information about all data assets. Metadata management is conducted in the data catalog end-to-end, from data sources to storefront. Data consumers access the data products and data services as products through the storefront. What is the Best Solution for Us? It depends. Your previous data initiatives, and with that your technology stack (enterprise architecture), and most importantly your data culture, are important when it comes to making the best decision. Understanding your current situation will allow you to make informed decisions about your next steps. You should ask yourself: What do I want to achieve? What is my target state? According to recent research findings, people are the biggest roadblock when it comes to organizations becoming more data-driven. With regard to your target state, you should consider how you want to foster collaboration to establish a data-driven culture. Connecting the data provider with the data consumer and emphasizing the critical role of business users is crucial for your success. But as they say, Rome wasn’t built in a day, so an incremental approach might be worth considering. Expanding the collaboration capabilities of your organization, use case by use case, offers certain advantages. Strong data governance and metadata management are the backbone of any data-driven organization. These capabilities might be a good starting point. Talk to our experts today for advice.

Many organizations expend considerable effort toward becoming more data-driven, but the results have been mixed. Governing data across multiple distributed data sources is one of the key challenges faced by organizations. The speed with which data change, the need for confirming their veracity, and the sheer volume of data render data governance a continuous, organization-wide exercise. Ensuring the trustworthiness, accessibility, and coherence of data requires joint efforts between the business in question and IT practitioners, necessitating strong engagement with the top management of the organization. While regulatory and compliance issues are the key drivers for data governance initiatives, an increasing number of organizations are beginning to appreciate the criticality of data governance for data value realization. The Data Management Body of Knowledge (DMBOK) defines data governance as The exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. Forrester defines data governance as The process by which an organization formalizes the fiduciary duty for the management of data assets critical to its success. The Data Governance Institute (DGI) provides the following definition: Data Governance refers to the organizational bodies, rules, decision rights, and accountabilities of people and information systems as they perform information related processes. Data Governance is how we ‘decide how to decide’. Data governance encompasses the various tasks and processes relevant to an organization’s information-related processes. It sets out the decision rights and accountabilities required for effective data and analytics management. Ultimately, data governance defines a framework for organization-wide ownership and authority. In this article, we discuss data governance frameworks. What is a Data Governance Framework? According to the DGI, a data governance framework is defined as …a logical structure for classifying, organizing, and communicating complex activities involved in making decisions about and taking action on enterprise data. A data governance framework provides the model/blueprint for enforcing data governance and managing organization-wide data. Several data governance frameworks have been proposed in the past. They vary with regard to their scope, focus areas, and addressed audience. For example, higher-level governance frameworks describe organization-wide decision rights and accountabilities addressing leadership, while the more granular, lower-level frameworks provide step-by-step guidance on policies and practices pertaining to technical professionals. Why is a Data Governance Framework so Important? A data governance framework is important, given its high relevance to data strategies and digital transformation as a whole. The growth of data and analytics, as well as the heightened requirements for service speed and agility, have rendered effective governance more important than ever. It is essential to Establish internal rules for data use Appoint people and organizational bodies to make and enforce those rules Minimize risk and meet data protection compliance Increase the value of data Reduce costs What are the Challenges in Establishing Robust Data Governance? Given the importance of data for doing business, it might seem quite obvious that data governance is essential to prevent “garbage in, garbage out” issues. However, many companies face challenges in establishing organization-wide data governance. The biggest barrier is the lack of a standardized approach to govern data across the organization. As multiple proposed attempts for data governance tend to be unlinked, they have failed to deliver the desired results. Another key challenge is insufficient funding. If an organizations’ leadership doesn’t recognize the value of data governance, it will perceive it as a textbook exercise. Moreover, organizations must possess adequate capabilities and the required maturity to effectively execute data governance across the enterprise. Other challenges arise from unclear accountabilities, inappropriate scoping of data governance initiatives (e.g., big-bang/all-at-once-rollout attempts), and insufficient technology support. How to Strategize the Evolution of Your Data Governance Capabilities As implementing data governance across the organization is a highly complex and ongoing task, the chance that the involved stakeholders will lose trust and interest over time are high. Therefore, you should start small and scale up fast. It is more prudent and manageable to begin with data governance user cases that are tightly related to your business goals, and then accelerate the endeavor by implementing the lessons learned thus far. It is crucial for you to understand your current capabilities and maturity. Going forward, this will allow you to choose the initiatives most appropriate to your enterprise. Next, you should define your organization’s desired target state; having a vision and knowing where the endeavor will lead are crucial. This entails developing your enterprise’s roadmap and defining the required initiatives. Get in touch with a member of our team to find out how Assefy can help accelerate your data governance journey. Choice of Data Governance Frameworks Assessing and increasing the maturity of your data governance capabilities with a holistic approach entails the development/selection of a suitable data governance framework. A multitude of such blueprints has been proposed by different entities (e.g., institutions, consultancies, and software vendors). Therefore, selecting an existing model and adjusting it (if required) to your needs is highly recommended. The two often-referenced and comprehensive data governance frameworks are: DAMA-DMBOK or the Data Management Body of Knowledge functional framework The DGI data governance framework To explain the aspects a framework should cover, DAMA-DMBOK envisions data management as a wheel. Here, data governance functions as the hub, with 10 data management knowledge areas radiating from it (see figure below). When establishing a strategy, each of the above facets of data collection, management, archiving, and use should be considered. The 10 data management knowledge areas comprise the following: Data architecture: The overall structure of data and data-related resources as an integral part of the enterprise architecture Data modeling and design: Analysis, design, building, testing, and maintenance Data storage and operations: Structured physical data assets storage deployment and management Data security: Ensuring privacy, confidentiality, and appropriate access Data integration and interoperability: Acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization, and operational support Documents and content: Storing, protecting, indexing, and enabling access to data found in unstructured sources and making this data available for integration and interoperability with structured data Reference and master data : Managing shared data to reduce redundancy and ensure better data quality through standardized definition and use of data values Data warehousing and business intelligence (BI): Managing analytical data processing and enabling access to decision support data for reporting and analysis Metadata: Collecting, categorizing, maintaining, integrating, controlling, managing, and delivering metadata Data quality: Defining, monitoring, maintaining data integrity, and improving data quality The framework clarifies the definitions of the environmental elements that provide structure to each knowledge area. The underlying processes, roles, technologies, and deliverables that guide the planning and execution of each area are defined (see figure below). These knowledge areas also explain how an organization’s culture must mature for data governance initiatives to function as intended. The DGI framework includes 10 universal aspects that address the why, what, who, and how of data governance. To simplify these concepts, DGI divides each of the above-stated aspects into core areas, namely rules, people, and processes.

The Russian invasion of Ukraine and the COVID-19 pandemic have led to a state of uncertainty, demanding operational excellence and the rapid detection of new business ventures from organizations. These recent developments and the accompanying need for resilience have fueled organizations’ ambition to become more data-driven. While investments in data, analytics, and AI are increasing, the results remain sobering. Most organizations have realized the importance of data but only pay lip service to the acceleration of their data journey. However, although much has been achieved, more remains to be done. In a Harvard Business Review article, Thomas C. Redman states, Data science, broadly defined, has been around for a long time. But the failure rates of big data projects in general and AI projects in particular remain disturbingly high. And despite the hype (e.g., “data is the new oil”), companies have yet to cite the contributions of data science to their bottom lines. In our previous articles, we discussed the biggest barrier to the ambition of organizations to become data-driven—culture—and described the critical role of business users and how concepts such as data mesh promote the perception of data as a product, as well as its domain-based organization-wide governance (see The Challenge of Becoming a Data-Driven Company ). Data-driven companies have deeply rooted the idea of data as a product in their organizational culture. Their goal is to democratize data and give power to business departments to govern their data and provide consumable data products to other units. This cultural change is further accelerated by self-service big data and analytics, as well as AI, since individuals now consume information and data whenever and however they want to. While data mesh describes the organizational perspective toward modern data architectures, a data fabric encompasses the technological aspects. In this article, we discuss how the emerging concept of data fabric helps organizations democratize organization-wide data. What Is a Data Fabric? A data fabric describes a modern data architecture with the required capabilities that encompass composable technologies and provide services across hybrid multi-cloud environments. In simple terms, a data fabric is a net that spans multiple data sources and applies machine learning to provide access and meaning to distributed data. While companies realize the challenges and shortcomings of monolithic data lakes as a single source of truth, a data fabric enables the management of the data where it resides. The core engine of a data fabric is metadata. Gathering, analyzing, and enriching metadata, paired with the ability to automate these processes powered by machine learning, allows for the analysis of the underlying data without the need to move and transform it right away. A data fabric is not provided by a single vendor or solution; it is a composable, flexible, and scalable architecture. Data Democratization There are several benefits of a data fabric, including enhancing companies’ architectures through increases in efficiency, making companies more scalable, enabling better integration, and helping companies gain more control and agility. However, the ultimate goal of a data fabric is to maximize the value of data and accelerate digital transformation through data democratization. This means giving power to business departments to govern their data and provide consumable data products to other units. Before data fabric, the gap between data and business users as data consumers had been artificially closed by expert data teams. Without their intervention, business users were unable to use, understand, or apply data. How to Build a Data Fabric There are several pathways to building a data fabric. The right pathway depends on the organization’s previous architecture decisions. These decisions determine the selection of the capabilities to start with. In any case, do not get overwhelmed with the complexity of the task; start small. With a case-by-case approach, you ensure faster results and keep the motivation level of your team high. Ideally, you can start with the most advanced business department to serve as an example of democratized data. Next, you may need to collect all sorts of metadata and give meaning to them. While metadata might not have much value in their passive form, their collection, analysis, and enrichment lead to active metadata with enormous potential. Applying machine learning to automate these tasks will ultimately lead to a full-fledged data fabric. Why Now? The key to overcoming the challenge of data culture is to empower business users and thus democratize data. Other areas of IT management have adopted a more business-oriented approach for decades. For example, operating systems are aligned with business capabilities, thus easily justifying their need and existence. Similarly, efforts in agile IT management focus on products with the end user in mind. Data-driven companies understand data as a product in their organizational culture. All of this has given rise to the emerging concept of a data fabric. Advancements in metadata management have further accelerated the transition toward modern architectures governed across distributed data sources.

The data and analytics mission has shifted from one of risk mitigation to one of the creation of business value with data assets. Among the top priorities for data teams are data quality, reliability, and access, as well as the enhancing of decision making and the driving of product and business innovation. For many organizations, the role of Chief Data Officer is still new, and the most effective positioning of data capabilities (data teams) inside the organization has yet to be defined. Where the role of the Chief Data Officer is undefined, the scope, priorities, roles, and responsibilities of data teams can quickly become inflated. It seems that once a data team is established, regardless of how small the associated budget, resources, and team, even the most minor data-related problem that comes up is regarded as falling within the data team’s remit. The challenge of finding the right balance between defining and aligning priorities, while leaving a buffer zone of flexibility for new things that come up, is common for almost any operating model. To achieve clarity of roles and responsibilities, senior leadership should act quickly and decisively to determine the data-driven ambitions of their enterprise, and to set the scope and actively communicate the purpose of their data and analytics teams. Key questions that have to be faced are as follows: How can the data and analytics function be organized inside the organizational model? Where should the data and analytics function be located inside the organizational model? What is the right degree of centralization for data and information management? How should responsibility be divided between business areas and data teams? How do you fill the data and analytics skills gap? Several options are available for senior leadership when it comes to the decision on where to locate a data and analytics team within their organization. For instance, the team could be located either within central or local management, or either within or outside the IT department. As the data value chain requires cross-functional skills, the split between business, data, and IT duties must be clearly defined. Furthermore, the data and analytics skills gap presents a hiring challenge for employers. Some organizations consider working with an external D&A service provider because they lack the right resources or are unable to develop or improve their current talent pool. Such a provider can support the organization by temporarily filling the D&A role deficit and/or upskilling or reskilling existing resources. Further to the challenges discussed above, organizations must define the roles and responsibilities for the core team along the data value chain. While emerging roles like in the domain of data ethics are gaining importance, there are a number of other roles that are essential for data and analytics teams. These roles are listed below. Chief Data Officer The chief data officer is part of the senior leadership team and is responsible for data governance, data value creation, data quality, and data reliability. The chief data officer’s targets are aligned closely with business goals. While being accountable for the data team, the chief data officer serves the business with data and analytics outcomes to help the business reach its objectives. The chief data officer is the data and analytics leader, and in some organizations the title can vary as ‘data and analytics head’ or ‘data and analytics director’. Data Scientist The data scientist holds a computer science, statistics, or economics degree. This role is responsible for modelling complex business problems, discovering insights with statistical methods, and applying visualization techniques. The analytics-related outcomes of the data scientist are more concerned with predicting future events and recommending the best actions to take next. Data Engineer The data engineer is responsible for creating, managing, and operating data pipelines that are needed by the business. The data engineer models and scales databases to ensure the flow of data within the organization. This role requires business and IT skills for providing data and analytics use cases with the right data. Technical skills are required to work on complex IT infrastructures and business skills are needed to understand the analytics context and be able to create the data pipeline and deploy the insights thus gained in business applications. Data Steward The data steward has close proximity to the business and holds a key role in data governance. This role is responsible for enforcement of data policies, ensuring data quality, and monitoring of information assets. The data stewards sit within business domains and have extensive business knowledge. They amend and manage metadata for data quality and data governance purposes. This role demands extensive business domain knowledge and some IT skills to understand basic data modeling concepts, data warehouses, and data architectures. Many other roles have emerged in the past 5 years, with data broker, ML validator, data literacy coach, and decision engineer, being just a few. However, due to recent global events (e.g., Covid or the conflict in Ukraine), there is set to be a convergence of many different roles and a rise in citizen roles. In so-called citizen roles, key data and analytics skills will be shifted to business users, self-service analytics will be embraced, and the overheads of expensive data teams will be reduced.

Managing data as a product is not a new concept—there is published material on it dating back to 1996 (see The Design and Development of Information Products ). However, due to the emergence of more recent trends like data mesh (see Power to the Business Users with Data Mesh ) and data fabrics (see How Modern Data Fabric Architectures Democratize Data ), the concept of data product management has gained renewed momentum. The idea behind managing data as a product is to generate more value from data while ensuring a high level of sustainability through efficient and effective product design and the reuse of production facilities. This entails the development of more compelling data products with reusable patterns. With the aim of overcoming current challenges relating to scaling data and using analytics successfully across the organization, data productization posits the idea of offering data as product to other business domains that have previously been isolated. Taking a product-centric approach gives data teams and business users an outcome and value–driven lens on data analytics efforts. Data products are not limited to reusable data sets; they can also encompass analytics and AI methods to analyze data. Here are seven recommendations for data productization. 1. Develop empathy for your data customer. Understanding customer needs is essential for introducing compelling data products. Data must be fit for purpose and meet quality constraints in order for the intended use and outcomes to be realized. Business context and customer (data user) needs must be well understood to derive quality requirements. Quality data should have the following characteristics: accuracy, completeness, timeliness, consistency, integrity, reliability, uniqueness, and accessibility (see What is Data Quality and Why is it Important for Business? ). For example, while a data set may meet the requirements for a business use case related to shipping, if the required location information is in the form of a postal address it may not be appropriate for use cases requiring more precise information about customer geographic position. Developing empathy for the data user and analyzing the use case allows a business to determine fitness for an intended purpose. 2. Allow data product customization. Empowering the customer to give their own flavor to the final product is common for products such as cars and trending for customization of sneakers. Similarly, data consumers should have the flexibility to make data fit their specific needs when it comes to designing the final product. For example, a data set should be applicable to multiple use cases as well as being compatible with a variety of end systems, such as business applications, advanced analytics, reporting, or external sharing. Additionally, the final analysis of the data set may vary based on business context and use case. 3. Design sustainable data products. Sustainability is more relevant than ever—and it should be for data products, too. Each product has a design that determines its functionalities, performance, and cost. For data products, production and maintenance efforts are key to efficiency and effectiveness. For example, a highly customized data set that only fits one use case while requiring high maintenance (cost exceeding value of output) would require a redesign. The levers used here could be to make the data product applicable to more use cases (and end systems—see above) or to find efficiencies in the data production and maintenance process. 4. Leverage data product families. Successful, fast-moving consumer goods companies take their offerings to the next level through product variations, e.g., if Coke doesn’t serve your needs, you can try Sprite, Fanta, or Mezzo Mix. Similarly, data products should evolve into product “families.” If there is a certain demand for customer sales data, a variety of sub data sets, reports, and insights could be offered under this product segment. Other product families could be created along employee, product/service, branch, or vendor lines. 5. Reuse production facilities and processes. Data products have (like other products) the ability to evolve into product families and with that they offer a wealth of synergies. Facilities and processes that have been established to develop the data product can be reused to develop the entire product family. Finding the right balance of abstraction from specific products to create a tool set of composable units is essential. For example, while the processes and facilities for capturing, ingesting, and cleansing data is the same for a product family, the retrieval, distribution, and presentation may very for each specific product. Analytics models can be developed and adjusted with minor effort to cover additional use cases. 6. Manage the data productization process. To ensure the provision of high-quality products it is important to have a clearly defined production process with dedicated roles. The data product manager is an emerging role in the data and analytics domain. Data product managers need to be able to manage cross-functional teams working on the development and deployment of data products. They also need some technical skills in order to be able to design the data production process, and business skills to communicate effectively with business leaders. A recent Harvard Business Review article addresses this topic (see Why Your Company Needs Data-Product Managers ). Beyond the dedicated roles successful data product management requires funding, best practices, performance tracking, and quality assurance. 7. Continuously enhance data products. Like other products, data products undergo several life cycle stages (introduction, growth, maturity, decline). Constantly enhance the data product according to its stage and deploy regular updates. For digital products, agile methodologies emphasize the importance of rapid development of first (minimum viable) products to test their acceptance by customers and then constant enhancement of these products. These learnings should be applied to data products, also. As organizations turn their attention toward data product management, additional aspects of traditional product management might become relevant such as marketing of data products or sales of data products. According to Investopedia, products are “made using commodities and are then put on the market and sold to consumers.”Due to the special characteristics of data, its pricing differs greatly from that of traditional products, e.g., the value of data (information) is known by the receiving party only after it has been disclosed (refund impossible), and the value of data increases as it is combined with other data (value depends on the receiving party). Additionally, company internal data is supposed to be shared as the value increases—pricing might be perceived as hindering for a sharing mentality. However, putting a label on data products to determine their value for an exchange could be beneficial. Even though data marketplaces have not taken off as many might have expected, the rearising of data products could lead to new ideas about how to realize successful enterprise-wide and external data marketplaces.