Data, now what? – Options for Organizations to Create Value from Data
Note: This blog post contains excerpts from the article “ Data Value Creation Matrix — Options for Organizations to Create Value from Data”.
Available at: https://aisel.aisnet.org/ecis2024/track07_busanalytics/track07_busanalytics/9/
Recommended Citation: Kakuschke, Nick, “Data Value Creation Matrix — Options for Organizations to Create Value from Data” (2024). ECIS 2024 Proceedings. 9. https://aisel.aisnet.org/ecis2024/track07_busanalytics/track07_busanalytics/9
As already discussed in previous blog posts (data value creation, GenAI), it is indispensable for organizations to deal with data value creation, especially as GenAI applications continue to make their way into organizations. Accordingly, as part of the CC Ecosystem’s research, we investigate, among other things, the conceptualization of value creation from data for organizations, especially in the financial industry. In our research, we focus on identifying opportunities for companies to create value from data in order to provide guidance to companies because data can be not only an asset, but also a liability, e.g. due to regulatory requirements and cyber security concerns. This blog post is dedicated to the options of data value creation for companies and presents excerpts from a research paper that was written as part of the CC Ecosystem research.
Data Value Creation Matrix
Based on a systematic literature review according to vom Brocke et al. (2009), 12 options for creating value from data for organizations were identified in the study as part of the CC Ecosystems (cf. Kakuschke (2024)).
To conceptualize data value creation, its definition serves as a starting point, which refers to converting the potential value of “data, analytics, or related topics” into actual benefit (Baecker et al., 2021, S. 4). As a baseline for distinguishing the underlying value object for data value creation, data and information can be distinguished (Dehnert et al., 2021; Lim et al., 2018). Based on the data–information–knowledge hierarchy (Braganza, 2004) data represent facts and observations with no associated meaning, whereas information is created through the analysis and interpretation of data, frequently referred to as insights. (Fayyad et al., 1996; Hartmann et al., 2016). Information-based objects such as algorithms and AI models that generate insights outputs were also included in the category of information.
On the other hand, approaches to creating value from data differ according to the type of value they provide to an organization. According to Repo (1986) values from data can be divided into exchange values and use values that differ in the time the value is created. The time at which resources are used describes use value, which represents the value for a user from the use of a resource. Exchange values refer to the time at which resources are exchanged with other actors for a consideration, where the value is embedded in the exchanged resource for a receiver (Bowman & Ambrosini, 2000; Chesbrough et al., 2018).
Accordingly, the options for creating value from data for organizations can be categorized based on the objects involved in the value creation process by data and information and based on use and exchange value, whereby on the one hand the focal organization uses the value objects and on the other hand the value objects are exchanged with another actor, which results in a two-dimensional classification framework for data value creation options with four quadrants: use data, use information, exchange data and exchange information.
The following figure visualizes the Data Value Creation Matrix with the options for organizations systematized within the four quadrants.
Data Value Creation Matrix
The quadrant use data, comprises options in which data, described as raw symbols, facts or numbers (Ackoff, 1989; Alavi & Leidner, 2001), is used by the focal organization, in the most basic form to increase the value of an organization’s data assets. This includes the preprocessing and enrichment of data to increase data quality (Baecker et al., 2020), as well as all modification activities that transform raw data into prepared data (01). Although this prepared data contains a higher value than the initial raw data, e.g. due to a higher data quality, it retains the value object data. Moreover, organizations in this quadrant could focus on creating information from data (02) and thus increase their assets of information objects by processing and analyzing data, e.g. the processing of census taker data by the bureau of the census, converting it into information such as statistical evaluations of population age trends (Ackoff, 1989). Although these first options for creating value from data are often precursors to implementing subsequent options, they add value to the intangible assets in form of data or information an organization holds.
In the quadrant of using information objects by an organization to create value, information can form the basis for improving an organizations existing products and services or can be used to develop new offerings (03) (Choo, 1996), for example in the context of market research in which information about customers and products is used to progress the development process of offerings (Baecker et al., 2020).The information used is only indirectly part of the provided product or service and does not directly become part of the offering. Further, organizations can use information to optimize the pricing of their offerings (04), for example through tailoring to customer groups, individualizing as in revenue management or dynamically adjusting depending on the context, such as weather or location (Schüritz & Satzger, 2016). Another way for organizational data value creation is to use information forcustomer interaction purposes (05). Information on customer needs and behavior can be used to optimize marketing, for example through targeted advertising and personalized product recommendations (Fast et al., 2021) or to adjust the external image of an organization, for example through tailoring the website design to target groups (Chen et al., 2012). The last option in this quadrant is to optimize organizational processes (06) (Lange et al., 2021), for example to improve operational efficiency through process automation, to complement and accelerate decision-making in organizations (Zolnowski et al., 2016), to reduce risks such as fraud, security breaches or privacy issues (Pierce, 2022) or to optimize the handling of organizational resources in inventory optimization or predictive talent management (Möller et al., 2020).
Referring to options in the quadrant of exchanging data as value objects, in the simplest way data can be exchanged in raw or pure form (07) with other actors (Thomas & Leiponen, 2016), for example like retailers selling point of sale data to consumer research companies (Wixom, 2014). However, the raw data typically needs to be prepared to ensure its further use in application scenarios, for example by aggregating, anonymizing or otherwise manipulating it, enabling the exchange of prepared data (08) (Buff et al., 2015) that is ready-to-use and integrable into the organizational systems of its receiver.
In the last quadrant of the matrix, insights representing outcomes of data analysis processes can be exchanged with other actors (09) (Parvinen et al., 2020). Insights comprise descriptive reports but also more advanced predictive and prescriptive analytics outcomes, for example through machine learning models (Buff et al., 2015). Going beyond these analytical results, so-called knowledge products (10) such as underlying models used to interpret data or model components like algorithms and parameters that contain knowledge to generate insights through their application can also be exchanged with other actors (Hirt & Kühl, 2018). In addition, information can be wrapped around core products or services (11) to create value from data to enhance offerings with additional information features as with FedEx, which was one of the first to offer package tracking as an additional and free service to package delivery (Wixom & Ross, 2017). Finally, organizations can offer services grounded on information and take action for customers (12) such as consulting, process automation and outsourcing (Buff et al., 2015). For example, with predictive maintenance, providers proactively maintain provisioned equipment and machines when anomalies and potential defects are identified based on predictive data analysis before failures occur (Dehnert et al., 2021).
Contributions of the Data Value Creation Matrix and Further Research in CC Ecosystems
The data value creation matrix consolidates different understandings of existing research, for example from the fields of data monetization, data-driven business models and business analytics, and provides a holistic view of data value creation for organizations. By highlighting the scope of available options for data value creation, organizations are supported in how to utilize data for themselves for example, as part of an orientation guide or white-spot analysis, especially due to the classification of options by value object and value type.
This systematization can help organizations to initially outline the necessary requirements of the respective data value creation option. For example, the positioning of an option within the data characteristic of the value object dimension indicates that organizations can leverage their available data directly to create value, for example by its exchange. In order to create value in the right hemisphere of the matrix, organizations must first convert the data to be leveraged into information value objects, which requires additional resources and capabilities, but in turn may represent a more scalable business option due to the number of potential customers and the effort required to make a sale e.g. for the exchange of analyzed insights from the data (Parvinen et al., 2020).
The categorization along the value type dimension in turn has implications for the actors involved in the value creation process. The options within the use value dimension of the matrix are oriented internally within the organization, while the exchange value options are oriented externally, towards the organization’s customers. Accordingly, organizations seeking to offer data or information products products or services to their customers should position themselves on the exchange side of the matrix whereas organizations seeking to address their internal characteristics should position themselves in the upper quadrants of the matrix.
Nevertheless, the options identified are in some cases too generic to identify specific data use cases and adapt them for an organization in practical application scenarios. Accordingly, the options for data value creation will be further detailed as part of the CC Ecosystems research to determine specific patterns for organizations, which in turn can serve as a starting point for entire data value creation portfolios.
Therefore, stay tuned so you don’t miss out!
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