Digital transformation within companies requires questioning your own elements of value-added in two respects:
- Development of a digital transformation strategy that evaluates how the digitization has changed, or newly defined, the previous value-added
- Development of a corresponding strategy for data value-added that defines on which data basis the digital transformation of the value-added can be accomplished
thaltegos focuses on the second of these aspects and offers a procedure model that is intended to act as a link between a company’s defined digital (transformation)-strategy and the operative realization thereof.
The 6 included modules put your company in the position to use the full potential of your data above and beyond your value-added. Additionally, this approach ensures a quick realization of the defined areas of action within the digital strategy. All of the modules can be implemented individually but, ideally, should build upon one another.
The outcome of modules 1 to 3 comprises the derivation of a data strategy, showing on which data basis the defined areas of action within the digital strategy can be realized.
The outcome of modules 4 and 5 is the realization of prototypical solutions to the realization of the defined use cases within the digital strategy.
The 6th module is intended to prepare and secure the operative solution for the use cases for which the digital intrinsic value was able to be confirmed within a proof of concept.
Based on the digital strategy, it is necessary to derive strategic use cases in a targeted manner. These can be defined for specific and/or general functions (marketing, distribution/retail, service, logistics, etc.). Significant components of these strategic use cases are the definition of a goal, the prediction of expected value contribution, the relevant data basis, and a rough approach to realization. The outcome are prioritized use case profiles that can be integrated as part of the digital roadmap.
In the second module, data sources necessary for the realization are identified based on one or more use cases. In the course of this process a data maturity assessment is completed, showing to what extent data within the value added have already been used and what potential lies within their use. By this means, data deficits are revealed and obstacles standing in the way of realizing the prioritized use cases are identified. External data sources are also included in this evaluation. The outcome of the data discovery module is a company-specific assessment of the “data level of maturity,” taking into consideration the strategically relevant use cases within the digital strategy.
An important success factor for realizing the strategic use cases is the quality of the underlying data sources. The standardized test criteria for determining the data quality include, in addition to things like data consistency and comprehensiveness, aspects of the utilized data management systems, such as their accessibility, performance and scaleability. The outcome of the data quality evaluation ensures the feasibility of realizing the strategic use cases within the proof of concepts and the operative application.
In order to realize digital use cases, it is standard procedure to prepare and, if there are data deficits, also to enrich the necessary data. Within this module, the steps of data preparation, including possible enrichments, are defined and carried out. One of the steps to successfully completing data preparation is taking possible analytical modellings into consideration. For this reason, this module is a mandatory prerequisite for realizing module 5. The outcome is the creation of a data basis for the necessary analytical modelling and/or realization of the operative application and the thereby resulting requirements of data retention (including performance and formats).
In module 5, an evaluation and prototypical run-through of different modelling procedures based on the established data basis for each individual use case is carried out. These cover everything from the simple description (in the case of pre-defined KPIs) to multivariate methods of analysis on to the application of cognitive or learning algorithms. The outcome is the identification of fitting analytical modelling procedures necessary for realizing the pre-defined use cases from the first module. This shows both the feasibility as well as the limits of these analytical procedures.
After the successful realization of the prototypical solutions, the final module also defines the requirements as to how these approaches can be translated into an operative solution. Even in the phase of prototypical solution, thaltegos makes sure that the process lends itself to implementation. This applies to both the technological application as well as the early inclusion of relevant stakeholders in the envisaged solution. The outcome is a concept for implementation that defines the solution to be realized as well as the corresponding approximate time and resource calculation. In order to realize a quick implementation, we recommend using an agile project approach, in which the details of implementation are defined and carried out in individual sprints.