Understanding the obvious. That’s the purpose of our activities centered around Data Driven Innovation.
By using the DDI Framework, any company should be able to develop a consistent strategy plus comprehensive content for exploring data-driven business opportunities. The DDI framework is used to run online and offline webinars, workshops as well as coaching formats with research projects, start-ups, SMEs and corporates. The framework consists of three parts:
- The DDI Canvas guides you in exploring all relevant dimensions on the supply and demand side of a data-driven innovation in systematic manner.
- The DDI Navigator will support you in exploring each dimension in more detail by carefully selected deep dives.
- Specific DDI Tools will help you to work through each of the eight DDI dimensions, producing a conclusive set of results that will guide a company-specific setup of new, data-based products and services.
PART 1: DDI CANVAS
The DDI Canvas is guiding users in exploring all relevant dimensions on the supply and demand side of a data-driven innovation in systematic manner.
Anticipate customer’s future demands
Understand the data assets that are available
Select the right tools for data-driven innovations
Deliver your innovation to specific users
Whom else is required to develop the data driven innovation
Understand keyplayer, dynamics and leverages of innovation ecosystems
Leverage network effects on data, infrastructure and marketplace level
How to develop a business and earn money?
Choose an optimal market position
PART 3: DDI TOOLS
Specific DDI Tools and Methods to work through each of the eight DDI dimensions, producing a conclusive set of results that will guide a company-specific setup of new, data-based products and services.
Together with carefully selected partners we are able to provide a range of offerings that will support start-ups, SME’s and larger organizations to successfully develop and expand their data-driven business.
- In highly customized, interactive workshops companies can gain practical experience of how to apply the DDI Framework and Canvas for either an in-depth check of all relevant factors of data-driven business opportunities, or as a tried-and-tested approach how to systematically address in-house challenges for data-driven innovation.
- Individual sparring sessions with key persons being responsible for driving data-driven businesses provide guidance for any question related to our DDI Framework and specific use-cases.
- Together with our partner Evalea we have developed a self-assessment and monitoring tool for CEO’s, decision makers and responsible project leads. This tool kit is meant to support the decision-making process for – often costly – efforts and investments in data-driven initiatives. Once the homework is done, it helps to stay on track while implementing the necessary steps within your own organization.
Please get in CONTACT with us if you are interested and want to know more about one of these tailer-made offerings.
The DDI Framework is based on a scientific approach, co-developed with the Technical Universities of Munich and Berlin and the Big Data Value Association.
The corresponding data set was obtained from an empirical study of more than 90 data-driven business models. The goal of this scientific research was to identify the patterns of successful data-driven companies in order to extract key variables for companies.
The DDI framework was developed and tested in the context of the Horizon 2020 BDVe project and is backed by empirical data and scientific research encompassing a quantitative and representative research study covering more than 90 data-driven business opportunities. The objective of our research study was to systematically analyse and compare successfully implemented data-driven business opportunities.
From the research study, we could derive findings along the main dimensions of the DDI Framework:
In order to understand typical success patterns of data-driven innovations, our research study relied on unsupervised cluster analysis.
Based on the cluster analysis, we could identify six clusters:
The central focus of companies in the Cluster A “Data Pre-Processing” is on delivering solutions for the pre-processing of heterogeneous data sources, such as images or videos. Due to the high technical complexity, companies tend to be very focused and do not provide additional analytics or automation capabilities. In addition, as companies of this cluster seem to focus on generic technical challenges without concrete customer value in mind, they are developing twice as often sector agnostic solutions compared to the overall sample.
Start-ups of the Cluster B rely on Internet of Things (IoT) technology as part of their offerings. As the IoT technology is integrated with multiple types of technologies as well as different types of data analytics, startups in this cluster are confronted with challenges in data pre-processing and data integration. Although companies of this cluster rely on industrial data twice as often as others and tend to use different kinds of data sources, they are less likely relying on unstructured data.
Companies of Cluster C are characterized by heavy usage of industrial data sources. The usage of unstructured data is less frequent in comparison to the overall sample. In terms of their value proposition, they tend to cover the whole range of data analytics as well as provide high value through process automation. Companies of this cluster seem to be prepared to make use of available services for processing IoT data, but do not include IoT technology as component of their overall offerings.
Companies in Cluster D are focused on descriptive analytical services for non-industrial data sources. The usage of other analytical services is significantly lower compared to the average sample. The same is true for match-making functionalities or process automation capabilities. In addition, Cluster D companies are more likely to rely on semi-structured data from media and time series. All Cluster D offerings are positioned in the market as data-driven services and are generating income mainly by a subscription model (94% of the offerings).
All Cluster E companies focus on predictive analytics, often combining other analytical values such as descriptive, diagnostic or prescriptive data. Compared to the average in our sample, they are 50% more likely to rely on unstructured data sources as well as 33% on personal data. They do not use industrial data at all. By relying on a smaller number of different types of technologies, they are confronted with less integration efforts, interfaces and partners. For generating revenues, they rely 50% more often on asset sales or selling of services and less frequently on subscription models.
The main value proposition of Cluster F startups is their match-making functionality, allowing them to connect the market side (supply & demand) with business and consumers. Start-ups in Cluster F are very likely to rely on commission fees (60% compared to 10% in average). They harness network effects on marketplace level and establish multi-sided markets / data-driven marketplaces. The high usage of personal data (87%) indicates that personal data is used for implementing match-making algorithms also in B2B marketplaces.
„The DDI Workshop worked very well. Companies were satisfied and they all thought it was very relevant to define their business strategies.“
„The DDI workshop we organized for the SMEs in Murcia was a great success. The expectation was very high and all available seats were booked within 24 hours after the workshop was published online. The participants appreciated very much to have a tool at hand that guides them in systematically analyzing new data-driven business ideas. After the workshop participants were looking forward to put the tool in practice in their companies.“
“The DDI Workshop was one of the highlights of the BDV PPP Riga Summit. It was skillfully managed to explain the methodology and to show its application on the basis of a real use case. The workshop was very practical and highly interactive, engaging everyone in the audience in active participation. This greatly helped to learn essentials of the method and to see the importance of all its elements for successful data-driven innovation.”
„The DDI Workshop is a great workshop format that helped us tremendously to efficiently deep dive and structure both the supply as well as the demand side of a concrete prescriptive Siemens SCM Use Case.”