Understanding the obvious. Platform Economy Data Quality Network Effects Digital Innovation Disruption Value Chain Management Digital Business Model Ecosystem Co-Evolution

That’s the purpose of our activities centered around Data Driven Innovation

While it’s meanwhile common language, that both Big Data and Artificial Intelligence (AI) have at least a growing, sometimes even disruptive impact on every aspect of our society, understanding the drivers and dynamics of those transformative forces is the key to unlock the potential of the ongoing transformation. When it comes down to questions of economy, the impact of those new technologies is striking: today, there’s barly any industry, market, value creation process or organization, that is not affected by the impact of data & algorithms.

Although the capabilities to address, analyze and use these forces to create new business opportunities vary from company to company, the need to deal with it is unquestioned and widly accepted across all industries. That’s the point where our Data Driven Innovation Framework comes into play. In order to capture these new types of value creation, any design of data-driven business opportunties needs to incorporate various perspectives in a structured and systematical way.

Ranging from specific users and their needs (plus their willingness to pay for new data-driven solutions) through questions of data quality and technological constraints up to measurable network effects in an existing ecosystem of customers and partners, those perspectives must be skillfully combined in order to exploit the power of any Data Driven Innovation. By using a coherent, thoughtfully crafted and empirically proven concept for addressing the business needs of corporates and SME’s alike, any company should be able to adapt its strategies and successfully address the biggest challenge of our times: transforming itself into a future-proofen, sustainable and competitive organization by exploring and exploiting data-driven business opportunities.

Here’s our claim and aspiration:

By using the DDI Framework, any organization – both profit and non-for-profit – should be able to answer the majority of questions centered around the irreversible rise of global data businesses. By transforming non-specific threads into (calculable) risks, organizations will be able to untap the full potential of data driven business opportunities and successfully manage their transition towards completely new business models and processes of value creation.

WHAT

The Data-Driven Innovation (DDI) Framework systematically addresses the challenges of identifying and exploring data-driven innovations. It guides start-ups, entrepreneurs and established companies alike in scoping promising data business opportunities by analyzing both the dynamics of supply and demand.

The DDI Framework is based on a conceptual model plus empirical research. The framework describes all relevant aspects of any generic data-driven business. On the supply side the focus is on the development of new offerings. For a clearly defined value proposition, this includes identifying and accessing required data sources, as well as the analysis of underlying technologies.

On the demand side the focus is on understanding the dynamics of the addressed markets and associated ecosystems. Our approach includes the development of a revenue strategy, a way forward of how to harness network effects as well as an understanding of the type of business. As data-driven innovations are never done in isolation, identifying potential partners and an viable eco-system helps to align those both sides of supply and demand in order to achieve a competitive advantage.

HOW

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:

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.

Download DDI Canvas

PART 2: DDI NAVIGATOR

DDI Guiding Questions that support users in exploring each dimension in more detail by carefully selected deep dives.

Download DDI Navigator

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.

Value Proposition Designer

Focus on a specific persona to understand needs and demands provides guidance in scoping and focusing the data-driven value proposition.

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Data Explorer

Get a systematic overview of the kind of data you need and analyze how feasible this data is for your recent and future business.

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Data Processing Analyzer

Understand the underlying functionalities of the various data analytics technologies and how they fit to the different types of data sources.

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Data Application Analyzer

Explore how to generate value with data and how to support and automate human decision-making processes.

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Partner Evaluator

Identify all your co-innovation partners, assess the importance of their contributions and identify strategic options to mitigate potential risks.

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Ecosystem Strategyzer

Analyze the most important co-adoption partners, identify involved risks and develop appropriate coping strategies.

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Network Effects Navigator

Understand network effects and their dynamics to build better data-driven products and services, and investigate strategies to leverage those effects.

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Revenue Roadmap

Select the most promising revenue strategy per user segment, combine the different revenue streams and continuously adapt them to support the growth of your business.

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Business Strategy Board

Assess the different ways of positioning your data-driven offering on the market and identify the most promising way to succeed in business.

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OFFERING

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.

SCIENTIFIC BACKGROUND

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.

Findings

The majority of data-driven start-ups (78%) are addressing B2B markets. Only two out of 90 start-ups of our sample focused on end-customer markets solely. Start-ups addressing end-user needs prefer already established channels to deliver their offering to the users.

They tend to rely on partnerships established business partners to bring their offering to users. A second strategy used by 19% of start-ups is positioning data-driven solutions as multi-sided market, combining complementary offerings to align private and business needs. 75% of our start-up sample have developed a clear sector focus. Companies with clear sector-focus have a specific customer segment in mind for whom a concrete value proposition is delivered. In general, solutions with a clear sector focus are more often used by other intra- or entrepreneurs to build data-driven solutions for end-users.

Value Proposition and Data Value

Two of three start-ups rely on data analytics in general for generating business insights. Among the start-ups using data analytics, 83% rely on descriptive analytics in their offering.

Compared to descriptive and predictive analytics, we have observed that diagnostics and prescriptive analytics are used less frequently. Only every fifth data-driven start-up is offering solutions for automating manual tasks or activities; match-making is observed only in 16% of our cases.

Data

A wide range of different types of data sources are relevant for developing data-driven innovations. Although only 19% of start-ups were addressing B2C markets, personal data was still most frequently (67%) used in the analysed data-driven offerings.

A second popular type of data source are time-series and temporal data. 56% of start-ups in our sample rely on this type of data to generate value. The high frequency might be due to the popularity of using behavioral data that is tracked within each user interaction on the web and mobile devices. Another very frequently used data source are geo-spatial data with 46%, and the usage of Internet of Things (IoT) data is seen in 30% of our sample. Industrial data sources have only been used half as often in compared to personal data, and open data only half as often compared to industrial data.

Technology

Among the five technology areas listed in the Strategic Research and Innovation Agenda of the EU (SRIA), Data Analytics is used most frequently. 82% of our start-up sample relied on some type of data analytics to implement data-driven value proposition.

Technologies of Data Management are seen in 41% of the cases; this is very much in line with offerings addressing the challenges of processing unstructured data sources. Solutions for Data Protection are the least frequently addressed research challenge with 13%. When looking to which extend BDV SRIA Technologies are used in combination, we observed that more than half of the start-ups, precisely 59%, combine more or equal than two, but only 22% combine more or equal than three SRIA technologies.

Network Strategy

For digital and data-driven innovations, network effects are one of the key resources to be reflected and understood. In our study, 57% of startups rely on those network effects. We further distinguished different levels of network effects.

First, when data-driven businesses can improve offerings with the sheer amount of data available, they are relying on network effects on data level. In our sample this was the case with 49% of startups. Second, when businesses are providing a technical foundation for others to build upon, network effects on infrastructure level can be observed. In our sample these have been used by 12% of the startups. Third, in cases where the number of marketplace participants is the key source of value, data-driven offerings can harness network effects on marketplace level. The low number of network effects on marketplace level (10% of startups) indicates the tremendous challenges of building and maintaining those marketplaces.

Revenue Model

Often information about the type of used revenue model was difficult to find. Especially in cases when startups have been focusing on emerging technical advances, such as drones or autonomous driving, information about revenue models was not available.

The most frequently used revenue model in our study was the subscription model. In this context we observed a strong correlation with the adoption of a “Software as a Service” (SaaS) approach, which brings a lot of flexibility when using it for deploying data-driven innovations. The second most frequent revenue model is selling of services payed on individual level. Those revenue models are often used for open software offerings as well as non-standardized solutions. We could rarely observe classic advertisement as a revenue model. In our sample, only 2% of startups are applying it. Most probably this is due to the high percentage of B2B models.

Type of Business

When positioning data-driven offerings on the market, companies need to reflect the associated business or innovation ecosystem. Data-driven service businesses that focus on establishing a partnership with a new business partner have been the most frequently used within our study.

78% of startups used this strategy to position their data-driven offering in the market. Compared to data-driven services, the development of data-driven marketplaces is significantly more complex, as a new marketplace / ecosystem needs to be established. Only 16% of companies in our sample rely on this approach. Another strategy is to identify and use an already existing ecosystem which gives the opportunity to position own offerings as niche applications. In our sample this strategy was observed in 12% of all cases.

RESEARCH

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:

Target User

Value Proposition