You can either start with the available data sources (the exploitation) or the possible applications (the utilization), depending on whether you want to identify new applications based on the existing data sources or specify the necessary data sources for a specific application. You then cycle through the individual boxes from left to right, right to left, and from top to bottom, and answer the following questions as well as further relevant questions.
Like any raw material, data must first be developed before it can be refined and eventually utilized. Data collection is usually preceded by an exploration, in which the potential data sources are identified and evaluated. In fact, only those data sources should be exploited whose data are subsequently utilized.
- Which data sources are already available and exploited?
- What internal data sources are available, but not yet exploited?
- Which external data sources of partners or customers can be accessed?
- Which public data sources can be exploited?
- Which data sources are also needed to enable the utilization?
- Which data (about customers, partners etc.) arise from the key activities, the customer relationships and the use of the (marketing, sales and distribution) channels?
- How can we use so-called data network effects, where the data leads to an improved offer so that more customers perceive the offer and in return generate more data?
- Green cards for data sources that are already available and exploited, e.g.: CRM data
- Yellow cards for data sources that are available but not yet exploited, e.g. external firmographics data.
- Red cards for data sources which are necessary to realize a case of application (see exploitation) but are neither present nor exploited.
- Owned data: sensor data, log files, data from ERP systems, transactions in online shops, anonymous surveys etc.
- Earned data: Customer data from CRM systems, social media data, user data of mobile apps etc.
- Paid data: data brokers, data marketplaces ("data places") and data exchanges
- Public data: Federal Statistical Office, Wikipedia, web crawler, social media monitoring, open data etc.
Data are the oil of the 21st century. Just as oil must be refined into petrol or diesel to drive a motor as fuel, data must be prepared for information to drive data-driven decisions, processes and business models in companies. Analytics (data analysis) takes care of the data refinement.
- Do we have to check data and if necessary, correct or filter the data?
- Can we supplement missing data with data from other sources?
- Do we need to anonymize or delete data?
- Can we aggregate different data sources and link data using common identifiers?
- Do we need to normalize the data or transform it into other presentations or formats?
- What metrics and key performance indicators (KPI) can we calculate?
- What models can we generate to calculate predictions or recommendations?
- What visualizations do we need to show key figures, trends or correlations?
- How do we integrate the results of the analysis process into existing business processes?
- Which manual steps are necessary in the analysis process to ensure quality?
- Green cards for processing steps already implemented.
- Yellow cards for processing steps that are in progress.
- Red cards for processing steps that are still to be implemented.
- Data transmission and normalization
- Data aggregation, integration & transformation
- Data sampling or data compression
- Filling of data warehouses (ETL processes)
- Exploratory data analysis (data discovery & data mining)
- Feature engineering & selection
- Predictive & prescriptive modeling (training & test)
- Visualization in dashboards and/or reports
- System integration and test
- Business Model: The process of refining the data is a key activity of the business model
- Analytics Maturity: the methods used determine the necessary degree of analytical maturity of the company or the customer.
After the raw material data has been refined in the fuel information, the information must be utilized, that is for example used to make more reliable decisions.
- What value propositions for the customers do we want to create from the data?
- How can the data improve our customer relationships?
- How can the data make our (marketing, sales and delivery) channels more effective?
- How do the data help us to make our key activities, the use of the key resources and the costs more efficient?
- How can we use the data to increase revenue?
- How do the data enable us to better understand our customer segments and partners?
- How can we develop new business models based on the data, e.g. Data-as-a-Service?
- Green cards for applications with high implementation probability.
- Yellow cards for applications with unclear feasibility.
- Red cards for applications where critical parts, e.g. data sources, are missing.
- Descriptive: monitor the performance of key activities such as marketing, sales & service to optimize costs.
- Diagnostic: identify the lucrative customer segments.
- Predictive: predict how much the need for key resources (e.g., employees) will be.
- Prescriptive: send the customer via the channels personalized offers to improve the customer relationship.
- Automated: adjust the prices daily, depending on the demand from the customers and the offer of the competitors.
- Value Proposition: to create value propositions for customers, partners or employees.
- Business Model: to identify possible fields of application
- Analytics Maturity: to prioritize various possibilities of development regarding the necessary analytical maturity of the company or the customer.
The exploitation, refinement and utilization of data requires specialized tools, which vary greatly depending on the data source, data volume, data format, purpose of analysis, framework conditions or application. Accordingly, the question of the necessary tools should be asked only after it is clear which data should be exploited, refined and utilized in which way and for what purpose.
- Which integration solutions do we need to integrate external data sources?
- Which database systems do we need to store the data?
- Which systems do we need for the aggregation, integration and transformation of the data?
- Which analytical tools do we need for descriptive and diagnostic analysis?
- Which modeling tools do we need for predictive and prescriptive analysis?
- Which visualization tools do we need for dashboards, reports etc.?
- Which integration and automation tools do we need for automation?
- Green cards for tools already available.
- Yellow cards for tools where it is not clear how the availability is.
- Red cards for tools that are still missing.
- Data management: ETL tools, SQL, NoSQL, graph databases etc.
- Data processing: MapReduce systems, in-memory analytics, data warehouse systems, etc.
- Data analysis: spreadsheets, statistics tools, mathematical programs, programming languages, etc.
- Data modeling: modeling tools, data mining toolboxes, SaaS offerings, machine learning clouds etc.
- Data visualization: self-service BI tools, infographic design tools etc.
- Data automation: marketing automation tools, service orchestration tools etc.
In order to use the specialized tools, specialists in the company need to know how the tools are operated, configured and administered. Instead of thinking in concrete persons, you can also think in roles and consider whether a person should fill several roles or have a role in the company several times (for reasons of availability, for example).
- Who takes care of data quality?
- Who is responsible for data protection?
- Who administers the IT systems?
- Who is designing the analysis processes?
- Who implements the analysis processes?
- Who carries out the analysis?
- Who interprets the results?
- Who generates and validates the prediction and recommendation models?
- Who designs the visualization?
- Green cards, if the role, ability or expert is already present in the company.
- Yellow cards when the availability still needs to be clarified.
- Red cards, if the role, ability or expert in the company is missing.
- Data steward
- Data protection officer
- IT administrator
- Big data engineer
- Software architect
- Data scientist
- Business analyst
- Information designer
- Business Model: (data) experts are a key resource for data-driven companies.
Certain roles cannot or may not be occupied internally because, for example, you would not utilize the employee concerned to the fullest. Then you need a specialized partner who is available to you as a service provider. Partners are also companies that provide you with critical data or tools that you simply could not obtain otherwise.
- Which partners or customers provide their data sources (as Data-on-Demand or Data-as-a-Service)?
- Which product providers provide us the necessary tools
(As Software-on-Demand or Software-as-a-Service)?
- Which implementation tasks can we assign to service providers?
- Which roles (persons) can we outsource to service providers?
- What expertise do we need in the form of consulting or training?
- Green cards for companies that are already partners.
- Yellow cards for companies where the status is unclear or in negotiation.
- Red cards for companies that are not yet partners.
- Market research company
- Data & address dealer
- Software & SaaS providers
- Platform operators
- Business intelligence & data science service providers
- Agencies and consulting firms
- Freelance experts
- Business Model: Partners that provide data, tools or personnel as part of a data strategy are among the key partnerships.
When you have finished all boxes, check your data strategy for consistency and completeness. If necessary, let the work rest for a day and / or present your data strategy to colleagues who were not involved in the development. Tell a story and pay attention to breaks and uncertainties.
For example, consider the following questions:
- Do I have the corresponding link data to connect the different data sources together?
- Do I have the right tools to tap the data sources?
- Do I have the staff to use the tools?
Highlight open questions, critical assumptions, or potential vulnerabilities with white cards and define tasks to answer these questions, test assumptions, and examine weaknesses. If necessary, rework your data strategy if, for example, a critical assumption has proven to be wrong.
Finally, you can integrate the building blocks of your data strategy into your Business Model Canvas. If you have identified more than one processing opportunity, use the template Analytics Maturity, to compare and prioritize them.