Data Driven Organisation Scan

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Data Driven Organisation Dashboard

The scan consists of a dashboard with questions in five categories: Organisation, Data, Security, People and Technology. Based on your answers, you will receive a score indicating your maturity in each category. The Impact category provides an option to give examples of how your organisation is using data to add value for your internal or external customers.

Using the dashboard below, you can complete the categories in any order. The number of questions in each category varies. The key shows a brief description of the different maturity levels indicated by different answers.

Impact

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Level	Answers Edit answers	Points
1	Data governance and controls are non-existent or fragmented. Data workers make decisions on data privacy, quality and availability are made locally. Privacy, security and compliance officers are not involved in data projects.
2	A cross-functional data governance body, organised at business unit level and comprising data producers and data users, promotes a consistent approach. Data roles, responsibilities and KPIs are clearly defined. There is explicit compliance with data-related regulations.
3	A data governance body at corporate level has senior involvement from all product lines and functions (including data producers and data users). Data quality is measured and managed at business unit level, but not visible or managed at corporate level.
4	We have an integrated data governance and control framework, and integrated, continuous monitoring of data quality. Data quality is measured and managed at corporate level. All data roles across the organisation have reached a mature level.
5	Data governance is integrated across our partner and client ecosystem. All aspects, such as data definitions, quality and privacy, are managed in the ecosystem.

Level	Answers Edit answers	Points
1	We have fragmented and local suppliers. Occasionally we make use of external data. Different departments have no knowledge of each other’s use of external data sources. Sometimes we pay twice for the same data.
2	We are starting to build a data supplier landscape and develop a data purchase strategy.
3	We work with a pool of data suppliers across business units, functions and product lines to deliver existing data propositions.
4	We have strategic partnerships with clients and suppliers with the aim of leading data innovation.
5	We can connect quickly with data suppliers and clients. We collaborate with innovative partners and make maximum use of smart technology, eg sensoring.

Level	Answers Edit answers	Points
1	Data is used at personal or department level. The primary focus is on reports and queries using Excel or similar standard tooling.
2	Data is used at business unit level. Users collaborate across functions and product lines, and work to improve their data capabilities and knowledge of more advanced analytics.
3	Data users are organised in a centre of excellence. They have internal clients and manage stakeholders proactively. The delivery focus is primarily on reports and queries.
4	Our centre of excellence works in short cycles in an agile manner. It facilitates self service for data used for reporting, and focuses on delivering advanced analytics for internal clients.
5	Our centre of excellence partners with external clients and other stakeholders, exchanging advanced analytics expertise.

Organisation
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Level	Answers Edit answers	Points
1	Analysis of data, stored in local data stores, leads to better reporting.
2	Analysis of data, stored in the data stores of other business units, leads to better reporting. It provides cross-functional information.
3	Analysis of data, stored in the data stores of other business units, leads to actionable insights that improve operational performance as well as tactical and strategic decision making.
4	Analysis of internal and external data (more than 50 sources) leads to actionable insights. Data can be stored in structured format (well defined table fields) and unstructured formats (written text, speech or pictures).
5	Analysis of data stored in a collective ecosystem leads to real-time insights, applicable across the organisation and available everywhere.

Level	Answers Edit answers	Points
1	Reporting processes are run within a department or business unit. Data definitions are locally defined. Data requirements apply to local business objectives. There is limited re-use of data by other business units and functions.
2	Reporting processes run across functional units but analytical processes are performed within business units. Business units define their own terms (eg what is a customer?) and manage core data assets, such as product data and customer data at business unit level (master data management).
3	Analytical processes are performed collectively across functional and business units. Data requirements are aligned with business objectives.
4	There is a glossary of business unit and corporate data terms and definitions. Core data attributes are managed at corporate level (master data management). Data quality is measurable and integrated at a corporate level and is fit for purpose.
5	Data is processed across an ecosystem with agreed terms, definitions and quality standards. The ecosystem enables agile data innovations.

Data
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Level	Answers Edit answers	Points
1	Data management actions are logged to allow retrospective analysis. The data security requirements for different data sources are known.
2	There is capability to detect data security events and respond, and to adapt to changes in data regulations.
3	Advanced analytics are carried out on internal and external data sources to assess threats to data security.
4	An incident management and response procedure is in place. Action is taken to reduce the root causes of vulnerabilities and to address threats.
5	We continuously improve our analysis of, and response to, dynamic threats and hazards.

Security
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Level	Answers Edit answers	Points
1	Our data workers perform descriptive analysis and create clear and effective reports. They understand where data is stored and how data attributes are defined.
2	Our data workers maintain the self-service environment where people can create and adjust reports. Business consultants help data users translate the reporting requirements to output.
3	Our data workers are data scientists and skilled in using advanced analytics to identify actionable insights.
4	Our data workers are data scientists and skilled in using advanced analytics. They understand the business and initiate actions based on their insights.
5	Our data workers are data gurus – outperforming peers and taking their expertise to a higher level.

Level	Answers Edit answers	Points
1	Data is a low priority. Senior management are not concerned with ownership of data, data management and data governance.
2	Early adopters are starting up data experiments and have easy access to budget for this.
3	The number of data experiments is growing. Business sponsors are getting involved, taking ownership, providing budget and acting on the results.
4	Analytics are a priority for our leaders and all staff are aware of how data generates value. Data experiments are commonplace.
5	Data is an explicit part of our mission and vision statement. An intelligence-driven culture across the organisation leads to innovative solutions.

People
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Level	Answers Edit answers	Points
1	To report on data; typically for scheduled and ad-hoc reporting.
2	To make analyses; descriptive use of data, such as average values, standard deviations and histograms, generated with traditional reporting tools.
3	To make predictions about what will happen using, for example, algorithms and neural networks.
4	To make predictions and define what needs to be done to realise desired outcomes, with games and simulations, for example.
5	To execute cognitive analytics, such as deep learning and natural language processing, to continuously improve models.

Level	Answers Edit answers	Points
1	Our data is stored in multiple locations, making it hard to combine data from different areas in the organisation. Projects typically cover a single product line or business unit.
2	Data from different business units and product lines is combined in a central data warehouse. There is often more than one version of the truth and reconciliation takes time or leads to quality issues.
3	We have one rationalised data model and definitions are clear at corporate level. We use a fast data platform to store and process large amounts of structured and unstructured data.
4	We can follow how data flows in the organisation and know at all times where data is stored and what the quality of data is (traceable data lineage). There is easy access to a variety of internal data (data lake) for all data-related functions.
5	We have a shared data ecosystem with suppliers, partners and/or customers, with clear data definitions. We can easily access external data sources.

Technology
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Legenda: Data maturity levels

1 Analysing
2 Monitoring
3 Predicting
4 Pro-Active
5 Self-Learning

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