Data culture is a term used to describe the set of values, attitudes, practices, and behaviours related to data within an organization or society. It encompasses how data is perceived, utilized, and valued in decision-making processes.
The importance of data culture
Organizations face numerous challenges in adopting and implementing data strategies effectively. Merely implementing advanced technologies is not enough to achieve the desired outcomes. Soft factors, such as vision and culture, play a pivotal role in technology adoption and the success of data initiatives.
A clear vision and a supportive culture within organizations are key enablers for embracing new technologies and aligning them with the goals and values of the organization. When technology solutions are designed with an understanding of the cultural context in which they will be used, they are more likely to be adopted and successful.
It is important to recognize that while every organization has its own dominant culture, different business units or departments within an organization may have varying views on how to best utilize data to drive value. By acknowledging these data subcultures and leveraging them through tactical empathy, organizations can build a data-driven culture that embraces diversity and empowers stakeholders.
In summary, a successful data strategy must consider and accommodate varying data cultures within and across the enterprise It involves cultivating the mindset, values, and practices that lead to the enterprise vision, while accommodating smaller group's needs.
By embracing tactical empathy, organizations can navigate cultural differences and build a data-driven culture that maximizes the value of their data assets.
Manage Cultural Differences before implementing technology
"Starting with a BI tool, dashboard, or report is a classic tail-wags-the-dog problem.
There’s no foundation. You’re trying to build data culture and practices to fit the flavor of the day"
- Jonathan Fowler, D.C.Sc., CEO, Logicle Analytics
When thinking about data management it is tempting to leap straight into technology.
In practice, we find that soft factors, such as vision and culture, play a crucial role in technology adoption. Organizations and societies that have a clear vision and a supportive culture are more likely to adopt new technologies that align with their goals and values. Similarly, technologies that are designed with an understanding of the cultural context in which they will be used are more likely to be adopted and successful.
New technology, familiar problems
| We have | a great BI tool | but | don't know what to look at |
| We have | plans for a data strategy | but | can't agree on where we will get value |
| We have | master data management | but | our users don't get what they need |
| We have | data governance | but | no one takes ownership |
| We have | a team of data scientists | but | they can't find the data they need |
| We have | a lot of data | but | can't get the insights we need |
| We have | self-service BI | but | our users aren't engaged |
| We have | spent a lot on data analytics | but | we don't trust the insights |
| We have | a data-driven strategy | but | no one is following it |
It's easy to focus on what you can see - data, technology, activities - but what you can't see can sink your data management efforts
Data strategy depends on data culture
How people respond to your activities depends on how you make your actions relevant to them.
Our holistic data management gap assessment helps key stakeholders to understand what is possible and to set their own targets across a range of soft and hard data management metrics, ensuring the alignment of your data strategy with desired outcomes.
The LDIS+ assessment, from Logicle Analytics, builds tactical empathy by painting a picture of various data subcultures in play across your enterprise.
Together, these tools allow you to plan and prioritise data management initiatives that will work for your key stakeholders, helping to ensure their support and drive adoption.
A common mistake is to assume that your organisation has a single culture.
While every organisation has a dominant culture, research shows that individual business units and departments have very different views on how to best use data to deliver value. Working with Logicle Analytics we help you to build alignment for your data and analytics programs by helping you to understand what drives your key stakeholders
"When building a data-driven organization, you must first build a functional data culture, enabled and driven by the different archetypes present in the organization. Each group’s strength complements another group’s weakness. Acknowledging these differences and using them to drive the organization forward are functions of tactical empathy. In its simplest form, tactical empathy means recognizing that you and I may draw different insights or calls to action from the same data, dashboard, or report based on our dominant archetypes. It also means that you recognize what is important to others who don’t see the same way you do.
"While this may all sound complicated, it can be summed up in two very simple truths:
- being a data-driven organization means more than having all the fancy tech, and
- a winning data culture is one that embraces its differences through tactical empathy." - Jonathan Fowler, CEO, Logicle Analytics
Our Approach
A holistic approach covering people, process and technology risks and opportunities
FAQs about Data Culture
What is Data Culture?
Data culture is the set of values, attitudes, practices, and behaviours towards data that an organisation or society exhibits.
It includes the importance placed on data-driven decision-making, data literacy and proficiency, and ethical use of data.
A strong data culture promotes informed decision-making based on data.
Why is data culture important?
Data culture is important because it encourages an organisation to make better decisions and achieve better outcomes by using data. It enables more effective collaboration across teams, promotes transparency, and supports continuous learning and improvement, and it fosters the investment necessary to supercharge data enablement of the business.
Can we have more than one data culture?
Every organisation will have a dominant data culture. However, research by Logicle Analytics indicates that multiple sub-cultures will also be at play. These are:
- Creative
- Collaborative
- Controlling
- Competitive
Depending on their sub-culture different stakeholders use data in different ways. Building an understanding of these subcultures is useful to engage across the enterprise
What is change management?
Change management is a structured approach to managing the people side of organizational change. It involves planning, preparing, and supporting individuals and teams to adapt to new processes, technologies, or strategies. Building an understanding of different data sub-cultures helps to ensure that your change plan uses tactical empathy to engage different groups.
Why is change management important for implementing a data culture?
Change management is important for implementing a data culture because it helps to address resistance to change, mitigate risks, and ensure that individuals and teams have the necessary skills and knowledge to effectively adopt new data-driven practices and behaviours.
Some common challenges when implementing a data culture include:
- Resistance to change from individuals or teams, often due to an attempt to impose a dominant culture without empathy for the subculture that may exist
- Lack of understanding or trust in data and data-driven insights
- Limited access to relevant data or data analysis tools
- Lack of skills or expertise in data analysis or interpretation
- Limited resources or budget to support the development of a data culture
To develop a data culture in your organisation, you can:
- Practise tactical empathy - build an understanding of individual subcultures and
- Communicate the value of data and data-driven insights to individuals and teams across the organisation
- Provide access to relevant data and data analysis tools
- Invest in training and development programs to build skills in data analysis and interpretation
- Encourage collaboration and knowledge-sharing across teams
- Establish clear metrics and goals for data-driven decision-making
- Foster a culture of continuous learning and improvement
To implement change management strategies to support the development of a data culture, you can:
- Develop a clear vision and plan for the data culture initiative
- Engage and involve individuals and teams in the process
- Address concerns and resistance to change through effective communication and engagement
- Provide training and support to help individuals and teams develop the necessary skills and knowledge to adopt new data-driven practices and behaviours
- Monitor and evaluate the effectiveness of the change management strategies and make adjustments as needed
Some benefits of developing a data culture include:
- More effective decision-making based on reliable data
- Delivery of new data products and services
- Monetisation of data
- Enhanced efficiencies, in particular as one moves towards digital channels
- Improved collaboration and communication across teams
- Greater transparency and accountability
- Increased innovation and creativity through data-driven insights
- Improved efficiency and effectiveness in business processes and operations
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