How can you create a culture of data sharing and innovation?

Data democratization is a term often used for defining data vision, but it's not just about making data available; it's about making it understandable and actionable for all levels within the organization.

The vision of data has to be customer backwards. Be it going into reports, dashboards or ML models. The kind of business decisions it's driving is vital to understand. Data usage and utility has to be one of the North stars that data vision should entail to align and buy in with leadership and the rest of the org.

The first step is to understand the “why” and gage the organisations readiness for change. There are many tested methodologies to do this. Unfortunately some places will never embrace a sharing mindset, it’s in the DNA. Short of a diktat from the CEO that hits managers in the pocket, it’s often best to start very small and work with friendlies in a attempt to showcase what can be done when everyone plays ball. For more progressive places where collaboration is already a byword for success, it’s much easier to launch these corporate-level initiatives. But to jump straight into strategy development without first diagnosing the lie of the the land is risky and can be shortcut to failure.

Raising data awareness and promoting data-literacy is a MUST in todays's world. Not only to data/business professionals but to ALL. Its a new language and most of all its a new mode of communication...so we all have to have the basics from schoolers to workers from all walks of life.

Defining a clear data vision and goal is a fundamental step in establishing effective data governance within organization. A well-defined data vision and goal ensure that data governance efforts align with broader business objectives. This alignment helps prioritise data initiatives that directly contribute to organization success. When everyone understands the overarching vision and goal, it’s easier for teams to work cohesively towards common objective. The data vision defines where the organization aims to be in the future in-terms of data maturity and effectiveness.

How do you identify your Data Champions to begin with? Do these: 1. Launch Data Literacy Training Bootcamps to upskill staff in data preparation, analysis, and visualization. 2. Host Data Analysis Use Cases Ideation Workshops where staff can brainstorm and develop innovative data-driven ideas and projects. 3. Establish a Data Analytics Community of Practice, where data enthusiasts can collaborate and share knowledge to build a sense of community. 4. Organize Data Analytics Hackathons to promote healthy competition among staff. Recognize and reward innovative data solutions and their creators with awards or incentives. 5. Data Champions will emerge naturally from these activities. Support them with resources to lead data-driven initiatives.

In my experience, I find people who are curious and like to solve problems have a lot of value to add. Asking exploratory questions such as ‘I wonder if we tried this or what would happen if we had this information’ can be powerful. It isn’t about the data itself.

When building out the organisational model, try to avoid forcing too much 'data terminology' on the business. If there are existing roles and labels that exist and work, use them rather than waste the limited attention of the business on unnecessary conversations are around the difference between technical data stewards and business data stewards, or business glossaries vs. data dictionaries.

Empowering them should go beyond providing tools and training; it should include career development opportunities focused on data specialization. Another critical consideration is fostering cross-functional collaboration. While having data champions within departments is good, creating interdepartmental teams can lead to more comprehensive insights and solutions. These teams can act as internal think tanks that tackle complex, organization-wide data challenges. Consider setting up a centralized platform or community where data champions can share insights, ask questions, and disseminate best practices. This can act as a knowledge repository and a communal space that fosters a stronger sense of mission around data.

Data culture is ‘democratizing’ relevant data to foster data-based decisions at all levels of the organization. It is taking people away from feelings toward facts to form opinions based on evidence instead of instinct.

Clear and concise documentation about the data sources, definitions, data quality and ownership is pivotal in building trust in the data. It is also essential to understand how the data would be used and shared further before proceeding with data sharing. Regular feedback from the data users is also needed.

If you've established data governance, you've got the right people identified for data sharing oversight. Data doesn't need to be hidden from others, especially in your own company. However, it also needs to be shared compliantly. If there are any rules, such as not sending it to certain countries without approval or not combining it with other data, those rules need to be known first. You also need to know where your data goes in case you have to provide updates or monitor for data breaches.

Defining the Data Culture Strategy with a vision of democratizing the data of the organization helps in building trust and bringing in the transparency of the data sources, processes, and tools used to load the same. Having a good data strategy that is aligned with the business brings in the required stakeholders and ensures that the value of the data is being acknowledged and adhered to for long-term usability.

Another dimension worth mentioning is proactive communication. Transparency is not just reactive; it involves actively communicating data strategies, changes, or incidents that could affect data users. Timely and transparent communication can go a long way in fostering trust even when things don't go as planned. Furthermore, while soliciting feedback is crucial, acting on that feedback is equally important. Acknowledgment without action may eventually erode trust. An agile approach to data governance, which iterates based on user feedback, can strengthen trust and engagement.

Data Literacy brings in clear understanding, interpretation, and lineage of the data. This can be achieved by starting with a business glossary, data dictionary, and finally a data catalog which will provide a better searching, classification, and categorization of the data. By doing this, organizations can use the data to solve business problems and lead to continuous improvements of processes.

In addition to promoting data literacy among employees, partners, customers...it is essential that data & AI ethics are socialized among students from high school to Univeristy...embedding data and AI ethics as early as possible will stabilize the ethical compass of data & AI.

Data culture means equipping people to tell data-backed stories.s to track data relevant to their work. Looking at problems and solutions from a data lens needs to become a part of teams’ routines across the board.

I'd like to add that Metadata Management, including tools like a Data Dictionary and Business Glossary, plays a vital role in enabling efficient data exploration and experimentation. These systems centralize essential information, offering users quick access to definitions and attributes through search capabilities. By maintaining standardized terminology and data lineage, they promote a common understanding of data. This facilitates the process of asking questions, testing hypotheses, and sharing findings, aligning with the goal of encouraging innovation. Additionally, Metadata Management helps mitigate risks by providing insights into data quality and dependencies, allowing organizations to balance innovation with data integrity.

Integrating "data storytelling" into this experimental culture is also essential. Data exploration often results in complex findings that are best understood when narrated well. Encouraging the skills to translate data into compelling stories can facilitate better understanding and decision-making across the organization.

Showcasing and sharing data stories, solutions and success helps in building the data culture and driving data literacy in an organization. This also inspires cross functional collaboration to drive innovation.

Celebrating data success and impact within organizations is essential because it fosters a culture of appreciation and recognition for contributors to Data & Analytics efforts. This practice not only motivates teams, but also promotes knowledge sharing, retains top talent, and strengthens stakeholder confidence. Moreover, sharing these achievements publicly acts as a powerful magnet for attracting skilled talent, enhancing the organization's appeal to individuals seeking a culture of acknowledgement and recognition for their contributions.

Additionally, consider implementing an internal "Data Impact Award" or similar recognition, which can be conferred periodically. This institutionalizes the celebration of data success and can serve as a motivational tool for continuous improvement.

Celebrate data success and impact is not just a form of acknowledgment,it’s crucial part of fostering culture of data-driven decision-making and continuous improvement. Recognizing data success stories raises Awareness about the impact of data governance throughout the organization. This can contribute to cultural shift within the organization. It emphasizes that data isn’t just a byproduct of operations but strategic asset that drives success. Builds trust with stakeholders, including customers and partners. It signals that the organization takes data seriously. This encourages agility in data governance practices. It sends message that organization is willing to adapt and refine its approaches based on data-driven insights.

Data sharing is a 'must' for innovation and unlocking opportunities for businesses. However, for an organization where this practice is new, a two-way approach should be adopted. The top-down approach helps in setting up a vision and objectives for the business and the bottom-up approach helps to define the 'which', 'what', and 'why' of the data. Data leaders and their teams are the boots on the ground and they are the ones who know their data well. They are the ones who can add value to the overall organization's goals by ensuring the right information is being captured and used for the business. Inclusion is the key! The more teams are involved in contributing to the shared vision, the more comfortable they will feel in data sharing.

In an increasingly interconnected world, data governance and sharing often cross national boundaries. Understanding global data regulations like GDPR in Europe or CCPA in California is crucial for multinational organizations.

Data governance project in organization is continuous effort to ensure data managed effectively, securely and in compliance with regulations and organizational needs. Data Lifecycle Management in organization is needed to manage data effectively with stages starting from creation of data to deletion of data and enforce policies accordingly.