You’re building a data governance framework. How can you make sure collaboration is at the heart of it?

The primary objectives of data governance are to: → Improve the agility of data-driven business decisions → Seamlessly share knowledge across the organization → Eliminate uncertainty and instill trust in data → Drive value through collaboration in current workflows → Make security and privacy compliance effortless The key focus of the vision and principles should be in areas that include data availability, usability, consistency, integrity, compliance, and security. Governance also encompasses the creation and maintenance of processes (and process owners) to ensure high-quality data can be used by all.

In my opinion, the specific objectives and activities of a company's data governance program should be derived from an overarching data strategy that is in line with other company strategies such as the corporate strategy, the digitalization strategy and the IT strategy.

In my experience it’s important to understand the company’s objectives and then the stakeholders needs, what is missing and what are the pain points they are dealing with. Making the stakeholders need as a priority when defining the data governance strategy is most important when establishing a collaborative framework.

Remember that governance is underway in the company no matter what you've been told. Get out there, talk to the entire production-to-consumption data pipeline, and map what's being done. Combine this with other advice to anchor a vision and principles in what the enterprise wants to be and what it currently is.

The principles that you want to define need to incorporate the collaboration from the beginning on. This is the only way to implement collaboration at the core of the new initiative. Afterwards it will not be possible to implement it. It would only end up as an “add-on”.

A data governance team has several roles, each playing a key part in leading your businesses toward a data-centric culture. Here are the four most common data governance roles: → Data admin: They oversee the implementation of the entire data governance program. → Data steward: Data stewards make sure that business users can consistently access high-quality data. → Data custodian: A data custodian deals with the movement, security, storage, and use of data. Unlike data stewards, the role of a data custodian is more on the technical side. → Data user: A data user is anyone within the organization who extracts value from data. Data users include marketers, researchers, executives, business managers, senior executives, and more.

A key consideration should be in the data governance operating model, focusing on decision making speed and the interface with data delivery. Organisational data lifecycles are often very rapid from generation to consumption of data. Traditional governance models are 'report upwards', with operations roles infrequently reporting into governance, and mostly not in person. This loses the day to day lived reality the organisation faces with its data, and dramatically slows down decision making. A good data governance operating model should blur the boundary between governing and delivering, by meeting frequently and including participants from across the business who are directly involved in data delivery and data accountability roles.

Will your data governance be democratic and distributed and include voices from throughout your organization? Or will your organization adapt to a centralized single source governance framework? A data governance strategy is best implemented and most successful when the framework reflects the political and social components that define the data landscape.

I equate Data governance with CGMP. It’s everyone’s responsibility. Establishing a RACI diagram of responsibilities is a great place to start. Cross functional participation is key and user SMEs are critical to risk assessments and work flow assessments. Ensure all governing SOPs capture the right controls.

Data governance standards are guidelines and procedures that an organization puts in place to manage and ensure the quality, availability, usability, integrity, and security of its data assets. These standards are a system for defining who within an organization can take action on specific data, when these actions can be taken, and the scope of these actions: → Data quality standards → Data integration standards → Data security and privacy standards → Data accessibility standards → Data lifecycle management standards → Data compliance standards → Data ownership and stewardship standards → Metadata management standards

In my role as a DG professional, establishing well-defined processes and standards is crucial for the success of our data governance framework. These guidelines will span the entire data lifecycle, encompassing collection, quality control, analysis, and even archiving or deletion. It is my responsibility to meticulously record these procedures and standards, ensuring that everyone on the team understands them and abides by them. Monitoring their effectiveness and continuously seeking improvement opportunities is also part of my mandate. This approach guarantees that our DG efforts are not only consistent but also adaptive, allowing us to maintain high-quality data management practices that support our organisational objectives.

Set data governance standards by setting protocols for following data governance verticals ??Metadata Management ??Data Policies and Rule ??Data Catalogs ??Data Lineage ??Data Quality and Analysis ??Data Profiling

Data governance tools are pivotal in driving strategic business decisions and shaping corporate destinies. These tools provide an efficient means to manage, control, and use the increasing volumes of data that businesses generate. Critical components of these tools include data stewardship, quality control, cataloging, lineage tracking, security, compliance, and data visualization. Each of these elements contributes significantly to maintaining the integrity and enhancing the value of data. In the absence of these tools, the process of data governance can be chaotic and error-prone. However, with a robust data governance tool, these burdensome tasks can be automated.

We know the power of tools to boost people collaboration or push people away. Given that, select tools that could be aligned with the defined roles & responsibilities and based on that could be engaging your data governance council to work in each part of the process as a team, respecting and orchestrating each role. Look for tools that will support your data governance and a more fluid way.

As a data management professional, I'm focused on integrating essential tools and technologies to enhance our data governance framework. This includes adopting data catalogues, dictionaries, quality and lineage tools, security solutions, and collaboration platforms. My priority is to ensure these tools are seamlessly integrated with our existing infrastructure and are user-friendly to encourage adoption among our team. By leveraging these technologies, we aim to automate and streamline governance processes, making our data management efforts more efficient and effective.

There are many vendors in Data Governance space . Chose the right vendor as per best fit and one who qualifies all the criteria’s . Collibra is a leader in this space but others are doing well , chose the best tool as per your fitment .

Modern data governance must be community-led, centred on collaboration and data democratization, with privacy at its core. Everyone in an organization should be able to access, understand, and use the right data to unearth valuable business insights. The first step is to change employee’s perception of data governance. It’s not about control, red tape, and bottlenecks. Instead, the data governance community ensures your data is trustworthy, useful, and easily available. The goal of data governance isn’t merely regulatory compliance, it is to increase the value of data. Everyone — not just data stewards or data governance steering committees — is responsible for data governance.

To strengthen organizational culture, I would recommend providing continuous education and training on data governance and management strategies to build a shared language and understanding. Knowledge Sharing Sessions: Hold regular seminars or lunch-and-learn sessions to encourage team members to share their knowledge and experiences. Recognize and reward the efforts of teams and people who work effectively together and contribute significantly to the governance initiative. Success Stories: Distribute success stories around the organization to highlight the value of collaboration in achieving data governance goals.

As with any collaborative effort clear communication channels and transparent decision making is important. Especially when establishing a shared data governance framework across functions and borders, having to align on different views of data policies and procedures. Forming a data governance committee that has functional representatives from IT, legal, business lines and so on might help.

Data governance policies/framework often require a myriad of professionals from different backgrounds to work and collaborate. The key to success is open communication, building a team spirit and establishing a respectful working relationship so that collaboration can be effective and successful.

N?o existe colabora??o sustentável fora de um ambiente que permita o erro e que possua seguran?a psicológica. Estamos falando de governan?a de dados neste fórum, mas isso se aplica para qualquer outra disciplina. Para implementa??o de uma cultura de colabora??o na área de dados, gosto de pensar numa abordagem 4E12P. Preciso definir a Essência do programa (Propósito, Premissas e Pilares). Depois preciso entender a Estrutura (Produtos, Pessoas, Processos). Com isso em mente eu come?o a refinar minha Execu??o (Painéis, Políticas e Publicidade) para depois potencializar a minha Estratégia (Perspectiva, Patrocínio e Plano de a??o). Quando conseguimos conectar e equilibrar cada um desses "Es" e "Ps", a colabora??o vira uma consequência natural.

I will put this as a farmer by nature due to where I originally come from, (Al-Qassim, Saudi Arabia). Imagine a farm where each plant represents a piece of data. To thrive, every farmer (team member) must communicate, share tools (resources), and nurture the farm collectively. Create a 'Farm Council', a mix of data stewards, users, and IT wizards, to regularly discuss the farm's health, seasonal changes (market shifts), and plant new seeds (innovations). Embed secret paths (channels) for easy sharing of insights, and establish a festival (quarterly review) to celebrate growth and learn from the seasons. This living ecosystem ensures collaboration is the sunlight, water, and soil nurturing the farm of data governance.

While developing your framework, stakeholder mapping should be done so that all the key stakeholders are involved in developing the framework. If this is done you can be sure that you are sufficiently consultative in your approach.

Creating a solid data governance framework involves getting leaders on board, educating everyone about its significance, and communicating the goals and benefits clearly. One needs to team up across different departments, assign roles, and highlight early victories to show the positive impact. Also remember to keep an open feedback loop for improvement, smoothly integrate the framework into daily tasks, offer ongoing training, and acknowledge those making noteworthy contributions. This approach will build a positive and inclusive data-driven culture across the organization.