How can you drive data adoption and usage in your organization?

Business will realize the value of adopting data if they see meaningful information in a timely manner. This can happen only if the data strategy aligns with the organization's business goals. As Business teams recognize the valuable information being delivered to them, the adoption will automatically increase.

As an interim Chief Data Officer (CDO) and Data Protection Officer (DPO) at the Middle East's largest bank, my mission was to implement GDPR compliance while elevating the bank's data maturity. I started by aligning leadership with the value of data, setting a data-driven tone. We streamlined data access, focused on stewardship, and invested in data literacy and culture change. Key to our success was establishing robust data governance, ensuring consistency and compliance. The outcome: GDPR compliance and a data-centric culture that boosted trust, efficiency, and readiness for a data-driven future.

If your company is in an initial stage of data-driven decision making, you must ensure to show its value as well as have a strategy. Showing the value can be done by focusing on a single high value project, having a detailed plan and bringing it to fruition. Try not to deviate from your plan to avoid cost and delivery time hikes. Once delivered, focus on communication of the added value. This will automatically generate interest to further build on the defined strategy which you already put in place. Ensure to advocate data-driven decision making at all levels of your company. Having data-driven engineers also provides leverage to your middle management to adopt the idea. This way, executives and management can find eachother in the middle.

An example we have done in the past was, showing people how they can calculate their own KPIs in reporting platform. Key is simplicity and visualization for this topic. With this approach, you can engage people more and make them feel like they can play with numbers very easily and understand their individual KPIs better in order to reach enterprise level KPIs. More importantly, get their buy-in with this simple method…

Nothing is more impactful than data that calculates, measures and tracks the business's bottomline. While prescriptive analytics can help navigate the business towards growth and profitability, descriptive analytics is the precursor to it all. Performance measurement should be the driving goal of enterprise data warehousing and business analytics. It helps the organization to identify its current position and status.

For business-side users, the key lies in providing them with tools and resources that enable self-service analytics. Self-Service Analytics: Creating sandbox where users can explore datasets within their access privileges (data marts). Self-Service Analytics dashboards should feature user-friendly drag-and-drop functionality and clear data definitions. User Manuals and Training: It's vital to provide comprehensive user manuals and training. This ensures that business users can harness the full potential of the analytics tools available to them. Promoting Data Literacy: Establishing an organization-wide data literacy program and fostering a community that encourage active participation and knowledge sharing.

Making it easy for business users to access real customer interactions that are on point is invaluable. Sharing the customer's own voice articulating frustration or unexpected effort to solve an issue can rally your team to act!

In my same role as a (CDO)and (DPO) for the largest Middle Eastern bank, where I led a GDPR program I focused on empowering our teams. We provided a user-friendly, scalable BI platform for easy data exploration and analysis. Comprehensive training ensured everyone could use it effectively while emphasizing data quality and governance. We encouraged feedback and shared best practices to create a community of data champions. This strategy transformed us into a data-driven powerhouse, a highlight of my journey.

This step is very important and it encourages a culture of data-driven decision-making throughout the organization. Consider this example: In a healthcare organization to drive data adoption, a user-friendly BI platform is provided for the medical staff. With this helpful tool: - Doctors access patient records and treatment data seamlessly. - Nurses analyze trends in patient vitals and medication. - Administrative staff track resource utilization and appointment scheduling. With the right tools and skills, we ensure that the medical staff use data effectively, improving patient care and operational efficiency.

- Promoting data quality and governance standards is key element of user empowerment. Users must understand the importance of data accuracy, consistency, and compliance with regulations. It ensures that data remains a reliable asset for decision-making. - Also, encouraging a collaborative environment is crucial. Creating a community of data champions where users can share feedback, ideas, and best practices with one another fosters a culture of learning and continuous improvement. - Peer-to-Peer support system helps users stay engaged and motivated to leverage data effectively. Users should be confident in their ability to navigate the platform and extract actionable insights from the data.

Data is always an integral part of the business processes. Consider this example: In e-commerce to drive data adoption: - Integrate data into the website's product recommendations. - When a user browses, the system uses data to suggest relevant items. - At checkout, it applies data-driven pricing and offers based on the user's history. By embedding data into the workflow, we create a seamless and personalized shopping experience. This encourages users to rely on data-driven recommendations, leading to increased sales and customer satisfaction.

Embedding data in workflows streamlines decision-making and enhances efficiency. For example, - sales team can access real-time customer data within their CRM, improving personalised recommendations. - Inventory management can automate restocking based on sales data, reducing manual oversight. - Marketing team can use customer behaviour data to personalize email campaigns, increasing engagement. - In manufacturing, sensors collect real-time data for predictive maintenance, reducing downtime. Such integration ensures data is integral to daily tasks, enhancing productivity, and delivering data-driven insights across the units. These data-driven approach ensures data is not just a resource but an essential part of daily operations

Data is everywhere in an organisation. From purchases made, to customer relationship etc. Integrate data into existing workflows and processes. If data analysis becomes a natural part of daily tasks, adoption will increase.

In my view, embedding data into workflows is essential for organizations aiming to maximize their data's potential. Automation plays a critical role in streamlining data processes, freeing up time for analysis and decision-making. By automating tasks like data delivery and updates, organizations can maintain data accuracy and reliability more efficiently. Implementing alerts is vital for staying informed about data trends. Lastly, fostering a culture of data-driven decision-making is key. By consistently using data to guide actions and outcomes, organizations can cultivate an environment where data is valued and utilized daily, driving organizational success.

We are using BI solution in our system of software usage monitoring to control the cost and volume of Oracle software use. The first step was to have the data processed and aggregated in one database. Secondly the data adoption. In complicated IT environment there are many factors that affect the result. The workflows in Software Asset Management require to take into account all of these factors since the attention to the little things are very important. That's why it is so crucial to follow up the data changes. The final result strongly depends on the algorithms in workflows and therefore it affect the management decisions. If you want to know the details, drop me a message, I will give some more observation on it.

By celebrating data successes,we not only motivate teams but also create a culture of continuous improvement. Others are inspired to explore data for their challenges, inculcating a data-driven environment that extends across the organization. Example: In a manufacturing company to drive data adoption these steps are followed: -Through data analysis acknowledge & appreciate the teams that reduced defects. - Host data hackathons, celebrating innovative data-driven solutions. -Regularly share success stories of how data improved efficiency & product quality. It’s not just doing/commanding work but appreciating & identifying areas of (continuous) improvement thereby leading innovations is equally important. That’s the true leadership aspect.

Celebrating data successes is a pivotal step in fostering a data-driven culture within an organization. - It's a way of acknowledging and appreciating the efforts and contributions of individuals and teams who have harnessed the power of data to drive positive outcomes. - celebrating these successes motivates data users but also underscore the significance of data in achieving strategic goals. - It can help in creating a sense of ownership and pride among users/contributors. Also, it reinforces the idea that everyone plays a role in leveraging data for the organization's benefit. - In turn, it encourages a culture of continuous improvement & innovation, where individuals are motivated to explore new ways to utilize data effectively.

Making your team realize a win or a improvement was made with insight gotten from data is a way to keep them motivated to do same.

Data when utilized to its fullest potential can drive user engagement and stakeholder ability in making more informed business descions. Showing users the value through dynamic visualizations will help encourage curiosity and can spur innovative thinking.

Fostering data curiosity is exemplified by Kaggle, a platform where data enthusiasts and professionals collaborate on data science projects. Kaggle's data challenges encourage participants to ask questions, experiment, and innovate with data. This collaborative environment stimulates curiosity and leads to groundbreaking data-driven solutions within a supportive community.

Nurturing a culture where data is seen as a tool for innovation and problem-solving is very important strategy for success. For example, in a tech company to foster data curiosity: -Encourage developers to explore user behavior data to improve features. -Offer data science/other relevant courses to team members interested in learning. -Hold regular brainstorming sessions where employees propose data-driven innovations. By promoting data curiosity, the team becomes more proactive in seeking insights. They start experimenting with data to uncover new opportunities, ultimately driving the company's growth and competitiveness in the market.

Fostering a culture of data curiosity & experimentation not only enhances data utilization, but also nurtures a dynamic and innovative environment. - Encouraging data users to ask not only WHAT but also WHY and HOW questions about the data; such inquisitive mindset could lead to deeper insights and innovative solutions. - Creating safe data playgrounds where users can experiment with data without fear of making mistakes. Such mindset encourages experimentation and learning across users. - Most importantly, enabling remote collaboration among different teams to foster diverse perspectives and cross-pollination of ideas related to data usage is the critical pillar. Leaders should set an example by actively engaging in data exploration

In our increasingly data-driven world, the essence of thriving doesn't lie solely in adopting data, but in nurturing a culture steeped in data curiosity and experimentation. It's not about hoarding numbers, but sparking questions: "What story does this tell? What if we try this?" By encouraging a mindset of exploration, where data isn't just consumed but questioned, we pave the way for innovation and fresh perspectives. In this environment, data isn't just a tool; it's a collaborator. So, let's not just collect data; let's converse with it, challenge it, and let it challenge us. The future belongs to the data-curious.

Enact Data Governance policies Implement data governance policies to ensure data accuracy, security, and compliance. When employees trust the data, they are more likely to use it.

Data has to be used ethically to achieve both organizational & societal goals. Example:- Let’s view how we can promote data ethics in a healthcare organization: -Enforce strict data access controls to protect patient privacy. -Provide regular ethics training to staff handling sensitive medical data. -Engage in transparent communication about how patient data is used for research, ensuring trust. This approach not only ensures legal compliance but also fosters a culture of responsibility. Patients trust that their data is handled with care, encouraging them to contribute more, ultimately advancing medical research & benefiting society.

Organizations must prioritize responsible data handling, safeguarding privacy, and ensuring transparency. By implementing clear data governance frameworks and adhering to ethical guidelines, we protect individuals' rights and build trust with customers and stakeholders. Additionally, fostering a culture of ethical data use encourages employees to make morally sound decisions when collecting, analyzing, and sharing data. Ultimately, upholding data ethics not only mitigates risks and legal concerns but also enhances an organization's reputation and fosters positive relationships with its data subjects and the broader community.

In the world of handling data, being ethical means more than just following rules – it's about doing the right thing and being honest. As leaders, it's our job to make sure that everyone who works with data understands this and uses it responsibly. One way is by setting up strong rules for how data should be managed. These rules shouldn't just cover legal stuff; they should also remind people of their duty to take care of data. We also need to think about how our actions with data affect society and the environment. When we use data, we should try to do things that make the world a better place, not just for our company, but for everyone. Being a champion of data ethics means treating data with respect.

Empower Data User (Cont) For technical side user (engineering teams junior data team members): Documentation and Knowledge Management: Implement robust documentation practices to capture technical details, best practices, and lessons learned. Maintain a centralized knowledge repository that facilitates easy access to information and project documentations for all team members. Retrospective & Progress Update Meetings: Conduct retrospective meetings at the end of projects to reflect on what worked well and what can be improved. Additionally, Progress Update Meetings and Meeting NOTEs help ensure that knowledge transfer occurs seamlessly, benefiting future initiatives.

Putting away ad hoc reports from meetings is beneficial. When users are obliged to report through a unique data platform and their performance be evaluated through that data platform, the organization would be forced to fill its data gaps and data flows will be shaped incrementally. Of course this will happen only if supported and followed by top managers and owners.

Driving data adoption and usage within an organization involves a multi-faceted approach. Start by fostering a data-driven culture, emphasizing the value of data in decision-making. Provide training and resources to build data literacy among employees. Ensure data is easily accessible and well-organized. Implement user-friendly data visualization tools and dashboards. Encourage cross-functional collaboration to share insights and best practices. Recognize and reward data-driven achievements. Continuously monitor and improve data quality. By addressing both cultural and technical aspects, organizations can promote data adoption and harness its full potential for informed decision-making and innovation.

A data-centric mindset is crucial for organizations to lead their industries, as it enables staff to explore data freely, challenge existing notions, and innovate. Such a culture evolves into a learning organization, enhancing agility, resilience, employee engagement, and job satisfaction. A real-world example is Café Delight, a coffee shop chain that reversed a downturn in customer visits by embracing data curiosity. Through analyzing customer feedback and sales data, an employee identified a demand for healthier snack options. This insight led to the introduction of a new product range, demonstrating the power of data curiosity in sparking innovation and achieving success.

Data-driven decision-making impacts business outcomes. Examples: 🔹In financial industry, suppose a bank wants to reduce customer churn.They analyze transaction data, customer feedback, & social media sentiments. By spotting patterns, they design personalized offers for at-risk customers, increasing retention. 🔹A healthcare organization aiming to improve patient care-by analyzing medical records, they identify factors leading to readmissions.They implement preventive measures, reducing readmissions by 20%. 🔹A retail company analyzed customer purchase history & identified a trend of declining sales in certain products. By adjusting their inventory & marketing strategies based on this data, they reversed the trend & increased profitability.