March 2024 (Part 3)
Transitioning from IT-Driven Reporting to a Self-Service Model: Making Change and Empowering the Business
The evolution from traditional IT-driven reporting to a self-service model represents a significant shift in how organizations access and utilize data. Empowering the business with self-service capabilities
Understanding the Transition:
1. Assess Current Capabilities and Needs:
Before embarking on the transition journey, assess the organization's current reporting capabilities and the specific needs of business users. Identify pain points, challenges, and areas where self-service can bring the most significant value. Understanding the current landscape provides a foundation for designing a tailored self-service solution.
2. Establish Clear Objectives:
Clearly define the objectives and goals of transitioning to a self-service model. Whether it's reducing time-to-insight
Making the Transition:
3. Provide Comprehensive Training and Support:
Recognize that transitioning to a self-service model involves a learning curve for business users. Offer comprehensive training programs
4. Collaborate Between IT and Business:
Foster collaboration between IT and business teams throughout the transition. IT professionals can provide expertise in data governance, security, and infrastructure, ensuring that self-service capabilities align with organizational standards. A collaborative approach encourages knowledge sharing and enhances the success of the self-service model.
5. Implement Robust Data Governance:
Strengthen data governance practices
Providing More Capabilities to the Business:
6. Scale Infrastructure Appropriately:
As self-service capabilities expand, ensure that the underlying infrastructure scales accordingly. Consider cloud-based solutions for scalability and flexibility, allowing the organization to adapt to changing data volumes and user demands without compromising performance.
7. Promote Data Literacy:
Promote data literacy initiatives to enhance the ability of business users to interpret and derive insights from data. Offer educational resources, workshops, and documentation to improve data literacy across the organization. A well-informed user base is better equipped to leverage self-service capabilities effectively.
8. Iterate Based on User Feedback:
Encourage user feedback and iterate on self-service capabilities based on user experiences. Regularly gather insights from business users to identify areas for improvement, feature enhancements, or additional functionalities that can enhance the overall user experience and address evolving business needs.
9. Ensure Data Security and Compliance:
As self-service capabilities expand, prioritize data security and compliance. Implement role-based access controls
10. Monitor and Optimize Performance:
Establish a robust system for continuous monitoring of self-service capabilities. Regularly assess system performance, user interactions, and data processing efficiency. Use monitoring insights to proactively optimize performance, identifying and resolving any issues that may impact the user experience. This iterative approach ensures that the self-service model remains responsive, reliable, and aligned with evolving business requirements.
Transitioning from IT-driven reporting to a self-service model requires careful planning, collaboration, and ongoing optimization. By understanding the current state, setting clear objectives, providing comprehensive training, fostering collaboration, and implementing robust data governance, organizations can successfully navigate this transition. Additionally, as capabilities expand, scaling infrastructure appropriately, promoting data literacy, iterating based on user feedback, ensuring data security and compliance, and monitoring and optimizing performance contribute to the continued success of the self-service model. In embracing these considerations and best practices, organizations can empower the business with greater autonomy in accessing and utilizing data, fostering a culture of data-driven innovation and decision-making.
Catch Up on This Week's Articles
Thank you to the Colorado Technology Sales Professionals meetup for hosting me as their featured speaker last Monday night at WeWork in downtown Denver. It was a great setting for me to return to public speaking after a 7 year hiatus!
Thank you to Bob McNeil and Jason August for all their work in the CTSP group and Whitney Webermeier for hosting us at WeWork.
Learn more about the Colorado Technology Sales Professionals: https://lnkd.in/gdmDg_5P
Check out their next meetup here: https://lnkd.in/gi3cZzTA
Contact Whitney to host your next event: whitney.webermeier@wework.com
Recommended by LinkedIn
In honor of Women's Month, the Denver and Houston Women In Data™️ chapters are hosting an exciting virtual panel event on data engineering from the female perspective!
Join us as we delve into career trajectories, challenges, and triumphs. Featuring three diverse speakers, including:
📊 Kanmani Thomas Xavier, a seasoned Senior Data Engineer with a Master's in Data Science and Data Engineering,
📊 Laura Lundell, whose inspiring journey from elementary school teaching to data analytics and engineering sheds light on diverse pathways into the field, and
📊 Cher Fox (The Datanista), CDMP, a trailblazer with over 35 years of experience in data and founder of her own consulting company.
Our engaging discussion will explore the intricacies of getting started in data engineering, navigating the industry as a female, overcoming barriers, and fostering diversity and inclusion.
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Hope to see you there! ✨
💻 Interested in joining WiD and gaining access to all the awesome resources and community groups to help you succeed?
Learn more and join➡HERE.
Thank you to Amanda Long , Silvia Onofrei, PhD , Jennifer DeBell , Emily Bocim and Ashlee Dutton !
🥳Join DAMA Rocky Mountain Chapter for our Q2 2024 event, Data Quality Management Best Practices. We are excited to welcome Cher Fox (The Datanista), CDMP, of Fox Consulting, as a featured speaker.🎤
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Data Quality Management Best Practices examines the key practices for effective data quality management, & will provide a practical roadmap to enhance data quality practices. I analyze the strategic definition of data quality standards, emphasizing various principles. The importance of proactive data profiling, coupled with regular assessments, is highlighted for identifying & addressing anomalies. The session reinforces the significance of a robust data governance framework, automated validation processes, & metadata management to ensure contextual insights.
Continuous monitoring, supported by alert systems, is discussed for tracking data quality metrics in real-time. Cultivating a data quality culture through user training & awareness is suggested as a vital component. The presentation also explores the utilization of data quality metrics & key performance indicators to benchmark & assess effectiveness. The commitment to continuous improvement concludes the discussion, emphasizing the importance of regularly reviewing & adapting practices to evolving business needs.
𝐖𝐡𝐞𝐧: Friday, April 26th from 2:30 pm to 5:30 pm
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Strategic Partners
Visit Cyber Qubits to learn more about their Cybersecurity certifications, education, and corporate training!
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Which of these articles resonates with you most?
Let's continue the conversation in the comments.💡
#data #dataengineering #womenindata #paneldiscussion #damarmc #damarockymountainchapter #dataquality #bestpractices #diversityinclusion #datagovernance #datasecurity #dataliteracy #thedatanista #dataanalytics #businessintelligence #datastrategy #datainitiatives #datawarehouse #datamanagement #testautomation
Data Architect at British Business Bank | Certified Data Management Professional (DAMA CDMP Associate) | Passionate about Solving Data Challenges & Stakeholder Engagement
9moFantastic insight about self service analytics! This comprehensive breakdown not only illuminates the critical path from IT-driven to self-service models but also emphasises the importance of strategic planning and collaboration across departments. Particularly, the emphasis on data governance and promoting data literacy underscores the nuanced approach needed to empower users while maintaining data integrity and security.
My mission is to help you thrive in a constantly moving world.
9moI appreciate the compare/contrast between Alteryx and PowerBI. Alteryx has evolved since I worked there! Thanks for the update, Cher Fox (The Datanista), CDMP
Senior performance marketeer (T-shaped Paid Social), that got tired of fixing attribution problems manually - so he initiated an AI solution.
9moEach article offers unique insights. I'm curious, which one sparked your interest the most, and why?