The webinar provides tips and strategies for effective data management, including embedding data understanding through business glossaries and data dictionaries, building a unified metadata model, Prioritising use cases and ownership in data strategy, implementing data governance strategies, and navigating culture and ownership in data management.
The importance of accountability and achieving value in data programs is emphasised throughout.
Tips for Effective Data Management
Organizations should have a single access point for information and encourage users to check it first before trusting other sources. Metadata management is important for making data findable, understandable, and ensuring accuracy. Implementing these tips can help organizations overcome challenges of not knowing what data they have, where to find it, or how to use it effectively.
Embedding Data Understanding through Business Glossary and Data Dictionary
Agreeing on a common data language is as important as using a metadata management tool. A custom built platform can be created using an open-source platform like Django, Apex in Oracle or Excel lists in SharePoint. Embedding data understanding can be achieved by surfacing the business glossary and data dictionary in reports, dashboards, and self-service applications. Including business rules, calculation, owner, data quality rating, and date last updated can help users understand data more effectively. A chatbot using the business glossary as the knowledge base can provide a user-friendly way to access information.
Building a Unified Metadata Model and Ensuring Value in Data Programs
The speaker confirms building their own metadata model, which takes longer and requires the right skills. The rollout has been limited, but its potential for driving data literacy and appreciation is high. Prioritization is crucial for delivering strategic insights and data products, and executive prioritization is crucial in addressing the chasm between requirements, demand, and capacity, allowing for focus on the right tasks. The real work of a data strategy lies in the nitty-gritty details of prioritization and execution.
Prioritising Use Cases and Ownership in Data Strategy
When building a data strategy, it’s important to prioritize use cases and identify super-use cases that include AI models, reporting, and data quality. Executives should make decisions based on these priorities, and it’s important to communicate that the business owns the data and IT and data providers are stewards.
Tips for Effective Data Governance
To effectively manage data, a clear ownership structure should be established with stewards assigned for each subdomain. Data governance should not be seen as just compliance, but also as a tool to drive business value. Aligning data governance with organizational and data strategies can help achieve this goal. Prioritising data goals using a strategy map, such as customer-driven analytics, can also contribute to creating value for the organization.
Importance of Data Governance in a Data Strategy
A successful data strategy requires a focus on customer-driven analytics, AI-enabled capabilities, data governance, and execution. A roadmap can help move towards future goals.
Achieving A Level Three (Defined) Data Governance
To achieve level three data governance, milestones from level one to level two must be plotted, and necessary measures in terms of people’s skills, processes, and technology should be put in place. Presentation, expectation management, and progress measurement are crucial. Defining and measuring data analytics’ value and success of a self-service BI program for decision-making is achievable. Achieving level three data governance requires understanding the desired shifts in a data strategy.
Creating a Data Scorecard for Effective KPI Ownership
Creating clear KPIs for data goals is important for understanding and measuring progress. Ownership of these KPIs should be shared between the central data team and the business. A data scorecard with clear measures and identified owners can help with this. Establishing a timeline for measuring progress is important, as is managing expectations for immediate benefit. Benchmarking at the start of a data strategy is crucial to understanding the benefit being measured.
Importance of quantifying manual reporting costs for effective data goal measurement
To measure the benefits of data goals and ensure a shift towards governed data usage, it is important to quantify manual reporting costs and focus on fewer deliverables. Creating a data scorecard and measuring usage of a data analytics portal can help with this process.
Tips on Implementing Data Management and Governance Strategies
When building a business glossary, focus on critical data and use existing IT resources. Have a conversation with executives about the importance of data governance and management, and translate outcomes into business terms to show real value. Understanding the benefits of data governance and management can help show ROI. There are metadata management software options available in the market.
Management Tools for Metadata
Metadata management is a challenging task, especially in cloud tools. IBM’s infosphere Suite has a module for handling business meeting glossaries. Data Hub is a free tool that works on the cloud and includes connectors to various data management systems. Categorizing and organizing metadata is crucial to prevent a data swamp.
Prioritising Data Management Use Cases for Executives
To ensure successful data management, it is important to define principles, policies, and procedures, prioritize use cases with executive air cover, link data strategy to organizational strategy and identify super use cases for each strategic goal, and present solutions that solve real problems for executives. Data stewardship must also be in place and enough information should be provided to executives without overwhelming them.
Understanding Value Proposition and Alignment in Data Value Chains
The discussion focuses on aligning the data value chain with the value proposition to stakeholders, with a need for regular strategic alignment assessments across all areas. The construct of subject areas blending and moving in a value chain description of the business is mentioned, raising the question of how to prioritize and steward such constructs.
Organizational Structures and Data Governance
The discussion focuses on how organizational structures can impact data management and governance. Simplifying complex business and data value chains is important by identifying super use cases that translate into subject areas and processes with clear ownership. It’s emphasized that data quality and presentation are crucial for every deliverable in data and analytics projects. Measuring data ROI is briefly discussed, including the development of a data ROI framework and a DataCo ROI for valuing data assets.
Navigating Culture and Ownership in Data Management
Effective data management requires examination of an organization’s culture and operating model. Ownership of data can become complicated in a Federated or decentralized operating model, but coordinating with executive data stewards can help. It is important to involve stewards and technical support and understand the story of what has happened in the business value chain for clarity in data management.
Ownership and Responsibility of Business and Data
Multi-ownership or multi-responsibility for data is gaining ground as a viable solution to the dilemma of data ownership. Maritza’s approach leans toward shared ownership between business and data, and depending on the position of the data in the value chain, data custodians in the data office can partner with the business domain’s owner responsible for that data. Multi-ownership is a reality for complex organizations, and sometimes it is easier to start with business processes and roll that up into subdomains before establishing data domains.
Prioritisation and ROI Calculations in Data Projects
The article discusses the perspectives of Yolanda and Roots on ownership in data projects and the need to create a cohesive environment for discussion. It also suggests using an opportunity matrix for prioritization based on business value and technical feasibility to avoid subjective decision-making. The article notes that CFOs commonly question the reliability of ROI calculations.
Importance of Accountability in Data and Analytics
Data professionals need to know how to measure and rank the ROI of a project, with the assistance of CFOs. Combining Finance with data professionals can help determine proper measurement and calculate commercial uplift. The goal is to determine the Return on Investment for data professionals and show the value they bring to the organization. It is critical to be intentional and certain with business cases and ROI calculations.
Notes from a Thank You Message to the Community
The speaker warns against hype and emphasizes the importance of delivering value, announces an upcoming chat on customer-driven analytics in the liquor industry, thanks Maritza Curry for her contribution and praises her practical approach to data analysis, and encourages the community to follow key female leaders in data.
Thank you Maritza Curry for sharing with the Modelware Systems community!
We greatly appreciated learning from you.
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