Guide to: Master data management Although 70% of respondents to Procurement Leaders’ Data and technology report believe data to be important or highly important to their organisation’s decisionmaking process, 59% of functions struggle with labour-intensive data cleansing while 50% do not have a master data strategy. Procurement teams face the challenge of making the most of organisational spend data and developing more digitally enabled functions. This guide is for procurement professionals who wish to improve data standards within their business by improving data visibility, managing disparate datasets and develop a comprehensive data stewardship model. Members should use this guide to benchmark their internal processes, avoid common pitfalls and shape their data strategies. It provides an in-depth analysis of the four key facets of master data management and data stewardship: Securing organisational buy-in. Mapping data processes. Developing a data strategy. Executing that strategy. Key insights Procurement executives should focus on gathering and managing data, rather than principally focusing on embedding data in decision-making. A comprehensive approach to data stewardship requires executive buy-in and support from across the business. Broken and overly manual processes, disparate data sources and ineffective change management strategies will act as barriers to successful data management. Phase one: Organisational buy-in A central preliminary step A comprehensive approach to data stewardship requires executive buy-in, and support from across the business. Cross-organisational cooperation is needed and changes to business processes may be required, both of which will need executives who are willing to drive change initiatives. Supporting the project team with executive oversight will empower them and mitigate potential problems in the strategy development and implementation phase. Working alongside wider organisational efforts Data quality has traditionally fallen to IT. But business functions have gained a degree of control and have been allocated additional responsibilities with regards to their data. While working closing with IT is an important aspect of developing data strategies, it is also important that procurement frames the project as one of functional transformation and retains significant control over how the project develops. Driven in part by regulatory changes, wider organisational efforts to control data are evident. This is important because any data management policies must fit within the foundations set by the wider organisation’s data policies. Useful lessons can be taken from these projects and, in terms of areas such as risk, a lot of the necessary work may have already been done. The variety of organisational governance models does mean the data stewardship responsibilities faced by procurement can vary significantly, however. Building the business case Constructing a business case is an important step in achieving senior executive support. Focus on outcomes, identifying the potential benefits of data quality improvements and the negative impact of poor data quality. Focus on: Risk to overall organisational performance and image from poor quality or compromised data. Improved ability to identify purchasing trends that can be incorporated in strategies and negotiations. Support objectives, such as compliance, supplier rationalisation and reduce supply risks. Advanced digital applications, such as machine learning solutions, rely on highquality data inputs for optimal results. Procurement Leaders’ Producing a Business Case Guide provides further advice. Building a project team This should be cross-functional and should have a significant IT presence. Attempt to bring senior executives on board too. If a cross-organisational data team already exists, working alongside or in the team will make building a project team easier. This team must have the flexibility to expand when needed, particularly to incorporate stakeholders and data owners as and when they are identified. Any conflict within the project team will be a significant barrier. This is frequently a result of competing approaches to data ownership, where business units compete to avoid relinquishing control of data. Ensure collaboration and clear leadership structures exist, whereby issues can be escalated if required. Phase two: Data process mapping As-is assessment Procurement teams must accurately map their current data practices, identifying how data is used, where it is stored and any data governance models. Couple this with a data-quality assessment to discover how complete and accurate the data is. Ask: Do we have a pre-existing data stewardship programme? If so, what is our model? What is the current quality of our data? Where are the main quality gaps? Who has access to data and how do they use it? Who has responsibilities for that data? How, and where, is the data stored? Shaping a vision of the target state Understanding organisational requirements and what resources are devoted to the project provides an insight into the objective state that the project should target. Consider how frequently these cleansed datasets need to be updated. The ability to monitor maverick spend on a recently signed contract may also be an important consideration. Ask what sort of data analytics projects procurement will want to embark on, such as mining e-auction data to optimise sourcing events, using purchasing data to forecast demand or exploring supplier risk with social media data. Understanding the ideal set-up for procurement data, and the possible resource limitations that may limit that vision, should underpin early strategy development. Deciding what needs to be incorporated within a data strategy Master data is the principal focal point. The ‘target state’ vision is likely to encompass datasets that would not fit within a strict ‘master data’ definition, such as social media data. However, the primary focus of procurement data strategies should be master data, while secondary datasets should be a lower priority. Definitions of master data can vary between organisations. On the whole, it represents data objects that are used by multiple applications. Such data includes customer, product, location, employee and assets. For procurement, supplier data is a central consideration. Master-data strategies vary across organisations, both in terms of the types of data considered and the structuring of that data. See the graphic, below, for a useful classification model. When deciding what data to incorporate into an MDM strategy, consider: Data changeability: while master data usually changes less than transactional data, data objects that change very rarely need not be incorporated in a formal MDM process. Multi-system requirements: if a data entity is used across multiple systems, it should be incorporated into your MDM strategy. Complexity and size: larger and more complex datasets are more in need of an MDM strategy. Developing a wider, more comprehensive data stewardship programme is significantly easier from these foundations. MDM projects are often expanded to be far more comprehensive than master data, but they can frequently retain the title. This demonstrates the centrality of MDM within stewardship efforts. Drawing a distinction between this preliminary MDM strategy and wider stewardship efforts is useful for the purposes of clarity. Phase three: Develop strategy Identifying the gaps Having constructed both an as-is assessment and a realistic target state, it is useful to identify the most prominent gaps between them, as well as the barriers to achieving the target state. Focus on: DATA SKILLSETS More procurement departments now develop internal data teams to improve the quality of their data and run analytics projects. This data team should also cover data stewardship, focusing on data quality, governance and capturing rules. The team can either have dedicated formal positions or can be staff from different teams with responsibilities in addition to their existing roles. Having a trained data steward is increasingly popular and important. Part-time data stewards can be hired in if business requirements are considered unnecessary for a dedicated full-time role. Experience working with your data architecture, such as Salesforce or IBM Database Software, will be advantageous. Knowledge of Structured Query Language (SQL) scripting and Macros can also be beneficial. INADEQUATE INFRASTRUCTURE Manual processes or Excel spreadsheets stored on desktops significantly hinder data collection and can undermine efforts to develop a comprehensive data ecosystem as part of your ‘target state’. Therefore, the data strategy may need to run alongside a wider digitalisation strategy. Create a central data hub to provide core services for data management. This should include a cleansing engine, a merging engine (matching data from multiple sources) and hierarchy management. Solution partners Grouped as master-data management (MDM) solutions, there are a wide array of digital tools that support the creation and maintenance of a centralised data record. These products help link and synchronise data between sources, and have monitoring and corrective action capabilities. Dedicated MDM platforms are offered by SAP, Oracle, IBM, Informatica, Riversand, Enterworks and TIBCO software. Data stewardship support solutions are far wider-ranging, including workflow solutions, IT consultants and even machine learning toolkits. This highlights the need to heavily focus on business needs and desired results when looking for a solutions partner, rather than being overly prescriptive with requirements. However, ensuring that digital or operational support requirements, if relevant, are met must still be a key to solution selection. Develop a selection committee as part of the project team. This should include representatives from IT. This selection committee must decide the following: Does the solution need to be multi-domain? What budgetary requirements do we have? How should solutions be evaluated? Is this solution scalable? Does this solution integrate with our systems architecture? How regularly do we need our data to be synchronised? What is a realistic timeframe for implementation? Develop a comprehensive model of ownership and responsibility It is important to understand and allocate responsibilities for data ownership. Develop a list of important stakeholders and MDM project participants beyond the scope of the project team. A significant proportion of this responsibility model may be allocated to employees outside of procurement – either from IT or a data team. The table, below, shows how a RACI (responsible, accountable, consulted, informed) model can be applied to data stewardship, in this case MDM programmes specifically. For the RACI structure, the participant list is broken into four participant levels: 1. R – the listed role is responsible for deliverables related to completing the task 2. A – the listed role is accountable for delivering the task's deliverables or achieving the milestones 3. C – the listed role is consulted for opinions on completing the task 4. I – the listed role is informed and kept up to date on the progress of the task Phase four: Actioning strategy Launch a pilot project Demonstrate project value with a small-scale pilot stewardship project, which focuses on an important dataset. This pilot must be a manageable size, not overly complex and should focus on a single subject. This allows the project to be a quick, isolated success. Develop transparent data policies Ensure your policies governing the use of data have been successfully communicated internally. It should be made clear that data governance is not just the responsibility of individuals, but an organisational objective. Ensuring users are inputting data correctly and are aware of procedures will drive policy effectiveness. Embedded change champions, who are trained in the new procedures and are willing to articulate it to the organisation more widely, will drive policy awareness effectively. E-learning modules and internal campaigns can also build internal awareness and knowledge. Learning retention It is important that lessons from previous stewardship efforts are captured, and the strategy development process is well understood. The ability to quickly redevelop a strategy is important: M&A activity or the consolidation of an organisation may mean the strategy needs to be revisited and undergo comprehensive amendments. Committing to ongoing stewardship and improvement Stewardship efforts should not be viewed as a one-off activity but an ongoing process of improvement. The project team should develop wide performance indicators, going beyond those of the actual project, which commit to objectives for higher-quality data. These indicators should include: accuracy; dataset completeness; timeliness; relevant (non-obsolete) data; and data security. Summary PHASE ONE: ORGANISATIONAL BUY-IN Ensure the project receives high-level executive buy-in to help drive it. Develop a cross-functional project team with the authority and capabilities to enact a comprehensive digital transformation strategy. PHASE TWO: DATA PROCESS MAPPING Identify the current processes surrounding data usage, storage and governance. Couple this exploration with a thorough data-quality assessment. Define the scope and central objectives of the data stewardship programme. PHASE THREE: DEVELOP STRATEGY Identify the steps required to achieve project objectives. Look at infrastructure requirements, process changes and skillset deficits. Map required models for ownership and responsibility onto the organisation. PHASE FOUR: ACTIONING STRATEGY Use a pilot project to demonstrate the potential successes of a data stewardship programme. Commit to ongoing improvements, with clear high-level objectives. Beneļ¬ts It provides procurement with a more comprehensive oversight, facilitating objectives such as improved compliance, supplier rationalisation and cost reduction. Clean data will allow for important analysis, including the identification of relevant contract terms, price variation and payment discounts. It develops a foundation for future digital applications, in the form of clean data, which improves system functionality or can be used as a learning set for a machinelearning algorithm. Pitfalls Failure to achieve senior executive buy-in will heavily reduce the scope and feasibility of any major stewardship project. Conflict can arise due to competition over the control of data, where some business units resist giving up access and ownership. Staff may resist changing their work processes in order to fit new data stewardship procedures. Further reading Guide: Spend Analytics Strategy report: Data & Technology Whitepaper: Data Quality About the research About the author: Daniel Owusu Daniel is a research analyst at Procurement Leaders who specialises in procurement technology. He draws upon an academic background in criminology and sociology and has experience in the legal services industry. Connect with Daniel on LinkedIn. Contact the author and feedback If you have enjoyed this report, would like some more information or feel it has not met your expectations, please contact us.
