STEP Data Governance: At a Glance

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1 STEP Data Governance: At a Glance Master data is the heart of business optimization and refers to organizational data, such as product, asset, location, supplier and customer information. Companies today deploy multiple systems that continually aggregate, consolidate, store and maintain a tremendous amount of operational information and master data. This data has the potential to frequently change; yet, in most organizations, there are few clear-cut roles, processes or tools for protecting or enhancing that information as it moves across the enterprise. As a result, information often becomes replicated and fragmented which leads to duplicate, conflicting, incomplete and erroneous information that hinders business responsiveness and decision-making. As the challenge to manage critical organizational data grows, businesses are increasingly embracing data governance strategies to protect the integrity of their valuable enterprise assets and to get the most from their Master Data Management (MDM) and Product Information Management (PIM) initiatives. Data governance is essential as data volume grows. Organizations of all sizes are challenged to ensure a single version of the truth exists for each of their critical data domains. The discipline of data governance combines people, processes and information technology to create seamless management of an organization s data across the enterprise. Successfully combining the three puts formal management responsibilities in place to ensure accountability and reduce the likelihood of errors. Organizations should take a centralized approach to their data governance processes which supports building out the correct teams to monitor data governance policies, the appropriate processes to implement them and the correct tools to manage a central repository of master data with real-time integration and synchronization between business systems. Data Sheet / STEP Data Governance

2 Data Governance Teams Data governance initiatives improve data quality by assigning a team responsible for data accuracy, accessibility, consistency and completeness. This team usually consists of executive leadership, project management and data stewards. Many initiatives are derived from previous attempts to improve data quality at the department level, which leads to redundant and contrasting information spread across multiple applications. Data governance initiatives should be targeted at increasing visibility of data across the enterprise, offering improved visibility to internal and external customers as well as compliance with regulations. A successful data governance team will be responsible for two complex activities: Change Management Enterprise data must first be aligned to define standards. Next, the team must ensure that standards are maintained and changes are controlled. If a change is deemed necessary, those changes must be managed across all affected areas of the business Compliance The data governance team must regulate the organization s compliance to any standards that it governs and act to improve the level of compliance Six Processes to Ensure Data Governance Success Once your data governance team is in place, they must determine the current state of the organization s data governance program and deliver a future state plan. After the assessment is complete, the team is ready to create a strategy for improving data governance practices and calculate the organizational risk probability. Knowing how data has been used, and possibly abused, in the past can help prevent data compromises. Every organization has this data readily available in loss and business reports. Collecting it, relating its meaning and studying loss trends can help transform risk management into a fact-based method for analyzing past events, forecasting future losses and changing policy requirements to improve mitigation strategies. Data Sheet / STEP Data Governance 2

3 After all, data governance is about organizational change and since companies are constantly experiencing change the value of their data and risk level is likely to shift. Many organizations only assess themselves once a year, which isn t often enough to be able to react to and change the organizational controls needed for weekly or even daily changes to the data. More specifically, data governance can be achieved using the following six steps: 1 Build a clear vision and scope your data governance initiative, so you can ensure that your organization is able to fulfill it 2 Define standards and assign business rationale as to why each exists, define benefits that can be achieved and what level of quality should be reached to realize the benefit (not always 100%) and create metrics that show whether benefits are being realized 3 Design a data governance organization that is suitable for managing the defined standards. This includes roles and responsibilities for processes used to manage activities (such as change management for standards) and changes to any external process that affects the organization s ability to govern (such as the IT project management process) 4 Engage a Data Owner to own standards and build a Data Quality Roadmap 5 Build a Data Quality Roadmap that documents current quality level, measures it against the requirements and proposes actions to bridge the gap and/or maintain good quality 6 Populate remaining data governance roles to operate ongoing compliance measures and manage activities identified in the Data Quality Roadmap 3

4 STEP Features for Data Governance Stibo Systems STEP MDM platform is built on a seamless architecture and includes features that uniquely allow organizations to implement data governance policies and processes that can be enforced within and across multiple domains of master data. OUR DATA Data Modeling The data modeling capabilities in STEP allow data stewards with even basic business analyst skill sets to quickly configure complex, yet flexible, multidomain data models in the STEP user interface with no programming or downtime required. Use case and vertical-specific import templates are provided to accelerate implementation and out-of-the-box features for importing industrydriven data models. Once a data model has been defined or modified, user interfaces, integration messaging and web services will immediately adapt to modifications driven by the model. Data modeling features allow data stewards to: Easily add new product categories with specific attribute sets Develop customer models tailored to end user requirements Easily organize products into multiple hierarchies to support industry-standard hierarchies (e.g. UNSPSC, ecl@ss or GPC), channel-specific hierarchies or legacy hierarchies (e.g. SAP) Model data variations across languages, markets and channels Link products to related digital assets such as images, videos, datasheets or certificates that are stored in STEP Model inheritance of attribute values and references in a family of products so as to reduce errors due to redundant data and to be explicit about common versus specific properties in a family Data Sheet / STEP Data Governance 4

5 Data Quality As a central part of a sound MDM strategy, STEP includes features designed to handle data profiling, perform data normalization, implement matching and linking and improve the quality of source systems. STEP customers use data quality tools to: Protect the quality of data by recognizing and disallowing bad data, while also warning users as early as possible at time of entry or at time of import Profile data to identify data problems and measure data quality across a data subset Compare customer and product records for attribute selection to match and link the best data into a golden record Improve data completeness using real-time updated KPI s Cleanse data using normalization and standardization rules and tables at time of data onboarding or in bulk as part of a data quality improvement project 5

6 Workflows As data is changed, updated or introduced into STEP, workflows allow the creation of a custom process to control, manage and validate the lifecycle of data object information about items, assets, customers and suppliers or in a defined method. Workflows can also configure business processes for content creation, maintenance, approval, and publishing. STEP workflow functionality allows data governance teams to: Map roles and privileges with actions that can be integrated into other business systems Enforce mandatory content creation processes and approvals Allow users and groups to receive pre-defined tasks Set deadlines and escalation processes for specific tasks WORKFLOW INSTANCE OBJECT WORKFLOW DEFINITION Data Sheet / STEP Data Governance 6

7 Business Rules Business rules are either conditions on data or actions that can modify data. STEP gives users the ability to separately govern business rules that are shared across different use cases (e.g. import, workflow, user interfaces, and approval). Rule changes are centralized, allowing rules to be changed once and the effect applied everywhere it is used. Since the rules engine is embedded within STEP s core application it has the ability to rapidly execute rules with minimal overhead. This is critical since both data volumes and requirements for better data quality are growing at the same time. Different business rule types cater for different types of conditions or actions, e.g. comparing two attributes, evaluating an Excel formula, changing an attribute or sending an . Business users can easily create rules and advanced rules can be authored using JavaScript. STEP s Business Rules engine allows data stewards to: Share business rules across multiple processes Edit business rule conditions and actions independently of where they are used Associate metadata with business rules for data governance Define business rule actions and conditions in the user interface Measure frequency and duration of a given business rule to analyze performance Digital Asset Management STEP provides a single shared repository for all digital assets across the enterprise. This solution enables all digital assets to directly reference products, services, customers, locations and other types of entities. STEP uses workflows to review all digital assets coming into the business, which ensures that they meet technical requirements such as size, format and resolution. What s more, by taking advantage of STEP s workflow engine, you can establish approval processes for all digital assets that need to be published throughout your channels. STEP s Digital Asset Management component allows data governance teams to: Ensure that all digital assets adhere to corporate requirements Automatically link digital assets with product or customer records to ensure that all relevant data is easily accessible Associate unstructured content, like doctor s notes, contracts or warranty renewals with an entity s Golden Record 7

8 About Stibo Systems Stibo Systems is the global leader in multidomain Master Data Management (MDM) solutions. Industry leaders rely on Stibo Systems to provide cross channel consistency by linking product and customer data, suppliers and other organizational assets. This enables businesses to make more effective decisions, improve sales and build shareholder value. During the last 30 years, Stibo Systems has helped hundreds of companies to develop a trusted source of operational information. A privately held subsidiary of the Stibo A/S group, which was originally founded in 1794, Stibo Systems corporate headquarters is located in Aarhus, Denmark. For more information, visit Data Sheet / STEP Data Governance data sheet_step Data Governance_EN_NA