The premise behind self-service BI is to enable business users to access and explore corporate information with minimal involvement from the IT department. Self-service BI projects struggle to move from a departmental to an enterprise solution due to the lack of consistent reporting stemming from the quality and credibility of their data.
Although self-service BI is based on empowering the users, it can be tough to convince them to use the data to make mission critical decisions without some form of Master Data Management (MDM). The first time a user finds a customer still assigned to a sales territory they were transferred out of last quarter, their confidence the data they are accessing diminishes. Without proper data management, adoption falls flat as the number of users encountering these inconsistencies grows.
Empowering your users means no longer relying on inconsistent or unmanaged data access methods. Instead of having a user email her friend in IT for a csv extract, or having a business analyst develop a custom data repository in MS Access, master data management enables unity between information governance initiatives and data access demand from the user community.
Below are the top 4 reasons why you should be considering master data management to compliment a self-service BI implementation:
1. Empowers Users and Subject Matter Experts
A proper master data management implementation encourages a reduced reliance on IT for the exploratory analysis as well as management of the master data list which will allow them to focus on other priorities. Domain experts will take ownership of the data by managing business rules and business hierarchies all without the knowledge of the underlying database structure. It can all be done right in Excel with tools such as the Microsoft Master Data Services (MDS) Excel plugin. There are several keys to this empowerment:
• Users can manage and maintain the data
• Users define and enforce business rules
• Users can optimize approval workflows
• Users can iterate safely through the built-in versioning controls
2. Conformance and Credibility of Data
Data quality issues come with very high costs and unfortunately poor data quality threatens most organizations that don’t implement standardized data management practices. One of the root causes to credibility issues is the lack of domain knowledge. The individuals that are most intimately familiar with the credibility and rules that govern the data in the disparate sources are the often the end-users not IT.
Enforcing validation rules and approval workflows onto data that flows into the master data management system (like MDS) will ensure accuracy and quality standards for your master data. For example when dealing with customer data, some fields that are commonly left blank or misspelled are city and state. Creating a rule that will populate these attributes based on the zip code will ensure the data is not only accurate but that it can be used correctly when mapping geographic data in reports. When the rules are set within MDS, all address data that is pulled in will assume that rule naturally. Applying such rules will increase the end user’s confidence that they are consuming clean, accurate, and credible data.
3. Integration of Heterogeneous Systems
It is very common that organizations will have several disparate systems for areas such as Customer Management, Order Processing, Warehouse Inventory, and Accounting. There is a good chance that these systems have overlapping data that is not often stored in the same manner which can be difficult to keep the data synchronized. An example is the order processing system may store states in their abbreviated format (i.e. MA) and the customer management system may store them as their full name (i.e. Massachusetts). Without proper master data management, managing these mappings between all systems can be a daunting task. With master data management tools such as MDS, the data from the various source systems can be consolidated and remain in sync to be used within the end user enterprise reporting system.
4. Positive Changes in Business Processes
Departments in an organization with little or no master data management, typically manage their own data and may be reluctant to grant access directly to other business units. This type of data management will create information silos that deters collaboration, information sharing, and information discovery.
A successful master data management strategy will overcome organizational and departmental boundaries by defining a framework for the processes and people involved. Roles such as Data Stewards will be defined and assigned, as well as steps for managing the proposal, review, and approval process for new or changed data. Enabling this level of collaboration will provide the foundation for a scalable enterprise information management framework to meet the evolving business demands of your organization.
At BlueGranite, we assist business and technology leaders interested in crafting Self-Service Analytics and Reporting strategies. Our flexible approaches can support the initiative by targeting a wide range of supporting activities, from building data repositories specifically crafted for Power Pivot use, to end user training, mentoring, and adoption programs. Be sure to contact us if you are in the process of deciding which flavor of Self-Service Analytics is most appropriate for your organization.