The essentials of a data governance framework
To perform at their best, your employees need consistent, reliable, accessible, and secure data to work with. Solid data governance enables this, ensuring your teams are all on the same page when it comes to the importance of data accuracy, shared understanding of how to manage company data, and safeguarding your information.
Below, we share key elements that a data governance framework should have to help clients to gain value-driving business insights.
Component #1: A Single Source of Truth for consistency and reliability
Data governance starts with true control and understanding of your data. Creating a Single Source of Truth enables this, organizing and clarifying your data to provide you with accurate, valuable business insights. A
Master Data Management (MDM) module should enable this in two core ways:
- Aggregating data from various locations and applications so that your data is securely housed in — and analyzable from — a single location
- Aligning scattered, duplicated information from across your data sources to prevent confusion (for example, collating different customer names or product codes from various systems)
Let’s explore a few aspects of MDM that enable this in more detail:
Acces multi-level data mapping
MDM provides a model for organizing your data by granularity level or in hierarchies, eliminating confusion.
Let’s take geographic locations as an example: Continents are divided into countries, which are divided into municipalities or provinces, which are divided into cities. If you have multiple data systems containing information on your operations’ geographic locations, MDM enables you to map these at any level.
Set a hierarchy for you data delivery systems
Often, specific data entities and attributes can be delivered into your Single Source of Truth by multiple systems. The ability to rank these delivery systems in order of accuracy, reliability, or preference ensures your Single Source of Truth always contains the best possible quality data.
If one of your data systems has more accurate data, its input will be automatically ranked as preferential to input from other, less accurate systems. Likewise, a rapid, rough estimate from one system can automatically be replaced with a more comprehensive data set, as soon as this becomes available.
Component #2: Granting specific access to generate reliable ownership
Defining which of your users can access and share which data within your company is vital for responsible, compliant data governance and preventing data breaches. By enabling access to specific data for employees with expertise in the relevant area, you can help generate a sense of ownership and motivation to become good governors (or stewards) of that data, ensuring its good quality.
With a data platform you should be able to build highly secure, yet adaptable, data governance structures. MDM enables assigning ownership and responsibility across multiple dimensions. For example, per domain (Customers, Suppliers, Finance, Organizational Structure, Products, etc.), per system (Salesforce, Exact, ERP, Kronos, etc.), per data attribute, and per data classification type (Public/Private/Sensitive/Secret). Assigning data classification type is particularly vital for determining access rights and protection levels for your most sensitive business data.
Component #3: Security and traceability via audit trails
An audit trail enables you to determine the origin of each piece of data your central system receives. This includes tracking which data sets have been edited or overwritten by delivery systems within your data landscape, or by individual users within your company.
If any errors, malfunctions, or gaps appear in your data, it becomes a straightforward process to identify exactly where things have gone wrong or where more information is needed. With a data platform, for example, all data additions, modifications, and removals should be logged by user, IP address, and date-time, maintaining both the prior and later value for your audit trail.
Enhanced input validation
A MDM module can also include an additional confirmation step requiring a third party (either internal or external) to validate data supplied by a certain system. This is especially relevant for businesses utilizing input data from partners, collaborators, suppliers, and so on. Receiving this external data and, in turn, using it to inform your own business insights means companies need to ensure correct data format and content as rapidly as possible. Unintentionally accepting invalid data will impact your entire data and decision-making chain, costing you time and resources to rectify later on.
The faster you can validate all your input data, the better: Flagging flaws, gaps, or inaccuracies early accelerates transforming your data into powerful business insights.n
Component #4: Ownership and accessibility for empowered business users
Enabling employees with expertise in a certain area to become masters of their data domain is hugely empowering, often resulting in higher data quality across your business.
Granting access rights per business line and per department enables your most informed employees to directly update, amend, and enhance the data you have across business-critical pillars (sales, finance, etc.). After all, they are the ones best positioned to flag inaccuracies early and implement the right corrections, as these are the numbers they produce and are responsible for in their daily work.
On the one hand, enabling this granular access works to generate trust, accountability, and motivation to achieve data accuracy throughout your workforce. On the other hand, enabling flexible view — rather than edit — access democratizes and de-silos your data. This allows multiple employees to inform their decisions with the latest data, while only your domain experts can modify it, avoiding accidental overwriting or deletion.
Bottom-up involvement for top-down insights: Forecasts, budgets, and KPIs
Opening up company data for bottom-up correction by subject-matter experts is an intelligent move from management: Ultimately, it delivers enhanced top-down insights to drive bigger-picture business decisions.
You can enable employees to manage not just actuals but forecasts and budgets too. Responsibility for data input and data approval for these scenarios is still restricted per person, ensuring as much accuracy as possible in your longer-term data analysis.
In addition, balancing stringent control of who can modify your company data, while enabling your experts to contribute and enhance it, is instrumental for clarity on your KPIs, ensuring these are delivered in a unified way across your teams.
A solid data governance framework is the backbone of valuable business insights.