Data analytics governance is the insurance in digital analytics. It is the key to making meaningful decisions and business growth through reliable and trustworthy data.
It’s extremely important to have a quality control mechanism that helps validate the consistency and reliability of digital analytics data. Hence, it is critical to invest time and resources for setting up a comprehensive data governance strategy.
Benefits of Data Governance
No strategy or campaign can start without planning. Go to whiteboard and jot down all the objectives, resources, cost, market, and other relevant factors that would fuel the digital campaign going forward. Plan how the data would be used and ask yourself these questions.
- Effective budgeting.
- Consistency and reliability in data.
- Core for Strong Digital Analytics Strategy.
- Enables Confident Decision Making.
- Reduces unwanted risks associated with the data collected.
7 important rules that define data governance
- Data Collection
- Data Quality
- Data Accessibility
- Data Security
- Data Privacy
- Data Integrity
- Data Presentation
Let us elaborate the above rules :
Data Collection
Analytics data can be collected in numerous ways but documentation on data collection needs to be in place for any future reference/amendments.
These are the few things we should document when it comes to data collection:
- Document the data collection techniques and steps involved in the process of these techniques.
- Document the technology or tools used in the data collection process which is very critical.
Data Quality
Good quality data assists in better decision-making.
- Once analytics tags are implemented, perform a comprehensive tag validation across multiple devices and browsers to ensure data quality & credibility.
- When the existing analytics implementation is impacted by site revamp or is not meeting reporting expectations or has any issues, then follow the below best practices.
Data Accessibility
Once digital analytics tracking is implemented on the site, next step would be who all need to have access to the tools and data being collected (this includes the data analytics team, marketing team, agencies, IT team, contractors and so on). Need and Role based data accessibility needs to be defined across organization.
Data Security
When we talk about data accessibility and data security, they are very closely related but data security is not only about restricting access of tools to people but protecting organization’s data warehouse.
- In the current trend we see data stored in cloud-based storage solutions and we know that these solutions have multiple levels of security involved but when the data is transferred to these solutions, the data governance team must be aware of to what location the data is transferred and if data is transferred to any other technology.
- Any storage devices should not be allowed to the workplace so that there is no data leakage from the team.
- Connecting storage devices to the computers should not be allowed (disable all the data transfer ports of all official desktops/laptops).
- You should also be aware that your data can be “revealed” unknowingly, when it is shared with external agencies. Therefore, it is best practice to document to whom the access is provided, what is the contract end date, when the access need to be revoked and act accordingly. This way the risk of data security can be mitigated.
- It is always recommended to get ISO 27001 certified (ISMS – Information Security Management System) if the organization is working with user data which proves that the organization has compliance with respect to data security.
Data Privacy
If the organization works on collection of user/customer data then the big responsibility of the organization is to safeguard the privacy of data.
- The user should be notified about the type of data which is collected and how it is used.
- If the user is not willing to share his data, then he should be able to opt out. This is a crucial thing to include in the website and analytics tracking.
- If any personally identifiable information(PII) required to be captured, it need to be encrypted before collecting the data.
- Organization needs to have a system in place to ensure that the collected analytics data is classified based on the level of sensitivity. This helps when it comes to providing appropriate level of access.
- As per the recent happenings w.r.t GDPR, a user should be provided with an option whether the organization can track the user’s activity or not. There are various guidelines to consider when it comes to GDPR which can be found in this link https://www.eugdpr.org/.
Data Integrity
Data integrity refers to the accuracy and consistency (validity) of data over its lifecycle. Compromised data is of little use to enterprises, not to mention the dangers presented by sensitive data loss. For this reason, maintaining data integrity is a core focus when it comes to data governance.
- Each time data is replicated or transferred, it should remain intact and unaltered between updates.
- Error checking methods and validation procedures should be in place to ensure the integrity of data that is transferred or reproduced without the intention of alteration.
- Identifying data is also a part of data integrity and proper naming structure need to be followed and documented so that it can be used as a reference at any point of time.
- With web analytics tools, we get to see processed data but not the raw data. Processed data should not be merged with raw data as it can potentially damage the data source. For example, analytics tool data should not be merged with enterprise data (CRM etc.,)
Data Presentation
Data presentation refers to how you are presenting the data being collected to the stakeholders. The end goal is that the metrics & insights what you derive from the data collected need to be presented to the stakeholders in appropriate manner.
- Every organization which deals with data has an end goal to bring the data to a presentable format that matches the business context.
- Few organizations attempt to allocate separate teams for data massaging, analysing and presenting. Data sets accumulate to petabytes in scale and access need to be granted to different teams which will be challenging as well as error prone.
- It makes more sense to allocate the same team working on data analysis to work on bringing data to a presentable format as well. This can be achieved by training the team to handle both. This minimizes improper data interpretation, builds a strong foundation for effective presentation and delivery.