Every growth-oriented company must ask this crucial question before implementing a digital analytics program!
Data governance should be a quintessential part of any digital analytics strategy. To have an efficient digital analytics program, you must have a robust data governance strategy in place. Your digital analytics venture will only pay dividends if you are able to trust the data that you collect. Data governance enables you to trust your digital analytics data. Remember, only reliable and trustworthy data will help you make informed decisions and propel your business forward!
Data governance may not be the holy grail of digital marketing, but it surely plays a significant role in maintaining an efficient digital analytics strategy. It’s extremely akin to a quality control mechanism that helps you validate the consistency and reliability of your analytics data. Hence, it is highly crucial to spend your time and resources for setting up a comprehensive data governance strategy.
Here are five important reasons why you must implement a data governance plan for your digital analytics efforts!
1. No Data Governance – No Data Reliability!
The absence of an effective data governance plan will only offer you data that’s unreliable, inconsistent, and non-repeatable. Data governance helps validate the consistency of the metrics specified in your organization. If your analytics standards and metrics are distinctly documented, communication between departments and business units within your company will be highly effective. You can avoid a potential disaster arising from an exchange of wrong data sets between departments.
2. Lack of Proper Data Governance Leads to Faulty Analysis and Reporting
It should be noted that ineffective analysis and reporting in organizations occurs not due to any technological glitch but due to bad data governance. So if your data analytics and reporting tool or other technology are not set up correctly, the analytics team cannot communicate the meanings of the metrics or its goals effectively. Analytics data will end up as nothing but utter gibberish to other departments. This will eventually lead to a lot of chaos about the analytics data in your organization. And any kind of chaos in organizations can only result in lost sales and revenue!
3. Efficient Data Governance Helps Build Strong Digital Analytics Strategy
Data governance ensures that your digital analytics implementation is flawless, the web page tagging is correct, and the reporting functionality is working fine. An efficient data governance plan will help the analytics team to recognize their most important tasks, organize those tasks and guide the team with those activities. It also allows the team to align the digital analytics strategy with the long-term tactical business goals.
4. Data Governance Enables Confident Decision Making in Key Business Areas
Effective data governance helps in understanding the metrics better and classifying them according to their importance. Data governance also tells how metrics figures are connected to the external rankings and the reasons for their disparities. With the precise and reliable analytics data at your disposal, you can make business decisions with confidence.
5. Data Governance can Reduce Unwanted Outlays
Data governance can give immense clarity on how some of the crucial metrics like “Page Views”, “Customer Lifetime Value”, and “Abandonment Rate” are defined by your company. This helps you save money in a number ways from not getting tricked by advertisers or ad networks to avoiding expensive (or unwanted) technology upgrade in your organization.
Before we dive into the next section, here’s an animated video that gives you a quick overview of Data Governance through an interesting case study!
Rules of Data Governance
In digital analytics, the phrase “data governance” is used informally in a variety of contexts to indicate various data activities. From our years of digital analytics and data governance experience, we’ve seen that organizations do realize the importance of governing their data. Yet every company has their own unique approach towards data governance.
We’ve often seen that the activity of accurately governing digital data is shrouded in mystery. So in order to demystify the misconceptions, we have come up with few rules that every organization collecting and utilizing digital data must follow. We’ve defined these rules after meticulously considering all the tasks that a digital analytics team must undertake at every stage of the data lifecycle. If you implement these data governance rules, you can expect to manage the analytics data with much greater efficiency and accuracy.
The following rules comprise a reliable data governance strategy!
Analytics data is collected by companies from a wide range of digital sources. If your organization is doing the same, you must be aware of the type of data that you are collecting, and how you are accomplishing this task. Are you collecting it directly, or utilizing the services of user agents or through other external entities? All these details are crucial!
You must categorize the types of data collection techniques that you employ and the steps involved in each technique must be appropriately documented. This will help you classify the data according to its type. You must know whether your analytics data has been studied upon, collected in a passive manner, openly collected using web pages, or other digital sources like mobile apps, mobile sites and so on.
It is also essential to document the technology used to collect the data. Did you employ some web analytics tool, log file processors, tag management techniques, panel-based trackers or other methodologies to collect different types of digital data? These details must be documented too.
The quality of the collected data is extremely crucial while undertaking data governance for your business. The first step towards guaranteeing the data quality is to assess the data collection techniques (or agents) to ensure that your company is actually collecting the data that it needs. From what we’ve learned from our years of data analytics experience, the data analytics setup can get decentralized with the passage of time. This can put the metrics tracking system completely out of gear.
Once this happens, your web analytics system will cease to function as required. This will result in erroneous metrics tracking that has no alignment with your business goals. This can also lead to the formation of obscure data elements due to change of analytics technology. Then it gets extremely difficult to interpret such incomprehensible data elements to find out what they actually stand for.
Hence, it is highly imperative to validate all the applications employed for data collection and also carry out regular audits of the collected data. Such audits will guarantee that the tags used for data collection (if any) are triggering correctly and the present tags are generating only unique non-duplicated data.
Apart from these activities, it is also recommended to perform regular checks to ensure data quality to validate the current data collection techniques. These regular checks can also warn you of any probable data collection issues in your analytics system.
After you have implemented data collection techniques for your organization, you must make it accessible for your data analytics team, and maybe also to your agencies, contractors, technology associates, and so on. However, this can put your data at risk, which brings us to the crucial topic of data accessibility. Every organization must be crystal clear on what data they should share – and to whom!
As the first and crucial step of data governance, only grant data access to your employees’ corporate email ids or to the agency partners whom you truly trust. As a rule of thumb, never give access to employees’ personal email ids (e.g. Yahoo, Gmail etc.). This will bring down the threat of an ex-employee obtaining access to your organization’s data.
Another scenario for governing data access is when it is shared by your technology partners. Generally, this type of shared data is aggregated and cryptic. But you should be aware of other risky situations when your data can get unrestricted access. This occurs when your digital data is shared by 3rd party entities, ad servers, data aggregators, targeted advertising technologies, and other applications.
Data security and data access are closely linked topics. However, data security is not just about restricting access to your organization’s digital data and allowing only eligible employees to access it. True data security comes into effect when you can protect your organization’s data warehouses. A vast majority of today’s data collection techniques accumulate huge amounts of data and make use of cloud-based storage solutions to preserve them.
There’s no denying that these storage solutions have several tiers of data security measures in place to safeguard your data. But you must be well-aware of their data transfer methods should they transfer your data to another technology. You must also know where your data is being stored after the transfer happens. You should also be aware that your data can get “revealed” when it is shared unknowingly with external agencies. However, this threat can be lowered by accurately identifying your data and maintaining a document that records all the storage, sharing, and security information employed by your data collection representatives.
If your organization collects and maintains user/customer data, then there is a huge onus on you to safeguard its privacy. The customer should be notified of the type of data being collected and how it will be utilized. However, if a user does not want his/her personal information to be stored, he must be provided with an option to opt out. This is an essential best practice that you must incorporate in your analytics tracking system. Another crucial data governance activity is data classification. Your organization must also have a system to ensure that the collected analytics data is classified as per their type.
It is very crucial that the collected analytics data is used and examined in its intended structure. Governing data integrity is all about ensuring that the data collection methods are presenting processed data as its output. Processed data in terms of your web analytics tool can be the string of actions that comprise a “user session” or “sales funnel” that leads to a successful sale! This processed data must never be separated and merged as raw data with your enterprise data. Such merger can potentially damage your analytics data.
Last but not the least among the data governance activities is how you present your data in an appropriate framework! Companies and organizations that handle digital data with responsibility know the importance of this crucial data governance aspect. Such agents will always do their best to present their data in a proper setting.
Efficient data presentation often becomes a challenging task for organizations due to the sheer size of data sets that can quickly grow into petabytes or more. Hence, for effective data presentation, every organization must set up a core team that is trained to precisely identify the collected data and presents that data effectively as per their data types. If a competent team is handling the data presentation activity, the data interpretations will have a high rate of accuracy. This will bring about efficient presentation and distribution of digital data resources in your organization.
Please be informed that these Data Governance Rules only form the basis for building your own data governance system.
Every data governance strategy presents its own set of challenges. But if implemented well, data governance will help protect your digital data as stipulated by the industry. We at Rawsoft have undertaken some of the most demanding data governance implementations for our clients over the past several years. So, if you feel that you need an expert to set up the data governance system for you, please do give us a call.