Data governance is the most important component for any digital analytics team. It is the key to making data-driven decisions across every business unit. Without it, businesses will limit growth. If data is the new oil, data governance is the literal oil rig. Sound data governance extracts, refines, loads, and visualizes all data points all while being consistent and reliable. Hence, it is imperative to invest time and resources on comprehensive data governance prior to any business intelligence solution.

What are the Benefits of Data Governance

There should be no data projects or data reporting without developing governance rules first, specifically for quality control and transparency. As a data team, grab a whiteboard and write all the objectives of data teams, cost of resources, ownership of data collection, structure of pipeline, and key software competencies to succeed. If each team member has a different answer after this exercise, then your team has an issue. Data teams must align on each of these questions and assign ownership within these pillars for success.

What are the 5 Data Governance Pillars

Data Collection

Let’s begin with data collection. Every function of organizations of any size are collecting data. Whether it is offline or online, all business units are generating and collecting mass quantities of data at all times. Who on your team collects data and how does it contribute to your overall data strategy? In addition to owning collection methodologies, can your team properly identify why this data source is important to measurement of key KPIs? At this stage, Engineers, Analyst, Scientist and the subject matter experts work together to create and enable data pipelines.

Data Enrichment

Once data is collected and stored, it must be transformed. Data enrichment refers to any ETL process that structures data in a usable format. Typically at this stage, Data Engineers are partnering with Data Analyst and Scientist to structure raw data points into more usable structured formats.

Data Quality & Integrity

Data Quality and Integrity checks include discussing with all team SMEs and Analyst/Scientist aligning on quality control. Data quality includes ensuring that the data received is actionable and aligns with the overall measurement goals. The key question with data quality is “Does our collected, enriched data give us answers we can trust?” If you answer is no, then you need to reconsider your enrichment and collection of the data. Data Integrity requires a sign off of SME’s can trust the received data in its raw and enriched form. The key question with data integrity is “Is our data impaired?” If the answer is yes, then you have poor data integrity.

Data Presentation

Once you have alignment on Collection, Enrichment, Quality and Integrity, it’s now time to interpret your data to provide actionable automated reporting for those making business decisions. Data visualization is the art of doing just that. Good data visualization is sustainable, impactful, consistent, trustworthy, accessible, and easy to read. Any lack of these key traits, presentation then becomes questionable. At this stage, analyst report key findings back to SME’s by interpreting key insights. Data scientist model data to gain future insights as well as help companies make more informed decisions.

Data Stewardship

Stewardship requires a fundamental shift in behavior and attitudes as it relates to data. There has to be an overall team support for data strategy and trust in those managing data pipelines. Teams also must prioritize this path to success over all other functions. Does your team have a love story with your data strategy? If not, consider rallying teams to be more enthused to take on projects and work together on building a strategy everyone can trust.

Data governance is the most important component for any digital analytics team. It is the key to making data-driven decisions across every business unit. Without it, businesses will limit growth. If data is the new oil, data governance is the literal oil rig. Sound data governance extracts, refines, loads, and visualizes all data points all while being consistent and reliable. Hence, it is imperative to invest time and resources on comprehensive data governance prior to any business intelligence solution.