Data Governance

Think of data cleaning as that feeling when you open a spreadsheet, and it looks like a kindergartener’s lunchbox exploded. That’s the not-so-glamorous side of analytics. But who gets stuck with this dirty work?

In a structured environment, that responsibility falls on the data engineer. What a data analyst or scientist cleans doesn’t always return to the source. Say company A employs three analysts/ scientists named Bob, Carol, and Dennis. Bob wants to create a report and clean the data before use, probably in PowerBI. The result doesn’t feed into the original system.

Now, Carol and Dennis need to use that same source. They both need to clean that data. The same cleaning process will probably be done thrice. But if B had informed the data engineer of the data quality issues, C&D wouldn’t have needed to do a process repetition. Basically, it’s the same mess, three times the work.

In an unstructured environment, whoever manages the data source is in charge of cleaning.

This is why good data governance is important, i.e. if you have a data engineer under your employment, your analysts/scientists shouldn’t be spending hours cleaning the data. It speaks to a bad data governance culture.

Data governance is a set of concepts, techniques, and technologies that assist companies in ensuring their data's availability, usefulness, integrity, security, and privacy. It essentially establishes a well-defined framework for managing data across its entire lifecycle, from generation to storage to analysis.

Here's why strong data governance matters:

1. Reduces redundancy and wasted effort. By providing defined roles and responsibilities for data cleaning and administration, data governance saves analysts like Carol and Dennis from spending hours re-cleaning data Bob has already cleaned. A data governance structure would ensure that Bob's data cleaning efforts are documented and incorporated back into the source system for everyone to benefit from.

2. Enhances data quality and consistency: Data governance encourages consistent data definitions and standards throughout the organization. This means that everyone has the same understanding of the data, resulting in more dependable and trustworthy insights.

3. Improves data security and privacy: Data governance enables firms to comply with legislation and secure sensitive data. It ensures correct access controls and minimizes data leaks.

4. Promotes data-driven decision-making: With clean, consistent, and trustworthy data, analysts can concentrate on identifying useful insights rather than dealing with chaotic data. This enables firms to make sound decisions based on accurate data.

Think of it like hiring a chef and then learning to boil eggs yourself. Not the best use of everyone’s time! A strong data governance culture ensures everyone uses their skills effectively. Data engineers focus on building data pipelines and managing data infrastructure, analysts use their expertise to analyze the data and identify trends, and data scientists delve deeper to uncover hidden patterns.

This is where we at TA Insight HUB come in. We’re like the data knights cleaning up the digital kingdom. We believe in strong data governance, setting clear rules and roles so everyone knows who’s responsible for what. With this, Analysts can focus on finding insights, not wrestling with messy numbers.

Who is the data-cleansing superhero, then? It takes a group to succeed! While analysts use their analytical wizardry, data scientists decipher the mysteries buried beneath the data, and data engineers construct the pipelines. Working together, we maintain the data spotlessly and are ready to show off its secrets.

Are you curious about how TA Insight HUB maintains the data kingdom’s shine? Simply give us a text! We are always delighted to discuss the fascinating field of data governance.

Keep in mind that dirty data leads to bad decisions. Let’s maintain the gloss on those spreadsheets!