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Letmeintroduce youtoSarah, a talented and passionate data scientist, who just landed her dream job at GreenEnv, a large company that makes eco-friendly cleaning products. GreenEnv has tons of data on customers, products, and other areas of the business. They hired Sarah to unlock the hidden potential within this data, uncovering market trends, competitive advantages, and more.

Her first task: analyze customer demographics and buying habits to create targeted marketing campaigns. Confident in her abilities and excited to apply data science methods, Sarah dived into the customer database. But her initial excitement quickly faded. The data was a mess inconsistent formatting, misspelled names, and duplicate entries everywhere. Data quality was terrible. There were variations of names like Jhon Smith and Micheal Brown alongside entries like Jhonn Smtih and Michealw Brown. Emails had extra spaces and even typos like gnail.com instead of gmail.com. along with many other inaccuracies. Sarah realized the hard job ahead of her data cleaning.

Inconsistent formatting, missing values, and duplicates would lead to skewed results, giving an inaccurate picture of GreenEnvs customer base. Days turned into weeks as Sarah tirelessly cleaned the data, fixing inconsistencies, filling in gaps, and eliminating duplicates. It was a tedious process, but essential to ensure her analysis was built on a solid foundation.

Who cares about data quality?

Every year, poor data quality costs organizations an average of $12.9 million. [1]

Thankfully, after weeks of cleaning and organizing this messy data, Sarah was able to get the job doneor at least for this part..

Her next challenge came when she ventured into product data, aiming to identify top-selling items and recommend future opportunities. However, she encountered a different problem a complete lack of metadata. Product descriptions were absent, and categories were ambiguous. Basically, there wasnt enough data to help Sarah to understand the products data. Sarah realized the importance of metadata management structured information about the data itself. Without it, understanding and analyzing the data was almost impossible.

Research Shows Most Data Has Inaccuracies

Research by Experian reveals that businesses believe around 29% of their data is inaccurate in some way. [2]

Frustrated but determined, Sarah reached out to different departments to piece together information about the products. She discovered that each department used its own internal jargon and classification systems. Marketing and sales refer to the same cleaning product with different names.

As Sarah delved deeper, she found that datasets were kept in separate applications by different departments, outdated storage systems struggling to handle the growing volume of data, and Sarah had to wait for a long time for her queries to be executed. Sarah noticed also there are no clear rules on who can access what data and under what terms, without centralized control and proper access controls, the risk of unauthorized access to sensitive information increases, potentially leading to data breaches and compliance violations. The lack of data governance, a set of rules and procedures for managing data, was evident.

Data Breaches Can Be Costly

According to the Ponemon Institute, the average cost of a data breach in 2023 is $4.45 million globally, an all-time high record, with costs varying by industry and location. [3]

Each of the above issues and hurdles in Sarahs story highlighted the interconnectedness of many pillars data quality, metadata management, and data governance all played a crucial role in accessing and utilizing valuable insights at GreenEnv.

Sarahs journey is a common one for data scientists and analysts. Many organizations have massive amounts of data, and everyone knows the saying: Data is the new electricity. Every organization wants to make the most of their data, as its a very valuable asset. But most people mistakenly (and practically) believe that simply hiring a data analyst or data scientist is enough to unlock this value. There are many pillars to getting the most value from data, and organizations need to account for and pay attention to these. The keyword here is data management.

Did you know..

86% of organizations say they believe investing in data management directly impacts their business growth[4]

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The Many Pillars of Getting the Most Value From Your Organization's Data - Towards Data Science