The myth of "100% data quality" is a classic one. It is not uncommon for teams to burn resources trying to reach an impossible standard when, in reality, fit-for-purpose data is often what matters more. Not every dataset needs to be flawless, but it does need to be reliable enough for the business decisions it supports. And Modern Data 101’s point about over-reliance on tools is unfortunately often true! Technology is powerful, but without clear ownership and a strong data culture, even the best tools won’t fix underlying quality issues. That’s why it's important to consider: People, process and purpose BEFORE platform. Another myth which often crops up: “Data quality is just an IT problem.” In reality, bad data affects every department! So, take this as your sign to work towards higher data quality, even if it's not perfect. Because data working for you on a functional level is so much more important than data being 100% or the sole responsibility of one department. 💯 #dataquality #dataissues #data
𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐌𝐲𝐭𝐡𝐬: 𝐖𝐡𝐲 97% 𝐨𝐟 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐅𝐚𝐥𝐥𝐬 𝐒𝐡𝐨𝐫𝐭? Though data remains a critical business success factor, only 3% of a company’s data quality scores are rated as acceptable as per a Harvard Business Review, meaning that 97% of data is below favorable. But why are orgs failing in their data quality mission despite continuous efforts to improve their data quality? This is often because organizations fall into the trap of data quality myths and misconceptions, chasing misguided data quality goals and unattainable targets that not only derail data-driven initiatives but also lead to a significant dip in overall project success rates. In this post, let's burst some of these Data Quality Myths that ruin the data-driven initiatives of orgs. 𝐖𝐞 𝐧𝐞𝐞𝐝 100% 𝐨𝐫 𝐚 𝐍𝐞𝐚𝐫 100% 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 As Ryan Duffy mentions in one of his posts, ‘You can’t get perfect in the way of good enough,’ striving for perfect data actually becomes a time-consuming trap. Achieving 100% data quality is nearly impossible, especially with large data volumes. Many professionals mistakenly aim to eliminate every duplicate, missing value, or inconsistency. Instead, they should treat data quality as an ongoing process, focusing on improving reliability and performance over time. 𝐀 𝐆𝐨𝐨𝐝 𝐓𝐨𝐨𝐥 𝐖𝐢𝐥𝐥 𝐄𝐧𝐬𝐮𝐫𝐞 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 Technology is always a strong enabler but not an omnipotent! Like a GPS that requires accurate input to guide you correctly, data tools are only as effective as the people using them. Over-reliance on tools can lead to complacency, causing critical human oversight to be missed. This risks perpetuating errors and undermining data quality initiatives. 𝐇𝐢𝐠𝐡-𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐃𝐚𝐭𝐚 𝐈𝐬 𝐀𝐥𝐰𝐚𝐲𝐬 𝐀𝐜𝐜𝐮𝐫𝐚𝐭𝐞 While thorough data cleaning is essential, it doesn’t guarantee accuracy. Data can become unreliable due to unexpected events or new information. Companies should continue using insights as a guide but must avoid relying on them as the sole decision-making driver. Diversifying sources and continuously validating data ensures more robust and adaptable strategies. 𝐃𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲, 𝐨𝐧𝐜𝐞 𝐚𝐝𝐝𝐫𝐞𝐬𝐬𝐞𝐝 𝐢𝐬 𝐠𝐨𝐨𝐝 𝐭𝐨 𝐠𝐨 As George Firican mentions in one of his articles, data quality is not a one-time project. As data is dynamic, it is subject to constant changes, decay, and obsoleteness. Therefore data quality management needs to be a continual process with constant vigilance and frequent maintenance to ensure data relevance, reliability, and decision-making. So, where does your organization stand on these data quality myths? Share your thoughts and experiences in the comments—let’s navigate the complexities together!