6 Major Data Quality Issues That Haunt Almost All Major Organisations
With the advent of data socialisation and data democratisation, many organisations are organising, sharing and making available the information in an efficient manner to all the employees. While most organisations are profiting by the liberal usage of such mine of information at their employees’ fingertips, others are facing problems with the quality of data being used by them. As most organisations also look at implementing systems with artificial intelligence or connecting their business via internet of things, this becomes especially important. Business analysts determine market trends, performance data, and even present insights to executives that will help direct the future of the company. And as the world becomes even more data-driven, it is vitally important for business and data analysts to have the right data, in the right form, at the right time so they can turn it into insight. The basic model that a company follows when implementing data socialisation is: However, many times, business analysts end up spending the majority of their time focused on data quality. This is a problem because data preparation and management isn’t the business analyst’s’ primary responsibility. But they also don’t need to depend on IT to do it for them either. Some of the most common data quality-related issues faced by analysts and organisations in general are: 1. Duplicates Multiple copies of the same records take a toll on the computation and storage, but may also produce skewed or incorrect insights when they go undetected. One of the key problems could be human error — someone simply entering the data multiple times by accident — or it can be an algorithm that has gone wrong. A remedy suggested for this problem is called “data deduplication”. This is a blend of human insight, data processing and algorithms to help identify potential duplicates based on likelihood scores and common sense to identify where records look like a close match. 2. Incomplete Data Many a times because the data has not been entered in the system correctly, or certain files may have been corrupted, the remaining data has several missing variables. For example, if an address does not include a zip code at all, the remaining information can be of little value, since the geographical aspect of it would be hard to determine. 3. Inconsistent Formats If the data is stored in inconsistent formats, the systems used to analyse or store the information may not interpret it correctly. For example, if an organisation is maintaining the database of their consumers, then the format for storing basic information should be pre-determined. Name (first name, last name), date of birth (US/UK style) or phone number (with or without country code) should be saved in the exact same format. It may take data scientists a considerable amount of time to simply unravel the many versions of data saved. 4. Accessibility The information which most data scientists use to create, evaluate, theorise and predict the results or end products often gets lost. The way data trickles down to business analysts in big organisations — from departments, sub-divisions, branches, and finally the teams who are working on the data — leaves information that may or may not have complete access to the next user. The method of sharing and making available the information in an efficient manner to all the employees in an organisation is the cornerstone in sharing corporate data. 5. System upgrades Every time the data management system gets an upgrade or the hardware is updated, there are chances of information getting lost or corrupt. Making several back-ups of data and upgrading the systems only through authenticated sources is always advisable. 6. Data purging and storage With every management level in an organisation, there are chances that locally saved information could be deleted — either by mistake or deliberately. Therefore, saving the data in a safe manner, and sharing only a mirror copy with the employees is crucial. “As business users grow frustrated that they can’t get answers when they need them, they may give up waiting and revert to flying blind without data. Alternatively, they may go rogue and introduce their own analytics tool to get the data they require, which can create a conflicting source of truth. In either scenario data loses its potency,” wrote Brent Dykes. If care isn’t taken to avoid incorrect or corrupt data before analysing it for business decisions, the organisation may end up losing opportunities, revenue, suffer from damage to reputation, or even undermine the confidence of the CXOs.
With the advent of data socialisation and data democratisation, many organisations are organising, sharing and making available the information in an efficient manner to all the employees. While most organisations are profiting by the liberal usage of such mine of information at their employees’ fingertips, others are facing problems with the quality of data being […]
With the advent of data socialisation and data democratisation, many organisations are organising, sharing and making available the information in an
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["AI", "artificial intelligence", "analytics", "RAG", "R", "Go", "Rust", "data quality", "GAN", "ViT"]
Prajakta is a Writer/Editor/Social Media diva. Lover of all that is 'quaint', her favourite things include dogs, Starbucks, butter popcorn, Jane Austen novels and neo-noir films. She has previously worked for HuffPost, CNN IBN, The Indian Express and Bose.