Clean data, clear path: mastering data hygiene in your organization

To unlock your organizational success, you need reliable and structured data. Our text explores effective data management strategies that empower organizations to harness the true potential of their data assets.   

From this post you will learn: 

  • how to evaluate data quality,
  • what database hygiene is and why it is important,
  • which makes data hygiene more difficult,
  • what problems does an unstructured database cause,
  • how lack of data hygiene hinders the functioning of the company,
  • how to take care of data hygiene. 

How to assess data quality? 

Data quality depends on many factors. High-quality data is:  

> up-to-date: created, managed and available immediately and as required,  

> concise: without redundant information,  

> consistent: without conflicts of information within or between systems,  

> accurate: correct, precise and up-to-date, 

> complete: containing all available and necessary elements,   

> compliant: stored in an appropriate, standardized format,

> correct: authentic and from known, reliable sources.

High-quality data serves as a solid foundation for your organization. It ensures that your systems and applications access accurate information, enabling informed decisions across various domains such as customer service, user experience, and business performance improvemen.  

What is data hygiene? 

Data hygiene involves managing data effectively. You make sure that both structured and unstructured data — whether stored in databases or files — are ‘clean’: reliable, up-to-date, and free from errors. In essence, data hygiene is akin to maintaining ‘data cleanliness’ and ‘data quality’. 

Why database hygiene is important   

Effective database hygiene (1) streamlines compliance with security policies, (2) optimizes performance and (3) ensures adherence to regulations. Achieving this state requires business applications and processes to operate with clean, accurate, and pertinent data. Database cleaning involves tasks like removing obsolete sensitive personal information and refreshing outdated or incorrect addresses. Establishing a clear data hygiene policy is essential to avoid overlooking issues or making misguided decisions.   

What makes data hygiene difficult?  

Once upon a time, companies relied solely on data from their internal systems (such as sales or inventory). Nowadays, they cast their nets wider, tapping into diverse sources including Internet datasets, IoT devices, and scientific experiments. The more sources they embrace, the trickier it becomes to ensure data reliability and authenticity. Each new system integrated into an organization’s processing engine introduces a heightened risk of data devaluation. Why? Because disparate sources yield varying data types. Notably, unstructured data—information lacking a specific organizational schema—now constitutes approximately 80% of the world’s data 

> Growing volumes of data   

We’re in the age of Big Data, where data volume grows incessantly. Since 1970, it has doubled every three years. The more data we accumulate, the more challenging it becomes to collect, clean, and integrate. Obtaining meaningful, high-quality data within tight timeframes is increasingly difficult. Moreover, as a significant portion of this data remains unstructured, processing times escalate further. Addressing unstructured data by adding partial structure is essential, even though it may impact overall data quality. 

> Faster use of data   

“Real-time data” has been a popular buzzword in recent years. The more data you generate, the faster you have to process it. You also risk clogging up your systems. A stream of increasingly fast-flowing data can damage them. The only way to manage the growing volume is to expand system capabilities. In the world of data, this means even faster processing: the speed of processing must match the speed of data flowing down. Real-time data processing, however, is still a relatively new field. We still have to deal with “noise,” that is, a situation where some important data is not used, while irrelevant data will go into processing. Decisions based on them will be suboptimal at best and wrong at worst. 

> Lack of clear data quality standards 

While product quality standards have been around since 1987, when the International Organization for Standardization (ISO) introduced ISO 9000, official data quality standards emerged much later in 2011 with ISO 8000. These standards are relatively new and continue to evolve. A 2015 study published in the Data Science Journal highlighted the persistent lack of comprehensive analysis regarding Big Data quality standards and methods for evaluating them. 

Disorderly data base means problems 

Inadequate data quality complicates management and can result in suboptimal decisions. These challenges are commonly encountered:  

  • Data duplication (or redundancy): when records in the database appear more than once. 
  • Data omission: when all the data required for a record is missing. 
  • Data inconsistency: when the same data is in different formats, but in several tables; this results in several files with different information about the same object/person. 
  • Data inaccuracy: when the data values for a specific object are incorrect 

How poor data quality hinders daily business operations  

> Sales and marketing  

A recent study by DiscoverOrg revealed that sales and marketing departments lose approximately 550 hours and up to $32,000 per sales representative due to incorrect data. Let’s delve into the implications: 

  • Unnecessary Expenses: Inaccurate data results in wasted resources and unnecessary costs. 
  • Customer Annoyance: Potential customers might receive duplicate content due to inconsistent data (when a database contains multiple records for the same individual but stored differently). 
  • Online Sales Challenges: Poor data hygiene or incomplete information can lead to selling the wrong product to the wrong customer. This often occurs when verified and organized data about products and target customers are lacking. 

> Finance  

In financial reporting, you can get multiple answers to the same question. This is a consequence of inconsistent data. So you will get inaccurate and misleading reports. You will gain a false sense of security or alarmist notes.  

> Supply chains  

Inaccurate data can also have serious consequences in supply chains. It is difficult to automate processes when you make decisions based on unreliable location information.  

At the corporate level, data quality can have a significant impact on the ability to achieve long-term goals. Your risks:  

  • negative impact on the ability to adapt and respond quickly to new trends and market conditions;  
  • aggravated obstacles in complying with key privacy and data protection regulations such as RGPD, HIPAA and CCPA;  
  •  challenges in leveraging predictive analytics with corporate data. 

Good practices in data hygiene  

While universal data quality standards are lacking, there are established best practices for data hygiene. These are worth applying today to achieve and maintain high data quality.  

  1. Compliance  

It’s crucial to lay out the ground rules for collecting data and its intended use, especially when dealing with consumer data. Setting up guidelines for storing and removing data is key. Having retention schedules that outline how long data stays before being deleted is invaluable. Keeping your data clean boils down to knowing what’s being stored, why it’s there, and when and where it should be deleted.  

  1. Data management  

Data management involves a set of processes, roles, principles, standards, and metrics. Applying them ensures effective use of information to achieve your organization’s goals. Data management requires specifying who can take what actions, on which data, in what situations, and using what methods. Good management is essential for maintaining high-quality data in an organization.   

  1. Automation  

Data hygiene also encompasses process automation. The main goal is to automatically update data as frequently as possible. It needs to stay current and accurate. Data cleansing systems filter through large volumes of data and employ algorithms. This allows them to detect anomalies or identify suspicious values resulting from human errors. They can also pinpoint duplicated records. 

  1. Deduplication process 

It involves eliminating duplicate data in mass storage volume or across the entire mass storage system (cross-volume deduplication). It utilizes pattern recognition to identify redundant data and replaces them with references to a single backup copy. It’s a proven method for organizing data collections.  

Challenges related to data hygiene – professional support is recommended 

Companies are aware of the importance of data hygiene, but often struggle to ensure the quality of their data. According to a study published by the Harvard Business Review, on average, 47% of new data records contain at least one critical error (i.e., one that impacts work). Only 3% of the results are considered “acceptable in terms of quality,” and that’s using the lowest standard.  

Ready to transform your company’s data game? If you’re a decision-maker grappling with the challenges of efficiently utilizing and managing your data, it’s time to take action. Unlock the power of your data to drive strategic decisions, enhance productivity, and fuel innovation. Let’s revolutionize your data management together and pave the way for unprecedented growth and success. Don’t let valuable insights slip through the cracks – seize the opportunity now and propel your business to new heights! Connect with us today to embark on your data-driven journey.