Data Quality Management
Why is data quality important?
Keeping data clean should be a collective effort between business users, IT staff, and data professionals. But oftentimes, it is just perceived as an IT glitch – believing that data becomes dirty when some technical processes for capturing, storing, and transferring data do not work correctly. Although this can be the case, data needs the attention of all the right stakeholders to maintain its quality over time. For this reason, it becomes imperative to build a case for data quality in front of necessary decision-makers, so that they can help enable it across all departments and levels.?
Below, we have listed the most common benefits of data quality.??
01. Accurate decision making
Business leaders do not rely on assumptions anymore, but rather utilize business intelligence techniques to make better decisions. This is where good data quality can enable accurate decision-making , while poor data quality can skew data analysis results, leading businesses to base crucial decisions on incorrect forecasts.?
02. Operational efficiency
Data is part of every small and big operation at a company. Whether it is product, marketing, sales, or finances – operating data efficiently in every area is the key. Using quality data in these departments can lead your team to eliminate duplicate efforts, reach accurate results quickly, and be productive throughout the day.??
03. Compliance
Data compliance standards (such as GDPR, HIPAA, and CCPA) require businesses to follow the principles of data minimization, purpose limitation, transparency, accuracy, security, storage limitation, and accountability. Conformance with such data quality standards is only possible with clean and reliable data.??
04. Financial operations
Businesses incur huge amounts of financial costs due to poor data quality . Operations such as making timely payments, preventing underpay and overpay incidents, eliminating incorrect transactions, and avoiding chances of fraud due to data duplication are only possible with clean and high-quality data.?
05. Customer personalization and loyalty
Offering personalized experiences to customers is the only way to convince them to buy from your brand instead of a competitor. Companies utilize a ton of data to understand customer behavior and preferences. With accurate data, you can discover relevant buyers and offer them exactly what they are looking for – ensuring customer loyalty in the long run while making them feel like your brand understands them like no one else.?
06. Competitive advantage
Almost every player in the market utilized data to understand future market growth and possible opportunities to upsell and cross-sell. Feeding quality data from the past to this analysis will help you to build a competitive advantage in the market, convert more customers, and grow your market share.?
07. Digitization
Digitization of crucial processes can help you to eliminate manual effort, speed up processing time, and reduce human errors. But with poor data quality, such expectations cannot be fulfilled. Rather, poor data quality will force you to end up in a digital disaster where data migration and integration seem impossible due to varying database structures and inconsistent formats.??
Data quality issues
A data quality issue is defined as:? AN INTOLERABLE DEFECT IN A DATASET, SUCH THAT IT BADLY IMPACTS THE TRUSTWORTHINESS AND RELIABILITY OF THAT DATA.?
领英推荐
Before we can move on to implementing corrective measures to validate, fix, and improve data quality , it is imperative to understand what is polluting the data in the first place. For this reason, we are first going to look at:
01. Lack of proper data modeling
This is the first and the most significant reason behind data quality errors. Your IT team does not expend the right amount of time or resources while adopting new technology – whether it is a new web application, database system, or integration/migration between existing systems.??
Data modeling helps to organize and give structure to your data assets and elements. Your data models can be susceptible to any of the following issues:?
a) Lack of hierarchical constraints:? This relates to when there are no appropriate relationship constraints within your data model. For example, you have a different set of fields for Existing Customers and New Customers, but you use a generic Customer model for both, rather than having Existing Customers and New Customers as subtypes of the supertype Customer.
b) Lack of relationship cardinality:? This relates to when there is no number defined that represents the number of relations one entity can have with another. For example, one Order can only have one Discount at a time.?
c) Lack of referential integrity:? This relates to when a record in one dataset refers to a record in another one that is not present. For example, the Sales table refers to a list of Product IDs that are not present in the Products table.
02. Lack of unique identifiers
This relates to when there is no way to uniquely identify a record, leading you to store duplicate records for the same entity. Records are uniquely identified by storing attributes like Social Security Number for Customers, Manufacturer Part Number for Products, etc.??
03. Lack of validation constraints
This relates to when data values are not run through the required validation checks before being stored in the database. For example, checking the required fields are not missing, validating the pattern, data type, size, and format of data values, and also ensuring that they belong to a range of acceptable values.?
04. Lack of integration quality
This relates to when your company has a central database that connects to multiple sources and integrates incoming data to represent a single source of information. If this setup lacks a central data quality engine for cleaning, standardizing, and merging data, then it can give rise to many data quality errors.?
05. Lack of data literacy skills
Despite all the right efforts being made to protect data and its quality across datasets, a lack of data literacy skills in an organization can still cause a lot of damage to your data. Employees often store wrong information as they don’t understand what certain attributes mean. Moreover, they are unaware of the consequences of their actions, such as what are the implications of updating data in a certain system or for a certain record.?
06. Data entry errors
Mistyping or misspellings are one of the most common sources of data quality errors. Humans are known to make at least 400 errors while doing 10,000 data entries. This shows that even with the presence of unique identifiers, validation checks, and integrity constraints, there is a chance that human error can intervene and make your data quality deteriorate.?