It’s no secret that data helps businesses make informed decisions. Backing business decisions with clean, informative data has become a common intent. However, if a business has poor quality data, it can hinder or even prevent businesses from making key decisions that could affect the future of the company. Because of this, high quality data is key to helping a business run smoothly.
What is Data Quality?
Data quality refers to the state of a data set or grouping. The quality of data can be measured based on the standards a company sets that outlines what high quality data looks like. It allows organizations to accurately assess information relative to purpose and intended function of the data.
5 Important Definitions to Know
Consistency, in relation to data, refers to whether data is formatted in the same way to ensure it doesn’t contradict itself. When data doesn’t follow a consistent format or lacks a standard method of entry, queries may not pull the intended results.
Data accuracy is another integral aspect of data quality. Accurate data reflects information that is true of the demographic, department, or sales information queried. It also provides summaries that are representative and furthers understanding of a particular area of business.
Having relevant data refers to collecting information that is pertinent to the needs of the business. Data that is no longer relevant sits unused, taking up space in the database. This data may no longer be relevant and should be eliminated from the system to clear data clutter and make room for updated information.
For data to be useful, it needs to be complete. Completeness refers to the ability of data to give you the full picture about metrics such as line efficiency, customer needs, and company sales information. If pieces of data are missing, companies will not have all of the information needed to make important decisions.
Timely data refers to how quickly new information is added to the database and made available for business use. Accessing data on current company operations such as recent energy usage or current sales numbers promotes agile decisions across an entire organization.
A Few Tips to Promote Data Quality
Maintaining high quality data can be a painstaking process, but here are a few key tips to promote quality data within an organization.
Tip 1: Have good organizational structure for managing data
This goes beyond a well-organized database structure. A good organizational structure will include designated positions with some structure of accountability in charge of overseeing data quality.
Program managers, change managers, business analysts, and other stellar leaders within a company help to ensure that data quality and any related projects, like data migrations, are overseen with great attention to detail. This system of leadership makes sure that data continues to serve the needs of the company.
Tip 2: Define data quality standards for the particular business and take steps to implement these standards
Specifying the standards that company data should follow is a necessary step to take. To start, define for what purpose the company intends to use the data. By outlining the purpose, businesses can decide which aspects of data quality need to be emphasized. For example, if a consumer sales company wants to emphasize accuracy, they might refine the standard of how customer contact information is saved within the system.
The next step is to set up a mechanism that dictates the frequency and methodology of evaluating data quality. Data quality metrics and standards will differ from business to business. Once the business purpose of the data has been determined, companies can establish key performance indicators (KPIs) to understand if the data is meeting the desired outcomes.
Finally, create a profile for the data and assess what changes need to be made in its current state. Does company data need to be edited or restructured to meet standards or business purposes? Ideally, what areas would renewed high quality data bolster the most?
Tip 3: Recognize what poor data quality looks like, where it comes from, and take steps to correct it
Poor data quality complicates decision-making and can lead to “false facts.” It also can cause a reduction in productivity and an increase in operating inefficiencies. According to Gartner, it is estimated that poor data quality results in $15 million in losses each year.
Low quality data manifests itself in many ways including duplicates within the system, ambiguous interpretations of data, restriction of access to data that is timely and relevant, and summaries that incorporate outdated information.
Problems with data quality can originate from several areas. Human error is a common source of data quality issues. Everyone makes mistakes, it happens! By examining the nature of these mistakes, companies can determine whether these errors are the result of pure accident or if employees lack a command of the standards the data must meet or the procedures of adding the data to the system.
Other areas where poor data quality can originate from include challenges with the system. Is the storage software altering the format or corrupting the data? If a data migration project was recently completed, is the data in its current format compatible with the new system? Understanding where potential issues lie within the system can alert leaders where changes might need to happen.
Data decay also causes issues with data quality. This isn’t necessarily the fault of human or system error– data sometimes trends toward inaccuracy with the passage of time. Case in point: customers may get a new phone number or move, making the contact information on file for them no longer relevant or accurate.
Tip 4: Consider using technology to monitor data quality
The rise of cloud computing and platform as a service (PaaS) choices brings numerous options for storing and maintaining data quality. With so many platforms to choose from, companies can better identify platforms with the tools that suit the needs of their data and data management priorities.
While every solution differs in its functions, options could include tools to assist in data profiling, data life cycle management, visualization, and reporting. These capacities assist businesses and provide valuable insight into the health of an organization’s data.
To assist with data quality management, Qlik® designed Qlik Data Catalyst®, an offering that oversees the health of company data in one central catalog. The Data Catalyst simplifies locating specific data with a keyword search bar, enabling a faster turnaround for data projects. The dashboard includes KPIs to measure the “health” of company data, and assists in other areas including data cataloging, management, and preparation. Interested in how Qlik can impact your business? Message us today and our team will be happy to answer your questions!