December 07, 2020
Whether you work in development, sales, management, or acquisition, utilizing quality data is essential for day-to-day business operations. However, because data is so abundant and comes in many forms (quantitative, qualitative) and formats (JSON, CSV, XML), obtaining and maintaining quality data can be challenging. This challenge is where understanding data quality comes in handy.
Due to its vast usage, data quality has many definitions and can be improved in various ways. This article will explore the ways in which data quality is defined, use cases for data quality, as well as six methods to improve your company’s overall quality of data.
Depending on who you ask, data quality has many definitions. Data quality definition can be applied to three main groups of people: consumers, business professionals, and data scientists. While the definitions change depending on their intended use, the core meaning of data quality stays relatively the same. We will touch on these core principles later in the article. For now, let’s take a look at the three most common definitions of data quality.
Consumers understand data quality as data that fits its intended use, is kept secure, and explicitly meets consumer expectations.
Business experts understand data quality as data that aids in daily operations and decision-making processes. More specifically, high data quality will enhance workflow, provide insights, and meet a business’s practical needs.
Data scientists understand data quality on a more technical level, as data that fulfills its inherent characteristics of accuracy, usefulness, and satisfies its intended purpose.
Ultimately, these definitions of data quality are all united by their emphasis on purpose and accuracy. While these are important, many other dimensions can be used to measure data quality. Let’s first examine why data quality is important, and some common use cases.
There are many risks involved in low data quality. Forbes cites that low data quality can negatively affect businesses' revenue, lead generation, consumer sentiment, and internal company health. Virtually, maintaining high data quality affects every aspect of a company’s workflow, varying from business intelligence and product/service management to consumer relations and security. Now let’s take a closer look at the major use cases for data quality.
Similar to data governance, data standardization involves organizing and inputting data according to negotiated standards established by local and international agencies. Unlike data governance, which examines data quality management from a more macro and legal perspective, data standardization considers dataset quality on a micro level, including implementing company-wide data standards. This allows for further specification and accuracy in complex datasets.
Data cleansing, with regards to quality, is a process in which data wrangling tools correct corrupt data, remove duplicates, and resolve empty data entries. Ultimately, this process aims to delete data that is considered “dirty” and replace it with sound, clear, and accurate data.
Geocoding is the process of correcting personal data such as names and addresses to conform to international geographic standards. Personal data that does not conform to geographical standards can create negative consumer interactions and miscommunications.
Data governance creates standards and definitions for data quality, aiding in maintaining high data quality across teams, industries, and countries. The rules and regulations that establish data governance originate from legislative processes, legal findings, and data governance organizations such as DAMA and DGPO.
Data profiling is a process that examines and analyzes data from a particular dataset or database in order to create a larger picture (profile summary) for a particular entry, user, data type, etc. Data profiling is used for risk management, assuring data quality, and analyzing metadata (that sometimes goes overlooked).
As mentioned previously, measuring data quality can be an incredible feat since many dimensions are used to measure a dataset’s quality. Here are the primary dimensions used to measure data quality.
Refers to the measurement of data authenticity as defined by any restrictions the data collection tool has set in place. For instance, inaccurate data can occur when someone reports data that is either false or an error occurred during the input process.
Completeness is a measurement of the degree of known values for a particular data collection process. Incomplete data contains missing values throughout a particular dataset. Missing data can skew data analysis results and can cause inflated results or may even render a particular dataset useless if there is severe incompleteness.
Data consistency refers to the measure of coherence and uniformity of data across multiple systems. Significantly, inconsistent data will contradict itself throughout your datasets and may cause confusion about which data points contain errors. Additionally, inconsistent data can occur when data is input by different users across different data entry systems.
Timeliness refers to the rate at which data is updated. Timely data is updated often and does not contain outdated entries that may no longer be accurate.
Data uniformity is a measurement of the consistency of the units of measurements used to record data. Data that is not uniform will have entries with different measurement units, such as Celsius versus Fahrenheit, centimeters to inches, etc.
Data uniqueness is a measurement of originality within a dataset. Specifically, uniqueness aims to account for duplicates within a dataset. Uniqueness is typically measured on a percentage-based scale, where 100% data uniqueness signifies there are no duplicates in a dataset.
As previously mentioned, data is an essential component of business intelligence. Likewise, compiling enough data for your datasets is just as crucial as collecting quality data. Evaluating datasets is a difficult but necessary task that can set you apart from your competitors. Data quality issues can occur in many instances during a datapoint’s life cycle. Nevertheless, creating clear guidelines and setting thoughtful intentions when analyzing your data will increase your data quality, allowing a more precise understanding of what your data is telling you.
With that, let’s look at some ways to better evaluate the quality of your datasets.
Uniqueness refers to the specific type of data you are collecting. It is important to utilize data that is specific to your business objectives and matches the intentions behind your data usage. Simply put, you should ask yourself: “Is this data relevant to my company’s established business goals?” If not, you may want to reassess what specific data you are collecting.
Frequency surrounding data collection, also known as timeliness (discussed above), indicates how fresh your dataset’s information is. Evaluating the frequency at which you collect new data or update the current information in your dataset will directly affect its quality. The goal here is to establish recurring data collection cycles that support your business objectives.
While sometimes accurate data is not possible, due to the data collection process’s human component, creating parameters on data intake reduces inaccuracies. For instance, analyzing datasets manually at random will give you insights into how accurately consumers are inputting data. At first glance, some datasets may look complete, but that doesn’t necessarily equate to accuracy.
Noisy data can create unnecessary complexities and convoluted datasets.
For instance, there may be duplicates in your data or misspellings in entries that cause errors in the data analysis process. Reducing noise in data can be done by data matching, which compares individual data points with one another to find duplicates, misspellings, and excessive data (data that is not necessarily a duplicate but is implied in other data entry points).
Incomplete or missing data can negatively affect the quality of your datasets, and in some cases create more extensive data reading errors depending on which data profiling tools you are using. The larger the number of empty values the more inaccurate your data becomes. Therefore, ensuring completeness throughout the data collection process is essential to guaranteeing data quality.
According to Experian, human data entry errors account for 59% of reported inaccuracies. Similar to implementing best practices in the workplace, it is also critical to have best practices for your dataset collection process. Communicating and interpreting your datasets consistently throughout your company will increase the quality in which your business utilizes said data. Establishing company-wide best practices for your dataset process will ensure consistency and quality.
Data quality affects all facets of business operations. With this in mind, obtaining and maintaining quality data is a priority in a successful business.