A recent survey reported that the top KPI for data teams in 2021 was data quality and reliability. But the majority of the respondents said that they do not use any data quality software or tool, and rely on manual data quality checks. And so, the biggest challenge faced by data teams was found to be low productivity due to manual work and a lack of automated processes.
Many business leaders and decision makers entertain the idea of adopting technology and automating processes, but very less actually do something about it. Same is the case with data teams at most organizations. It’s true that introducing new technology for digitizing any aspect of your business can potentially disrupt existing processes and uncover multiple challenges. But resolving these challenges can prove to be very beneficial in the long run – especially for your team’s productivity and performance, as well as consistent business results.
This blog will help you to understand various features and functionalities that are packaged in data quality tools and the factors you should consider while choosing a data quality solution for your specific business use case.
Features to look for in a data quality tool
While buying any software tool, there are three important feature aspects to consider. These include:
- The real-world processes the solution can facilitate,
- The additional features and capabilities that improve the execution of these processes,
- The intrinsic platform features that improve work efficiency.
Below, we cover all these aspects in more detail for data quality tools:
1. Data quality processes
Your data is probably polluted by a variety of data quality errors. And to fix these issues, it must be subjected to a complete, end-to-end data quality management lifecycle.
Data quality management usually includes a list of systematic processes. The exact number and nature of these processes depend on your needs as well as the state of your data. Let’s look at the most common and crucial data quality processes a data quality tool must facilitate, and what each of them means.
The ability to connect, ingest, and integrate data from a variety of data sources – including support for various file formats, databases, on-premise and cloud storage, as well as third-party applications.
The ability to get instant 360-view of your data quality by identifying blank values, field data types, recurring patterns, and other descriptive statistics that highlight the state of your data and potential data cleansing opportunities.
c. Data parsing
The ability to analyze long strings and identify important components – so that they can be validated against a library of accurate values. For example, parsing full names to identify first, middle, and last names, and converting nicknames and other abbreviations to proper names.
The ability to eliminate inconsistent and invalid values, create and validate patterns, transform formats, and achieve a standardized view across all data sources.
The ability to select, configure and execute proprietary or industry-leading data matching algorithms, and finetune them depending on the nature of datasets to identify potential record matches.
f. Data match result analysis
The ability to assess the match results and their match confidence levels to flag false matches as well as determine the master record.
The ability to flag and eliminate duplicate records – meaning, the records that relate to the same entity.
The ability to merge records together by designing a prioritized list of custom rules for automated master record selection and conditional data overwriting.
i. Data export or load
The ability to load or export results back to the source file or any other destination source.
2. Additional features to improve process execution
Many vendors and service providers claim to facilitate the digitization of certain processes. But the features offered to improve the execution of these processes is an important aspect to consider to assess what a software tool can do for you. Some examples of such features in a data quality tool are highlighted below:
a. Bulk standardization to remove noise
Oftentimes, a dataset contains certain words that don’t add much value to your data columns and just increase noise. Such words can cause problems during data standardization and data matching processes.
To remove noise, the data quality team manually verifies, replaces, flags, or deletes each noisy word present in a dataset. This is where a specialized wordsmith tool can be very useful. As the name suggests, a wordsmith tool profiles a data column for the most repetitive words present in that column and their count, and allows you to perform bulk operations on those words.
For example, in a Company’s dataset, you can have three different values:
- XYZ LLC
- XYZ Inc.
- XYZ Incorporated
You can see that all three company names are actually the same, and the words ‘LLC’, ‘Inc.’ or ‘Incorporated’ are just adding noise and producing duplicates of the same entity. A wordsmith tool can help you to remove such words from the entire column, leaving behind the actual company names.
A data quality tool that profiles and standardizes your datasets down to the word-level in bulk can exponentially improve your team’s productivity – since it can save them a lot of time and effort.
b. Inbuilt and custom pattern templates
While cleaning and standardizing datasets, you often have to validate the patterns and formats of the data values. Data quality tools that come with in-built templates for pattern recognition improve the efficiency of your data standardization and validation processes.
These pre-built templates can help in validating the pattern of common fields, such as email addresses, US phone numbers, date time stamps, and much more.
Moreover, if the data quality software supports designing custom regular expressions and validating proprietary patterns, this can prove to be very useful for your special requirements.
c. Schedule data quality jobs for batch processing
Although data tools can digitize and automate many processes, they still require human interaction for:
- Initializing the process and providing input,
- Overseeing the execution of the process,
- Verifying results, and moving output to destination source.
Scheduling data quality jobs for batch processing is a crucial feature that can help you to manage large amounts of data efficiently. You can schedule the most frequent or repetitive data quality tasks and they will be triggered on specific date and time each day/week/month, as scheduled.
This is something that can reduce maintenance time, minimize human error, and provide consistent results on a regular basis.
d. Real-time integration of data quality functions
As opposed to batch processing, some businesses require data to be stream processed. This means that incoming data is tested for data quality in runtime, and transformed as required before being loaded to the destination source.
This can probably add some complexity to your data quality management process at the start. But once you have the real-time data quality flow figured out, it can be very beneficial. Some vendors offer this capability packaged as an API or SDK so that you can adopt industry-grade data quality functions and implement them in your custom data quality flows.
e. CASS-certified address verification
Many master data objects at any organization contain address fields. For example, Address of a Customer, Address of a Store Location, Address of an Employee, and so on. When it comes to address verification or address standardization, simple or generalized data quality tools do not offer much value. And verifying that an address is a physical, mailable location in the county, and follows a globally-acceptable format can become a big challenge.
An in-built capability of data quality tools to verify addresses against an authoritative, official database (such as the USPS in the US) is a necessity in such cases. And while looking for such features, ensure that they are certified to be offering such services.
For example, CASS (Coding Accuracy Support System) is a certification program by the USPS to ensure that software vendors are accurately using USPS information to validate and standardize address data for their users. To qualify for CASS certification, software vendors must offer delivery point validation (DPV) and a locatable address conversion system in their services.
3. Intrinsic platform capabilities
In any organization, the main reason for digitizing processes and adopting technology is to improve work efficiency. This is why it is not enough for a software tool to facilitate real-world scenarios only. It must offer some basic features that make work easier and faster, and enhance result accuracy.
For a data quality tool, such features can include:
Data quality processes mentioned above are generally computationally complex and resource intensive. And an unoptimized and poorly-architected software tool can take hours to process simple jobs. Before choosing a tool, it is important to test tools and assess their speed of producing results on different data samples. In addition to this, you should also check if the tool is able to consistently process records at similar speed.
Faster speed does not help when the results are inaccurate or inconsistent. Data quality tools that implement industry-grade and proprietary algorithms for data profiling, cleansing, standardization, matching, and merging can generate more accurate results as compared to the ones that utilize simple statistical or conditional algorithms.
Of course, even the best of tools cannot prove to be 100% accurate all the time. The goal should be to look for a tool that offers maximum accuracy consistently across a variety of data samples.
Assess whether the data quality tool is scalable and can withstand an increasing amount of data as well as users. You may not have big datasets at your company right now, but the size of data can exponentially increase with time. Furthermore, you may be starting with a single team member initially who will be using the tool but you may want to scale and add more users to your plan later on. Make sure the vendor offers such scalability features and plans.
A data quality tool that has a simpler user interface and focuses on user adaptability is an important thing to consider. The tool must be self-explanatory and should guide the user step-by-step through various data quality processes. An intuitive interface with easy UX writing can help business users to perform technical tasks comfortably within the software, such as connecting to databases, assessing data profile reports, tuning match algorithms, and so on.
Cleaning and matching huge amounts of data can seem overwhelming even in the presence of a suitable data quality tool. If a vendor offers support, training, or other professional services to help you get started or navigate through the process when you get stuck, it can be very useful for your team.
How are these features packaged in software tools?
After assessing the features and capabilities of a data quality tool, it’s important to understand how vendors commonly package these capabilities in their product and service offerings.
1. Stand-alone, self-service data quality tools
These tools have more or less the same features as mentioned above. They do not connect to other data sources in real-time, and so these tools are mostly used for batch processing (including data profiling, cleaning, standardizing, matching, and merging), and then loading the consolidated records back to the destination source.
Some additional benefits include:
- Quickest and safest way of consolidating data records.
- Easiest to fine-tune matching algorithms and merging rules depending on the current nature of data.
- Some of these tools come with specialized word dictionaries that allow finding exact words (for example, first, middle, and last names), and replacing misspelled or missing fields.
- Some tools also support scheduling data quality management tasks, and generating consolidated records at specified times.
- Especially helpful for consolidating email marketing lists, contacts, and customer records.
2. Data quality API or SDK
Some vendors expose necessary data quality functions through APIs or SDKs. This helps you to integrate all data quality management features in your existing applications in real-time or runtime.
This may require some additional efforts, but some benefits include:
- Useful while implementing custom flows (especially for data governance) that are important to your business requirements.
- Can potentially act as a data quality firewall for your data warehouse, where incoming data is tested for quality before entering.
3. Data quality embedded within data management tools
It is important to understand here that some vendors embed data quality features within centralized data management platforms so that everything is taken care of in the same data pipeline. Although this might seem like a very good approach, there are certain challenges to consider while choosing a data management plus data quality tool. For example, to design an end-to-end data management system with embedded data quality functions, you would have to conduct detailed planning and analysis as well as involve key stakeholders at every step of the process.
Such systems are often packaged as master data management solutions. The term ‘master data management’ refers to a collection of best practices for data management – that involves data integration, data quality, and data governance.
Depending on the purpose and use of an MDM, they can be packaged as operational (used in routine data operations) or analytical (used for analytics or business intelligence purposes).
4. Custom in-house solutions
Despite various data quality and master data management solutions present in the market, many businesses invest in developing an in-house solution for their custom data needs. Although this may sound very promising, businesses often end up wasting a great number of resources – time and money – in this process. The development of such a solution may be easier to implement, but it is almost impossible to maintain it over time.
To know more on this topic, you can read our whitepaper: Why in-house data quality projects fail.
Factors to consider while choosing a data quality tool
Now that we have seen the primary capabilities and features of a data quality solution, as well as how various vendors package them as tools, there are a few more factors that you should consider before making the final decision. These include:
1. Business requirements
Not every solution will fulfil all your requirements. The goal should be finding the tool that checks most boxes for you. Another helpful step is identifying your core data quality key performance indicators (KPIs). Data quality can mean something different for different organizations. Once you realize and identify your own definition of “data quality”, it will be easier to know which solution will best facilitate it and help you to introduce, maintain, and sustain data quality in your core data assets.
2. Time and budget
Adopting any technological solution requires time and budget investment. Some tools – especially the ones that cover end-to-end data management – need more time, consideration, pre-planning, and stakeholder involvement.
Furthermore, you can compare the prices and plans offered by various vendors to understand which tool best suits your budget.
3. Preferences of data quality team
This is the final and definitely a key decision-making point. Many people may generate data at your organization, but the responsibility of managing its quality may be assigned to your data quality team – that includes data analysts, data stewards, or data managers. For this reason, it is best to allow them to choose the tool that they need and will use in their day-to-day operations.
No matter how skilled your data quality team is, they will still struggle to sustain acceptable levels of data quality until they are provided with the right tools. This is where a data quality management tool can come in handy. An all-in-one, self-service tool that profiles data, performs various data cleansing activities, matches duplicates, and outputs a single source of truth can become a big differentiator in the performance of data stewards as well as data analysts.
DataMatch Enterprise is one such tool that facilitates data teams in rectifying data quality errors with speed and accuracy, and allows them to focus on more important tasks. Data quality teams can profile, clean, match, merge, and purge millions of records in a matter of minutes, and save a lot of time and effort that is usually wasted on such tasks.