Introduction to Data Quality Tools
In the Digital Era, the availability of big data facilitates new generation Industries to design novel business models and automate their business operations. It also helps them in inventing new technological solutions which in turn generate new business opportunities. Big data is generated from multiple sources like sensors, machines, social media, Web sites, and e-commerce portals. Data is the new oil and it is an asset to any organization and there are attempts to monetize the data. There are bound to be variations and inconsistencies in the data collected from so many heterogeneous sources. There should be a mechanism to correct the anomalies either at the source or post collection and to ensure high data quality. Unless the data is accurate, the insights we get from it will have no sense and we will not be able to make any meaningful comparisons and draw inferences from it.
In this article let us study Data Quality tools and their features.
What is Data Quality Tool?
Before examining the data quality tool let’s dwell on data quality and understand its importance.
Data Quality
The success of any organization depends on the quality of the data collected, stored, and used for deriving insights and the quality data forms the core part of any business and it is in the bottom layer in the information hierarchy. The information layer, Analytics layer (knowledge), Insights (wisdom) layers are on top of the data layer in the respective order mentioned (refer Fig-1).
Data quality can be defined as a characteristic that makes the data fit for its intended use and it can also be defined as the characteristic that makes the data to represent the true picture it is supposed to project. The above two definitions are in total contrast as the first one insists on the completion of the day-to-day transaction and the other one aims to achieve the end-to-end purpose for which the attributes are designed.
For example, Employee master in payroll contains so many attributes, few of them are mandatory for calculating the monthly payment. If all such fields are present correctly that will be sufficient to run payroll and this will meet the first definition of data quality. For doing manpower planning, skill planning, dynamic work allocation, and effective utilization of manpower most of the attributes should have the right quality of data and this will meet the second definition of data quality.
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Importance of Data Quality
- Accurate data produces accurate analysis & dependable results avoid wastages and enhances the productivity and profitability of the Organization.
- Reliable data provides an edge to the business in fighting competitive markets.
- It facilitates the system to be compliant with all local and international regulations.
- Companywide digital transformation and cost-saving programs can be implemented with adequate data backup.
Steps to Improve Data Quality
- Have the right mix of People, Process and Technology with adequate support from top management is the first step to improve data quality.
- Install a system to measure and improve a set of Quality Dimensions like
- Uniqueness, b. Precision, c. Conformity, d. Consistency, e. Completeness, f. Timeliness, g. Relevance.
- Data accuracy, Data validity, Data Integrity are the other aspects of good data quality management.
- There should always a single source for Data and we should avoid getting it from multiple resources.
Data Quality Tools
Any DQ tool typically does data cleansing, data integration, Managing master data, and Metadata by adopting the guidelines in the various disciplines of DQM as given below.
- Data Governance
- Data Matching
- Data Profiling
- Data Quality Monitoring and Reporting
- Master Data Management (MDM)
- Customer and Product Data Management
- Data Asset Management
Apart from cleansing the data as it is getting created, these DQ tools suggest processes and procedures generate quality data at the source.
The ways of selecting right DQ Tools are:
- Enumerate the data issues an organization is facing.
- Clearly understand the features of the various DQ tools in the market.
- Study the pros and cons of using shortlisted DQ tools.
- Choose the right one.
DQ Tools & Features
Below are the various DQ tools and their features:
| DQ Tool | Key Features | Value to the users |
| Informatica Quality Data MDM solutions | Data Standardization, deduplication, validation, consolidation, and robust MDM solution. | MDM supports structured and unstructured data. AI features enabled. |
| SAS Data Management | Data Integration and Cleansing. Uses Data governance and metadata management disciplines of DQ management. | unstructured data. AI features Graphical interfaces and powerful wizard for effective data management |
| Experian Aperture Data Studio | Data discovery and profiling, Data monitoring, Data cleansing. Works with any type of data. | Easy to use DQ management tool. Workflow designer enables easy data quality monitoring. |
| IBM InfoSphere Quality Stage | Data cleansing and Data management. Data profiling helps deep analysis of data. | Machine learning-enabled delivers high data accuracy. |
| Cloudingo | Data integrity and cleansing. Removes duplicates and human errors. | Used extensively in Salesforce. It has a drag and drop interface. |
| Talend Data Quality | Data Standardization, Deduplication, Validation. | Uses ML features to maintain clean data. |
| Data ladder | Data Cleansing. Uses data matching, deduplication techniques for cleansing. | Very high data accuracy. Manages multiple databases, big data. |
| SAP Data Services | Data integration, transformation and Master data Management. Uses text analysis, auditing, and data profiling techniques. | Handles data from multiple sources and provide a reliable data for analytics. |
| OpenRefine | Data Cleansing including big data. | Open-source tool. Supports multiple languages. |
Advantages
Data Quality tool enhances the accuracy of the data a. while it is generated at the source, b. as it is getting extracted before storage, c. transformation post its storage. Its main benefits are
- Builds confidence in the business to venture into transformation exercise.
- Scales up revenue, profits, new business, and productivity for the business.
- Reduces wastages, saves cost, shrinks time to market, and makes business agile.
- Makes business digital-ready and builds a vibrant brand.
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