How to Introduce Quality in Data?

Quality represents the positive aspect of anything. In terms of data, it refers to the accuracy, consistency, completeness, and relevancy resonating the goal.

However, it’s challenging to name any specific example. Multiple examples of data breach surface the headlines in every few days. Recalling the incident of Cambridge Analytics in 2018, it was charged for harvesting data from millions of Facebook users without their consent. Nor did it take consent before using it for political advertising. It proved a big scandal that levied fine on Facebook. 

The incidents like this created urgent need for managing data and its quality. That’s why almost every company tries to meticulously handle the quality of data in the present scenario. This post will help you to discover some simple ways to integrate quality in data so that sensitive records can be handled with care.

Data Quality Platform

The very first is data quality platform, which can be a software solution that is developed to handle, maintain, and enhance the quality of stored data. These platforms have multiple in-built tools and functionalities that can identify, audit, refine, track, and validate the stored records with laser-fast speed.

These software (like Microsoft Azure, Amazon Web Services or AWS, Talend, etc.) automate multiple sub-processes that are included in the data handling process. This is how many organizations become able to minimize errors, streamline work-cycles, and manage records effectively.

Overall, the software is standalone solutions that can manage multiple tasks like data integration, segmentation, verification, standardization, etc. so that accurate and realistic business intelligence can be drawn. That properly managed data may have structured and unstructured data entries. With these platforms, organizations can employ servers or cloud to handle it. Certainly, it would depend on organizational needs, budget, and preferences.  

Benefits a Data Quality Platform Brings

Well, the reasons to require such platforms are many. The first and foremost is the accuracy of records that strategists can trust and rely for making strategies. Moreover, there are many more reasons, which are compelling:

 

• Better decision-making

People make decisions every day, but only a few prove a milestone and realistically working. If the data is clean, it represents high quality, which is the main ingredient of making better and realistic decisions to improve productivity, performance, and efficiency. Employing any quality platform for data can prove great help for decision-makers who can use clean data, while becoming fearless in making decisions. It simply because the risk of disastrous mistakes and missed opportunities would be minimal.

 

• Increased efficiency

The second reason is improved efficiency. It is obvious that manual efforts won’t match the efficiency of automated tools. Put simply, software has the capacity to stringently follow quality protocols (which are fed in the form of functionalities). On the flip side, a data cleansing executive may make mistakes in verifying entries, removing types, etc. Overall, the software has cutting edge in managing data quality during data profiling, cleansing, monitoring, and validation. It takes minimal time and effort to fix mistakes and inconsistencies, no matter how massive the database is. This is how data engineers, analysts, and scientists receive proper support from software in handling errors, and they focus on value-additions and innovation.

 

• Improved compliance

The next thing is regulatory compliance. It is a significant concern for many businesses and industries that have a pool of sensitive datasets like healthcare, telecommunications, and finance. The software like AWS and Talend can effortlessly introduce accuracy, consistency, and security. These are some strong pillars of compliance, which offload the burden of non-compliance and associated penalties.

·       Enhanced customer experience

Customer experience comes first for every organization or entity. Interestingly, the data-driven digital world provides multiple applications or software that can help in drawing customers’ data. This data consists of customers’ preferences, needs, intent, and behaviour. Its analysis can guide on what customers expect and how to make them happy by living up to their expectations. This is how customers can be satisfied and loyalty can be nurtured.

·       Increased revenue

Revenue is the return on investment, which is something that every business works for. The error-free data can help you get insights into their needs, expectations, realities, gaps, trends, etc. Its analysis will guide to pass through hurdles and discover potential opportunities for overwhelming revenues. These platforms can help in automating the cleansing process to increase the likelihood of possibilities.

Key Features of Data Quality Platforms

Data profiling

Data profiling can be considered the technical process of discovering the structure, content, relationship, and quality of datasets so that effective decisions can be made. The quality management software comes with robust data profiling capabilities. These capacities allow users to tap and filter them on the basis of identified patterns and anomalies. This is how the quality is audited. These tools can be user-friendly and valuable, which enable you to get into insights without wasting many precious hours.

Data cleansing

Typically, data cleansing is also called data cleaning and scrubbing. This process helps in exfoliating data by identifying and fixing errors, inconsistencies, and other flaws. The cleansing tools have incredible features working the backend for validation, standardization, deduplication and enrichment. The data cleansing services providers utilize these features to define the flexibility to customize solutions, which create scope to integrate desirable rules and criteria. Also, the user becomes able to automate cleansing process and ensure quality over time.

Data monitoring and validation

Data monitoring and validation are essential to verify the seamlessness of data. The cleansing software also come with monitoring features, which measure the quality metrics and indicators. They facilitate user to automatically get informed about potential risks and requirements. Also, the predefined rules and criteria can be modified for customised validation. These settings enable orgnizations define their own protocols to measure quality, route alerts, and integrate validation rules as per requirements.

Error detection and root cause analysis

Error detection is a proactive feature that helps in identifying the root causes swiftly and eliminating errors. A cleansing software should have such features that can detect them and highlight the sources of those inaccuracies. This potential of determining root causes enable users to investigate and understand what the problem is and how to resolve them. If it is not resolved, it should be able to avoid its recurrences in the future. 

 

Integration with data sources

This is crucial because the tool without data can never work. The cleansing tools have integrated features that allow easy and on-going handling of data and systems after integration. After integration, the organizations find it easy to handle data and ensure quality of broader data management throughout the data cycle. This cannot become achievable unless you have pre-built connectors, APIs, and other features that let in data from various systems and manage effectively.

Conclusion

The quality is an important aspect of data management, which cleansing process makes easier. Certainly, multiple platforms like AWS and Talend software are available that can help in automatically executing cleansing subsets, and integrate refined data with analysis tools. This is how various organizations ensure quality and results shortly.