Big Data is revolutionizing the way the world of business operates. From product creation to customer support, Big Data is empowering enterprises to make data-driven decisions, making the decision-making process more objective. In a recent survey ranking the benefits of Big Data, making better strategic decisions came out on top.
The business benefits derived from Big Data highlight the importance of data analysis. Aside from making better and faster decisions based on data, Big Data analytics enable enterprises to create products that customers want, as well as drive significant efficiencies in various business processes, most especially in data management.
In order to harness the power of Big Data, enterprises must first ensure the quality of their data. In this sense, Big Data depends on effective and efficient data management. However, data quality and data management can also be impacted by Big Data analytics and its corresponding tools. Effective data management allows businesses to build predictive models and accelerate and automate the decision-making process.
But before enterprises utilize Big Data analytics, they should first establish the metrics through which they can measure data quality. They should establish what makes data high quality and identify the different criteria that make for an effective and efficient data management system.
Data Quality Metrics
The success of data management can be measured by the quality of the business decisions and outcomes that can be derived from data. These business decisions and outcomes rely on the quality of data. Transitively, the success of data management can also be measured by the quality of data it produces.
An article published by the Data Science Journal established five key metrics of quality data:
1.) Availability. Availability means that data can be easily accessible across an enterprise through a data access interface. The timeliness of the collection, processing, and delivery of data also plays a part in its availability as it determines whether the required information will be in the hands of the intended user when he or she needs it.
2.) Usability. Usability pertains to the correctness of data. Data is usable when it is in an acceptable format or value and comes from credible sources such as specialized organizations or experts.
3.) Reliability. Quality data is accurate, consistent, and complete. Accuracy means that the data truly reflects the source and should not cause ambiguity. Consistency means that data is verifiable with other sources. Also, consistency ensures that the key attributes of data such as concept and format are still matching before and after processing. Completeness means that data is not missing significant components that will impact its integrity and accuracy.
4.) Relevance. Relevance pertains to the fitness of data with the users’ requirements such as the parameters established when retrieving data.
5.) Readability. Quality data should be understandable and clear with regard to its content and format. Readability also necessitates that data classification, description, and even coding are easy to understand and meet required specifications.
Big Data Analytics Tools That Impact Data Quality and Management
Many enterprises make the mistake of focusing entirely on master data management (MDM) to meet the key data quality metrics and ensure the reliability of their data. However, MDM is only one tool among many others that ensure not only the quality of data, but the quality of the entire enterprise data environment and lifecycle. There are other Big Data analytics tools that impact data management and quality:
Data Storage. Big Data is impossible without the capacity required to store and process it. A simple definition of Big Data is information that traditional enterprise systems can no longer contain, let alone handle. Data storage and its scalability should be among the first considerations of enterprises looking to harness Big Data.
Cleansing. Big Data comes from numerous and complex sources. Some data is structured like that coming from enterprise solutions such as SAP and Oracle, while other data is unstructured or semi-structured such as documents, metadata, and email correspondences. In order to ensure that data is of the highest quality, enterprises first have to clean their data or transform it into readable data sets using data cleansing tools. These tools modify data values to meet data integrity and quality requirements, and meet domain restrictions and other data rules to unify all types of data into one usable data set for multiple users. Cleansing may also involve data standardization and parsing which transforms values of data into consistent formats based on local and industry standards and user-defined rules.
Profiling. Successful data management demands overall visibility into an enterprise’s data environment. Profiling is a data analytics tool that captures metadata, delivers insights into data quality, and identifies potential data quality issues. Profiling aids in creating a comprehensive inventory of an enterprise’s data environment and enables it to fully manage and utilize their information assets.
Discovery. Successful data management and data quality management also require data access efficiency. Data discovery or data mining tools enable enterprises to quickly identify the information they need to make decisions. These data quality tools also enable enterprises to extract not only information from databases, but also actionable insights.
Mapping. Data mapping tools improve data management and ensure data quality by helping enterprises understand the flow of data within data environments and ecosystems. At the same time, these tools help identify potential data risks and leakages. Mapping also enables enterprises to identify and link related information across or within data sets, in a process called matching. This process further adds value to data by providing additional context to individual objects.
Analysis. This tool is at the heart of Big Data analytics. Analysis enables enterprises to break down data, identify patterns, measure the impact of those patterns, and create actionable insights from their data. Analysis helps achieve the main goal of data quality and management: utilize data effectively and efficiently to create insights and drive outcomes. Analysis may also involve not only data processing, but also machine learning features and predictive capabilities. These features and capabilities further accelerate the insight-creation process of Big Data analytics tools and, thus, enterprises’ decision-making processes.
Visualization. One of the challenges of Big Data analytics tools is communicating the results of analyses to professionals who don’t necessarily have backgrounds in data science. Visualization bridges the gap between incomprehensible analytics results to actionable insights for the enterprises’ non-IT employees. Visualization transforms data results from spreadsheets and SQL databases into user-friendly graphs and charts. Visualization aids data management in meeting the presentation metric of data quality.
Monitoring. This tool ensures enterprises’ ongoing compliance with data quality rules and standards. Monitoring enables consistency in data quality and data management efficiency over time by periodically running quality assurance tests and controls on the data environment. Some monitoring tools also empower enterprises to automate quality assurance processes, further enhancing data quality and management.
Together, these Big Data analytics tools significantly improve data management and data quality. The higher quality data then improves the reliability of the tools, resulting in a cycle of continuous improvement in data quality, data analytics tools, and, most importantly, business outcomes.
Go Beyond Data Management
Liaison’s enterprise data management solutions deliver more than just quality data to the enterprise. They offer customized data management which consolidates, cleanses, and enriches data coming from any number of disparate sources across any industry.
Our data management solutions are built upon the Big Data architecture of our proprietary ALLOY™ Platform, which natively supports the storage, integration, and syndication activities required to supply quality data to the enterprise. Contact our data experts to learn more about how Liaison can help you achieve improved data management and quality.