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Data Quality Powered by Big Data
Data Quality Powered by Big Data

August 2, 2022

AI

14

0

Enough has been said about the importance of data in enterprise. Data has the power to drive decisions, deliver actions, bring efficiency, and directly impact the bottom line. To realize the true potential of data, organizations need to make sure that their data is accurate, complete, concise, easily accessible, secured, and consumption ready. In the highly competitive environment today, companies don’t have the luxury of vetting through many spreadsheets and documents. Data-driven decisions must be timely to be effective.

Almost every organization has many sources of data inputs containing the same or different data attributes for the same entities. For example, information about customer entity can flow-in through web and mobile self-service, social media outlets, census and other government data sources, credit agencies, and log files, etc. Many times, information received for a unique customer is conflicting and a lot of times information about two different customers seems too familiar. These bitter-sweet problems are usually addressed by Master Data Management (MDM) software.

Traditional MDM software licenses are expensive for enterprises. Also, they have scalability issues for Big Data and cannot manage unstructured input sources like social feeds. To make the data work efficiently for enterprises, Big Data technology platforms come in handy to ensure optimal data quality with the value addition of automation and discovery of hidden opportunities within data.

From our years of experience working on various data platforms - ERPs and CRMs, we have developed a reference architecture to implement optimal and cost-effective Data Quality technology using open-source Big Data platforms for enterprises. The strength of our reference architecture lies in scalability and openness of these platforms. We can scale this architecture to work with smaller data sets or for Petabytes of data. Also, there are no limitations on input formats. Using open-source ingestion technologies, this implementation has the ability to ingest data from virtually any source in any format.

Technology

Hadoop: Hadoop core and ecosystem components are best suited for ensuring optimal data quality for the growing amount and complexity of data. It offers reliable, scalable, low-cost, and a high-speed storage and processing engine that is essential for data processing needs. Ingestion technologies like Flume and Sqoop enables Hadoop to collect data from virtually any source, including databases, cloud applications, social platforms, logs, documents, FTP, or any venue for electronic data input. Hadoop Distributed File System (HDFS) enables reliable and scalable storage of any form of data, mainly processing efficiency-based designs. MapReduce is Hadoop’s processing engine that delivers high-speed processing of data that is already stored in HDFS. These components are perfectly suited for collecting data from discrete sources, aggregating data, and standardizing.

Apache Spark: Spark is an in-memory computing framework designed to bring real-time factor to Big Data analytics. Spark excels at loading data in memory for complex data processing resulting in lightning-fast results of complex data exploration, sampling, mining, and analytics processes. SPARQL, which is the query language of Spark, is perfectly suited for ad-hoc data analysis. Also, Spark ships with the MLlib machine learning platform that enables organizations to build predictive models based on historical data.

Apache Solr: Solr is designed as a high-speed index and search engine around unstructured data. For data quality purposes, Solr can run matching and cleaning processes using fuzzy-matching algorithms. Depending on the business rules configured, Solr can automate duplicate identification and merging processes with no or minimal human intervention.

Apache Hue: Hue is a rich and interactive administrative and reporting dashboard used mainly for Hadoop. It offers monitoring, scripting, data exploration, and dashboard capabilities. Additionally, it can integrate Spark and Solr results as plugins to dashboards for centralized access to all data from various tools in this reference architecture. Depending on data quality needs of the organizations, we have configured Hue to optimize the power of data without reinventing the wheel. But in some cases, we have also developed custom user interfaces to interact with data using Node.js and Angular.js.

Data Quality

Based on our years of experience ensuring optimal data quality for large organizations, we have devised standard processes, components, and tools enabling our clients to get a head-start on automated data quality process. We bring our Big Data technology and data quality functional expertise together to ensure that data quality becomes an effortless but tremendously valuable tool for businesses.

Data Accuracy: In the world of discrete, best-of-breed applications, companies often deal with numerous data formats. Data standardization helps companies mine, explore, visualize dashboards, and monetize data with ease. Our aggregator adaptors can collect data from various source systems and execute real-time standardization of algorithms. Standardization is determined by our clients as it best suits them, but we can recommend industry standard formats based on our experience. In addition, we embed USPS address matching and cleansing, email address verification, change of address (NCOA) service, and individual demographics (based on public and credit data) and organization demographics (Duns & Bradstreet data) as part of our standardization process. These components allow us to run high-speed, weighted duplicate identification and merger of duplicate records near real-time using the Big Data technology stack.

Data Management: Our data management process enables focused structure on large amounts of structured and unstructured data from numerous source systems. Using our data management processes and tools, our clients can implement layers of security and enforce industry and government compliance requirements while making data available to the right people at right time. Also, our specialization in data modeling and change management enables clients to implement lightweight but efficient data governance. At the end of the day, technology is only a part of what ensures optimal data quality. Data management processes and tools are key in identifying data quality needs and solutions.

Data Discovery: Our data discovery tools allow companies to fill-in-the-blanks enabling them to see more dimensions of their historical and transactional data. We utilize fuzzy data generation and ML algorithms to generate additional data fields, unlocking the full potential hidden in existing data. We also utilize publicly available data sets (like census), credit files (with authorization), demographic information, and web crawlers to generate additional data fields. Data discovery always brings a positive surprise to large companies as they start discovering information they never knew they could.

Platform: Our reference architecture for data quality management using Big Data technologies, comprised of open-source platforms, fit into any enterprise technology footprint without disruption. Our experts specialize in extending, customizing, installing, configuring, administering, and implementing these tools for data quality needs. The entire architecture is designed to be flexible, scalable, high-speed, and cost-efficient. We also offer a managed service environment for this reference architecture in our private cloud offering.

 

This blog was originally posted by Jade Global at Data Quality Powered by Big Data (jadeglobal.com)


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