Enough has been said about importance of data in enterprise. Data has the power to drive decisions, deliver actions, bring efficiency and directly impact the bottom line. To realize true potential of data, organizations need to make sure that their data is accurate, complete, concise, easily accessible, secured and consumption ready. In highly competitive environment today, companies don’t have 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 same or different data attributes for the same entities. For example, information about customer entity can flow-in through web & mobile self-service, social media outlets, census and other government data sources, credit agencies, log files etc. A lot of 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 handle unstructured input sources like social feeds. To make the data work efficiently for enterprises, big data technology platforms come handy in ensuring optimal data quality with 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 ability to ingest data from virtually any source in any format.
Hadoop: Hadoop core and ecosystem component are best suited for ensuring optimal data quality for the growing amount and complexity of data. It offers reliable, scalable, low-cost and high-speed storage and processing engine which 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 with mainly processing efficiency based design. 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 disrete sources, aggregating data and standardizing
Spark: Apache 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 MLib machine learning platform which enables organizations to build predictive models based on historical data.
Solr: Apache Solr is designed as high-speed index and search engine around unstructured data. For data quality purposes, Solr have ability to run matching and cleaning processes using fuzzy-matching algorithms. Depending on business rules configured, Solr can automate duplicate identification and merging process with no or minimal human intervention.
Hue: Apache Hue is rich and interactive administrative and reporting dashboard mainly for Hadoop. It offers monitoring, scripting, data exploration and dashboard capabilities. Also it can integrate Spark and Solr results as plugins to dashboard 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 power of data without reinventing the wheel. But in some cases we have also developed custom user interface to interact with data using Node.js and Angular.js.
Based on our years of experience ensuring optimal data quality for large organizations, we have devised standard processes, component 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 the business.
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, dashboard 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. Also we embed USPS address matching & cleansing, email address verification, change of address (NCOA) service, 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 big data technology stack.
Data Management: Our data management process enables business focused structure on large amount of structured and unstructured data from numerous source systems. Using our data management process and tools, our clients are able to implement layers of security and enforce industry and government compliance requirement while making data available to right people at right time. Also our specialization in data modeling and change management enables clients to implement light-weight 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 machine learning algorithms to generate additional data fields unlocking full potential hidden in existing data. We also utilize publicly available data sets (like census), credit files (with authorization), and demographic information and web crawlers to generate additional data fields. Data discovery always brings positive surprise to large companies as they start discovering the information they never knew they could.
Platform: Our reference architecture for data quality management using big data technologies, comprises of open source platform those fit right 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.
Read the blog: Content Management Using Hadoop
At Jade Global Inc., we specialize in data quality management using big data platforms. Our offerings include big data strategy, road-mapping, architecture, business case, implementation, technical support and managed services.
Feel free to send your questions, comments and inquiries to us via firstname.lastname@example.org.