Data is now being generated at an astounding rate. While it took from the dawn of civilization to 2003 to produce 5 exabytes of information, we now produce that same volume in just two days! Billions of connected devices—ranging from PCs and smartphones to sensor contrivances such as RFID readers and traffic cams—are now contributing to this flood of structured and unstructured data.
This flood of Big Data is bringing disruptive changes in the payments industry. Daily operations produce massive numbers of real-time transactions, and each transaction record contains a large amount of historical data. The huge volumes and dimensionality of business data make it difficult to run real-time analytics using standalone processors; to manage huge storage resources, and to accept and process data from different sources and different formats. Advanced analytics can help firms sift this data to find the actionable information needed for business success.
The Mixed Blessings of Big Data
Big Data technology makes it possible to process and analyze large amounts of data and to track spending patterns day to day, which help identify opportunities for business growth. While Big Data is enabling faster payment operations, it also contributes to the volume and types of data the business must manage.
If real-time analytics is performed on big data using standalone processors, then the potential of big data will be unrealized. Parallelized, multi-core processors can be used to provide better performance, but these resources significantly increase IT investment.
Using cloud technology for Big Data deployments lowers the IT investment costs. It allows businesses to scale up or down quickly by adding or removing resources as needed — paying only for the capacity used. The cloud has made it very easy to implement Big Data applications, which deliver excellent performance and unbelievable speed. The challenge lies in analyzing the related operational data for business intelligence.
Technologies for Structured and Unstructured Data
The explosion of data from payments systems is in both structured and unstructured data formats. NoSQL databases such as MongoDB® can accept data from different sources and provides faster performance than the alternatives. In its latest version (3.0), this database software is compatible with the WiredTiger open source storage engine, which excels at processing read-and-insert workloads as well as more complex update workloads.
The WiredTiger product features document-level locking functionality and excellent data compression, facilitating even faster operations of the MongoDB. The data compression function can be used to conserve disk space, an important capability in managing Big Data volumes. Either of its two compression algorithms — zlib and snappy — can be used, as per the organization’s data-storage requirement.
Apache Spark™ And Apache Tez™ are trending technologies which enhances the optimization of Big Data for payment platforms. Apache Spark is an open source, general data processing framework in the Apache Hadoop ecosystem that make it easy to develop fast, end-to-end Big Data applications combining batch, streaming, and interactive analytics on all the data. It comes with an in-memory processing engine that provides an SQL interface on top of NoSQL databases. This interface enables the execution of SQL JOIN functions for MongoDB and the generation of real-time analytic reports.
Apache Tez™ is an extensible framework for building high-performance batch- and interactive-data processing applications, coordinated by the YARN function in Apache Hadoop (aka MapReduce 2.0 or MRv2). It improves the MapReduce paradigm by dramatically improving its speed, while maintaining MapReduce’s ability to scale to petabytes (PB) of data. Apache Tez also provides “fit-to-purpose” freedom to create highly optimized, data-processing applications that offer an advantage over end-user-facing engines such as MapReduce and Apache Spark. Its customizable execution architecture allows users to create complex computations as dataflow graphs, permitting dynamic performance optimizations based on real information about the data and the resources required to process it. It also provides highly improved performance for executing queries — 100X faster than Apache Hive™ on MapReduce — and a highly scalable SQL interface that can execute queries scaling from TB to PB.
Impose Structure on the Unstructured
Unstructured data is ubiquitous. In fact, today most individuals and organizations conduct their lives, as well as their business activities, by processing unstructured data — with their minds and the aid of their smart devices. As with structured data, unstructured data can be generated by machines or people. Some examples of machine-generated, unstructured data include satellite images, scientific sensors, photography, and video recordings. People — more specifically customers and employees — contribute text documents and email messages, convivial media (i.e. contributions to social media channels such as Facebook, Twitter, and LinkedIn), mobile-device data, and ad-hoc Website content (e.g. uploads to YouTube, Flickr, or Instagram).
To assist data managers, companies can overlay, or impose, structure on these unstructured data sources. The following is a step-by-step approach to that end.
Step 1 – Relegate unstructured data. Most corporate data environments are pretty chaotic. Word documents, email, PDFs, spread sheets, and other data files are scattered across the enterprise. The good news is that most unstructured data is, or can be converted to, machine readable forms that can be read, indexed, compressed, and stored fairly easily. Organizing unstructured data is the first step before parsing and utilizing data for visualization implements.
Step 2 – Impose storage policies. Most data has a shelf life. Incipient data is frequently accessed during its first 90 days of life and utilization gradually diminishes after that. Because of these utilization trends, data should be routinely examined for dates — the most recent utilization — and then discarded or archived as per the data-retirement policies enforced by the IT organization.
Step 3 – Evaluate your business intelligence infrastructure and adjust as needed. Before organizations begin analyzing unstructured data, it’s important to evaluate the current business intelligence (BI) infrastructure that’s in place and how it all fits together. It’s not always easy to establish structure for and definitions of this non-traditional data. The data management team should identify the steps that are needed to integrate unstructured data into a structured BI environment.
Step 4 – Don’t overlook metadata. Making effective use of unstructured data requires an approach to organizing and cataloguing content. In order to utilize the content, it is necessary to contextualize the content. Some systems automatically capture process-aware metadata, or key attributes such as the source date, author, denomination, etc. However, applying metadata to authentic content such as content summaries, companies or people mentioned, or topic keywords can be considerably more labor-intensive.
Step 5 – Procure and configure technologies and applications for unstructured data analysis. Specialized data analysis technology can be used to analyze unstructured data as well as to structure a data model that business intelligence applications can process, as described previously. With the advent of Big Data, storing information in a data lake in its native format has become more important. It preserves metadata and anything else that might support analysis.
Mining Unstructured Data for Intelligence
Unstructured data analysis can commence by utilizing a natural-language engine to quantify keyword density. This approach, along with the utilization of metadata, can enable data scientists and decision makers to get at the heart of what business stakeholders want: insight into the origins of positive or negative comments about the company, its products and services.
Before you begin, ask yourself what sources of data are consequential for your analysis. If the information being analyzed is only tangentially related to the topic at hand, cast it aside. Instead, utilize only sources that are absolutely pertinent. The following, subsequent steps will aid in accomplishing your objectives.
Step 1 – Specify the goal of the analysis. Carefully define the questions you want answered. Do you need numbers, the identification of an emerging trend, or something else? The analyzed result should produce the predictive information about business or some part of business information.
Step 2 – Configure the analytic-technology applications. Evaluate your technology stack given the goal, objectives and related requirements. Then set up the project’s information architecture. Factors paramount to culling data storage and retrieval often depend on scalability, volume, variety and company-policy prerequisites.
Step 3 – Determine authentic-time access. Authentic-time access has become especially consequential for e-commerce companies so they can provide authentic-time quotes. This requires tracking authentic-time activities and providing offerings predicated on the results of a predictive analytic engine. It’s withal crucial for ingesting gregarious media information. The technology platform you employ must ascertain that no data becomes “disoriented” in an authentic-time stream.
Step 4 – Protect and prepare source data for processing. Protect the source file by creating a replica. Then, prepare an input file by auto-cleaning “noise” such as white/blank spaces and non-ASCII characters; in other words, transforming informal “social-media shorthand” text to formal text. Through analysis, you can establish relationships among the sources and extracted entities so that you can design a structured database to meet project specifications. This can take time, but the insights may be worth it.
Through natural language processing and semantic analysis, you can utilize components-of-verbalization tagging to extract designated entities, such as “person,” “organization,” “location,” and their relationships. Then you can build a term- frequency matrix to recognize the word pattern and flow in the text.
Step 5 – Statistical modeling and execution. Once you have prepared the database, relegate and segment the data. Supervised and unsupervised machine-learning tools — such as K-betokens, Logistic Regression, Naïve Bayes and Support Vector Machine algorithms — can save time. Utilize these to find related attributes to determine customer attitudes, targets for a future campaign and overall source-document classification. You can determine customers’ disposition with sentiment analysis of reviews and feedback. This intelligence can support future product recommendations, guide the design of new products and improvements, and identify overall market trends.
Step 6 – Analyze key customer topics. The key topics customers write about can be analyzed with temporal modeling techniques that extract the topics or events customers share via social media, feedback forms and any other accessible channel or platform.
Step 7 – Communicate — and facilitate feedback on — the findings. Provide answers to the questions that were the subject of the analysis in tabular and graphical formats. To ensure that the information generated by the analytics process is easy to understand and that the intended audience can utilize it, provide a single point of access where the report can be reviewed securely via mobile or Internet connections. That way, the users can make recommendations in authentic-time, or on a near authentic-time substratum.
Traditional business intelligence software works best on structured data, not on the unstructured data that is rapidly becoming the dominant component of the Big Data, which payment firms are attempting to manage and mine for actionable intelligence. To find meaningful relationships and patterns within and between these two types of data, IT organizations are turning to combinations of Big Data and predictive-analytics technologies. The right application of these technologies can unearth patterns from astronomically immense amounts of data — structured and unstructured. The analysis of these patterns can provide the intelligence that business executives need to plan for the future, as well as to improve operations for current market realities.