There are different data processing approaches when it comes to enterprise data management as the needs and priorities of each organization differ. Among these, batch data processing can be a very efficient and time-saving mode of processing vast data volumes. A group of transactions can be collectively done over a certain period. In batch processing, the data is gathered, stored, processed in batches, and the results are rolled out. The top-notch Hadoop tool is focused on a batch processing approach. Executing batch processing of data requires different programs for data input, processing, and output. You can see payroll and billing systems as a perfect example of the same.
On the other hand, real-time processing of data offers ongoing data input in real-time. The data must be provided at small time intervals, which is near real-time. This is a critical advantage for modern-day businesses as the market conditions, and consumer trends fluctuate now and then. For space research, radar stations, ATM devices, etc., real-time data processing is very important.
Even though most organizations still tend to adopt batch data processing model, sometimes it is necessary to use real-time data processing. Analytics and real-time data processing will let an organization take immediate action in response to emergencies and leverage opportunities as soon as this pop-up. Real-time processing aims to gain the needed insights to act promptly and prudently at the right time, which means ensuring on-line response.
Concept of CEP
CEP or complex event processing is the approach of combining data availed through various sources to derive the patterns and identify the possible threats or underlying opportunities. The ultimate goal of CEP is to respond quickly to significant events. Examples of such situations are sales leads, product or service orders from consumers, quick service calls, etc.
Operational Intelligence
Operational Intelligence or OI will use the CEO concepts in real-time data processing to better understand operations. It can do effective query-based analytics against stored data as well as live feeds. OI can also be a real-time analytics approach over the operational data, which offers better visibility across many data sources. Here, the goal is to get real-time analytical insights based on continuously flowing data and allow the organizations to take quick actions. Operational Intelligence is a subset of BI which offers a greater value to business processes. Incorporating operational intelligence to the database administration process can be consulted with expert providers like RemoteDBA.com.
Many organizations now build and use their customized OI systems for the retail chain operations and customer service processes in different industries. The overall return on investments from this is enhanced customer satisfaction and reduced churn rates. Operational Intelligence can also be used to identify and remediate problems before affecting the customers directly. It is one of the vital ways to take care of your business requirements.
Real-time operations intelligence in customer care can be used for optimizing the customer experience. Recommendation applications will help the agents get a better insight into the customers they are dealing with and offer a personalized service to them by knowing their needs. Business enterprises can more effectively collect data about their customers over the phone and how they previously interacted with the organization. OI’s objective is to assess the total customer experience and recommend the personalized scripts to guide an agent who attends to the customer for optimal customer interaction. This, when done well, can lead to more customer satisfaction, improved sales, better problem solving, etc.
More and more retail businesses are starting to use real-time Operational Intelligence to track their customers’ buying patterns and preferences to provide more personalized suggestions to them. A huge volume of historical data about customer activities can be analyzed and used to optimize the brand’s overall consumer experience. This will also offer a competitive advantage to even smaller businesses to improve their business. The real-time processing of data based on the POS systems also helps organizations update inventory, track inventory history and sales of any given item at specific times to allow an organization to manage real-time payments and planning.
The assembly lines in manufacturing or processing units can also use real-time processing to reduce time, effort, cost, and the number of errors. When a specific process is completed in an assembly line and moves on to the next phase, it is easier to determine whether there are any errors in the previous step and who to correct them in the subsequent steps using real-time data processing.
Another wider use of real-time OI is to monitor social media activities by allowing an organization to react to any negative activities like posts or tweets promptly. Doing this will help mitigate the negative impacts before snowball and turn into something potentially very damaging.
Some other examples of real-time operations intelligence are to take care of dynamic pricing, supply chain management in live scenarios, social analytics to facilitate dynamic upselling, and more successful brand and utility management.
Real-time computational systems
In a Hadoop-based data analytics environment, the task of offering real-time data analysis is an in-memory layer between CEO and Hadoop. The storm is a widely used near-real-time open-source distributed computation system, which can process live data streams. The storm will also assist in real-time analytics using online machine learning, real-time computation, distributed ETL, and RPC. MapReduce of Hadoop can also process the jobs in batches, whereas Storm focuses on processing data streams in real-time. In any case, the overall idea is to reoncile in real-time and doing batch processing when we deal with big data sets. Detection of any transaction-related fraud in banking in real-time while incorporating the data from a data warehouse or cloud clusters is another example.
Comparing batch processing and real-time data processing, both have many advantages and disadvantages based on the given use case. The choice of the apt data processing approach in the given project may largely depend on the type of data and the source of the same, and the processing time to get the job done.
