The 3 Most Important Factors for Real-time Data Processing

Our previous blog on building an enterprise-wide data strategy highlighted the need to prioritize users over data. Understanding and communicating the goals, plus seeking input from users, are critical steps to achieving a successful enterprise-wide data strategy.

What about coping with the infusion of data? Large amounts of data can either overwhelm or run away from your users as it sits in data warehouses, swims in data lakes, or runs through process-dependent business applications. 

The time-to-market demand to address business challenges is critical in establishing real-time or near-time data processing. However, addressing these challenges requires buy-in from management, including investment and commitment to the people, processes, and technology needed to implement a successful real-time data integration.

About this Article

This article describes the three key pathways to reaching real-time data processing and integration goals:

• Abandoning traditional data-analysis processes
  
  
• Establishing resiliency by decoupling your data
  
  
• Shortening development time with data profiling, data/process modeling

Real-Time Data Integration Defined

Real-time data integration is the processing and moving of data as soon as it is collected. Real-time is frequently referred to as "near real-time" because it's not actually instantaneous. However, it only takes seconds for the data to transfer, transform, and be analyzed.

The Impact of Time-to-Market Demand

Most businesses need to provide information for analysis in real-time rather than at a point in time (a.k.a. delayed). The need is for the speed of the data. Decision-making must be done quickly for businesses to be agile and on top of the ever-changing market landscape. 

Here are a few examples of how real-time data is implemented:

• Manufacturers deploy technology to collect machine data, more generally categorized as IoT data (Internet of Things). That data must funnel to the users immediately to enable decision-makers to monitor and troubleshoot manufacturing equipment and processes as they happen.
  
  
• CRM/ERP data can be leveraged in near real-time through data pipelines to facilitate more timely analytics and reporting.
  
  
• Healthcare data must be routed in real-time to support both the operational and analytical reporting needs of patients, administrators, and providers.

Pathway 1

Don't Rely on Traditional Methodologies and Protocols, such as ETL

Traditionally, ETL (extract, transform, load) is done in batches overnight with a built-in lag time to the user (one day, one week, etc.) based on business processes. Users bring the data into the system for automated processes or intuitive business decisions. Thus, ETL is based on a point in time and the data is typically processed sequentially. 

On the other hand, real-time data integration:

• employs a constant flow of data (i.e., not just in overnight batches)
  
  
• has multiple threads where critical processes proceed independently
  
  
• employs a purpose-built processing approach 
  
  
• focuses on the application logic vs. the processing framework
  
  
• is up and running 24/7 
  
  
• handles the elasticity of the various data streams 
  
  
• employs the real-time approach of parallel processing* and execution

*Note: With real-time parallel processing, a new pipeline is opened when an established threshold is reached based on the amount of data being moved around or changed. The data is then shunted to portals and processes already built into the real-time data integration system. 

So, the framework for real-time data integration involves constantly processing data through a pipeline. In the pipeline, the data is simultaneously enhanced, cleansed, and standardized into a format/layout/info content planned and set in motion well in advance.

Pathway #2
Establish Resiliency by Building a Decoupled Data Architecture

Data resilience and decoupled data architecture result in a single process that can feed the next step, but there isn't dependency between the steps. For example, the step that's doing the data cleansing isn't dependent on some other prior process. Each stage has a gate and is linked but is not tightly coupled. If one part of the process fails because of a data glitch, the rest do not stop. They can continue to run. 

So, resiliency and decoupled data architecture in real-time data integration make the system run more smoothly. In addition, ongoing monitoring with the human touch can ensure that throughput is optimized across the framework.

Pathway #3
Save Development Time with Thorough Data Profiling, Data Modeling, and Process Modeling

Your data needs to be profiled and modeled. So do your processes before you dive into the deep end of the real-time data processing stream. This means iterating through the requirements like (but with an accelerated version) ETL. With more planning ahead of time, less testing time is needed during development.

Definition of Data Profiling 

Tech Target defines data profiling as "the process of examining, analyzing, reviewing, and summarizing data sets to gain insight into the quality of data." Data quality measures how complete, accurate, consistent, timely, and accessible the data is. 

Data profiling examines, analyzes, and creates summaries of data. Among other things, data profiling can smoke out costly errors common in databases—null values and irrelevant information. 

So, the GIGO (garbage-in-garbage-out) rule applies. You need to go back to basics and examine the condition of the data you are bringing in, evaluate it, and fix problems before injecting it into your data stream.

The Goals of Data/Process Modeling

Data and process modeling provides a blueprint of a software system with the data elements it contains. Modeling includes definitions of data and diagrams to demonstrate the data flow. It is, in essence, a flow chart that helps business and IT teams document requirements and discover problems before the first line of code is written.

Modeling asks and answers the following questions:

• What is the target data store going to look like? 
• How will you store it once you land somewhere?
• What are all the components that are needed? 
• What will the data look like at each component?
• What does that component need to accomplish?

Summary

With the advancement of networks and the proliferation of IoT, the current generation of real-time data has grown at an unprecedented rate. As a result, organizations are collecting more data than ever before. 

To keep up, organizations must start processing data in real-time rather than days or weeks behind. There's simply too much data, and the enterprise won't be able to catch up with it if they rely on outmoded data processing methods. The data integration approach must be in real-time to take advantage of every second of the working day.

Next Steps

Organizations must work with an experienced partner who has built data processing pipelines in the past. That expert partner is familiar with the technologies and can guide or mentor the project team through the implementation of real-time data integration establishment. 

So, whether you need to build a new real-time data integration strategy or an existing setup that's having trouble, you need a partner with real-world experience to leverage the available capabilities in the industry today (technology, approaches). 

Want to learn more? Take the first step by downloading the eBook, "The Executive's Guide to Building a Data Strategy That Leads to Business Growth & Innovation."