Adapting Business Processes to the New Paradigm of Data Velocity


Big data moves faster, changes more rapidly, and has the potential to provide deep insights that weren’t previously possible. It makes sense, therefore, to adapt our business processes to handle big data and data velocity.

In this post, we’ll cover what to consider before making any changes. Let’s start by reviewing the basics of Big Data and Data Velocity. These terms are common in the data world but have less-than-clear definitions to the rest of us.

Understanding big data

The first step to understanding big data is understanding its components, which are divided into the five Vs. These are:

  • Value – To realize value for your stakeholders, you need to breakdown information silos and expand data consumption.
  • Veracity – You must continue to address the need for good data governance and information accuracy.
  • Variety – More types of data are available – and required – than ever before to drive a profitable business strategy.
  • Volume – With increasing digitization, channels and systems, the volume of data available to your enterprise has expanded exponentially.
  • Velocity – The pace at which data is generated and consumed across your business, has changed how you need to engage.

Understanding data velocity

While each component plays an important role in today’s data management and analytics strategies, data velocity is especially important. That’s because the speed of data growth has many implications for the planning, and security, of an enterprises’ data.

Among the technical challenges to data velocity include:


The vast amounts of data pouring into your enterprise can leave you vulnerable to cyberattacks. Typically, IT relies on firewalls to keep out harmful information. However, the presence of large volumes of data may allow harmful packets to sneak through. Furthermore, sinister users are encrypting malicious packets, making them harder to detect.

Data Management

Data velocity means that you need to anticipate future data storage needs. This not only includes the amount of data storage needed, but also deciding where to store it. Traditionally, enterprises choose to have their data stores in their own server areas. Yet they now have the choice of turning to cloud (i.e., third party) providers as well.

Turning to a new architecture

Addressing these challenges requires a change in the ways you set up your data architecture. To handle large amounts of data, your data architecture should include the following components: 

  • Data Capture  Your enterprise data architecture should be able to capture high-velocity data from all incoming sources. The architecture should be able to manage spikes in incoming data and guarantee message delivery.
  • Transformation   Your data architecture should be able to transform incoming data into a format acceptable by your enterprise. The architecture should also have excellent source connectivity to ensure incoming data can be combined with internal reference data. 
  • Storage & Analytics   Your new data solution must be able to accept millions of transactions per second. Data requests made by employees must also be handled with no disruption to other data traffic. Finally, analytics tools and dashboards should be able to receive new data updates in real-time.

Changing business processes

Changes in data architecture aren’t all you need to focus on. As with all technological changes that have come before, enterprises need to adapt their processes to fit the times. Among the areas to consider:

  • Cybersecurity   Review your current cybersecurity plan and current resources. Do your IT and cybersecurity employees needing additional assistance? 
  • Data Resource Planning   Take the time to evaluate your company’s current data storage capabilities. How much additional space will you need over the next few years? Will you house this additional storage inside your own server space or turn to third-party providers? 
  • Data Sharing Policies   How will data be shared internally and externally? Before, data requests were typically handled on a case-by-case basis. Today, larger datasets and the increase in users who need data require protocol and procedures be put in place.

The new data paradigm

Big data—and data velocity, in particular—present new challenges. As a result, you need to reconsider your data architecture, policies, and security measures. In other words, high-level changes to business processes are becoming a requirement for success. 

Big data can bring many benefits to enterprises, but not without preparation. No matter how in-depth an enterprise's analytics strategy is, reliable and up-to-date data is essential to achieving real insights that will move the needle.


See how Antuit can help you build an adaptive, modern framework that unlocks the hidden value in your data.

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