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