Why Fast Data Matters for Business Why Fast Data Matters for Business
This posting discusses the importance of fast data for business. Did you know that humans are creating more data now than... Why Fast Data Matters for Business

This posting discusses the importance of fast data for business.

Did you know that humans are creating more data now than they ever have in the history of the world? Of course you did. I’ve cited that knowledge tidbit in a few articles myself. And if that reminder does nothing but fill you with anxiety as a business owner, researcher, or developer, you aren’t alone.

There are so many negative statistics about data modernization and business—so many dour articles about how business isn’t ready for big data. You know this, and you know you’re supposed to be working on data somehow.

It’s time to pivot, however, from obsessing over your big data woes to something that might produce business value—fast data. Let’s take a look at the difference between big data and fast data and fast data for business is important.

[Related article: Practical Ways to Integrate Data Science Into Your Organization]

But First—What is Big Data?

Big data refers to the amount of data produced by our activities online and the devices we choose to connect to the grid. Humans are creating around 2.5 quintillion bytes of data per day, a staggering number we’ve only just begun to harness.

That data isn’t all relevant or usable. When your company decides to move to data-based decision making, cleaning, and processing data is where you’ll spend most of your time. It’s a giant dumping ground, and without the right pipeline, most of your data is unusable—so much anxiety.

Big Data Versus Fast Data

Fast Data is the real-time data produced from streamingthink event-driven applications or IoT devices. It promises accelerated access to data insights, giving companies a chance to make decisions in real-time. 

Fast data presents its own set of challenges. Where batch jobs may run for a short time to process large amounts of data, streaming processes could run for weeks or months. You’ll have to consider traffic spikes or network failures, for example, and systems have more time to succumb to security breaches.

Big data is at rest, but fast data is data in motion. If your organization is data-dependent, building applications on top of fast data could be a game-changer. Financial institutions, energy, and telco companies are some of the biggest recipients currently, but other organizations will soon be recipients of the 

Fast Data Applications

Fast data offers a lot of potential business value. First, it’s a requirement for hyper-personalized recommendation engines, as programs must process user data in real-time. It’s also necessary for IoT, creating loops of continuous intelligence and predictive analytics, including maintenance.

Businesses pushing towards analytics-based decision making will need to master the fast data cycle to help produce those continuous insights. Building applications on top of fast data operationalizes data by delivering continuous intelligence.

One of the biggest applications for fast data is building on top of streaming applications for continuous intelligence. Users demand that devices respond to deliver personalized experiences in real-time, and the only way to manage that kind of movement is through utilizing principles of fast data. 

Challenges When Utilizing Fast Data

Fast data doesn’t respond and doesn’t provide insights the way traditional big data did. You can’t pull a massive amount of information from your data lake and process it overnight to reach some conclusion. Fast data evolves and could take weeks or months to provide long term insights while still offering real-time capabilities in operation.

Fast data is tricky to characterize, so your team must be ready to work through the unique requirements of fast data first (hello, data engineers), and build a scalable pipeline for any models. Driving the architecture are questions about the data shape, output expectations, and process tolerance.

Data capture and scale are both continual challenges, especially for organizations without the budget for giant data science teams. Edge to core applications do provide a wealth of continuous intelligence, but building applications safely and securely on top of those processes requires a lot of upfront labor and continued maintenance. Try to scale, and it’s a challenge.

[Related article: 3 Signs Your Business is Ready for a Recommendation Engine]

Data Is Fast Before It’s Big

Companies have a vested interest in utilizing the principles of fast data to build responsive customer experiences and continuous intelligence loops. Data storage and analysis will evolve as we respond to the speed of data, but our challenges with big data aren’t going to go away any time soon.

We probably won’t see many standalone applications just yet because of budget and resource constraints. Still, we do already have the data-oriented frameworks to begin building out applications with the help of existing frameworks. Developers and data scientists could continue to focus on the business value of fast data while clearing the way for responsive applications in a competitive world. 

If you’re successful, you can drive scalable, responsive solutions to customers while remaining flexible and innovative. The agility in fast data provides you end-to-end data-intensive applications and dynamic operations.

Elizabeth Wallace, ODSC

Elizabeth is a Nashville-based freelance writer with a soft spot for startups. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain - clearly - what it is they do. Connect with her on LinkedIn here: https://www.linkedin.com/in/elizabethawallace/