'Big Data' Is No Longer Enough: It's Now All About 'Fast Data'
Data is growing at a faster rate than ever before. By 2020, every person online will create roughly 1.7 megabytes of new data every second of every day, and that’s on top of the 44 zettabytes (or 44 trillion gigabytes) of data that will exist in the digital universe by that time.
Given this ever-increasing amount of available data -- and the fact that most of it is live in the moment -- the benefits of big data will be lost if the information isn’t processed quickly enough. Here’s where the concept of “fast data” steps up to the plate.
Processing data at these breakneck speeds requires two technologies: a system that can handle developments as quickly as they appear and a data warehouse capable of working through each item once it arrives. These velocity-oriented databases can support real-time analytics and complex decision-making in real time, while processing a relentless incoming data feed.
As complicated as this system seems, it’s an absolute must for anyone looking to compete, particularly in the enterprise space.
So much data, so little time
On Google alone, users perform more than 40,000 search queries every second. But when every second -- or millisecond -- can lead to mountains of lost data, each business needs a dedicated platform to capture and analyze data at these increasingly rapid speeds.
John Deere is a company taking full advantage of these models. All new John Deere tractors come equipped with sensors that both inform new product offerings and serve as a benefit to customers.
The data provides insights into the exact use of the equipment, while the technology helps diagnose and predict breakdowns. That means better products and better customer service. For consumers, the sensors offer access to data on where and when to plant crops, the best patterns for plowing, etc. It’s become an entirely new revenue stream for an old company.
How companies use big data to solve problems, test hypotheses and improve product offerings will vary by industry. Being on the very precipice of fast data, startups in the enterprise space must consider the following to get real value from their data:
1. Empower all employees through data.
Central business teams will no longer “own” software and be responsible for disseminating insights to the other departments; the time lag can hurt a business. Everyone within the organization needs access to that platform -- not only to analyze data, but to also gain insights specific to their individual roles.
Enterprise companies need to take data analysis one step further. This requires a contextual understanding of each person’s role at the company, offering tangible insights to improve job performance and efficiency through speedy updates and the streaming of initial analytics.
2. Leverage multiple data sources.
Across the world, 90 percent of all existing data developed within a period of just two years. And, whether it’s transactional data from POS terminals or sensor data from home appliances, the sources of data are predicted to keep increasing in the coming years. Startups utilizing big data need comprehensive coverage of all these data sources.
But it’s often difficult for companies to build these “integration pipes” on their own, so it’s important that they ally with partners or utilize public APIs. You can use either one as leverage to scale at the necessary rate needed to compete.
3. Use data proactively.
Big data isn’t just a guide for the inexperienced. It’s a tool for solving problems and testing hypotheses. Take a recurring business problem -- such as low sales during the third quarter -- and use the massive pool of data resources to determine the root cause of the concern.
Understanding the underlying data sets behind big data analysis is the key to utilizing the technology properly. While third-party vendors such as BrightFunnel are spending millions to teach their sales teams about ROI, they will never fully know the ins and outs of the client businesses they serve. Thus, it’s up to business leaders to understand how the dots connect across the raw data, and make those correlations themselves.
Big data is only as useful as its rate of analysis. Otherwise, businesses won’t gain access to the real-time suggestions and statistics necessary to make informed decisions with better outcomes. With fast data, information becomes more plentiful, more actionable and more beneficial to an organization.