Big Data vs Fast Data
Solving Problems With Big Data
Solve Growth, the fine purveyor of this blog, is an agile growth marketing agency with expertise in Fast Data. But what exactly is Fast Data? There has been a lot of press associated with Big Data in the past few years, but not much has been written about its counterpart, Fast Data. In this post, I’ll lay out both and offer up pros and cons so that you can make the best decision on how to implement each in your business.
Solving Problems With Big Data
Big Data has rapidly grown in prominence with large enterprise organizations. Big Data refers to a suite of technologies associated with analyzing large, unstructured datasets. Big Data benefits organizations with large collections of historical data spread across numerous databases. Historically, it requires experience with a collection of technologies known as Hadoop (which requires a previous knowledge of Java). Recently, though, Apache Spark has emerged as an alternative to Hadoop. In either instance, technical expertise in database architecture for both Hadoop and Spark is required, which is a steep learning curve.
- Useful for analyzing large amounts of unstructured data across multiple databases
- Assumes historical data trends hold true in current market conditions
- Extracted insights can be implemented top down from management
- Typically incremental benefits that can be executed at any time
- Requires an expensive team of highly experienced professionals
- Team familiar with complex ETL processes and integrating legacy data sources
- Familiarity with Hadoop and Java is more common among more experienced professionals
- Technology suites are provided by enterprise software companies and integrators
- More expensive
- Requires a longer lock-in duration
Solving Problems With Fast Data
Fast data refers to real-time analytics and rapid implementation of insights. Technology suites for Fast Data analytics teams vary greatly and change often. Collected data typically comes from structured data with a data collection process that is pre-planned. It requires continuous testing, collecting, and integration of real-time or recent data. Datasets analyzed are smaller relative to Big Data analytics. As a result, analytics does not require a large database architecture team.
- Data driving decision making comes from real-time or recently collected data
- Useful in environments where recent data is more accurate than historical data
- Requires team to know what to collect and why in advance
- Fast data analytic tools and suites are typically cheaper as a result of greater competition
- Requires a team to stay up to date on a larger variety of technology
- Requires a team with authority and trust to implement insights in real-time
- Less compatible with top-down management
- Greater levels of accountability for mid-level and junior professionals
Fast Data Usefulness
Solve Growth has found that two groups of clients have outsized benefits from Fast Data:
- New companies gain the most since they lack the historical data to justify Big Data teams and techniques
- Established companies that are offering new products or entering new markets also reap large benefits from Fast Data since there is no historical context for product/market fit
The insight integration timeline for Fast Data processes is measured at a scale of days and weeks as opposed to months and years for Big Data. Due to this, Solve Growth specializes in Fast Data techniques and utilizes an agile process to drive our client results.
But, we increasingly look forward to the days when client data can be both big…and fast.