Big data processing techniques to streamline analytics
Analyzing raw data has become a complex task that companies are often underprepared for as data sets continue to grow. Here's how to create smarter data processing techniques.
Data is often referred to as the new oil -- the fuel that drives our industries and creates new billionaires. But data has several advantages over oil because it's cheap, easy to transport, infinitely durable and reusable. Data also has far more applications, and its profitability explains why Uber's market value is higher than traditional carmakers and why Airbnb gets more clients every month than Hyatt.
However, data needs processing and refinement, just like oil. When dealing with personal data, transactional data, web data and sensor data, implementing useful big data processing techniques has been a tough job even for computers. Companies used to have to take a representative sample of the data for analytical purposes, but this process changed with the evolution of big data analytics.
Problems when data becomes 'big'
Market research firm IDC forecasts that by 2025, the "global datasphere" will grow to 163 zettabytes. Analyzing data in more than a million records would require special techniques as is, let alone trying to analyze data sets more than 163 billion times that.
Simply adding more memory and hardware to existing systems only provides temporary solutions -- and at an extensive cost. Unlike oil, data is never consumed. Instead, more data needs to be stored every day.
"Modern cars have 60 to 100 sensors and those companies managing fleets of vehicles have to deal with terabytes of data being generated every second. … The cost becomes prohibitive when you deal with petabytes of data," said Felix Sanchez Garcia, lead data scientist at U.K.-based GeoSpock Ltd.
Addressing big data processing techniques requires innovative algorithms and programming, rather than simply adding hardware power. A solution widely used is indexing and partitioning the data to provide better access. GeoSpock's infin8 uses data indexing to process and organize data for subsecond data retrieval by ingesting and processing raw data at any scale, then creating an organized index that preserves every record of the original data set.
Making the algorithms smarter has another interesting effect, too, allowing companies to reliably harvest data from images, video and audio that opens the door to new generations of applications that can "look and hear." These advancements let machines scan footage and tag the objects or people they detect. It can also be used as part of companies' intelligence-gathering arsenal.
Getting most out of big data
Quite often, companies spend significant resources in collecting data hoping that they will be useful in the future, only to realize when that time comes that missing elements or data quality issues render those data sets unfit for purpose.
Felix Sanchez Garcialead data scientist, GeoSpock
Artificial intelligence provides big benefits in this realm. Advancements in artificial intelligence require large amounts of data to operate properly, and these AI tools provide a better view of the data to see what parts of the data set are more useful and which parts have less value that can be deprioritized. Thus, we can query against what the AI has learned is most beneficial for analytics purposes, instead of the full data set.
Another highly efficient and necessary big data processing technique is visualization. Visualization is the core of big data analytics as it aggregates data in meaningful ways that allow underlying patterns to surface. This data proves invaluable when answering questions about sales performance and the effectiveness of targeted advertisements.
Visualization can also determine if any important data is missing early on in the process.
"Quite often, companies spend significant resources in collecting data hoping that they will be useful in the future, only to realize when that time comes that missing elements or data quality issues render those data sets unfit for purpose," Sanchez Garcia explained. "Interestingly, one positive side effect of GDPR is that companies are being forced to perform a data inventory and think about what they hope to do with their data."
Making informed decisions cuts off wasted resources and efforts, while honing focus on how to automate as much of the data collection process as we can. While recent failures -- especially in the self-driving car industry -- cast doubts on AI's capabilities, the underlying big data structure has a stronghold. Whether it's used to train machine learning algorithms or help humans make better decisions, knowing what data to collect, where to collect it from and how to store and process it allows us to extract the most value from big data processing techniques.
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