5 Different Ways Businesses Use Big Data
Blog: Oracle BPM
By Michael Chen
Can your business use big data?
Yes, absolutely.
How you use big data depends on a number of things. Big data is all about insight. The sheer volume of numbers and metrics provides enough scope and scale to present a clear picture about, well, whatever it’s applied to. Processes, customer behavior, logistical issues—all of these can be identified, drilled down, and segmented with big data. Then, when coupled with tools like analytics and machine learning, your business has all the capabilities for data-driven decisions that elevate and accelerate your goals.
So what can you do with all that? Let’s take a look at five very different real-world scenarios. The following examples show how flexible—and powerful—big data can be, in practically any situation.
Health Care: Simplifying Logistics
As medical records become electronic, the ability for big data to streamline processes extends to both health care management and patients. On the management side, big data can reveal many critical variables that affect staffing and logistics. For example, it’s a given that cold and flu season will necessitate more patient visits, but identifying variables such as weather, proximity to holiday travel, percentage of patients having received flu shots, and other such individual factors can provide a bigger picture.
The result allows health care facilities to appropriately manage everything from staff size to time allocated for booking appointments to stocking flu shots and other seasonal needs. This ultimately benefits patients, and they have more transparency and accessibility into fulfilling their needs. At the same time, big data allows an organization’s data scientists to develop models for things like patient reminders or identifying who is at risk (or could benefit from) new medical research.
Banking: Minimizing Fraud
Fraudulent activity is the nemesis of the banking industry. When fraud happens, it takes up valuable time and resources from all parties—the victims, the bank’s staff, and the location that processed the fraudulent purchase. It also damages trust, which is perhaps the most important element of banking. The longer fraud goes undetected, the more people get hurt and the more resources get drained. Big data, however, is the most significant innovation in fraud prevention in decades.
For the banking industry, big data means countless bytes of information—transactions, metrics, payments, etc.—that provide details into user behavior. At scale, this is a blueprint for how money is used. When coupled with machine learning and analytics, patterns can be identified, and as machine learning increases this ability, anomalies become much easier to spot. This allows banks to catch fraudulent behavior as soon as it starts, minimizing the chance that it can spread and damage more accounts.
Manufacturing: Identifying Bottlenecks
The manufacturing process has many moving parts in a workflow, from parts procurement to final quality control. Each of those steps comes with numerous variables: for example, procurement may stall with vendor inventory problems or shipping delays; and assembly may have an issue with tool or machine failure. By applying digitally tracked metrics to all of these steps and taking in large volumes of records, big data can act as the foundation to identify potential sources of bottlenecks.
This can work both directly and indirectly. As an example of direct improvement, big data can show if a certain inventory provider is consistently late in shipping or the source of quality failure. In this case, big data can be the flag that leads to an eventual vendor change. As an example of indirect improvement, big data can help procurement teams identify ways to maximize vendor discounts, thus freeing up budget to be applied at other levels (e.g., new assembly machines, or more quality control staff).
Software: Identifying User Behavior
When software is released, be it a video game or workplace application, the development team’s goal is to have all of its features properly and regularly used. This, of course, isn’t always the case. But the how and why of feature usage can be explained using big data. Big data metrics can collect data that identifies which features are used, not just activated, and how long users remained engaged. It can also tell you whether any bugs or failures were triggered, and what else was activated.
Analytics tools can then break this data down into more isolated segments to create definitive looks. For example, perhaps a crash bug always happens within a piece of software’s feature, but only when another feature is concurrently activated. Big data collects situational metrics to build a roadmap for future iteration, whether that’s to fix buggy features or obsolete them due to lack of user interest.
Government: Optimizing Resources
All branches of government deal with massive amounts of data. Stereotypical jokes about government bureaucracy have a certain level of truth, but in the digital world, all that paperwork has gone online. This actually turns a negative into a positive: all that paperwork laid the foundation for the metrics to be tracked in the digital space. With big data, suddenly that information is dynamic and fluid, and in many cases, it’s much more accurate as clerical errors become minimized.
This leads to an overhaul of resource usage in many ways. Big data can lead to the development of automated processes, which optimize human resources to more appropriate uses. Big data can also provide insight into things like traffic patterns and utility usage, identifying problems and creating a path to infrastructure improvement.
Big Data: The Future of Everything
The above five examples stem from vastly different businesses and industries, but they all have one thing in common: they show how data can identify problems in almost any circumstance. As device technology and data communications evolve, the volume of data is continuously growing, and that means that big data will only get bigger. At the same time, the power of analytics tools and machine learning/artificial intelligence is growing as well.
Thus, the amount of connectivity in our world is only going to increase, and the importance of big data for any organization in any industry is only going to grow more significant. The lesson? Regardless of what you do or how you do it, there’s a way to integrate big data into your processes and workflows. In fact, doing so isn’t just a good idea; it’s probably the best idea to future-proof your business. Because if you’re not integrating big data into your organization, chances are your competition is already way ahead of you.
Michael Chen is an Oracle senior manager of product marketing.
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