Big data analytics examples: In this blog we’re going to discuss some common big data analytics examples and how they can have an impact on your day-to-day business operations.
Big data analytics has been a familiar concept in digital transformation for years now, but there are still many businesses that fail to make the most of big data and its business impacts.
Forrester reports that between 60% and 73% of all data within an enterprise goes unused for analytics.
From marketers to project managers, organizations are increasingly seeing the importance of collecting data from all aspects of a business to help guide their operations, and that is reflected in ERPs now being one of the most in-demand applications for SMBs to adopt.
Industry leaders can use big data for a variety of purposes such as cost reduction, more efficient business processes, and the ability to better judge the needs of the customer.
Since 2017, at least 53% of companies have leveraged big data to make informed decisions—and that number is growing. Developments such as robotic process automation (RPA) are helping fuel this rise in big data, making it easier to sort through and process vast amounts of data.
Now, to remain competitive, analytics needs to play a significant role in the operations of a modern SMB.
We’ll be taking a look at the business case for big data analytics and how big data analytics can be used for competitive advantage—here are three different ways businesses can leverage big data, and how these big data analytics can have key impacts on the business processes.
1. Big Data Analytics Examples In IT
Big data analytics can be used for competitive advantage by supporting a robust IT infrastructure, which is vital to enhancing the efficiency of an organization while also ensuring cost savings and security.
So what exactly do we mean by this and how does business analytics contribute to business value?
Analytics support the creation and deployment of a more robust IT infrastructure by giving professionals the tools they need to stay on top of everything. In particular, IT leverages analytics in two primary ways:
Analytics shed insight into network performance for things such as traffic, speeds, uptime and downtime, user habits, and even the printing environment.
Using the data collected from this monitoring, IT professionals can help understand the movement of traffic across a network, and managers can tweak processes as needed to encourage efficiency.
This is done by a software engine assessing data from a variety of sources, like connected devices, servers, and the flow of traffic.
Network analytics helps your IT team spot bottlenecks early, check the health of devices on the network, and fixing issues as they arise.
From an operational standpoint, the network analytics we’re talking about is automated and compared against how your network should be performing. If, during analysis, your network is found to deviate from operating at optimal capacity, the information fed to your IT team helps them discover what issues are slowing you down and how to remediate them.
In other words, the use of network analytics allows you to ensure that your operations are running smoothly at all times, catching network performance issues in real-time and keeping costly downtime to a minimum. This is a good example of big data analytics which is frequently deployed by SMBs today.
Cyberattacks are increasing—some 95% of IT decision makers believe they are susceptible to external threats. Analytics are most frequently deployed to study the behavior of breaches in order to predict the next one.
It’s historically been incredibly difficult to predict a cyberattack.
However, according to the IDC, big data may be just the key that the industry needs in order to provide analysis and shed light on best practices for avoiding attacks.
Data can be analyzed and used to determine, for example, when users are most frequently working in order to understand what unusual activity might warrant an alert to be checked; a login attempt at a strange time in this case.
This is done by analyzing big data sets, both current and historical, and using machine learning to help the system understand patterns and trends.
The more data your business is able to analyze, the stronger your defense. Through big data analysis, your security solution can build a clear picture of what’s “normal” in your business—who logs on when, who has access to what information, and data handling behavior.
This makes it a lot more difficult for cybercriminals to target businesses that utilize big data analytics, as any deviation from predicted patterns in the business network will be flagged and tracked by IT.
This is a common technique that is used in threat-hunting cybersecurity solutions which you’ll find in many MSSP offerings.
2. Big Data Analytics and Marketing
Analytics first arose in marketing as companies began to uncover how to entice customers best to respond to their advertising efforts—through value propositions and calls-to-action.
Since then, analytics have proven useful in marketing for several reasons. Big data analytics can be used for a competitive advantage by:
- Helping companies get a better sense of market segments and potential audiences
- Providing more in-depth insight into customer behavior and preferences
- Experimenting with new products and better marketing approaches
- Revealing the best strategies for augmenting the user experience
- Making A/B testing easier
- Assisting with the optimization of pricing strategies
With markets and consumer preferences so rapidly changing, it’s critical to be constantly testing new ideas. Analytics make the entire process easier by providing pointed clues into what works and what doesn’t.
For example, big data analytics can help provide information on what particular customers are most interested in, and that information can then be used to target them with more specificity in your email campaigns.
If you receive promotional emails from e-commerce sites recommending certain products, you can be assured that they’ve made a judgment on your tastes using data about you that’s been compiled for them through an ERP.