3 Big Data Analytics Examples That Can Help Your Business
Big data analytics has been a familiar concept in digital transformation for years now, but there are still many businesses that fail to utilize the concept as fully as they could.
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 the most in-demand application for SMBs.
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. Here are three different ways businesses can leverage big data, and how analytics can improve business processes.
1. The Importance of Big Data Analytics in IT
A robust IT infrastructure is vital to enhancing the efficiency of an organization while also ensuring cost savings and security.
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.
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 which 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.
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:
- Help companies get a better sense of market segments and potential audiences
- Provide more in-depth insight into customer behavior and preferences
- Experiment with new products and better marketing approaches
- Reveal the best strategies for augmenting the user experience
- Make A/B testing easier
- Assist 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 you certain products, you can be assured that they’ve made a judgement on your tastes using data about you that’s been complied for them through an ERP.
3. Analytics with Employees
In addition to finding what works for customers, analytics can also provide insights into the best strategies for encouraging productivity in the workplace.
More and more businesses are using analytics to identify the best way to drive employees to work more efficiently.
- Sorting resumes and cover letters during the hiring process
- Analyzing video interviews to assess a candidate’s personality
- Spotting patterns of behavior in employees and departments
- Tracking the real-time effects of training and employee coaching
- Identifying areas of payroll leakage or poor hourly time management
- Collecting performance data for employee energy, wellbeing, and pain points
- Ranking employees by quality and reliability
In other words, analytics within the workplace helps companies gain a much better sense of exactly how their employees work, and how to support them to drive productivity to the next level.
For example, if your data insights are showing you that your customer support team is spending an inordinate amount of time answering the same customer queries again and again, you could create an FAQ section on your website that answers those repeat questions.
Better still, you could implement a chatbot, which you can program to answer these queries in real-time for customers.
The end result is that staff are freed up and able to spend their time on tasks that need a human touch.
The same can be applied to virtually any environment; even the warehouse floor.
If analysis determines that workers are following an inefficient process, you can now see this in your insights, and work to rectify it, whether through a change in policy, or maybe even a custom app which addresses a specific workplace bottleneck.
The point here is that data analytics helps uncover working processes that were previously an invisible drain on your operations.
With this increased visibility, decision makers have actionable insights which they can use to effect change.
How Analytics, Big Data, and Business Works Together
At its core, the use of big data to drive decision making is meant to make businesses more cost-effective, efficient, and competitive in their market.
SMBs are the most likely to leverage data and analytics software to gain a competitive edge.
When done correctly, analytics and big data work together to provide valuable business intelligence on your processes and provide you with new opportunities.
In IT and cybersecurity, data analytics helps companies stay ahead of threats to keep their customer, employee, and company information safe, a particularly important consideration for today’s cybersecurity environment.
In marketing, big data allows companies to go straight for what works, leaving guesswork out of the equation and allowing businesses to nurture leads and customers with more precision.
Finally, internally, the use of big data helps rid companies of dated processes which may be contributing to poor efficiency in business operations.
This is especially the case with manual processes, many of which can be alleviated through employing automation solutions.
Every business should be using big data to identify critical metrics, potential problems, and insights on their customers.
These analytics assist in moving a business forward by providing essential insights across the breadth of a company.
From IT to human resources, big data is becoming increasingly vital for companies to make informed, cogent decisions to drive productivity and profitability.
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