You have finally reached your breaking point.
You’ve read too many emails. You’ve tested too many products. You’ve looked at too many spreadsheets.
You have reached data overload.
The team at Software Advice recognized that this was such an issue that they collated some information around the topic in a convenient infographic:
- To store the data we produce every day, we would need to erect a tower of hard drives more than four times the height of the Burj Khalifa, the world’s tallest building.
- Ninety-three percent of IT professionals are overwhelmed by how much data there is to process, manage, and analyze.
- As businesses generate more revenue, the more the need for data analysis grows.
- About 1 in 10 people rely solely on spreadsheets or paper files.
These are staggering numbers.
What Causes Data Overload?
Practically any digital object generates data.
Three sources of data include sensors, electronic health records, and logistics information.
As sensors multiply on the manufacturing line, they create more data. In fact, Gartner predicts that by 2020, more than 21 billion devices will be connected.
Electronic health records (EHRs) include health data on each patient. The size and scope of each EHR may vary based on the hospital’s specific uses.
Finally, logistics information creates data. Think of the last time you tracked a package for something you ordered. You likely saw information about your order’s current location, the time it came in, and the time it left the post. More data will transpire as ecommerce continues to grow.
How Do I Solve Data Overload?
In general, there are two solutions for overcoming this challenge.
The first is to hire a data analyst.
Jobs for data analysts are on the rise, according to the Bureau of Labor Statistics. This is likely due to organizations being overwhelmed by the flood of data they generate.
Data analysts manage data collection and provide context for data. This allows leadership to make key decisions. Nearly two-thirds of respondents to a 2013 Deloitte survey said that analytics plays and important role in driving business strategy.
The second way to combat data overload is to invest in analytics software.
Analytics software, among other features, often provide predictive analytics. This means that, based on the input data, the tool can make predictions about what’s to come.
Manufacturing companies have used predictive analytics to better forecast demand and improve their ordering process. Healthcare systems have analyzed care paths to get patients out of the hospital more quickly and safely. Retail companies have improved the customer experience—when customers veered away from their typical purchasing tendencies, retailers can send a nurturing email. As a result, one online retailer saw a 2-percent lift on its business. All of these are examples of predictive analytics in action.
Analysis doesn’t have to lead to paralysis. Avoid data overload by investing in data analysts and analytics software.