5 Data Analytics Trends that Appear Overhyped

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Big data has revolutionized how organizations do business. Data analytics has revealed so much about business productivity, customer behavior, even recruiting and staffing information. Data-driven decision making is critical to staying competitive in a fast moving and rapidly evolving market. But while some trends are becoming more important and more influential for companies looking to use their data to make smarter decisions, some data analytics trends seem to have had their time in the sun.

1. Hadoop

Apache Hadoop was the place to store and process big data for several years. But now, its multi-layer modular format has quickly become overly complicated, particularly as more businesses start to explore how big data can help them grow their business. With the wide variety of related vendors and projects, involved in the analysis of data stored and processed in Hadoop, simplicity seems far out of reach for the enterprise customer. Many of those customers may soon find themselves migrating to simpler and more user-friendly experiences.

2. IoT

The Internet of Things (IoT) may well be the most overly-hyped technology, ever. The excitement around interconnected and digitally linked devices has been palpable, for both consumers and industries at large, but it is not without its challenges. In fact, IoT is a huge perpetrator of lost digital security, on a global scale. That lost security is a big part of why we think IoT may not be as wonderful as everyone has been thinking.

As consumers and companies rush to adopt and benefit from the promises of a more interconnected network of devices, everything from smart cities, smart grids, the industrial internet, connected vehicles, connected medical devices, retail, agriculture, and even personal wearables become vulnerable to cyber-attack. We are only just realizing just how troubling this lack of security is as hackers take advantage of the lag in security technology.

3. Batch Analysis

Real-time data is the way of the future, and running batch jobs overnight to analyze data is what we did in the 1970s. Given our growing functionality and capacity around data analysis, there is no longer a strong rationale for the reliance on slow, legacy systems.

4. Caffe

The deep learning project known as Caffe started out as a framework for image classification, but lately seems to be slowing down in terms of its actual effectiveness. The deep learning community has moved on to greener pastures as Caffe models often need excessive amounts of GPU memory, with buggy software and a lack of quality documentation.

5. Monthly BI Reports

As self-service business intelligence becomes more popular, BI reports produced by IT specialists are becoming less effective for businesses looking to quickly leverage their data to make critical decisions. Businesses that want to be agile can’t wait an entire month for actionable insights. For that reason alone, monthly BI reporting is quickly becoming a thing of the past.

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