Getting More from Analytics by Emphasizing 'Quality' over 'Quantity' in Data Logging

IT suppliers and data management managers are experiencing a major pain point with efficient data logging management. The availability of NoSQL open source software has enabled enterprises to collect large volumes of data from different sources, and software companies have implemented “call back home” features that allow their software to send information to data collection centers within various parameters, creating additional run time configurations and data traffic. And as the Internet of Things and a “connected everything” approach to businesses become increasingly popular, more and more data will flow in and out of data management systems, leaving IT managers muddled with millions of pieces of data they must properly manage and store.

Embarking on these projects of data logging management often leads to disappointing levels of success as most of the data is a complicated and tangled web of information. And with the influx of data from various connected “things,” IT managers find themselves frustrated with the limitations of making accurate calculations and predictive analytics of IT efficiency due to the sheer quantity of data.

Instead of writing off predictive analytics products as failures, businesses should improve their data collection processes, focusing on the quality of the data rather than the quantity. Not only does this improve the process, it allows IT managers to make room for more solutions that can generate real-time insights from large amounts of data coming from connected devices. This ultimately provides more useful, qualitative analytics and insight to improve business efficiency.

There are several critical factors to consider in this situation:

  • Scaling quality data when logging information within the Internet of Things: Upgrading existing software to collect missing information is an expensive operation and not an easy one. Sometimes, this can be because of a scaling problem, such as upgrading software for products that are out on the field, or due to customers who do not have the bandwidth to upgrade their software at a particular moment. These impediments delay data collection and incorporation of analytics into the business progress.
  • Building proper solutions into applications and connected devices using an adaptive logging framework: Offering IT managers an adaptive logging framework is critical to managing an Internet of Things environment. This allows information to be controlled remotely and managed properly using a central server to make accurate analytics updates. Not only does it reduce data volume and bandwidth requirements, but it also improves data quality instead of quantity and allows for remote control data logging.
  • Better data leads to better insights: While this factor may seem obvious, the discipline is much more difficult to achieve without striking a perfect balance between data quality and quantity. In practice, it is essential to leverage both quality and quantity synergistically to gain and retain greater profitability and competitive advantage. Using quality data leads to better predictions, and better predictions yield better business decisions.

The Internet of Things will continue to shift the way businesses run and manage their back-end organization. Without an emphasis on better data management as well as quality transactions and a more organized, analytical approach, businesses ultimately risk financial gains.