The Power Of Integrating IoT and Data Analytics

Marketing Team
Published on March 24, 2021

When you dive deeper into the realm of data analytics, you would realise that the landscape looks so much better and efficient with IoT. At the end of the day, analytics is all about assessing high-quality data and generating actionable insights. This implies, the larger the data inventory, the more accurate would be the insights. Now take a moment and list down the functionalities of devices connected by the Internet of Things. These devices collect information about customer behaviour from every source they are connected to thereby, making it easier for companies to derive valuable insights for smarter decision-making. Additionally, as IoT is based on the cloud technology, it conducts the process of gathering data from multiple points and sharing it in a more proactive way. This is why, collecting information and organizing them on a single platform for running analytics comes with so much potential for improving existing business processes.

Here’s Why IoT Devices Should Be the First Choice For Expanding Your Data Repository

As we have already mentioned before, when it comes to drawing patterns and comparing statistics it should be always based on large volumes and variety of information. Therefore, to meet these ends, here’s how IoT devices could help:

  • Collect both structured and unstructured data so that their values could be mined effectively.
  • Continuously add to the collection of data so that your organization can gain an edge with its marketing efforts in the industry.
  • Get a 360-degree view of the needs and preferences of your customers and the key factors driving sales and revenue.

Sorting Data With a Powerful Business Intelligence Tool

Now that the first stage of data analytics, that is, information gathering is broken down and achieved, it is time to move on to segregating useful data from the inaccurate shreds. Needless to say, when you have information flowing in from innumerable sources, it becomes all the more important to skim through it and only take the high-quality data forward. With IoT, the benefit is, the data that makes the lives of your consumers easier, can be capitalized on by your business to improve products/services and enterprise decisions based on their real-time versions.

This is where a powerful business intelligence tool equipped with the latest technologies could prove useful. A business intelligence tool like Microsoft Power BI can make your journey to perfecting data analytics smoother. The clear advantages of Power BI over similar tools are embedded in the facts that it can be integrated with the existing systems and business applications of the company, configured without extensive coding and allows customization of dashboards for sharing real-time data graphs and reports. Once embedded with key performance indicators (KPIs) depending upon the unique requirements of your business, the BI tool can sort credible data from their insignificant counterparts and prepare it for analytics. With some assistance from a trusted technology consultant, businesses can determine which BI tools and data storage solutions will work perfectly to provide a connected and streamline the entire data infrastructure.

Things To Consider When Using IoT For Data Analytics

Create a Clear Data Management Strategy

Understandably, IoT produces a lot of data but not all of it can be labelled as useful. Companies must outline clear strategies for managing their IoT data, resolving which of them can be eliminated and where it could be stored for easy access. One thing that must be remembered here is that the data must be consistent across all datasets and departments relying on them and compatible with the analytics tools. To be on the safer side, you can bring your enterprise data and the information collected by IoT together and make way for precise predictive analytics.

Automate Predictions and Analytics

Considering the amount of data that a company can utilize, thanks to IoT, automating analytics is imperative. The tools and solutions implemented in the analytics system should be able to gather and evaluate different forms of historical and real-time data. Once you have reliable sources for pulling up relevant data, configuring the BI and analytics tools with machine learning algorithms to improve business predictions and forecasting becomes a cakewalk!

Data Security

One of the most vital things to keep in mind here is that the integration of IoT data should be protected by strapping security. To ensure that the information hoarded by the IoT devices is reliable and not vulnerable to breaching threats, confirm their security levels by setting up a few checkpoints throughout the process of analytics.