Role of Data Analytics in Internet of Things (IoT)

Role Of Data Analytics In IoT - InfoCleanse

From the 80s to throughout the 90s, when people began experimenting with the thought of adding sensors along with intelligence to objects, no one could’ve guessed the impact IoT would go on to have on the future.

Whether it’s Artificial intelligence, supply chain management, or healthcare center – today, people can’t stop talking about IoT implementation in various businesses. It has truly become a bridge that connects our digital & physical universes.

Healthcare IoT Market Growth - InfoCleanse

According to 2019 estimates from Mordor Intelligence, the IoT market is currently expected to reach $6.1 billion in value by 2024 along with a CAGR of 31.8%. This clearly indicates how the global market highly favors IoT development.

What makes IoT even better is the potential it carries to perform much faster and one such component for growth is through data analytics. IoT and Data, no doubt, remain intrinsically linked.

The Big Picture – IoT meets Data!

If you have already become familiarized with IoT (Internet of Things) connected devices, you may be aware of how their relevancy and existence largely rely on the data they obtain.

But, the thing is, for the end-user, the raw data is not something they find valuable, instead, it is the digestible explanation found in the gathered information (i.e. data analytics).

As data achieved from IoT devices is deemed valuable for marketing if it is only subjected to analysis, this brings data analytics into the frame.

Types Of Data Analytics - InfoCleanse

By definition, Data Analytics or DA is a process used in examining both big & small datasets with data properties that are varying in nature for extracting meaningful conclusions along with actionable insights.

These conclusions typically exist in the form of patterns, trends, and statistics that assist business organizations in engaging proactively with data for implementing efficient decision-making processes.

Why IoT & Data Merging Matters?

What attracts consumers to an IoT solution is its ability to offer data in an absorbable and meaningful way. Anyone can print and hand off data sets, however, it requires time and effort from the user’s end to manually analyze that information and carve it into something workable.

Data analytics enables users to pick up the trends or patterns within the information gathered by their device with ease. Through the insight DA provides, it makes sure users are equipped with the right knowledge to confidently navigate an effective business/personal product decision.

Peter Sondergaard Quote On Data Analytics - InfoCleanse

Regarding IoT technology, consumers are more than prepared to invest upfront due to the probability of the solution paying for itself after some time. This is possible by pinpointing those areas with wasted resources or saving time & effort through task automation.

Once again, powerful & intelligent data analytics maintain a key role by providing them those metrics that are integral in making these realizations come true. In this, individuals and businesses carry significant power as they can be picky regarding data quality.

As IoT integration becomes much more present in our daily activities and life, DA remains imperative in assisting users to draw key insights.

How Merging Data Analytics & IoT Impacts Your Business

Data Analytics And IoT Merging Process - InfoCleanse

Data analytics can play a significant role in the growth & success of IoT applications and/or investments. Here’s how analytics tools can allow businesses to create an effective use for their datasets:

  • Volume – There are a massive collection of data sets IoT applications can utilize, hence, business organizations must manage and analyze these data to extract relevant patterns. Data analytics software can analyze these data sets (including real-time data) with ease and great efficiency.
  • Structure – IoT applications can often include data-sets that contain a varied structure such as semi-structured, structured, or unstructured data-sets and it may carry a significant difference in terms of data type and formats. With data analytics, business executives can analyze all of the varying data sets through automated tools & software.
  • Driving Revenue – Utilizing data in IoT investments allows businesses to gather customer insight like their choices and preferences. This leads to creating services and offers according to the customer expectation and demand and in turn, it improves profits and revenues earned by organizations.
  • Competitive Edge – In our current technological era, IoT is no doubt a buzzword and there are several IoT application developers & providers available in the market. As such, utilizing DA in IoT investments can allow businesses to offer enhanced services and thus, provide the capability for gaining a competitive edge.

Types of DA (Data Analytics) applied in IoT

Data Analytics Process - InfoCleanse

Data analytics come in different types to be utilized and applied in IoT investments for gaining advantages. Some of these include:

1. Streaming Analytics

This is a form of DA that is also referred to as event-stream processing. What it does is analyze large in-motion datasets and during this process, data streams in real-time are analyzed for detecting urgent situations along with immediate actions.

IoT applications that are based on air fleet tracking, financial transactions, traffic analysis and more can all benefit through this method.

2. Spatial Analytics

This is a DA method that’s utilized for analyzing geographic patterns for determining the spatial relationship that exists between physical objects. IoT applications that are location-based can benefit greatly through this method of data analytics. For instance, smart parking applications.

3. Time Series Analytics

As suggested by its name, time-series analytics is another form of DA that is established upon time-based data and it is analyzed for revealing associated trends & patterns. The IoT applications that can largely benefit from this method are health monitoring systems along with weather forecasting applications.

4. Prescriptive Analytics

This is a combined form of DA deriving from a combination of descriptive & predictive analysis. It is often applied for understanding the best actionable steps that can or should be taken during a specific situation.

With this type of data analytics, commercial IoT applications are more likely to gain and utilize it for drawing better conclusions.

Final Thoughts

Advanced data analytics no longer remains a fancy add-on, instead, it proves to be quite integral in any IoT solution as it provides users with enough knowledge for making smarter decisions and pointing out certain issues.

It is safe to say, IoT is sustained through the capability & power of data. Despite how valuable pure quantitative data may be, there’s far more power involved in how data is categorized and the type of insights users can draw from it.