This article was published as a part of the Data Science Blogathon.
Introduction
Businesses have always sought the perfect tools to improve their processes and optimize their assets. The need to maximize company efficiency and profitability has led the world to leverage data as a powerful tool. Data is reusable, everywhere, replicable, easily transferable, and has exponential benefits for the business. It can provide useful business insights on customer lifecycle, anomaly or issue detection, real-time data analysis, etc. However, even if data could be a fantastic tool, it is limited if you can extract and interpret the knowledge from the information.
The question now relies on how to process, understand data, and infer useful insights more efficiently and acceleratedly.
This article looks into Big Data and how it develops into Smart Data. Additionally, we will look into the concept of Smart Data and its benefits for businesses.
Source: shapelets.io
What is Big Data?
Five main characteristics often describe big data: volume, value, veracity, velocity, and variety, aka the five V’s. Many experts also consider an additional one: variability. All these attributes compose what we know as “Big Data.” Each of them is key for understanding and analysis of the data.
This concept is not new for companies, as they collect a great volume of information that increases daily. As I understand it, we collect and analyze large amounts of data to obtain actionable insights businesses use to enhance their processes. This is why Big Data is so important for any industry sector.
Did you know that it is estimated that the volume of data generated worldwide will exceed 180 zettabytes in 2025? According to Seagate’s report, that same year, 6 billion consumers, or 75% of the world population, will interact every day with data, and each connected person will have at least one data interaction every 18 seconds. In other words, the volume and the velocity of information will force businesses to increase their data processing speed. Consequently, over the next few years, Big Data will continue to be a key support for strategic development, decision making, enhanced streamlining operation/ business operations, and customer relationships.
Nevertheless, the volume, value, veracity, velocity, and variety of information will force companies to focus on adapting and starting to use tools that help them process the data quicker and smarter. This is where the concept of “Smart Data” emerges.
What is Smart Data?
Smart Data tools help pre-process the data when ingested to reduce the time of the analysis. What makes “smart data” smart is that the data collection points are intelligent enough to understand the data immediately. Not all data provides the same value to companies; in this scenario, the quality of the information will prevail over the amount of stored data. For example, it allows a device sensor to output useful human-readable data before sending it to a database for storage and/or detailed analysis.
Consequently, Smart Data analytics is the natural evolution of Big Data that aims to treat volumes of data intelligently, as it allows companies to obtain, among others, the following key benefits:
Time-saving
The volume of information that companies ingest doesn’t have any value raw. It needs to be cleaned and then curated to extract any knowledge. By implementing smart software, the data stream or batch will already come partially curated, which could be extremely important when there is a time restriction. For example, a self-driving car can’t afford to wait for data to be sent to the cloud, analyzed, and sent it back. It requires the data to be gathered through a sensor considered “smart,” so the data can be immediately analyzed and then sent to actuators (all internally) who are going to take whatever decision is required at this moment.
It is a great opportunity for SMEs.
Variety of data is as important as the volume and the velocity because many different types of data are available; it can be challenging to treat it if the data quality is not nearly perfect. When creating a smart data strategy, businesses must be careful about the type and quality of data ingested. Bad data quality can cost 12% of the business revenue. Here, Smart Data helps to improve the quality of the information by pre-cleaning it.
For this reason, if small and medium-sized companies use a vast amount of data in a short period, implementing a smart data strategy will help them carefully select the data they are looking for and have a better quality of analysis.
Better customer or consumer service
Traditionally in analytics, the data was amassed, groomed, and then processed at a fixed time (during the week or the day). That workflow means that the data was already obsolete because of the time series analysis.
For example, when monitoring patient health in the healthcare industry, data must be analyzed quickly and use predictive analysis to determine if its life can be in danger. Anomalies detection combined with a decentralized automated data analysis can make it easier for businesses to find the most relevant information for each customer or consumer. Therefore, it would facilitate the hyper-personalization of customer service and improve it.
Prevent problems
Anomalies detection is critical in some industries, where time is an important asset. For example, detecting an anomaly in the supply chain is extremely useful in a factory and avoids an opportunity cost of millions of euros. To avoid this issue, smart data or edge computing use pre-analysis of the machinery to detect any anomalies and then send the information to the centralized cloud. It will help the company to plan when to do maintenance or determine if machinery has a loss of productivity.
A step toward automation
Tools that automate the collection and transformation of data are vital, and the need will only grow as you try to extract value from the ever-growing data volumes coming from an ever-increasing number of sources. Smart data is the tool that will allow you to automate your collection and let you focus on more important tasks.
Know the competition better
Intelligent data analysis allows companies to obtain information about the market, the sector in which they operate, and the competitive situation, providing them with useful tools to improve their position, such as price monitoring or change trends.
Conclusion
In short, Smart Data is a complementary value to Big Data, enabling it to make faster analyses with better data quality and automating data collection and processing. Smart data solutions and strategies will be time-saving thanks to their decentralized data collection and analysis. Additionally, it is an opportunity for SMEs because it will help to select and clean data for a better quality of analysis. It will improve customer service thanks to the hyper-personalization of clients’ data. It will help to detect anomalies before it even occurs, and its automation will let the business focus on more important tasks.
In this article, we dive into the concept of Smart Data and its value in the business sector and data science world. In essence, this feature covers the following:
– Concept of Big Data and why it’s important.
– Concept of Smart data.
– Key benefits of Smart Data in business and data science.
Remember: business intelligence is now key to development and success! Don’t wait, and start reshaping your world!
If you wish to understand more about the application of smart data, big data, or data science, I recommend you have a quick look at the following articles:
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