Data Streaming To Becoming a Truly Data-Driven Company in '24

Businesses generate massive amounts of data every second. Each touch point such as social media, email, website, or employee interactions generates its own data as well. Touch points are important for understanding how customers interact with businesses, what channels they use to search and purchase products, etc. This allows companies to understand who is interacting with their products, where, and when.

However, it can be challenging for organizations to process, store, and analyze data that is continuously generated from multiple sources. In this research, we explore how data streaming can help with these challenges. 

What is Data Streaming?

Data streaming is a continuous flow of data with no beginning or end, such as activity logs from company web browsers. It is generated in a variety of forms and volumes by a variety of sources. Unlike traditional solutions, data streaming solutions help the process, store and analyze data in real-time or near real-time. 

Typically, streaming data is generated from website activity, monitoring systems, and sensors. For instance, real-estate websites monitor user activity and make property recommendations based on their geo-location by using dynamic location data.

3 main components of data streaming

1. Datastream management systems (DSMS): It is a computer software system used to manage data streams. It differs from a database management system in several ways:
   • Database management systems deal with persistent data, while data stream management systems deal with volatile data streams.
   • Unlike a data management system, DSMS performs continuous queries since data streams are continuous.
2. Messaging buffering: Data is gathered from various sources such as sensors, monitoring systems, and web browsers. All of them generate their own data streams. Message buffers collect data from the data aggregation system and store it temporarily.
3. Message Broker: It collects streaming data from multiple sources and translates it into a common format.

What is the importance of data streaming for businesses?

1. Process and analyze massive amounts of streaming data in real-time

Traditionally, companies ingest data from various sources, process it after downloading, and store it in a database usually in data warehouses or data lakes. Data is processed all at once within a specific time frame. 

However, the nature of data has changed with technological improvements such as IoT and sensors. These technologies generate stream data, and with stream processing technology the data is processed as it is generated. In-stream data processing, data is analyzed in real-time and continuously. There is no need to wait for data to be processed in batches since batch processing takes longer.

For instance, gaming companies monitor player interactions, collect customer streaming data, and process data in real-time. They use real-time data analysis to provide recommendations to their gamers based on their gaming activities. 

2. Respond to changing conditions or possible issues in real-time 

Data is processed and fed into an analytics system immediately as it is generated. With real-time data processing and analysis, businesses can detect changing business conditions quickly. Fast detection enables businesses to respond to potential problems before they materialize. 

As streaming data is generated in real-time, it can be difficult to access and analyze the data as it is produced. Keeping a record of data streams is useful for not missing new data and analyzing it later.

For example, the automotive industry is leveraging sensors in the production of self-driving as it provides real-time insights. Sensors generate stream data, and it provides real-time outcomes for businesses. 

If you have other questions about data streaming, let us know: