Streaming Analytics: What It Is and How It Works

Data continues to be one of the most sought-after attributes in today’s modern economy. Companies of all sizes are turning to data and analytics for answers to their business questions. But data is only as good as it is timely. Meaning stale data is just as dangerous as no data. 

Enter streaming analytics. This is where you bring in data as it’s created, creating a source of real-time information. As you evaluate how your company needs to utilize data, let’s look at the power you can harness from your data as you adopt a streaming analytics platform.

What is streaming analytics?

Streaming analytics refers to the continuous process of collecting, processing, and storing data records in real time or near real time. Data comes from source systems in small packets throughout the day. Oftentimes, this data needs to be cleaned and further processed in order to store it in a format that is helpful to the business.

How streaming analytics works

In the past, data was often collected in a single batch process. With a streaming analytics platform, your data is collected in continuous streams from various sources such as Internet of Things (IoT) sensors, social media, or transactional systems, and processing them in real-time. This is achieved through the use of specialized streaming analytics platforms and frameworks that can handle large volumes of data in motion. 

These platforms often employ different techniques from traditional analytics sources to help end users extract meaningful patterns and trends from the data. The insights derived from real-time streaming analytics can be used in a variety of ways, but mostly helping companies that need real-time monitoring of business operations. 

Streaming analytics vs traditional analytics

Streaming analytics is different from traditional analytics in that it focuses on using the data as soon as it is collected. Traditional approaches use batch processing, which lumps the data together into different time increments—sometimes once a day or every hour. Once the batch of data is processed, it can then be cleaned and processed for analytical use.

Streaming analytics platforms, on the other hand, gather, process, and clean data in a continuous or near-continuous cycle. This allows organizations to view data in near real time. 

While traditional analytics processes require less processing power because they’re processing data at consistent times regularly, companies that need data right as it’s created won’t be able to wait for the delay. Streaming analytics are critical for organizations that need real-time insights into how machines are performing, where products are, and what users are saying on social media platforms. 

Benefits of streaming analytics

Businesses of all shapes and sizes can benefit from streaming analytics. With more data being generated today than ever before, it’s important for an organization to be able to analyze and make decisions based upon it. Data streaming allows for this real-time analysis of data.

Employees who have access to data tend to perform better within their given job responsibilities. Streaming analytics takes it one step further by providing employees with real-time data, which will enable them that much more in their job functions.

Streaming analytics can benefit organizations of all types and every team within your company. In this article, we’ll talk about some common use cases for streaming analytics.

System integration

Streaming analytics can be used as a system integrator between the software that you’re already using in your organization. Software powers the modern economy. Oftentimes, even small businesses can have 20+ software vendors they work with.

Data streaming can be used to integrate these systems to help reduce the manual data entry performed by your employees. A common use case could be creating data streams between your email provider and other software such as Salesforce. By integrating these two systems, your sales reps will have a better picture of the communication that has taken place with their prospects, allowing them to sell more efficiently and effectively.

Alerting and messaging systems

Streaming analytics platforms can also be used to create custom alerting systems within your business. Integrating systems with messaging tools such as Slack, email, mobile apps, or text messages can help provide real-time insights.

A great example here is customer service representatives who multitask between phone calls and chat support. By integrating chat and call center software, the business can create an internal alert system that can notify the rep whenever a chat hasn’t been answered or a call not responded to. Managers can also take advantage of streaming analytics to analyze the current workload of their support teams, providing them with better information to make informed decisions.

Reporting and dashboarding

Streaming analytics can also be used to create real-time reports and dashboards for executives within the business. Stale data is the enemy of decision-making, and real-time data streams help combat this by providing easy access to all data within the organization.

Sales leaders can benefit from streaming analytics through the creation of dashboards that monitor the current sales pipeline. As sales reps close deals and interact with customers, these dashboards will update in real-time to paint a better picture of how different sales teams are performing.

Self-service analytics

With access to a streaming analytics platform, users are able to get the data they need, right when they need it. There are no more bottlenecks waiting for a batch to process or getting held up in IT. For organizations and team members that need to be agile and respond to events as they happen, a stream analytics platform gives users critical information they can quickly act on. 

Key considerations with streaming analytics

Being such a new technology, it’s important to understand what is required in order to get started with streaming analytics. New vendors are making it easier than ever before to get access to your data in real-time. Here are a few things to consider when starting a streaming analytics initiative:

Batch vs stream processing

Traditional data processing occurs in batches instead of streams. Many tools still rely on batches when it comes to handling your data. It’s important to partner with a vendor who understands the importance of streaming analytics and real-time data.

Refresh times

Data refresh times between systems can also differ. Software tools will have different protocols for data updates, and it’s important to understand how often the data refreshes. Some databases may only be set to update once per day or per hour. By understanding the refresh rates of your different systems, you can be better prepared to start a streaming analytics initiative.

Data source systems

Understanding which data is most important to you and where it is stored is also critical to streaming analytics. Write down a list of all the different software and data systems your company uses on a daily basis. Once you’ve identified these systems, it’s important to pick the most important ones to start with, as streaming analytics initiatives can take time and resources to implement.

Size, velocity, and complexity

Probably the biggest consideration is to understand how your data is structured in terms of size, complexity, and velocity.

Size refers to how much information you are collecting with your data and is usually measured in megabytes or gigabytes. Understanding the quantity of data will help you know how robust your streaming process will need to be.

Complexity refers to the structure of the data. Data can come in structured (such as data tables), semi-structured (such as videos and images), and unstructured (such as email or phone conversations).

Velocity refers to the speed at which data exits a source system and enters the streaming tool. Data held in a stream is often referred to as an event, and many systems can produce millions of events per day.

Streaming analytics examples and use cases

Consider some of the following examples and applications of how companies across different industries can utilize a streaming analytics platform. 

• E-commerce: Using real-time data from streaming analytics can transform how an e-commerce business understands customer data. Using a streaming analytics platform to look at near-real-time information on customer behavior enables businesses to enhance product recommendations, personalize user experiences, and optimize marketing strategies on-the-fly.
• Finance: For the financial industry, streaming analytics plays a crucial role in real-time fraud detection and risk management, critical concerns for many across the industry. Continually monitoring transactions and market data allows financial institutions of all sizes to swiftly identify and mitigate potential threats, ensuring the security of financial transactions.
• Healthcare: Healthcare can be a fast-paced business, especially in critical and traumatic care. But across a healthcare facility, having access to streaming analytics and patient data in real time can help facilitate early detection of anomalies, timely intervention, and overall improvement in healthcare outcomes. From monitoring vital signs to analyzing electronic health records, real-time insights contribute to more efficient and effective patient care.
• Manufacturing: By monitoring equipment and machinery in real-time, organizations can detect potential issues before they escalate, reducing downtime and optimizing maintenance schedules. With real-time data, companies can also better forecast future production capabilities and needs to meet user demands.
• Supply Chain: Streaming analytics enhances supply chain management by providing real-time insights into inventory levels, demand fluctuations, and logistics data. This enables organizations to make quick and informed decisions, ensuring efficient and cost-effective supply chain operations.

The bottom line

Harnessing the power of streaming analytics can provide huge benefits to your business. By identifying key areas where data can impact the decision-making process, you will be able to incorporate real-time data into your organization. Business leaders and employers will begin to see the importance of analytics as they make more informed decisions.