/ Streaming analytics: The key to harnessing the potential in your data

Streaming analytics: The key to harnessing the potential in your data

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. Data is only as good as it is timely, meaning that stale data is just as dangerous as no data. Streaming analytics is a relatively new concept. In this article, we will talk about the power you can harness from your data as you adopt streaming analytics.

What is streaming analytics?

Streaming analytics refers to the continuous process of collecting, processing, and storing data records in 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.

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.

 

 

Who can benefit from 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.

 

How do I use streaming analytics in my organization?

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 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 multi-task 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.

 
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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.

Conclusion

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.

Check out some related resources:

How Sony Interactive Entertainment creates an advantage by sharing data across its massive partner network

10 Ways to Turn Data into Actionable Insights

How a private equity firm uses data to expand and enhance its business portfolio

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