How to Implement a Real-Time Data Analytics Strategy
Content Marketing Manager, Sigma
Nobody will argue that our modern world operates in real time. We increasingly expect information to be up to the minute, or even the second, in many cases.
These changing expectations require companies to collect and analyze data in real time to compete, make informed decisions, and predict future trends. This is why so many companies have invested in modern data infrastructures and real-time analytics tools in the last few years.
Of course, not every business requires the ability to access and act on data in mere seconds. But real-time and near-time (minutes, not seconds) analytics are growing increasingly necessary and popular.
While mobile devices, the Internet of Things (IoT), and social networks are primarily responsible for driving the need for real-time analytics, it’s the rise of cloud data warehouses and lakes that are making real-time analytics possible.
Like many companies, yours may be considering a move to a real-time analytics strategy. Which begs the question: where do you start? In this post, I explore some steps to take before beginning your journey into the world of real-time data collection, processing, and analysis. And I’ll help you understand how to evaluate the latest real-time analytics tools before you make your next investment.
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Step Understand what your company needs
Before you pull out your checkbook and set your engineering team to the task of building a modern real-time analytics stack, you need to understand your company requirements. No two companies are identical. Just because one solution worked for a competitor or partner, doesn’t mean that it will work for you.
Here are some questions you’ll need to answer before you can get to work:
- Is this solution for the entire company or an individual team?
- What are the company/team’s goals with real-time reporting?
- How will the company/team use real-time insights to hit broader company goals?
- What types of data sources matter, and what do you want to measure?
- What existing tools does the company already have in place?
- Who will utilize real-time analytical insights?
Step Pinpoint your data sources
So, where are your getting data?
According to SaaS management platform Blissfully, the average SMB uses 20 paid SaaS products. Each of those SaaS applications probably generates data that is critical to at least one department in an organization.
If you’re a technology startup, then there’s also the data generated from your product. Information about reliability and customer usage probably guides your decision-making process. There’s also data directly related to your company’s bottom line from payments and financial sources.
Before you can build the right data infrastructure to process and analyze all this data in real time, you will need to pinpoint precisely where you want to collect that data.
Common types of data you may want to analyze in real time can include:
- Customer relationship management (CRM) data
- Product, website, or app data
- Enterprise resource management (ERP) data
- Third-party SaaS vendors or solution providers
- Customer support system data
Make sure to take a broad look at your company or team’s needs and map out all the sources of data you wish to centralize and glean insights from as part of your real-time analytics strategy.
Step Build your data infrastructure
Now that you know what types of data you want to analyze, you’ll need to build a data pipeline to collect and store that data in a cloud data warehouse or data lake, so it can then be analyzed. There are three main components in the modern data stack you’ll want to consider:
Cloud ETL or ELT
ETL (Extract, Transform, & Load) is the process of moving real-time data from a source like a database or application into a data warehouse where it can be analyzed. Some cloud ETL vendors also support ELT (Extract, Load, & Transform) in which data is converted into analyzable form after it is already in the data warehouse.
In the past, companies usually had to build a custom data pipeline. But today’s cloud providers have taken much of the complexity out of the process. We don’t recommend building custom data pipelines unless it’s absolutely necessary—they can be challenging to manage and can add additional burdens to your engineering teams.
You can read our online guide to ETL/ELT here for a complete breakdown.
Cloud Data Warehouse
To analyze data in real time it has to be stored in an analytical database. Modern cloud data warehouses like Snowflake make it possible for companies of any size to store massive amounts of data, and query that data in real time. In the past, on-prem data warehouses capable of such tasks would cost a fortune and require an army of IT talent to build and maintain. Again the cloud has simplified the data warehouse, making it possible for virtually any company to generate insights from its treasure trove of data in real time.
Read our online guide to choosing the right cloud data warehouse.
Real-Time Analytics Tools
With data collected, processed, and stored in a queryable data warehouse, you will then need a real-time analytics tool that is up to the task of searching through all that data and returning actionable insights in seconds. Modern analytics tools are much easier to set up and use than the legacy solutions of the past. Read on to learn what things to consider before you invest in a real-time analytics tool for your company or team.
Learn how real-time analytics fits into the broader analytics category. Read our definitive guide to big data analytics.
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Step Choosing a real-time analytics tool
While there are a variety of analytics tools on the market today that can analyze real-time data, you want to be sure you invest in the right one. The wrong choice could leave you with more than an empty wallet—think low employee adoption, headaches for the engineering team, and downtime if you have to replace it in the future.
It’s essential to get a good idea of what you need from your real-time analytics tool. Every company is unique, and needs will vary. We recommend evaluating internal requirements and aligning purchasing decisions with those goals.
It’s also important to note that not all BI tools are the same (or solve the same problems). Often, companies will deploy multiple tools for different use cases, teams, or business units. With that in mind, here are some guidelines to consider when selecting an analytics tool:
- Take a user-driven approach
- Determine who will engage with the data and how they will use it
- Look for an analytics tool that allows teams to collaborate and share insights
- Make sure it’s easy to use; otherwise, it won’t be adopted
- Ensure it has the capabilities to scale with your team as analytics requirements change over time
- Keep security, compliance, and data governance top of mind
Still have questions?
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