Data analytics is nothing new. For decades, businesses have been deploying a "stack" of data analytics tools to collect, transform, evaluate and report on data.
However, as data has grown larger in volume, and as the ability to analyze data quickly and accurately has become ever-more important to business success, the data analytics stacks that businesses depend on have evolved significantly.
So, if you haven't taken a look at data analytics stacks recently, they're worth revisiting. As this blog explains, the modern data analytics stack looks quite different from the analytics stacks of old.
What is a data analytics stack?
A data analytics stack is the set of tools that businesses use to collect, prepare, analyze and report on data.
Because analytics processes can vary widely depending on which types of data an organization is analyzing and which types of insights it seeks from the data, data analytics stacks come in many forms and sizes. There is no singular set of tools that you can deploy to build an analytics stack.
In general, however, a typical data analytics stack includes tools to perform the following functions:
- Storage: Data could be stored in a data warehouse, a data lake, a cloud-based object storage service like AWS S3 or a conventional database.
- Collection: If data can't be analyzed at its original source, data collection tools can move it to a different location to prepare it for analysis.
- Quality control: Data quality tools may be used as part of the analytics process to identify and correct inaccuracies, missing data, redundant data or other flaws inside data sets that could hinder analytics results.
- Transformation: In some cases, data needs to be transformed by, for example, migrating it from one type of database to another.
- Analysis: When data is ready to be analyzed, data analytics tools interpret it by running queries against the data and displaying the results.
- Reporting: Data reporting tools help teams to interpret and share the results of analytics operations.
Watch the Webinar: Rethinking Data Analytics Optimization
How the data analytics stack has evolved
The types of data operations described above have long been important to data analytics. However, the types of tools used to perform those functions, and the way those tools are integrated to form a data analytics stack, have changed for several key reasons.
Migration to the cloud
94 percent of enterprises today use the cloud. That means that a lot of the data that businesses need to analyze lives in the cloud by default. Having data analytics tools that can analyze that as readily as possible – ideally, without having to move it from its original source – is a key aspect of the modern data analytics stack.
That's especially true for businesses that take advantage of cloud-based data warehousing services. If you can perform analytics operations on data inside a data warehouse without having to move or transform it first, you'll get faster, more actionable results.
Cost control
As the volume of data that businesses generate has grown, so has the challenge of ensuring that they can ingest, analyze and store all of the data cost-effectively. One way to control costs is to deploy a data analytics stack that minimizes the amount of data movement that needs to take place within the analytics pipeline. In this way, businesses can reduce data egress fees, which they typically have to pay when they move data from one environment into another.
Administrative complexity
While open source data analytics tools (like ElasticSearch) are powerful, deploying and managing them requires significant time and money. That's one reason why businesses today increasingly opt for fully managed, easy-to-deploy data analytics tools, which reduce their total cost of ownership and administrative burden.
Data security
Data security and compliance requirements are tighter than ever. That means that maintaining the security of the data analytics stack is a key priority. So is ensuring that data analytics can deliver effective insights for security operations teams.
Read: [REPORT] Best Practices for Modern Enterprise Data Management in Multi-Cloud World
Who needs a data stack?
Not every business needs a full suite of data collection, transformation, analytics and reporting tools. If you only need to perform one type of data analytics operation (such as analyzing log data or performing security analytics), an analytics tool that is purpose-built for that use case may suffice.
But for businesses with multiple types of data to analyze, and multiple analytics use cases to support, data analytics stacks provide the foundation for achieving actionable, data-based insights over the long term. That's especially true if your data stack is flexible enough to adapt and scale as your business needs change.
Read: Unlocking Data Literacy: How to Set Up a Data Analytics Practice That Works for Your People
How to build a modern data analytics stack
Given the many considerations at play in creating a cloud-friendly, cost-effective, easy-to-maintain and secure data analytics stack, building a stack suited to your business is no simple feat.
To make the process as easy as possible, however, it helps to prioritize data storage and analytics solutions that are agnostic, meaning they can work with any type of data and support any analytics use case. Be sure, too, to think about the total cost of ownership, rather than looking just at the direct cost of your data analytics tools. And make sure your stack can deliver not just the insights you need today, but also those that your business may require in the future.
Additional Resources
Watch the Webinar: DATAVERSITY Demo Day
Check out the Whitepaper: The New World of Data Lakes, Data Warehouses and Cloud Data Platforms
