By now, it’s widely regarded that the “big data” era is over and we’re now solidly in the era of cloud data. Once considered the pinnacles of tech, systems like Hadoop, Apache Spark, and others are now seen as legacy systems. They require a lot of resources to maintain and scaling generally takes advanced planning.
Their replacements have come in the form of systems like Snowflake. These new systems require much less maintenance and scaling is as easy as clicking a few buttons. There’s no need to plan scaling out for years, as these systems allow you to scale up and down at will.
What is Data Agility?
In order to achieve the best ROI from your new cloud data storage, you need to understand the concept of data agility. Data agility is defined by TDWI as: “How fast can you extract value from your mountains of data and how quickly can you translate that information into action?” For business, the translation is about getting your data into the hands of your analysts and business stakeholders in a way in which they can easily and quickly find insights about your business.
Why Do I Need Data Agility?
While legacy systems such as Hadoop and Apache Spark were great at storing and processing huge amounts of data, getting that data into a usable format typically requires someone with an advanced knowledge of the respective technology. However, that person with the advanced knowledge of the technology rarely also understands the business. This gap in knowledge continuity allows insights to fall through the cracks as the two sides — typically IT and the business — negotiate on what’s important and what’s not.
Having a single system that provides an industry standard mechanism for data analysis allows analysts with the business to sift through the data directly and discover insights — without the need to negotiate and translate business needs into IT actions. Having knowledge continuity contained within one team also allows quicker iterations of discovery and shifts in analysis as business needs change.
How Do I Get It?
In order to be Agile there are some must-haves and items that will help based on your situation and types of users. Let’s start with the must-haves.
First, you have to use a platform that allows you to easily integrate disparate data sources and do away with data silos. After that, your technical team must be able to complete rapid data discovery using industry knowledge and discovery tools to understand data quality and take corrective action quickly.
After all of the data is in one place, data transformation is critical to preparing the data for analysis. Expecting end-users to do this transformation is not a path to success. It is essential to have a platform and tools that prepare the data for different uses. For example, the transformation for a data science team is different from that of a strategy team. However, in the past, most companies have acted like these teams could turn the exact same data into value equally.
IT teams can not predict all of the ways different business groups will want to use data, so they must be flexible in how that data gets in the hands of various teams. For example, a sales team may need a final set of data that answers specific questions that can be investigated easily. Whereas a data science team may want as much raw data as possible, but with common keys that connect different sources easily, like customer IDs, account hierarchies, and product codes. There will be a greater chance of success if these linkages are identified and established ahead of time.
How Can Snowflake Help With Data Agility?
Given what’s needed to obtain data agility, one of the first things to figure out is what tool or tools check off the boxes. You’ll need something that’s easy to use, allows massive amounts of diverse data, and has the ability to scale itself up and down as needed. One such tool is Snowflake.
Snowflake uses standard SQL syntax for queries, and allows you to bring your data in from many systems into a single Data Cloud. It also offers a data marketplace, where you can access data from sources outside your organization to help enrich and diversify your data.
The Combined Benefits of Atrium and Snowflake
At Atrium, we use our customers’ Snowflake instances to accelerate our point of business impact when building machine learning models and analytics. Having all your data in a single place allows our data scientists to build better models, and without the burden of data collection, it shortens the overall time needed for the project.
Our analytics consultants and data scientists use Snowflake’s SQL language to quickly build insightful and intelligent dashboards and models to help your teams track progress day-to-day or over many months/years as needed. Since SQL is a common querying language used by most databases, knowledge transfer to your teams is easier and less of a mountain to climb.
Having access to large amounts of data and being able to quickly scale processing power as needed allows our consultants to meet the needs of your various business units without having to compromise speed over granularity or vice versa. And since this processing power is scaled on demand, once the heavy lifting is done, Snowflake can “sleep” the powerful compute power to avoid unnecessary costs for your organization.