The cloud data warehouse has become the bedrock of most analytics programs, and a lifeline for executives to glean insights into their business. Truth be told, cloud data warehouses– like Snowflake, Amazon Redshift, and Google BigQuery–are here to stay. As its popularity has increased, however, its primary use cases have shifted away from data storage and reporting into a more common notion of a data lake, where raw data can not only be stored but also magically transformed once inside.
But how exactly do businesses achieve this magical data transformation within their cloud data warehouse? Who owns that strategy? And most importantly, how can businesses action these insights directly within the systems where that data lives?
Last week, we sat down with a cross-functional executive panel to discuss the new rules of business data in 2021. The panelists covered a wide range of topics from how companies prioritize the right data to why integration solutions have struggled to help businesses achieve a unified data model. One major topic of discussion focused around the data warehouse’s role in articulating the wide variety of data coming in from ever-increasing SaaS vendors. Here are some highlights:
Ilya Kirnos, CTO and Founding Partner, SignalFire on the need for better solutions for data variety
“When talking to people about big data, I break it into 3 buckets: data volume, data velocity, and data variety. For the first two, we have great tools now to store lots of data and process it quickly. The third one: having to deal with data coming in from lots of different sources –then canonicalizing and unifying it – is still an unsolved problem. One new entrant, the cloud data warehouse, provides this notion where you can extract and load data into your data warehouse, and [once there], you can magically transform it inside.”
Ross Mason, Founder of MuleSoft and Digg Ventures on ownership of data strategy within the organization:
“Too often, when people start down the path of unifying their data in a data warehouse or data lake, they dump way too much into one place, hoping to glean future insights out of this store of information. Unfortunately, it ends up creating a data swamp that nobody goes near because it’s too complicated — nobody understands how to really reach in and get [what they need]. The key is to break things down into manageable pieces and keep the domain of your data warehouse narrow in scope based on the users who will interact with it.
It comes down to centralized owned data infrastructure, with business unit owned data warehousing and data capabilities scope.”
The difference between a data warehouse and a data lake:
While data lakes and data warehouses are both widely used for storing big data, they are not the same thing, and should not be used interchangeably. A data lake is a vast pool of raw data, where the purpose is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.
Nick Bonfligio on the perils of data warehouses in creating insight siloes
“Cloud data warehouses are here to stay. {…] I advise our customers to think about their warehouse as a part of an overall data solution and unified data model, not just something that’s off to the side for reporting Business Intelligence Insights. You need to ensure insights gleaned from your data warehouse get back into the operational systems where you can act on them, otherwise they rapidly decay in value. And while this is possible today, it often involves gobbling together four disparate solutions to get data and insights into and out of your warehouse. Syncari brings all the technology our customers need to create and distribute insights from customer data in one complete platform.”
Eileen Treanor, CFO at Inkling on the role executives play in data strategy decisions-making:
“Sometimes we view data warehouses as panaceas. A big part of my role is to orient the business around the five or six pieces of data we need to consolidate that will give us more insights into how the business is doing. There has to be a strategic lens to any data initiative that can answer the C-level question of “what are we going to do with this data.” And let’s not try to do 500 things, let’s do five things. I always think it’s best to start small and build on that.”
To learn more, check out our full panel discussion, now available on-demand.
