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TDWI Upside - Where Data Means Business

To Unleash Data Potential, Enterprises Need to Fully Embrace the Cloud

Embrace the scale and performance benefits of the cloud to improve data engineer productivity and reap the benefits of real-time, data-driven insights.

During the past several years, the importance of data-driven decision making has become a top priority for business leaders across the globe. It's clear that the ability to glean meaningful insights from volumes of data is no longer a competitive advantage but a requirement for making real-time decisions and remaining relevant in today's changing business landscape. As recently as five years ago, accessing these insights in time to still be relevant was nearly impossible, but this all changed when cloud data analytics arrived.

For Further Reading:

Plan Carefully when Migrating to a Cloud Data Warehouse

Executive Q & A: Bursting Data to the Cloud

How Replatforming to a Cloud Data Warehouse Could End Your Career

Enterprises continue to invest heavily in the cloud, putting immense pressure on data teams to deliver more useful insights across their organizations. Despite this, most data teams still spend 80 percent of their time looking for and preparing data and just 20 percent performing analysis, according to a survey from CrowdFlower. To fully capitalize on the potential of data talent and uncover business insights, data engineers must spend less time on maintenance and reactive tasks and more time fully leveraging the scale and performance benefits of the cloud.

Let’s first consider some of the overarching challenges for data engineers:

  • Volume, variety, velocity. Today’s data teams struggle with these “Three Vs” of modern data. Businesses need to have data insights ready so they can act on information as close to real time as possible.

  • Growing skills gap. Businesses lack the resources to handle these escalating data demands. Data engineers are in high demand and an average tenure is less than two years, according to research from Zippia. Realistically, it’s just not feasible from a human resource or cost standpoint to hire enough highly skilled data engineers to keep up with these changing data needs.

  • Outdated legacy systems. Many organizations are dealing with outdated legacy tools that are complex, inflexible, slow, and costly. Not only does this make data processes more time-consuming for data engineers, but it eliminates the possibility for the democratization of data across the enterprise.

Creating a Modern Cloud Stack to Unleash Data Potential

When it comes to adopting a cloud data approach, there are elements of a modern cloud stack that can help data teams be more strategic, solve these key problems, and move insights across the organization more efficiently.

For instance, modern data tools that automate tedious, error-prone boilerplate efforts can free time for teams to focus on the unique business logic of the data processing itself. A cloud-native data integration platform is a great example of a tool that can significantly reduce time spent on manual data efforts. Through its ability to transform raw data into the refined, analytics-ready data required to support business intelligence, teams can better manage the “Three V’s” of data and automate non-critical tasks to adapt to the pace of today’s data insights and make critical decisions faster.

Low-code and no-code tools that enable rapid development of applications, automate data integration and support data visualizations can provide scalable systems that bridge the growing skills gap. By enabling more business users to easily analyze data sets, a low- or no-code approach can broaden data teams and empower more users across the organization to quickly unlock key business insights. This approach democratizes data use and frees up valuable time for skilled data engineers to focus on more technically challenging and value-adding tasks and take full advantage of what the cloud has to offer.

However, to fully capitalize on modern cloud data tools, a business must have access to all of its data and have a modern data integration strategy to bring that data into a cloud data platform and transform it to make it useful for analytics. On-premises and legacy extraction, transformation, and loading (ETL) approaches to transforming data are inflexible, time-consuming, and no longer viable considering the unprecedented amounts of data organizations are dealing with today.

Alternatively, adopting modern cloud ELT allows data teams to be more strategic with their cloud data platforms. Unlike traditional ETL methods mentioned above, this modern approach is a more effective way to consolidate data by extracting it from source systems, loading it into the cloud data platforms, and then using the power of these platforms to transform the data there. An increasingly important capability beyond this initial consolidation of data into an analytics system is the ability to move it back outside of the cloud data warehouse back into business applications (such as Salesforce). This approach is much more agile and intends to automate and operationalize data insights and allows teams across the organization to access and act on the same data that’s being used by the analysts in real time.

The Bottom Line

Every business today needs to compete using data, and the adoption of a modern cloud data stack is a critical step toward handling its increasing volume, complexity, and speed. By embracing the scale and performance benefits of the cloud, businesses can drive higher data engineer productivity and reap the benefits of real-time, data-driven insights. This ultimately allows them to flip that 80/20 rule and spend a majority of their time on strategic work that will move the needle for their organization -- and keep them fulfilled at work.

About the Author

David Langton is a seasoned software professional with over 20 years of experience creating award-winning technology and products. David currently serves as the VP of Product at Matillion, a data transformation solution provider. Prior to his role at Matillion, he worked as a data warehouse manager and contractor in the financial industry.

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