In a Data-Driven Economy, Data “Real Estate” Must Be Modernized

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Click to learn more about author Mike Potter.

The rush to become data-driven is more heated, important, and pronounced than it has ever been. Businesses understand that if they continue to lead by guesswork and gut feeling, they’ll fall behind organizations that have come to recognize and utilize the power and potential of data. But the transition isn’t occurring fast enough. According to a report by the Business Application Research Center, most companies (58%) still base their decisions on gut feeling or experience, not data and information.

This has created a divide between best-in-class enterprises, which are more likely to base their decisions on information, and laggard companies, which mostly rely on gut feeling. Businesses cannot afford to guesstimate any longer. If they are to keep up in the current market – which, make no mistake, is absolutely driven by data – organizations must strive to monetize their data in every way possible with analytics. This is not limited to one particular industry, so it will be vital for every firm if they intend to survive and compete.

Businesses need to evaluate existing legacy systems, which could be incapable of supporting their new goal to become data-driven. Legacy systems tend to make it nearly impossible to achieve analytics at scale. To become a data-driven organization, businesses must modernize their data “real estate” by utilizing both the cloud and automation, which can bring data warehouses and data lakes together. They must also stream data in close to or at real time to meet the requirements of competing in this challenging market.

Shift to the Cloud and Embrace Automation

No one questions the core benefits of data warehousing, particularly how it makes possible acquiring and maintaining massive amounts of information. It completely transformed the way organizations operated when it came on the scene, but solely is not enough to match the pace of today’s market. Cloud technology has successfully eliminated the constraints of physical data warehouses and data centers, allowing businesses to expand or reduce their data storage needs based on individual requirements. 

Capable of storing information from a variety of disparate data sources – IoT, CRM, and finance systems, to name a few – a cloud data warehouse is highly structured and unified. Due to its design and flexibility, cloud storage can serve a wide variety of specific business intelligence and analytics use cases, allowing organizations to gain insights that improve operations, enhance customer service, and provide a powerful competitive advantage. A cloud data warehouse is not only scalable and less expensive to maintain than an on-premises solution, it paves the way for faster insights from real-time data.

When combined with a modern data integration platform that automates the entire data warehouse lifecycle, businesses can rapidly accelerate the availability of their analytics-ready data. This is critical in today’s challenging and highly competitive business landscape. Enterprises that want to thrive must use every data-driven advantage at their disposal but can only do so if insights are delivered in real time, if the lifecycle is automated, and if the data is always in an analytics-ready form.

Use Automation to Bring Data Lakes and Warehouses Together

Data lakes can be used to fuel data pipelines to make available the information data analytics tools need to create insights that inform key business decisions. With a low cost, high degree of flexibility, and easy scalability, data lakes are well-suited for supporting modern analytics, especially for any use case that requires large-scale data processing.

Automation can be a differentiating factor here as well. With the right technology, data warehouse and data lake automation capabilities can come together in one unified user interface. This enables businesses to plan and execute projects with ease, eliminating the hurdles and stopgaps caused by a lack of unity and automation, which can limit or prohibit the successful use of data. Any limits, no matter how small, will negatively impact how a business operates and ultimately reduce the likelihood that decisions are made with data.

Stream Data to Meet Today’s Stringent Requirements

The third and final step in modernizing your data real estate is to swap out ETL (extract, transform, load), a key technology of the past, with a solution that offers a continuous stream of data that can be accessed at any time. Traditionally, ETL has been rather limited because it transforms large amounts of data in one go, and that transformation needs to be complete before it is loaded into the target. 

With Change Data Capture (CDC), businesses can speed up and smooth out their data onboarding process with automation. CDC removes the challenges associated with integrating data by replicating and streaming information from all sources in near real time. That data may be sent to one or more destinations of choice.

CDC uses ELT (extract, load, transform), a more modern alternative to ETL. ELT is much faster because it extracts data from the source and loads it into the target system in its original format and in near real time. It saves the data transformation component for a later period, preventing delays that were inevitable with ETL. When implemented effectively, CDC can meet today’s scalability, efficiency, real-time, and zero-impact requirements. 

Prepare for a Future That’s Led by Data, Not Guesswork

Businesses that base their data architectures on these three core principles can be more agile, scalable, and resilient. By modernizing data real estate – using not only cloud data warehouse technology but also the power of automation, which can bring data warehouses and data lakes together – organizations can rise above their data latency challenges. They will also be able to better plan and execute projects with much less effort. And when combined with using CDC to replicate and stream information from any source in near real time, businesses will be prepared for a future that is being led by data, not guesswork.

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