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Optimizing Data Integration to Enable Cloud Data Warehouse Success

Without a doubt, the business environment of the past few years has proven that data – and the efficient and effective utilization of it – is critical to a company’s success. Today’s organizations have an enormous amount of data at their disposal – and they will win or lose based on the speed with which they can glean meaningful insights from it. Proper optimization of their data assets enables the creation and delivery of better products and services for customers, while at the same time streamlines and strengthens their core operations.

Successful organizations require a seamless process for their employees to access data and make better decisions. The goal is for employees to always have the information they need, without even realizing the back-end operations that took place to deliver that data to them. 

The Data Journey

Organizations have data living in a variety of locations, often creating data silos. Most also have data they don’t even know about, necessitating a discovery phase that can give them complete insight into what data they have and where it currently resides. 

Much of this information is valuable to improve your employees’ decision-making – and your bottom line – but not in its current state and location. It needs to be managed, synchronized, replicated, ingested and stored for proper discovery and usage. 

It’s only when those data sources are brought together in a central, trusted, and consistent location, that a complete, holistic perspective can be created. This enables employees to make better business decisions and take faster actions. 

Speed and responsiveness anchored on accurate, trustworthy data-driven decision making is what every organization strives for. Your organization’s access to data and ability to use it needs to reflect the speed with which your business – and the world – evolves. 

Rise of the Cloud Data Warehouse

Cloud data warehouses (CDW) provide a consistent source of data and information for an organization. The reason they’re so popular is that they solve multiple issues historically inherent in capturing and using an organization’s data. 

CDWs change the way data is consumed, improving the speed with which queries are answered, while lowering costs at the same time. CDWs create a single layer of data for analysts to interact with, and the data coming out of a CDW is highly-structured and unified – making it better for direct use by employees.

As CDWs mature, there are several key areas that are important elements to your organization’s CDW success:

  • Agility and Scalability – The ability to easily bring a new CDW online, or to shut it down just as quickly, depending on need. There should be no unused capital overhead to deal with. Just as important is the ability to scale as your data volume – and number of data users – continues to grow. CDWs can expand exponentially alongside your business.
  • Accessibility – Today’s global economy demands that data be accessible anytime, anywhere. In addition, CDWs should make data accessible to users of all skill levels, in all positions. This is critically important to meet the goal of becoming a data-driven organization: data cannot remain locked up with data scientists. That’s why the modern user interfaces and ease-of-use that today’s CDWs provide is such an advantage over traditional data warehouses.
  • Affordability – CDWs are less expensive to set up and roll out, due to their pay-as-you-use nature. While the costs associated with hardware, operations, maintenance and upgrades are absorbed into the total cost, the overall expense to the organization is less than on-premise data warehouse management.
  • Performance – Last but not least, CDWs have no need to balance data loading and analytical performance; and functionality can easily be tailored to current needs. They simply answer queries and provide value faster than earlier approaches.

But CDWs cannot do it alone. Regardless of whether you’re using an Extract, Transform, and Load (ETL) or an Extract, Load, and Transform (ELT) approach, if the data in the CDW is bad, then bad data will make its way into applications or analytics. The bottom line is that it’s important that the transform function is done so that you have quality data to work with. In addition, in order to maximize productivity, the process for employees – both business and IT users – to move data into the CDW needs to be as easy as possible, so they can concentrate on leveraging that data for analytics, machine learning, and AI applications. 

The other issue is that, in the modern enterprise, it’s rare that only one CDW will be in use. Many organizations use multiple CDWs for different capabilities. The bigger the organization and the more data it collects, the more likely it is that data will be coming from and going to multiple destinations. 

Proper data integration is necessary for CDW success.

Data Integration – the Key to CDW Success

Data integration involves combining the data residing in different sources and providing users with a unified view. Modern data integration tools are comprised of the practices, architectural techniques and tools needed for organizations to deliver insights and increase time-to-value. 

In order to use data across the organization – and realize true value from CDW investments – the way data is moved, processed, manipulated and analyzed must be done correctly and consistently, regardless of who in the organization is accessing it. 

Data integration comes in many forms and is generally organized into a couple of broad categories:

  • First is analytical data integration, which is necessary to sift, sort, organize and transform data for quick analysis and insight. Organizations have data spread across both on-premise and cloud systems. Therefore, both types of data integration are required to optimize performance and enable automation.
  • Secondly, operational data integration involves the movement of data across and between applications, systems, and databases. It keeps systems up-to-date and in sync, so data users have the most relevant information available. 

Data integration technologies and platforms have also continued to evolve and mature with greater use by organizations. There are several requirements for data integration success – and resulting CDW success – in today’s modern organizations:

  • Ease-of-Use – Be sure your data integration solution is designed to be easy-to-use by a range of employees including non-technical business users, and not just data engineers. The enterprise of the future will be structured around self-service, low-code applications and tools – and for these to work, they need access to the organization’s data. Modern integration tools recognize this and make it easy to move data into the CDW, and leverage that data across the organization for analytics, machine learning, and AI.
  • Speed – Time-to-value is critically important when it comes to decision-making. How fast can the data integration solution integrate data into the CDW? How much work will be needed by your internal team to make this happen? Old, dated data presents your team with old, dated results and inaccurate analyses.
  • Supports a Multi-Cloud Strategy – As history has shown us – especially in the enterprise technology space – needs and technologies evolve, often when you least expect it. In addition, it is rare that an organization uses (or will use) only one CDW. There are multiple clouds of data at every organization. In addition to data in the cloud, there is also the issue of legacy data and on-premise systems. Data in these systems is also relevant and needs to be incorporated into your CDW as well. To be prepared, you need an integration solution that can connect with any and every CDW platform to deliver seamless integrations and enterprise automations. At a base level, any solution you select should be able to support the major cloud data warehouse platforms – namely Amazon Redshift, Google BigQuery, Microsoft Azure, Snowflake, and Databricks. 
  • Single Integration Platform Approach – Even the most powerful tools and technologies in the world are useless if organizations make them too difficult for users to learn and incorporate into their everyday workflows. With that in mind, there is a strong case to be made for embracing a single integration platform approach. In addition to data integration – which we’ve already discussed the importance of – organizations should look out for opportunities to bundle in application integration and API management in the same platform. Application integration is important to incorporate because there are thousands of applications in use at any one time – and all of them have critical data flowing through them. Ensuring you have the tools to make them connect and speak a common language as needed could be the difference between data project success and failure. APIs have grown in importance as well, enabling partners, customers, and business managers to integrate with the organization’s products, solutions, and services – and its data. The ability to automatically build integrated APIs where needed to further the creation of your data-driven organization is an important capability to incorporate as well. If there is a single platform for all of your integration needs – incorporating data, applications and APIs – then users only need to learn a single set of rules, while organizations ensure more data consistency and a better return on investment. 
  • Utilizes AI – The incorporation of AI and machine learning capabilities into modern data integration platforms help to improve productivity and decrease time-to-value. AI-powered data integration solutions can more readily find useful pipeline patterns, move relevant data from a given source quicker, and drive faster, more accurate analysis and insights. Some of the additional benefits of AI and machine learning are the ability to automate compliance, to utilize self-correcting pipelines, or to analyze in-flight data. AI can help deliver insights not only about an organization’s data, but also about its process flows, execution efficiency and operational efficacy, while acting without intervention.

In short, properly managed data integration enables the CDW by mobilizing data and automating the business processes around it that are needed to make data-driven decisions. 

Using Data to Win

The convergence of data across every function, every customer, and every geography is happening at full speed. As the data landscape continues to evolve, your cloud data warehouse and data integration platform should be evolving as well. It will be those organizations that can harness data from every source and turn it into powerful insights that will win in the modern data economy.  

About the Author

Mark Gibbs at SRK Headshot Day in Menlo Park, CA

Mark Gibbs is Vice President of Products at intelligent integration provider SnapLogic, where he leads product management and promotes the company’s data driven strategy. He has worked with a variety of enterprises throughout his career, enabling them to create real business value through the use of data. Mark’s vision and leadership has led product teams to create compelling integration and analytics solutions that empower enterprises to become better informed.

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