Preventing cloud data warehouse failure is possible through proper integration. Utilizing your data is key to success. That message is echoed by every business and technology pundit working today, every C-level executive, every Board member – and even every article like this one. The importance of using data to make better decisions has been drilled into every CIO, CTO, and technology manager’s head several times over. The argument no longer needs to be made – the importance of accurate data, served up to who needs it, when they need it, is now the baseline for operating a modern, competitive business.

There’s no lack of data to work with. Customer data. Employee data, sales data, marketing data. Industry data. The problem is using that data correctly to provide a foundation for your company and its employees. Data can be unwieldy and hard to corral into the buckets you want it to be in. Preparing data and getting it in the right place can be the biggest challenge in creating a data-driven business.

The convergence of your company’s data across every function, every customer, and every geography is happening at full speed, with no signs of slowing down. Those companies that can harness data from every source and quickly and easily turn it into powerful insights will be the ones that succeed.

The need for cloud data warehouses

The growing amount of data, paired with the difficulty of storing and managing it yourself, led to the rise of the Cloud Data Warehouse (CDW) and the variety of vendors who provide this service.

As data pours into an organization, it needs someplace to go. Otherwise, data silos can be created where different departments have different types of data stored. There is no synchronization happening to ensure everyone has the most up-to-date information. Some data can be lost, some duplicated, and in some cases, newer data overwritten with older versions. In addition, siloed data tends to breed a great deal of wasteful duplication of efforts.

The bottom line is that it was time for some collaboration and organization, where CDWs came in. CDWs provide a consistent source of data and information for an organization. They solve multiple issues around an organization’s data, making it easier to manage, synchronize, replicate, ingest and store data for proper discovery and usage.

CDWs create a single layer of data to interact with. The data coming out of a CDW is highly-structured and unified – improving the speed at which data is used for decision making while helping to lower overall costs. Speed and accuracy are key to data success in an organization, and that is what CDWs promise.

CDW challenges to overcome

While organizations embraced cloud data warehouses as a way to help make the most of their valuable data assets, organizational and technical challenges can hinder success if not recognized and actively mitigated.

One of the biggest challenges comes in the data discovery and loading phases. Data comes in multiple sizes and from various locations. There are multiple users of the same data and sometimes a large, uncontrolled flow of data throughout a business. Organizations quickly find out that the transition to a CDW won’t be as smooth as they initially expected, as there are complex data types to deal with, varying data formats, legacy technology to overcome, regulations and compliance issues to deal with, and data that needs to be released from silos. In fact, research conducted by my company found that almost half (49%) of the average enterprise’s distinct applications and data sources remain disconnected from one another.

When data is used without accounting for and correcting these issues, then CDW initiatives run the risk of failure. Without the proper data integration processes being undertaken at the start, companies will never be able to trust that they’re using accurate, real-time data. Or the data may become unwieldy, inconsistent, and difficult to consolidate. It’s the old “bad data in, bad data out” situation all over again.

In addition, there can be problems with the ongoing management of your data that can lead to CDW failures. Suppose data is not loaded fast enough into a CDW. In that case, you risk company leaders making decisions based on outdated information, leading an organization down a path that’s difficult to rebound from.

Preventing Cloud Data Warehouse Failure Through Proper Integration
Preventing cloud data warehouse failure through proper integration

On top of all that, situations where the entire data process is broken, can lead organizations to either spend too much time and money to fix it or settle on an inefficient process and only mostly accurate to get data to employees and check that task off their list. In truth, several processes can be initially set up correctly and then automated, relieving valuable time and resources.

Integration drives CDW success

Organizations must also embrace data integration to make CDWs live up to their promise. In today’s organizations, there are, more often than not, several CDWs in use at the same time. Data is coming to and from multiple organizations, and while the situation is an improvement upon the siloed approach, proper data integration is necessary for CDW success.

The process of moving data into and out of a CDW needs to be as easy as possible, so employees can concentrate on providing value – leveraging that data for analytics, machine learning, and AI applications – not on maintenance and worrying about whether their data is recent and/or accurate. Data needs to be a consistent source, regardless of who is accessing it or how they are doing so.

Data integration tools should comprise the modern-era processes, backbone, and tools needed for organizations to deliver insights and increase time-to-value quickly. There are several key functions that any organization looking to optimize CDW use should demand of their data integration platform. These include:

  • Single Platform Approach: There are many reasons that a single platform for your organization – one that empowers control of data integration, application integration, and API management – makes sense. One of the initial reasons for moving to a CDW in the first place is to streamline operations, making them more efficient to use, operate and grow with an organization’s needs. Given how integral data integration is, there is no reason to complicate matters while trying to make a CDW and the processes that surround it work.

The use of data and applications go hand-in-hand, which makes incorporating application integration into the same platform an advantage. There are thousands of applications in use throughout your organization simultaneously, and using the same tools to manage them alongside the company’s data is more efficient. It can help cut down on any usage errors.

APIs are favored by organizations looking to empower users inside and outside of an organization to utilize data and make better, more accurate decisions. Integrating API integration and control functions in the same platform helps speed time-to-results. It improves overall efficiency, as employees only need to learn a single approach to managing all three.

  • Easy User Interface: Keeping with the efficiency theme, integrating data into a CDW and promoting its use throughout the organization should be easy as possible. You want employees working on higher-value activities – ones that advance the company’s goals. You also want the ability to empower non-technical employees to use data integration technology so they can build applications and use information as needed to be successful. Democratizing usage requires an easy-to-use interface.
  • Ability to Curate Data with Ease: The long-standing debate has been whether ELT (Extract, Load, and Transform) or ETL (Extract, Transform, and Load) is the best approach to utilizing an organization’s data. The truth is, you need tools that can do both without dictating a specific method. There will be times when speed is most important, necessitating an ELT approach, and others where data should be pre-transformed before a specific use, meaning an ETL approach is needed. Be sure to ensure that your platform can handle both.
  • Ability to Quickly Scale: Your data integration platform should be able to grow as the company does. No company stands still. There is new data to incorporate and old data to update. New markets to conquer, new decisions to be made. The platform you use needs to not just work in the present but be able to quickly support you in the future without the need for re-architecting or re-training.
  • Multi-Cloud Strategy: Already, organizations have several CDWs in use at the same organization. This will only increase over time – different divisions and departments have different needs, and companies are bought and sold every day. The bottom line is that the data integration solution you invest in needs to be able to handle multiple different clouds at the same time, providing seamless integrations and automations. Support for all the major CDWs – Amazon Redshift, Google BigQuery, Microsoft Azure, Snowflake, and Databricks – should be the starting point of any discussion.
  • Use of AI and ML: If ease of use and driving organizational efficiency are the reasons for embracing a CDW, then it only makes sense to enable the ability to automate further. This means ensuring your solution can incorporate artificial intelligence and machine learning technologies to improve data use and integrations.

AI-powered solutions can move relevant data from a given source quicker, drive faster analysis and insights, and help employees determine how best to make a connection or integrate various data sources. AI and ML can also help identify and correct issues that human operators could miss and automate reporting and compliance.

It is clear that data and automation are closely tied to a company’s success. The rise of the CDW platforms has improved how data can be organized, analyzed, and used at all levels of an organization to drive better decisions and business results, but it is necessary to surround your CDWs with the solutions that make the desired efficiency and effectiveness gains possible.

Investing in modern integration solutions enables your organization to actively ensure both its data initiatives’ short-term and long-term success. Those that do so will quickly see the difference maker that data can be.

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