Overcoming Four Key Data Transformation Challenges

It is safe to say that when data and analytics leaders built their data management and data analytics strategies in late 2019, they did not foresee the macroeconomic impacts of 2020. The upheaval of many of the best-laid plans touched all industries as executives looked to data to provide insight into how to withstand the fallout and course correct.

While enterprises have long understood the need to migrate to the cloud, the COVID-19 pandemic served as the catalyst for quick plans to get started right away. And many organizations are ready to make the shift to the cloud or hybrid model for data management. In a recent survey of enterprise IT and data professionals, more than one-third (38%) said they are already using cloud data warehouses (CDWs). Long term, 43% expected to have all of their data in the cloud, with the remainder planning to pursue hybrid models that leverage both cloud and on-premise data warehouses. 

The business uncertainty experienced by organizations across the world accelerated the need for flexible, agile tools that could help businesses reaquickly and use data effectively to plan their next move. Data transformation—the ability to join together raw data and format it for analytics—is a process that benefits data teams to quickly gain insight into business processes and areas of opportunity. Many businesses agree and are looking for a way to speed the data transformation process in 2021. However, there are challenges identified by top enterprises propelling the growing demand for data transformation. 

1-Lack of standardization among cloud vendors poses challenges for data transformation strategies

Modern data management includes a tech stack that is complementary to managing and preparing data, such as a data ingestion tool or data transformation software, data governance tools, data cataloging solutions or business process automation applications. These solutions must work with data governance policies to support the goals of the enterprise that maintain and protect data. Keeping data governance policies consistent across the business is a hurdle for many enterprises as data volumes and varieties continue to increase. Standardization of data rules and policies are made even more challenging by the onboarding of multiple data and analytics vendors.

For all businesses, the vendor evaluation process is a key decision when moving to the cloud. Organizations first need to determine which cloud data warehouse works best for the business use case, and then figure out the complementary ETL, data preparation, and data visualization tools that are required. But as the number of vendors and solutions in this space continues to increase, the need for consistent data governance and standard business logic poses a challenge due to varying standards by each platform. 

Proper data integration and transformation can help facilitate best practices for data governance and data management. To help overcome concerns, businesses should look for data transformation solutions that are tightly integrated with cloud data warehouses to ensure standards can be met across technology platforms. It is also important to ensure that the solution can help provide specific capabilities that encourage standardization of governance such as data lineage, audit logs, auto documentation. Technical integrations between complementary vendors such as data catalogs, data preparation, ETL/ELT, and governance tools are helpful for customers and provide a better end-to-end integration solution.

2-Modern methods for data collection are timely and make it difficult to manage data quality

Getting data into a cloud data warehouse does not automatically mean it is ready for analysis. Loading data into a cloud data warehouse breaks down data silos but preparing data for analytics is a time-consuming task for data professionals. For the typical data analytics project, aggregating and prepping data takes about a week on average, according to an IDG survey. That same survey found that nearly half (45%) of the time spent in bringing a data analytics project to production. 

Moreover, new data sources such as IoT or real-time streaming are not available for older, legacy ETL systems, creating a big barrier to real-time insights and information. This is especially true for businesses that rely on timely data to monitor performance or telemetry data. In order to provide data and analytics for insight, businesses need to figure out how to deliver the data quickly without compromising data governance and data quality.  Data users need to clearly see and understand where data is coming from, what it means, and how it was calculated. This is of critical importance as the plethora of available data makes this more difficult, while also making it easier to read the wrong data.

To ensure quick time to insights, as well as the accuracy of data, it is imperative to transform data from its raw source into an analytics-ready state. Data transformation tools can provide better, automated data preparation to enable quicker data collection with formatted, joined-together data for analysis. 

3-Data-driven culture is still an aspirational approach within organizations

There is a problem with getting metadata spread inside the business—not only with tools, but with the culture and processes. Widespread adoption of metadata across the business would help business analysts and stakeholders understand how to use data to make decisions within their functions. Without this ability, it is nearly impossible for people in any enterprise, apart from those in IT, to be truly data-driven.

There is also a problem understanding exactly who owns the data. Many data professionals see data control issues as the biggest challenge to data analytics projects as the lack of clarity for ownership and centralization of the processes managing data are barriers to insights. This leads to a more IT-centric approach where IT requests all of the requirements for the project and hands off the data to the business user.

Companies should provide equal digital citizenship among its employees, enabling all workers with low-code and no-code solutions that give them access to data and an intuitive interface that helps them perform transformations for analytics ready datasets for their roles.

4-Business users need data and IT needs to provide it

But in order to provide the necessary data access, IT must set guardrails around what data can be shared and how it should be accessed. Data democratization is meant to lessen the dependency on IT and greatly improve the efficiency of the business as a whole. To quickly compete using data, business users should go directly to their data and avoid the IT bottleneck.

In order to make data available to business users, enterprises should consider a few key criteria for their data transformation strategy. First, IT must be involved in the selection process so that any new tool supports the existing business logic in place. Next, it is important to work with a modern, cloud-native technology as these will likely have an easy onboarding experience. Modern data transformation has simple graphical user interface, drag-and-drop features, and other intuitive capabilities that feel familiar to tech-savvy business users. Finally, it will likely help with adoption to find a platform that supports the growth of skill sets and new users so that the transition from small use cases to complex data transformation and orchestration seems seamless. 

The demand for data transformation will only grow faster

Operational business needs will only grow as enterprises work to uncover the impact of 2020 on their businesses and understand how to use data to find opportunities to maintain or increase growth. Data transformation is a valuable part of making data useful and setting organizations up for success in 2021. Taking data out of silos, into a single view, and transforming it all enables enterprises to gain clarity and confidence in their data-driven decision making.