Cloud-based services and applications benefit organizations in many ways, including increased flexibility and cost savings. As more business processes and workflows become data-driven, companies are prioritizing cloud migrations. Relying on additional servers and onsite space for data storage and retrieval isn’t practical.
Onsite data management is less scalable with increases in volume and frequent shifts in how employees use and access information. IT staff often don’t have the resources to keep up with an expanding array of hardware. More equipment also means performing additional maintenance, finding ways to make it all sync, and locating enough space to house it.
However, movement away from on-premise models to cloud or hybrid setups also impacts data teams. Both roles and responsibilities within these groups change when companies move to cloud-based solutions. Below are some of the more significant ways cloud migrations affect data teams.
1. More Platforms and Tools to Learn and Understand
A cloud-based data pipeline often involves several data processing applications, web-based tools, and storage solutions. Data teams might view and interact with information through online tools and dashboards. However, these applications aren’t usually the powerhouses behind pipelines. The information data teams see, enter, and pull actually comes from database management systems and server- or storage-based apps.
While all these systems, applications, and platforms work together to create pipelines, they can use various algorithms and data models. Information that’s fed into a customer relationship management platform might originate from or pass through multiple sources and processing apps. Some records might show up with errors or not at all during the process. The complexities and data structure variances involved are usually invisible to humans and hard to understand.
However, data observability solutions help teams discover and correct potential problems throughout an entire data pipeline. With AI and machine learning capabilities, observability applications not only diagnose blockages in data pipelines, but they also observe data flowing through the pipelines so they can detect issues like data drift or schema drift. Data observability applications can let you know about data reconciliation which makes sure that all the data has successfully made it from one application in the pipeline to the next application. Observability apps flag duplicate or missing data and send alerts about suspected performance issues
2. Developing and Maintaining Vendor Relationships Becomes Crucial
When teams are managing and storing data onsite, their skill sets usually have to be more technical. Groups need to maintain and troubleshoot hardware and network connections and keep everything in the system up to date. This includes researching and deploying security patches and software upgrades.
However, in a cloud-based model, there are a variety of vendors that handle all of this. It’s up to each vendor to oversee its portion of the company’s data, services, and hardware and software maintenance. While internal data and technical teams may have control dashboards within each tool, what keeps them running is now offsite. For data and IT teams, there is less of an emphasis on technical prowess as soft skills become more prominent.
Data teams are charged with continuous relationship building instead of a few interactions with vendors during bidding and sales processes. Organizations become more dependent on vendors’ service level agreements and support capabilities. When things go wrong or a new integration is on the horizon, it’s the vendor who has to step in. Poor or strained relationships can make it challenging for data teams to overcome temporary setbacks or significant difficulties.
3. Deeper Dives Into Business Processes and Solutions Are Necessary
Since vendors provide data management and business process solutions in cloud-based environments, teams need a deeper understanding of workflows. Groups should also have a clear and detailed grasp of how different platforms support business processes. Data teams have to look at company objectives, practices, and tech solutions with a critical and holistic eye.
Simply put, what can a specific cloud-based solution do, and how does it facilitate or improve workflows? Additionally, teams must determine whether a platform helps them meet the company’s end goals. If a solution doesn’t support and elevate business processes and objectives, it’s time to find one that will. Proactive data teams are constantly on the lookout for alternatives and developing deep understandings of internal processes and environments.
As a result, various group members may become specialists or subject matter experts in different workflows or functional areas. Perhaps a member of the team dives into what marketing’s goals are and what technology the department relies on. That individual may learn that one of marketing’s objectives is to better target customers and increase personalization in their outreach. But current database solutions and platforms are not syncing, and the information is unreliable.
The specialist might perform a detailed analysis of each platform, how information gets in, and the ways that data flows. They could determine the current solutions aren’t cutting it and find a centralized replacement all departments can use. Alternatively, they might find that just a few tweaks are necessary for full integration and support of marketing’s goals.
For many organizations, continuing to operate with legacy systems and applications is impractical. There’s too much data and hardware to manage onsite, and the cloud provides convenient answers and solutions to these obstacles. Not to mention, remote collaboration, automated workflow support, and dynamic information management are nearly impossible without cloud-based applications.
Although the cloud solves problems related to space, access, and oversight, it transforms the roles of data and technical teams. They must learn the capabilities of a broader range of tools and develop close ties with vendors. Data and technical teams also need to increase their understanding of business processes and how platforms can support them. Moving to the cloud requires integrating the details, the big picture, technical know-how, and people skills.