Automation at scale is key to running a data-driven business

Growing organizations are undergoing fundamental changes in how they operate and manage growing data. Until fairly recently, data management strategies for most companies were based on trial and error, historical modeling, or educated guessing.

While this approach worked in the past, it is becoming obsolete. Organizations now have access to huge reams of data which drive our understanding of every aspect of their business, including customer demand generation, advertising, revenue operations, profitability, inventory, and more. Every department in forward-thinking, growing organizations are becoming data driven, because using information to make decisions is always better than guessing.

With increasing amounts, variety, and complexity of data available, these organizations have never had a better opportunity to leverage this power to their advantage. However, as the sheer volume and complexity of data continues to grow, they also realize it is unsustainable to hire more and more data engineers or data scientists to get the actionable insights that they need in a timely fashion. Moreover, manual data aggregation increases the likelihood that the data will have errors. And using bad data to make decisions could be very costly to their organization.

Enter data automation.

Thanks to data automation, organizations of all sizes now have access to clean, unified, normalized and centralized data to measure performance in every part of the organization. Not only does data automation allow you to scale your data efforts, it also provides more accurate data to make better decisions.

Launching data automation starts with an ETL strategy – which stands for extract, transform, and load. ETL is the process data engineers leverage to transform disparate data sources into a single, usable, and trusted source, where end-users can access the data to solve business problems.

At Switchboard, we learned that growing organizations have two options:

1. Hire a ton of data scientists and engineers to contextualize and manually transform the data into actionable insights for every department. This process is expensive, error-prone, and fundamentally not scalable, and therefore not truly sustainable.
2. Embrace data automation and ETL so the data is normalized and unified in a process which is mostly automated. With data automation, every department can glean the real-time insights they need to run more efficiently and profitably. This approach is much less expensive, far more accurate, and more scalable as you grow than hiring data engineers.

The sheer complexity of disparate data sources and volume makes data automation with ETL increasingly necessary. In future posts I will share more detailed insights into data automation and ETL, and will provide practical examples of the benefits of ETL. In the meantime, we suggest you learn as much as you can about data automation and ETL to launch your data journey.

Read more from our blog HERE