5 steps to develop a data automation strategy

New and exciting technology advancements, especially as it relates to data analytics, have brought forth a growing problem for businesses of all sizes: data management. Traditionally a time-consuming and painstaking process, data management is being completely reinvented thanks to data automation.

The data management technique enables organizations to store, process and analyze data through technology tools and software. It drives organizations, regardless of the amount of data, to find more efficient and effective data analytics and business processes. It isn’t a one-size-fits-all solution; however, there are some common steps to consider when developing a data automation strategy. 

1. Determine which processes and tasks to automate
2. Ensure you’re using the right automation tools
3. Make incremental steps
4. Seek out a trusted advisor or consultant
5. Monitor and improve the strategy

Data automation is a complex process that benefits from a strategic evaluation before implementation. Responsible parties should evaluate which data processes are taking up the most time. This can include processes that have redundant manual steps, like data entry, integration or analysis, or those that require excessive time and energy from a data team. 

Once the processes to target for data automation are identified, the next step is to evaluate those processes looking into the manual steps of each process or pipeline. By looking into these tasks, the organization might choose different directions to take or focus more attention on one pipeline over the other based on the complexity of the automation. 

What to do: Seek out processes that can save data teams the most time and yield the highest return on investment. By strategically evaluating and ranking the processes, leaders can create a proper data automation strategy. This can help data teams and engineers focus on deriving insights and more productive workflows than those from traditional data management.

Identify what tasks require automation and rank them from most to least complex. While time-consuming, this is a worthwhile exercise as it relates to an organization’s long-term data automation and data management efforts. Separately, understand the technology requirements for automating the tasks at hand and make sure they align to your capabilities and business goals.

Your organization should have a good idea of which processes to target for automation, and which specific tasks within the processes need the attention. Now it’s time to choose the right data processing automation tool that fits your organization’s specific requirements. It’s also imperative to consider other necessary and related capabilities, including scalability, security, observability and integration.

What to do: Take all the information that you have collected regarding processes and tasks to identify the right automation tool for your organization. Evaluate the capabilities of each tool and find the match to meet your organization’s business goals.

An organization might choose to bring data automation solutions into its business, but it doesn’t need to be an all-encompassing shift from the start. The approach might be more incremental, requiring data teams and other employees to be patient as data management capabilities ramp up. As data automation means something of a learning curve, an organization's most business-critical data processes might not be automated until down the road.

What to do: Start with patience. Take an incremental approach to automation. Help ensure that your team gets experience with automated tools and the new strategy before applying the tools to the most important aspects of a process or pipeline. Take the time to see the benefits of data automation and then scale up as necessary. 

Some of these incremental steps might be taking employees in for training, team by team, to better understand the data automation strategy goal. Begin by targeting one part of the business for implementation. If that is successful, then the organization can consider expanding to other parts of the business. Take time to establish a baseline for how the strategy functions.

There is a range of data automation tools available. Having a trusted advisor with skills in data management and analytics is key to your organization’s automation success. While some business leaders might think to keep all work internal, it might not be the best decision. By bringing in an outside expert, the organization can get the most up-to-date insight on data engineering and business intelligence.

Without a trusted advisor or seasoned consultant, the organization might get stuck in old mindsets and a resistance to change. This can result in faulted implementation and long-term issues related to processes. If a data automation strategy isn’t implemented correctly, it might mean that some processes never recover or take much time to restore. This costs the business two critical resources: time and money.

What to do: It might be in the organization’s best interest to bring in a third-party advisor or consultant. Seek out an expert that has experience with the business goals your organization has set out to achieve. This expert should have experience with the processes and tasks you’re looking to automate.

Automation is an iterative process that builds with incremental improvements. These types of processes require continual updates and tweaks after the development stage. The world of data and automation evolves at a such a quick pace, a relevant, effective strategy is almost always a work in progress. While some processes can be performed, implemented and left alone, automation requires constant feedback and discussion.

What to do: There should be a team that monitors the automation processes in place. Other employees who interact with it should also have some mechanism of providing feedback about how the automations. The team in charge of monitoring needs to have an open dialogue with business leaders about which processes need updates and which might no longer be of use to the business.

Any organization trying to keep up with evolving technology is seeking out new strategies that push them ahead of their competition. A data automation strategy is a crucial solution that can drive the organization to make informed decisions based on real-time data insights. It requires an organization to put time and effort into the implementation process and follow steps that help ensure a positive outcome for the business.