“Digital transformation” has long been a buzzword for organizations looking to remain competitive but undergoing a full digital transformation can be intimidating, especially when your organization has been using the same familiar processes for years. With complex legacy environments and the risks associated with change also being considered, it’s often unclear where to begin.
Introducing automation-based technology into your current processes provides an opportunity to begin driving digital transformation throughout your organization. Leveraging machine learning and analytics, automation is a powerful tool that can perform actions with minimal or no human assistance, providing significant improvements in efficiencies across the business. The key benefits of automation include decreasing time to resolution, reserving human skills for high-value tasks, boosting agility and speed, improving quality of service and the customer experience, while reducing costs.
When talking with customers about service assurance transformation, we often begin by looking at their initiatives in terms of what their actionable and achievable goals are. Many transformative strategies seek to improve the entire service operating environment in one giant plan. However, the road to success is laid brick by brick, using an approach that builds on itself over time to deliver more achievable and sustainable business results. Here are a few thoughts to help with the definition of scope and how to leverage automation as part of your digital transformation.
Define the problem
The end goal of automation is always to drive business value. So, before undertaking a project to streamline operations, you must identify a specific problem or task that needs improving. Once we have this, we can determine how automation can help achieve the objective. For example, any business can say, “We want to raise trouble tickets faster” but you’ll have greater success with implementing automation if you set specific, measurable objectives.
Here’s another example; “We currently have a lot of poor-quality data because it’s all coming from one source and much of it has been modified by downstream systems. By next year, we aim to add two related sources of data and eliminate one legacy system that modifies the data. Using automation will allow us to gather more raw data points, which will improve accuracy.” Establishing measurable and achievable goals at the beginning of the process will help you determine which processes to focus on automating first and lay the groundwork for your overall digital transformation strategy moving forward.
Splitting out each step of the process, analyzing it, automating it and moving onto the next step to do the same is the most efficient method for automating operations. Although all individual tasks are part of the same business process, each plays its own role in supporting the overall automation. Because automated actions are solely based on what you program it to do, it’s crucial to take the time to design and implement a process that you trust. Then, you can repeat the three distinct steps—input, processing and output—for each task. The output from one process can then be used as the input into the next and so on.
Input data to be automated
Understanding the data used as your input is important for establishing automated processes because it will ultimately trigger actions without human intervention. As the polite version of the saying goes… rubbish in means rubbish out!
Data is a multi-dimensional entity, with three key attributes to consider:
- Quantity (height) – The more data points you have, the more accurate and certain you can be that any analysis and subsequent automation will achieve the required results.
- Breadth (width) – Having multiple related data sources increases your ability to perform more intelligent correlation.
- Quality (depth) – Unmodified “raw” data is the objective. Data that has already been modified by downstream systems or processes can cause event and root cause analysis and correlation processes to be less accurate and, in some cases, fail entirely.
Use the above to evaluate if you have enough high-quality data to achieve the desired output at the end of your process. If you don’t, then fix the issues before you carry on.
Look for process improvements
Data processing involves collecting, organizing or manipulating data to create or classify information. Examine your current data processing methods, for example, when you raise a ticket or create an event. Then, look for ways to improve the process. What data is involved? Do you need it all or need more? Is every step in the process needed? Is everyone involved in the process adding value?
Often, you’ll find that outdated, inefficient processes are still in place for little reason other than, “We’ve always done it that way.” Improving the basics of data processing first will pave the way for even smoother automation. While you may add some business value by automating an inefficient process, you’ll drive much more value by automating a streamlined process. After evaluating and improving your processes, you should be able to achieve a clear set of data outputs that can be used to trigger an action or serve as an input for another process.
Test automation output
Use test data to check if your input, processing and output actions are all correct. Refine all three steps as needed. Driving digital transformation requires confidence in the initial triggers and automation processes, so it’s crucial to clearly understand your data and define your processes to satisfaction.
Leveraging automation to drive digital transformation simplifies the transition. By using the steps above to help automate processes, you can lay a solid foundation as part of your digital transformation strategy – whether you are removing unnecessary costs by unifying legacy systems to a single platform, preparing for 5G, implementing AI or looking to increase your customer experience levels by being more efficient. Incorporating automation into your operating environment today is essential for future-proofing your organization and developing your digital transformation strategy.
For more on this topic, please read my article in Pipeline this month.