Are You Getting the Best Return on Your Investment?
ROI, or return on investment, is a critical metric in measuring the success of any investment, or in this case, automation investments. In today’s competitive technology landscape, organizations are taking on more work and offering more products and services to stay afloat. But taking on these additional ventures can be time consuming and costly, especially if you need to hire more staff to accommodate the growing business. That’s where automation comes in.
Automation can help cut costs and reduce time spent on time-consuming tasks that are essential to your business. But if you aren’t effectively automating these processes, are you gaining the most on your investment? An Automation ROI Assessment can help you figure out which areas of your organization could be improved upon with automation, or which areas could be automated more efficiently.
How does an ROI assessment work?
In 4-6 weeks, our team at Data-Core will assess all of your current automation efforts and recommend any new solutions or areas to improve upon where you can get the most return. Depending on the scale at which you automate, our assessment can even be done in as little as one week.
Our Automation ROI assessments follow a risk-based approach where we assess risks like application exploit potential, DevOps defect tracking analytics, incorrect utilization of data, performance bottlenecks, uncovering unknown dependencies in your microservices architecture, future-proofing your current cloud and hybrid infrastructures to assure scalability and cost efficiency, cultural disparities and other general operational inefficiencies.
Some other examples of metrics used to measure your ROI are:
- Automated test execution speed
- Accuracy and quality of results
- Resource analysis, or resource requirements, cost of training, etc.
As an example, let’s look at an automated machine learning (AutoML) assessment project we have previously done for a client. A machine learning project can require hiring professionals with a data science or statistical background, which can be very expensive. We found that when using these resources, and spending some time and money, we saw results, but it took 6-8 months.
During these 6-8 months we tried a few different algorithms to test with the client’s data. Through trial and error, after 6 months, we finally found an algorithm that would best fit the data. Even then, we were only producing a 55-62% accuracy rate after running the algorithm, which was not satisfactory.
By developing an AutoML solution, we eliminated the need for data scientist personnel and enabled the client to run this algorithm with little statistical knowledge. By using the AutoML solution, the client was able to produce a result in just one day, versus 6 months manually.
Although it’s not necessary to automate all tasks, automating as many as you can will save time and money in the long run, so your organization can operate more efficiently.
Not sure how to assess your automation efforts? Contact us and we’ll help you assess your needs and get the best return on your investment.