The value of Kensu to our customers

In my role leading US Enterprise Sales at Kensu, I have the opportunity to work directly with our customers and partners. 

Since joining the company, I have seen data observability take off, with new projects appearing constantly. I think there are several defining factors for this, including technological advances (e.g., artificial intelligence and machine learning), process advances (e.g., data mesh and data as a product), and the market maturing. However, probably the most important one I get involved in is the justification for investing in a platform like Kensu. 

As a strong believer in the benefits of data observability, I find this one of the easiest parts of my role. We have provided transparent positive returns on investment (ROIs) to organizations with as few as three data engineers all the way up to enterprises with over one hundred.

With that in mind, I thought I would highlight in this blog post different examples of ROI that can be achieved through implementing Kensu. These are hard dollar figures and do not consider many of the softer benefits of data observability, which include:

• Improved trust in data across the business,
• Better morale throughout the data management and engineering teams,
• Reliable business decision-making.

For the benefit of the ROI calculator, we will say that the average salary for a data engineer in the US is $95k (this figure was provided by payscale.com), although often, our customers tell me it’s higher than this. In the calculation, we load the cost of the salary of the data engineer by 30%, to give us a loaded data engineering cost of $123.5k (the 30% is taken from figures published by the US Government at www.sba.gov).

On average, each data engineer manages 2.5 data products a year. A data product includes several pipelines as well as applications and data sources. Typically we see 6 pipelines per data product, and an engineer is currently spending around 8hrs per week maintaining, troubleshooting, and fixing data product issues. We always use a conservative figure in terms of the reduction in troubleshooting time with Kensu, and in this case, we are using 50%.  

Now that we have our baseline, let's see how it breaks out. 

Kensu can help companies across all sizes in several different industries. Based on these inputs, the Kensu solution would typically save an organization of around 7 data engineers $91,350 across three years and $30,450 in year one. On the other hand, a company with a data team of around 56 data engineers could see a positive ROI of $1,549,800 across three years and $516,600 during year one.

The true value of Kensu and Data Observability is best realized over a longer period of time. But it might not be as long as you think. On average, Kensu customers begin seeing a positive ROI within 5 months of using the platform. The cost of a Data Observability project is on a case by case basis, but the benefits are standardized across all: 

• Less time spent troubleshooting
• Faster time to resolution for the data engineering team 
• A proactive approach to data incidents 

Implementing data observability makes sense, both operationally and financially. Operationally you will improve the data reliability and hence the business users’ trust in data, and financially, you will free up data engineering time.

So my argument always is don’t delay; the longer you take to move forward on a project, your costs will compound and grow at an exponential rate.

If you found this blog interesting, feel free to reach out to me and I can share the ROI calculator and we can walk through your company’s use case and discuss the ROI Kensu could deliver for you.