In this two-part Blog post, we will examine the pros and cons or building an in-house IOT ANALYTICS SOLUTION versus licensing a solution from a leading industry player like Glassbeam…
Stakeholders representing enterprise companies spanning multiple industries of all sizes are acknowledging the value of Machine Data Analytics. The value proposition promised by implementing an Analytics solution initially centers on customer support and the quantitative and qualitative benefits derived from lowering the cost of customer service, increasing customer collaboration, and increasing CSAT.
Enterprises wishing to embark on this journey have 2 broad choices – Do It Yourself (DIY) Analytics (typically using a combination of Open Source tools and in-house expertise) or procuring a commercial license. We accept the reality of the appeal of DIY analytics for the enterprise intrapreneur, and we wish those embarking upon the development journey well – but tempered with the subtle warning that not many enterprise companies have the breadth and depth of talent to create a DIY Analytics Application that meets stakeholder expectations.
Calculating the Risk of Failure
We define the Risk Value = Chance of Failure X Cost of Failure
One can easily search the Internet on the success and failure rates of enterprise development projects. Typically failure rates range from 30% to 70%.
Cost of failure, measured in dollars, is the cost of building a Minimum Viable Product (MVP), through a Do-It-Yourself (DIY) approach for 1 year (Since most “Go/No-go” decisions are made within Year 1, especially by discerning CFOs). The Cost of failure incorporates the following variables:
- Cost of human resources.
- Cost of infrastructure.
- Cost of exceeding the project timeline.
- Opportunity Cost.
In our next post, we will walk you through a couple of real-world examples where we worked closely with a customer to weight the costs and benefits for a DIY approach versus licensing our SaaS platform.