Yesterday we all read about a news that Brightpoint sued Emptoris - a leading Spend Analysis Service provider based at MA over failed to deliver the service as expected - Brightpoint suing software vendor for negligence
There are multiple posts and opinions started pouring the web about why this would happen and what Emptoris should do to respond. Competitors may start using this as a point to win some of the deal and so on. But I think there is a larger message in all this - and that’s about how you sell your product / service, how you setup customer expectation and how your delivery mechanism has been setup - business sensitized or mere a product delivery and lastly whether you stress on value or volume.
In today's Spend world, what are the components that one needs to ideally talk about while engaging with customer for Spend Analytics requirements?
1. A strong analytical BI tool - which allows you to maximum slice dice capabilities
2. A strong backbone data service - which provides you good cleanse, classified data which boost your ability to see holistic trends and identify savings
3. A process consulting to guide customer on how they really need to look at the data to get maximum value.
When you look at the market - First part of BI tool takes center stage, as sales person understand and provide visualization to end customer well by "Showing" something. The second part of Data service - which is more critical takes a back seat, as one can rarely show something without executing a free of cost POC - which makes sales process very long. Also Data Service is usually over committed in sales cycle without looking at customer data. Now if customer's data is really bad in legacy systems - who the hell in the world can provide 99% accuracy for that - a sales person can. And least we talk about the third part. As I mentioned in my last post of "What is the go live point for Spend Project" - Once the product is implemented (installed, running reports) - everybody claps about a "successful" go live - but does anybody care about the educating and guiding user community on what the value they get out of the product? How they should be looking at data - and most importantly how they should not be.
The second important point is - expectation management. Spend analysis is a collaborative efforts between customer and vendor. By imposing a usual "Project" tags on it - everybody tries to "manage" milestones. By its nature spend analytics are engagement driven efforts, to keep fine-tuning the data, keep enhancing process to sensitize it for specific business or organization, enhancing visibility through product features and as a end goal - getting a maximum value - identifying potential savings out of it. These are all "Analytical" processes and "business intelligence" tools - which does not get you any savings automatically but just make sure you have right advice, full picture where you can go. Now it’s still business user’s ability to use that tool which actually saves money in longer term. So the question is - are they really engaged in cycle well, are they well educated by vendor on expectations, are they advised enough on what not to expect from the process? Are we telling them if there data is not good enough - it’s not going to be "cool" to start with - but as fine tuning happens - there are opportunities to make it "cool".
The last point is delivery mechanism. In my opinion it should not be a mere "data factory" kind of setup. I fully understand that the data service thing cannot be fully automated in any case - so one does require a manual intervention to look and add human intelligence to make decisions about the data. But that should be a repository based - engine based auto learning process. Also the product implementation should not be a delivery goal. BI product should be seen as a medium to achieve that savings goal.
Any thoughts or comments - let me know - pmendki (at) gmail (dot) com
Thanks
Prashant Mendki
There are multiple posts and opinions started pouring the web about why this would happen and what Emptoris should do to respond. Competitors may start using this as a point to win some of the deal and so on. But I think there is a larger message in all this - and that’s about how you sell your product / service, how you setup customer expectation and how your delivery mechanism has been setup - business sensitized or mere a product delivery and lastly whether you stress on value or volume.
In today's Spend world, what are the components that one needs to ideally talk about while engaging with customer for Spend Analytics requirements?
1. A strong analytical BI tool - which allows you to maximum slice dice capabilities
2. A strong backbone data service - which provides you good cleanse, classified data which boost your ability to see holistic trends and identify savings
3. A process consulting to guide customer on how they really need to look at the data to get maximum value.
When you look at the market - First part of BI tool takes center stage, as sales person understand and provide visualization to end customer well by "Showing" something. The second part of Data service - which is more critical takes a back seat, as one can rarely show something without executing a free of cost POC - which makes sales process very long. Also Data Service is usually over committed in sales cycle without looking at customer data. Now if customer's data is really bad in legacy systems - who the hell in the world can provide 99% accuracy for that - a sales person can. And least we talk about the third part. As I mentioned in my last post of "What is the go live point for Spend Project" - Once the product is implemented (installed, running reports) - everybody claps about a "successful" go live - but does anybody care about the educating and guiding user community on what the value they get out of the product? How they should be looking at data - and most importantly how they should not be.
The second important point is - expectation management. Spend analysis is a collaborative efforts between customer and vendor. By imposing a usual "Project" tags on it - everybody tries to "manage" milestones. By its nature spend analytics are engagement driven efforts, to keep fine-tuning the data, keep enhancing process to sensitize it for specific business or organization, enhancing visibility through product features and as a end goal - getting a maximum value - identifying potential savings out of it. These are all "Analytical" processes and "business intelligence" tools - which does not get you any savings automatically but just make sure you have right advice, full picture where you can go. Now it’s still business user’s ability to use that tool which actually saves money in longer term. So the question is - are they really engaged in cycle well, are they well educated by vendor on expectations, are they advised enough on what not to expect from the process? Are we telling them if there data is not good enough - it’s not going to be "cool" to start with - but as fine tuning happens - there are opportunities to make it "cool".
The last point is delivery mechanism. In my opinion it should not be a mere "data factory" kind of setup. I fully understand that the data service thing cannot be fully automated in any case - so one does require a manual intervention to look and add human intelligence to make decisions about the data. But that should be a repository based - engine based auto learning process. Also the product implementation should not be a delivery goal. BI product should be seen as a medium to achieve that savings goal.
Any thoughts or comments - let me know - pmendki (at) gmail (dot) com
Thanks
Prashant Mendki
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