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Keep up to date with our news and thoughts about Telecom Expense Management, Contract Value Intelligence and Spend Analytics.

Posts by Mark Selby:

Optimising Enterprise Telecom in Digital Transformation

Optimising Enterprise Telecom in Digital Transformation

Enterprise telecom expenditure and strategic cost management is still quite a niche subject once you get into the detail of it, but the rise of Digital Transformation and Digital Procurement means it is quickly becoming a focus area for many enterprises.
 
From a data perspective, it overlaps all the services that make up the essential communication network within companies, the onion-like layers of supplier terms & conditions, and vast complexity that goes into the supplier-end operations to supply a service.
 
It’s very common today to focus on the top-line issues, i.e. what’s your overall service requirements, and what’s the best deal I can negotiate from a supplier. Once these top-line issues are agreed and a Telecom contract is implemented, there is a hidden gold mine of efficiency opportunities that can alter how company departments operate while providing large improvements to the bottom-line expenditure.

Why is this hidden? Because finding the efficiency opportunities, and executing them, are found through rigorous data & business analytics that extend past the capabilities most organisations have today.
 
This marks part of the Digital Finance Transformation, recognised as a key driving force for companies in the coming years. It’s also been found that most CFOs aren’t sure where to start on such a mammoth objective, evidenced by the level of funds still weighted towards traditional ways of working.

Contract Value Intelligence: Optimise your Telecom contract savings

Contract Value Intelligence: Optimise your Telecom contract savings

As a buyer, how do you prevent Value Leakage in your complex Telecom contracts? Simple, Spend Analytics! 

Telecom is increasingly a prominent feature in enterprise budgets. Annual spend on Telecom equates to an average 1-2% of enterprise revenue. Therefore, significant savings and efficiencies found in Telecom will make a significant impact on the bottom line. To beat bad data, information silos and increasing value leakage, enterprises must focus on spend analytics.

Today, a fragmented information environment characterises Telecom category management for buyers:

  • Contracts and invoices hold tens of thousands of line items containing references to different definitions.
  • Definitions made up of implied rules defined somewhere else across the contract or standard supplier T&Cs.
  • Implied rules are tangled in an undefined web to other sets of definitions.
  • ‘Shifting sands’ of contract variations and regular updates of standard supplier T&Cs.

As a result, Telecom Category Management has no standardisation and will mean something different depending on who you ask. Eliminating value leakage is not seen as a routine operational activity today because there has been no feasible way to systemise operations. When enterprises perform related activities, they are performed manually and only done at significantly timed events due to the high time, effort, and specialisation required to complete the work. These significantly timed events typically are: 

  • During the RFQ / negotiation stage, when price benchmarking is completed to give the buyer an indication of what they should be paying.
  •  Resolving a longstanding commercial issue, when billing issues have compounded to the point that extraordinary intervention is required to get things under control.

Digital Procurement: What is a Thinking Machine?

Digital Procurement: What is a Thinking Machine?

When we thought about our approach to enterprise data, telecom expense management, spend analytics and digital procurement, the name came quite naturally.

Our corporate background was in a company with one of the most complex data warehouses in the world. Legacy data warehouses were built on top of one another, each with an initial scope to be the ‘source of truth’ but ultimately finding it too complex and expensive to implement.

Instead, build was made on a compromise of requirements and provided piecemeal reporting that fed into an ever-growing web of manual analytical processes for end users to achieve their outcomes.

I was lucky, in that my early days as a customer service consultant inadvertently afforded me access to the source feeds across dozens of key applications. Having no technical literacy at the time, my only frame of reference was the end business purpose of the data I was looking at.

Seeking technical mentorship, I slowly started translating my business knowledge into data modelling and development. There was clear transformational potential in having complete, holistic datasets that represented what was really going on in the systems day to day.