Enterprise telecom expenditure is still quite a niche subject once you get into the detail of it.
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.
These points are raised in this McKinsey article, which also points out the approach of working in small pilot projects and successfully digitising the most critical tasks within finance, which allows the CFO to establish proof points and ease the eventual roll-out of digital technologies across the function (and other parts of the company).
What is the case then for using Telecom as a proof-point?
- Have you ever heard rumblings about the bills never being correct, even when they’re in your favour?
- Locked into commercial negotiations at end of year, because the bills need to be paid but there’s so many discrepancies that must be resolved first?
- Have a dedicated team reviewing every inch of the bill every month, on top of the normal administration?
Nothing is a greater opportunity sucker than not having a clean base.
And all these issues are not all tech-related, it’s caused by project updates not being communicated correctly, wrong assumptions about order requirements, discrepancies not being flagged quickly enough – compounding on a daily basis and when you have a large account these issues are exponentially magnified.
We all know the issues are solvable, so why aren’t they screams the account director?!
I managed an account with unpaid bills of around £7m, thanks to a national comms network that was never billed according to what was actually supplied and in compliance with the contract.
What was the guiding solution?
- Step 1) Establish a common database for every system and project report to be stored, with an abstraction method that related the raw data against how operations & contract managers would interpret the information.
- Step 2) Automate analytical patterns to gauge real-time project health and operational failures
- Step 3) Leverage the abstracted data to drive process simplification reviews
At the core, these three steps can be adapted to just about any use case within business operations that involve a complex number of data sources and co-ordination. It’s worth noting that while this example was implemented on the supplier-end it’s just as applicable on the client-end; data availability is a little different, but what is there all leads to the same outcomes.
So, why aren’t teams already doing it?
Given the rise of self-service analytic tools, business teams are becoming more tech & data savvy than ever before, empowered to look deeper and wider in their subject area, and has brought far better management reporting and general productivity.
Limits are found where there are a wide number of data sources that don’t integrate easily – think the dozen different reports that only make sense when the resident finance specialist sits down and looks through it all – made worse with unstructured documents (think legal contracts, emails, spreadsheets) that have multiple technical layers of data extraction, cleansing, interpretation, and attribution that aren’t possible to perform at scale without the right resources and focused technical leadership.
We’ve spoken with many managers who are savvy of these challenges and actively look to work out solutions, but with little success due to lack of time & resource and a general challenge to quantity benefits beyond a general sense, ultimately making this type of work a distraction from core functions (sounds familiar to the CFO’s challenge breaking down finance digitisation strategies). Existing packaged solutions focus on existing well-defined processes that can have a measured step-change improvement.
Industry trends like cheaper ML (though still only at the beginning of democratising appropriate, powerful use cases), user-friendly data integration tools, and the big cloud platforms (Azure, AWS, GCP etc.) are making it easier to spin up production-grade data solutions with the right knowledge investment.
These factors point to a rise in packaged data platforms that solve the data integration and abstraction effort for business teams, automate predictable analytical patterns, and provide a path for business teams to self-improve their work models. At TMS, we’ve called this Contract Intelligence.