Business Assurance: Decision-driven Versus Data-driven
Having a data-driven Business Assurance (BA) process is an aspirational goal for many Business Assurance teams. This post is not going to be about the technology per se but will talk about what comes before and after technology: humans and our flaws.
Three Key Aspects of Business Assurance
Before delving into human-before-and-after-technology, let’s review three aspects of Business Assurance, which form the backstage onto which most of the Business Assurance activities unfold.
Since its inception, Revenue Assurance (RA) and, later, Business Assurance (which we’ll use interchangeably for the purpose of this article) was a borderline discipline in telco organizations. It has always sat in-between Finance, IT, and Technology, fighting to influence decisions and processes. We have evidence from RA surveys across the years that shows us that RA/BA has consistently resided in different parts of a telco organization.
Apart from being a borderline activity, RA, done operationally, is dominated by FOMO – fear of missing out. In RA, FOMO works from two perspectives: from the perspective of the process owners that RA is checking and also from the internal RA one. When looked at from other process owners, FOMO is an actual driving force behind RA actions: RA is winning when discovering what others missed out. But FOMO has a play from its own RA perspective such as the fear of missing out on creeping revenue leakages, missing out contributions to impactful projects, missing out on budgets, or missing being relevant within the organization.
And finally, RA/BA is hard to be conceived without data. Business Assurance delivers rich analytics aimed at enabling action. There is, however, a previous step before action: the decision. Here is where most RA/BA strategies miss out on: the decision is an implied, taken for granted, step. In the same way, being data-driven is often taken for granted.
The combined effects above make RA/BA a fragile component for any telecom organization, exposed to the political play between major verticals driven mostly by fear, and a weak decision-making process.
Now, BA leaders cannot change the first two aspects, but they can definitely understand and improve the ‘taken-for-granted’ part of the whole activity: the decisions.
Decisions impacting Business Assurance
A telecom organization makes decisions when it launches a new product or implements a new technology. But automated decisions also include the rating and billing ones we take for granted. Each rated amount in the customer usage is a decision – to apply a certain value, according to the customer’s rate plan, for the specific service and usage type.
There are also internal BA strategic and operational decisions: The extent of risk coverage to implement, the tools to be used, and processes to be implemented are strategic decisions made internally by BA departments, aligned with the larger parts of the organization. BA’s operational decisions are, amongst others, to validate the alarms/findings of the controls, raise them with the process owners, manage the cases, update configurations, etc.
|Business Assurance Decision Space||Business Assurance|
With this high-level inventory of the decisions BA is called to make, or to protect, we can glance at how aiming to be a data-driven organization can actually hamper decision-making.
Decision-driven or data-driven?
We already know that being a data-driven organization is a catchy phrase in business lingo nowadays, which comes with the effect of digitalization.
But being data-driven has some hidden implications. Making decisions solely based on the available albeit massive, data is the equivalent of looking for keys under a lamp post.
A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he has lost his keys, and they both look under the streetlight together. After a few minutes the policeman asks if he is sure he lost them here, and the drunk replies, no, and that he lost them in the park. The policeman asks why he is searching here, and the drunk replies, “this is where the light is”
This is called the “streetlight effect”, a type of observational bias that occurs when we are searching in places where it is easier to look.
Put in the context of the data-driven organizations, business assurance included, it means that we are exposed to a limitative fallacy, where even though we have plenty of data, we still make poor decisions.
Such effects have been highlighted in academic literature, where the risk of decisions made, is not based on a purpose, but based on a preference for using a certain set of data because it is available or it validates a preference already made.
How would such a bias affect the decision outlined above? For BA, it would mean that a sound risk assessment and decisions to cover certain areas will be impaired by the data available: a BA team could start covering the usage because that is the data IT want to give them access to; or because it is better documented, even though the risks would be higher in another area, like subscriptions or dealers, just to give a few examples.
Operational BA decisions would be impaired as well, if the cases are validated as false purely based on data available while the flawed result may be caused by the out-of-date reference data.
Another impact of such bias would be that decisions tend to be made only based on available data, without using statistical inferences, hypothesis testing, and judgement.
The way out
The way out is simple, even though simple does not necessarily imply easy.
The purpose should precede the data. This means, as professor Bart de Langhe puts it in the article cited above:
Find data for a purpose, not a purpose for your data
In the context of decision-making for Business Assurance, it means that the decisions for covering certain risks, revenue streams or lines of business should guide the use of data, and not the data available to drive such decisions.
It also means that BA professionals should get acquainted with hypothesis testing and statistical thinking in order to make better decisions in conditions of incomplete information.
The latter point is a call for modern analytical tools and platforms, which are enhancing the existing capabilities of RA/BA teams with advanced analytics and ML capabilities, able to deliver analytics in an agile manner and to support the rising citizen data scientists.
 Why decisions should drive your data analytics, https://dobetter.esade.edu/en/decisions-data-analytics, retrieved June 30, 2021
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Stefan has more than 17 years’ experience in telecoms risk management, helping telecoms organizations navigate risk and technology. He has worked on various roles related to Revenue Assurance, Fraud Management, Credit Risk Analytics solutions as a consultant, trainer, subject matter expert, and project manager for various operators worldwide. Currently he’s a principal consultant in Subex’s Business & Solutions Consulting European team.