Drawing parallels between Cloud Native, Cloud Enabled and Data Native, Data Enabled

Just sharing a playful analogy to end the week (and month).

Like me, you’ve probably heard lots of conversations that contrast cloud-enabled applications with cloud-native. We’ve heard the arguments that an OSS or BSS application might be “on the cloud,” but not really the gold-standard “cloud-native.”

We know there are lots of legacy OSS / BSS applications that are really just the same monolithic applications, perhaps with just a few minor tweaks to ensure they run on a cloud-based Virtual Machine (or machines). Scaling of infrastructure / resources is mostly manual. That’s cloud enabled.

Cloud-enabled solutions aren’t considered to be cloud-native because they haven’t been architected to take full advantage of the autonomous scalability or resiliency features, nor modularity of cloud-hosted resources. They don’t tend to have the closed-loop mechanisms to re-shape the app to meet current conditions like cloud-native apps do.

I’m not going to get into the religious war of what’s the right fit for any given OSS/BSS, nor whose solutions are more modern / sophisticated / cloud-X / superior than others. To be flippant, there’s probably some sort of continuum between cloud-enabled and cloud-native that almost any OSS/BSS product resides on (except the really legacy ones!).

Instead, I’d like to borrow the wording to bring an analogy to the use of data to drive decisions within an organisation, especially OSS/BSS data. 

To state the bleeding obvious, there’s been an increasing shift towards the importance of data in recent years. We collect a mountain of the stuff with every OSS and BSS application. The question comes down to how we actually make use of it (or in some cases, IF we make use of it).

If you’re a data-native organisation, you have mechanisms that take that mountain of data and make operational decisions with it, in an automated or semi-automated way. You immediately re-shape the organisation based on information as it arrives. The most extreme example of data-native is a DAO (decentralised autonomous organisation).

I don’t know of any network operators that are even close to resembling a DAO yet, even though OSS and BSS solutions probably have a direct line to most important data points from which a telco’s decisions could be made. They collect information about customers (BSS), revenues / sales / profits / margins (BSS / billing), sales pipeline (CRM), the efficiency of the workforce (WFM), state of projects, the state / health of assets and almost any other KPI you’d want.

Reality is that most operators are more data-enabled. They have data. They make some use of it. They don’t really have a closed-loop mechanism to make use of much of it though. Sometimes execs have dashboards driven by the automated collection of KPIs. Mostly they don’t. Sometimes they use data collected manually. Sometimes by consulting houses. Sometimes their executives make key decisions based on data. Sometimes not.

The question for you and your organisation is where are you on the continuum between data-enabled and data-native? The other question is how we can better make use of our OSS/BSS data to create insight / decision support loops that help take us more towards the data-native end of the continuum (assuming executives actually want it).

What do you think?