… and make it make money for you instead, by enabling monetisation?
This is to all business owners, executives and leaders, who are not as tech-savvy as their best programmer, but they know there is an issue with data department and their costs.
As per A.M.E. Metwally, 90% of world’s data was collected in the past two years, but only 3% of it is actually monetised (lecture during MEA Data Innovation Summit in Dubai, 2025).
What’s more, Hyperight reports (link), that 60–73% of enterprise data is not even used in analytics, not to mention monetisation.
If you ask me — that’s a pretty terrifying statistic. Enterprises spend annually anywhere from $100k to $1M+ on their data processing and cloud infrastructure, which generates pure losses of at least $60k — $600k+ per year, simply because their data is not being used.
How to spot if I’m the one wasting my data potential?
There is a quick and easy test for that — go ahead and ask your IT/Data department to provide you with documentation on the data pipelines they introduced and their expected vs actual return on investment.
I bet, that in 99% of the cases you will not receive a full, comprehensive report (damn, even a report would be already a success), clearly showing the amount X that pipeline costs generates for you Y sales / leads / savings etc in return.
This is because most IT teams are running at full capacity, struggling to keep up with never ending business demands about new features, rarely looking at the actual return for the stuff they build. There simply isn’t enough time.
What preventive measures can be taken right away, at little to no cost?
Expect your team to say no
No should become a default answer. It is that simple indeed. If you hired a skilled team, you have the trust that they know what needs to be done. Therefore, they cannot reply to every single business request. You can force something if necessary, but let stakeholders be creative when coming to your team. Once they have to support their argument, they will think twice before they put a new request no one really needs.
Establish definition of project requirements minimums
Automate it in your ticketing system, put a procedure in place — whatever works for you. Make mandatory for requesting stakeholders to provide business context, ideally predicted ROI, but at minimum the predicted output on one (or more) of your business metrics (your team will be responsible for estimating costs). That way at least you know what it is intended to do.
Implement data lifecycle policy
It is a fairly simple process and if this sounds like gibberish to you — no worries, here we come: the idea is that for each file / table or any data that you store, you have a set of automated rules. These rules monitor how and when is your data used. Once it reaches a certain threshold, it is being archived and or discarded.
Example:
You have a monthly generated sales report in Excel. Your data team stores these files in the cloud. You only need these files on a monthly basis and at the end of the year. So you ask your team to implement automation, which will archive/delete the monthly Excel reports after the current year is over. Simple as that, yet many companies do not do it, or only do it in very few cases.
The measures above won’t cover it all, but they are a great start. Once implemented you will have:
- Your team clearly thinking more in terms of data monetisation.
- Your business stakeholders thinking twice before they request something from your team; and when they do — they specify the ROI.
- You will periodically get rid of unused and not needed data — Hyperight statistic in your case is likely to fall at least a good 50%.
What should be done at scale to ensure data monetisation?
Existing processes
Assuming you implemented the first steps, now you need a comprehensive audit of your data processes. It should focus on identifying key information:
- which business metrics are impacted by this process and by how much?
- what is the infrastructure cost of maintaining this process (that’s all your Cloud costs, custom tools costs etc.)?
- what is the people cost of maintaining this process — time spent on development, how often this process has an issue, how much time on average it takes to fix this issue?
Some of you may have already noticed, that you won’t get the people cost without proper incident reporting procedures — that’s out of the scope for this article, but I will write a separate one and link it here.
Now, if you are able to collect or, at least, estimate monetary value for all three points mentioned above, a simple ROI calculation will suffice:
Business Impact x (infrastructure cost + people cost) = ROI
New processes
You should review existing processes that you have in place. If you don’t have either of these, introduce them with help of your team:
- Calculating impact of new data pipeline / product / dataset on your business metrics.
- Separating costs related to specific data process out of all data costs (tagging assets, separate environments, warehouses, resources etc.).
- Incident management procedure (how we report a broken process, who handles it, what’s the SLA, how are we reporting on steps taken to mitigate the issue, what are the next steps after it was fixed etc.)
Once you’ve cleared these steps, the “simple” thing that remains to be done is to put a process in place that will ensure:
- Measurement processes above are in place.
- ROI / expected ROI is calculated as soon as possible, becomes part of the development requirements.
This way, not only you will have more control and information about the productivity and costs within your data processes, but also, with time, you will educate your team to make informed business decisions which clearly impact the business metrics you are trying to improve. This is the definition of data monetisation: to use the data that you have, to directly improve business metrics that you are working on.
How can I get help with these initiatives?
All of that sounds great, but you’re thinking: “where do I get started?” “I am not sure I understand it fully?” “My team is too busy anyway — I can’t ask them to implement all of these new procedures and optimisations..”
We get it. It’s more common than you think. This is why the data monetisation statistic hits that hard and is loosing money in 97% of the cases. That’s what we’re here for — book a free, introductory call with us to get yourself started; you can choose a time of your convenience here:
How to avoid loosing money on 97% of your enterprise data? was originally published in Lortech Solutions Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.
