Our approach intertwines financial acumen, agile methodologies, and evidence-based management to guide product owners and managers in shaping business cases that hold water.
Resulting in:
Optimizing
data investments
with business cases
that resonate.
Do you struggle to show the efficiency of your data investments? We can help.
- Sharper Business Cases: Abykys zeroes in on refining your business cases. Our emphasis on evidence-based management ensures that your data investments are backed by solid reasoning and contribute directly to your business’s growth.
- Efficiency Through Clarity: Wave goodbye to endless meetings. Abykys seeks to streamline processes by offering actionable insights, allowing you to make informed decisions without getting lost in discussions.
- Holistic Alignment: Business success relies on the seamless harmony between data and overall strategy. Abykys bridges this gap, ensuring your data investments become a driving force for your business’s advancement.
Exploring the ramifications of ineffective business case management in data investments
Data is considered a must-have. A typical example is compliance or protecting the organization’s future. This can become a compelling excuse for every data investment, leading to costs running out of control and losing focus.
Especially applicable to
Financial industries where processes are mainly data-driven and compliance requirements grow yearly. We hold experience on this topic with a digital bank.
Understanding the effects of an IT budget reduction is for many organizations complex because the IT cost has lost the connection to the benefit or requirement it was once approved for. This can destroy precious intellectual property or ongoing waste of money. It is leading to unpleasant and political debates based on little facts. Measuring efficiency will ensure a link between cost and benefit and understanding what cost leads to what benefit.
After a slow start, the business is entirely on board, providing more ideas and a budget the organization can digest. How to prioritize all these different business cases that all claim business value but are incomparable?
Especially applicable to
Organizations that needed digital during Covid to survive. For example, retail must shift to online and now needs to scale back because people are moving back to the stores and spending less. We have experience with one of those retailers.
Creating a business case and getting one approved has become a process everybody hates. There are no understandable templates, we end up convincing each other on technical terms, and when the decision on the business case is finally made, all discussion points are forgotten. The only information registered is the businesses get an increase in target and IT in budget.
Both are especially applicable to
Large organizations which already dealt with multiple cost reductions over the years. Examples are infrastructure organizations. We hold experience with mobility and telecommunication organizations.
What’s the difficulty,
you might ask?
No reference material.
We must respect that many of these data implementations have never been done in the organization. Therefore we should allow assumptions and increase the validation pace, learning how to make money out of data. Especially with hyped expectations from often biased big tech and consultancy agencies. Financial tools applied to traditional business investments cannot deal with data investements as they expect a much higher degree of certainty.
Very flexible.
Data and its supporting systems are nontangible and highly flexible in usage. Components used to build solutions comprise a large amount of pay-per-use features and a mix of insourced and outsourced elements, while it is also very reusable at almost no extra cost.
A lack of ownership.
Data is incorporated in more and more business processes where it used to be a generic cost that could be easily linked to the number of users. This has led to unclear ownership, and although various new C-level roles have been introduced, ownership of the efficiency of data is still unclear and often end up to be a responsibility for a product owner or portfolio manager to deal with.