Having spent the past 30 years ‘working in IT’ with almost 20 years focusing on data, I’ve seen plenty of key events & challenges – the introduction of the internet, Y2K, the rise of big data and now AI, but one thing has remained constant – businesses don’t always get what they expect from their ‘investments in IT’.
This is my simple guide of the Do’s and Don’ts that will impact the success of any data related project.
It is imperative that any solution be driven by the needs and priorities of the business and not solely by IT’s view or understanding of what they believe to be the need. Without any defined and understood business value, any project will be IT led and therefore treated as a technical implementation to deliver ‘functions & features’ rather than something that solves a business problem.
If it’s purely a re-platforming project to move away from an unsupported platform or to reduce license/usage costs, then make sure that this is clear and business users know that it’s going to be business as usual, at least in the short term with a view that the move to this new platform will enable future business value to be achieved.
All projects should have a business sponsor working alongside any IT sponsor even if the project is purely a migration onto a newer platform, for example a migration to Microsoft Fabric.
For projects where the business case includes delivering value to business users, a business sponsor must have the authority to make decisions and will champion the project internally. This assists with management’s expectations as business challenges are understood, use cases developed and any impact on existing processes/data management procedures is determined.
This leads nicely into another reason projects don’t deliver as expected – a lack of solution adoption. Business users are unwilling to embrace analytics solutions for two main reasons: 1. They do not see any benefit/value which can be linked to poor change management, and 2. Employees in general do not like change and can be reticent about adopting a new solution unless they can see it delivers value to their working day.
It is very easy for the project team to make assumptions around the level of data literacy of key business users, this is a particular challenge when the business case includes a move to self-service style interactions with analytics solutions. Even if business users see the value of the new tool then they must not only be allowed the time to familiarise themselves with it’s technicalities but given the base skills to get the most out of the solution – an effective user adoption programme is essential.
If there is too much focus placed on the product itself and ‘it’s wondrous capabilities’ rather than taking time to see how the product and it’s features solve existing problems or delivers real business value then the full potential of the product will not be realised. Technology itself will not solve the problem, there must be a robust delivery, implementation and business value methodology. The technology itself is only the enabler to delivering business value. Having a sole focus on delivery of this new technology rather than demonstrating how it will deliver business value and enable sustainable user adoption will not in itself deliver success.
At Coeo we ensure that our engagements are linked to delivering business value and that any specific success criteria are clearly defined and communicated from the outset. Our delivery teams can support you at any and all stages of your data transformation journey, whether it’s a data platform engineering team to assist with your move to Microsoft Fabric, a data modelling/visualisation team to help drive up user adoption or a full programme delivery team that assists with defining the business case right through to delivery and beyond.
One key reason projects often fall short, especially as technology advances with increasing rapidity – is their long delivery lifecycle. Coeo’s up front focus on discovery and design plus adoption of agentic AI accelerators such as Coeo Alloy leads to real business value being delivered at the earliest opportunity. Identifying quick wins or a simple use case that takes no longer than three months to deliver will quickly create a community of solution advocates. A ‘big bang’ approach is unlikely to work as business sponsors and users will lose interest, priorities and business requirements will change leading to a delivery of an outdated and no longer relevant solution.
To find our more about Alloy and how we are using agentic AI to build repeatable assets and speed up delivery without compromising quality. Register for our lessons in Agentic AI live session