Why data insight projects fail and how to succeed – Part 1
Reports across every industry tell us that a very high rate of IT projects will fail to deliver on one or more aspect.
On average, only around a third of projects are delivered on time, in scope and without budget overrun. Data Insight initiatives are no different. As data analysis solutions become more and more important to organisations if they want to stay competitive, manage processes and improve operations, the drive for these projects to be successful is on a steep rise.
In this 4-part blog series, we look at some of the common reasons data projects fail and the key underlying solutions that can help businesses improve project success rates.
Reason 1 – Outcome Alignment. What does the future hold?
The first and most important part of any data analytics project is to map out its real purpose and create alignment with the business strategy.
Organisations are often presented with requests for such solutions accompanied by the case “we need to bring our data together to create more value” or “this will solve the data transformation problems we currently have”. However, these reasons don’t address the true benefits that could be realised and this makes return on investment (ROI) measurement very difficult.
You’re about to allocate valuable time, resources and investment into your next solution, so in order to deliver a fit for purpose solution and measure ROI you should always begin at the end. This means mapping out what improves in your organisation once your project is delivered. Alongside this, do these improvements align to your overarching business strategy, goals and objectives?
A common error is to confuse the features and benefits of a solution with the outcomes delivered through implementation or its use.
One such example of this scenario is where businesses believe procuring a model toolset is an outcome. In the world of analytics, standing still is no longer sustainable and businesses must continually evolve with the ever-changing technology landscape. By recognising that the outcome from the analytics project is to maximise the value that can be derived from data assets, the roadmap is set for a solution to be sought. This may involve the use of modern data tools, a strengthening of the data governance or could be a change in the approach to how data is utilised.
The following represents an example of a well-constructed outcome:
- Feature: Embedded analytics in selected solution
- Benefit: Users are able to easily analyse data and generate insight
- Outcome: Company saves 15% in costs from identifying operational inefficiencies
By establishing the desired outcomes before undertaking a project, you can better manage expectations and are more likely to deliver a solution that drives tangible, positive improvements and experiences.
Without aligning the outcomes of a data project to your business goals and objectives (as well as your IT strategy), you run the risk of failing to deliver tangible improvements. When ROI is evaluated at a later date, if there are no measurable improvements, it can create challenges when seeking approval of future projects.
Proposing a solution mapped to outcomes that align to your business goals creates a direct correlation between your project and a positive impact. By taking this approach you build better relationships between IT departments and senior stakeholders, as well as ensuring everything you do is continually helping your organisation. This is a virtuous circle, which generates an appetite for continual improvement through the use of data insight.
In order to put data at the heart of a business, it must be used to deliver outcomes that shape, develop and advance the business in all areas.
In part 2 of this series, we look at estimates and how to build a well-developed business case to secure investment for your project.