4 Ways AI Analytics Projects Fail and How to Succeed
“How do I de-risk my AI-driven analytics projects?” This is a common question for organisations ready to modernise their analytics portfolio. Here are four ways AI analytics projects fail—and how you can ensure success.
Artificial intelligence (AI) will offer a tremendous benefit to businesses modernising their analytics tools. Many enterprises are already gaining valuable insight from analytics in some form—with traditional business intelligence, automated reporting, dashboards and more. Yet decision-makers may find themselves in uncharted territory when considering AI, deep learning or machine learning capabilities. How relevant is AI to them? How best to proceed?
Even the most accomplished and experienced IT leaders have worked on a transformational project that failed at some point in their careers, and some understandably view advanced analytics projects with scepticism. The challenges of integrating data from diverse silos are well documented. We’ve identified four common pitfalls that can derail a project—and three corresponding approaches that help organisations avoid trouble and realise a successful project.
4 Awry Analytic Approaches that Increase Risk
For many decision-makers, the thinking goes that risk can be reduced by piloting something early. Then, if it fails, at least we’ll fail early before making a significant investment or spending a lot of time. This view is based on experience with other approaches that expose limitations. Organisations that have adopted one or more of the following approaches, however, can raise the risk of failure past an acceptable level:
Starting by collecting all the available data and then determining how to use it can be tempting. The problem is that any organisation may have petabytes of data, only a fraction of which has true value to the business. Placing it all in a large data lake, for example, may not necessarily lead to failure, but it can use a great deal of energy without any assurance of positive outcomes.
Pursuing several opportunities at a time is tempting because of the large number of AI opportunities available. Unfortunately, pursuing many opportunities this way dramatically increases the risk of failure because it dilutes effort, and may increase the complexity of implementation.
Starting by building out an AI capability with the intent to gain clarity on the question or objective later can lead to avoidable risk. If the insight to be gained by the analytics project doesn’t clearly demonstrate how it’s expected to provide business value, hitting the target is all the more challenging.
After investing substantial time and money in an initiative, only to realise that the question went unanswered or the gains unrealised can court failure. For example, you may risk the loss of support from key stakeholders who needed a more expedient return. Getting sign off for future projects might become an uphill battle.
How to Succeed
How can organisations de-risk their transformational AI analytics projects and help ensure successful outcomes? Three strategies can help organisations avoid the common pitfalls we’ve outlined when seeking the AI insight you need to boost business value:
Get Clarity on the Business Question
Bring together a team of stakeholders that possess business and analytical skills and apply critical-thinking techniques to question your suppositions. What does success look like? Where does the data come from, and which decisions might it support? How does the organisation integrate this new insight into operational processes? The team also needs to determine what form the analytics data should take so that business users can consume it.
Enable Faster Exploration
Use a data science toolkit to assemble an ad-hoc workflow that is tailored to the specific problem identified.
Empower a Quicker Win
Use a data science sandbox to create a prototype. If an idea is successful, the prototype provides an easy way to demonstrate benefits and enables fast scaling to production. Leaders can gain confidence in the idea early, rather than waiting for the fully developed analytics capability to be demonstrated.
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