Imagine hiring a contractor to build a house that only had a 30% chance of success in being a completed, functional home. It sounds absurd, doesn't it? Yet, in the world of IT, this scenario isn't far from reality when building a data analytics platform for an organisation. Shockingly, at least 70-80% of data analytics projects globally end in failure despite over 40 years of projects and evolution. In this article, we'll explore the alarming failure rate, the role of data analytics automation as a solution with the suggestion that major consultancies should champion automation for a higher likelihood of success on behalf of their clients.
The Staggering Failure Rate
In 2017 Gartner analyst, Nick Heudecker asserted that a 60% data analytics projects failure rate estimate was far too conservative, with the real figure closer to a staggering 85%. That's a significant majority of projects falling short of expectations and investments going down the drain.
The Root of the Problem
Why do data analytics projects fail at such an alarming rate?
Complexity: Data analytics projects involve collecting, processing, and analysing vast amounts of data from disparate sources. This complexity often leads to scope creep and project delays.
Data Quality Issues: Poor data quality can lead to incorrect insights, making it challenging for businesses to make informed decisions.
Resource Constraints: Limited budgets and a shortage of skilled professionals can hinder project execution.
Insufficient Stakeholder Involvement: Successful data analytics projects require active involvement from various stakeholders, including business users, IT teams, and data scientists. When stakeholders are not engaged or do not have a clear understanding of the project's value, it can lead to misalignment and project failure.
Major Consultancies and the Status Quo
Looking at the role of major consultancies in this scenario, who are vital in providing technical expertise and experience on these projects, there is a preference in some cases for manual, labour-intensive approaches over automation. Why? It often comes down to a business model conflict. For consultancies, manual projects are labour-intensive which leads to more staff and billable hours, a factor that significantly boosts the bottom line for consultancies.
The Case for Data Analytics Automation
So, how do we break free from this cycle of failure? The answer lies in data analytics automation.
Efficiency and Speed: Automation streamlines the data pipeline, reducing development time and effort. This translates to quicker insights and a faster return on investment.
Consistency: Automation ensures that data processes are executed consistently, minimizing the risk of errors caused by manual intervention.
Resource Optimisation: Automation leverages existing resources efficiently, reducing the need for extensive manual coding and specialized skill sets.
Scalability: As data requirements grow, automation can seamlessly scale to meet those demands, ensuring your analytics project remains future-proof.
Cost-Efficiency: In the long run, automation proves to be more cost-effective than manual approaches, offering substantial savings.
Considerations for Consultancies
With all these advantages in mind, it's time for major consultancies to throw their weight behind automation vendors. By doing so, they not only increase the likelihood of project success but also align with their own interests:
Client Satisfaction: Successful projects lead to satisfied clients, repeat business, and positive referrals.
Resource Allocation: Embracing automation allows consultancies to allocate resources more strategically, focusing on tasks that genuinely require their expertise.
Competitive Edge: Automation can be a unique selling point, setting consultancies apart in a competitive market.
Moreover, in this climate of tighter financial constraints, harnessing the power of automation can also enable some consultancies to offer a competitive fixed price quote or be able to manage multiple projects with positive delivery outcomes. Win! Win!
Conclusion
In conclusion, the shockingly high failure rate of data analytics projects demands our attention. Automation offers a clear path to higher success rates, efficiency, and cost-effectiveness. Major consultancies need to recognise the value of automation and its alignment with their own goals. It's time for a positive change in the world of data analytics.