Every business knows that their data and analytics capabilities are probably their best weapon in today’s digital world. Unfortunately, many haven’t been successful at implementing it.
This failure has left a sour taste in many businesses mouths, and they are somewhat wary of taking on enterprise-level data and analytics efforts. But, by refusing to give the necessary focus to these entities, organisations are doing themselves a disservice.
So how can businesses embark on a successful data mission? By looking at the key reasons why these initiatives often fail we can learn about what organisations should – and shouldn’t do – if they want to succeed.
- Inaccurate current state assessment – If an organisation doesn’t know about their current state it’s nearly impossible to formulate a strategy and approach that will work.
- Misjudged value proposition of new technologies – There is an enormous buzz around the likes of machine learning and artificial intelligence for data science, but sometimes these solutions aren’t feasible.
- Reliance on technology deployment as the primary answer – Technology is not the be all and end-all of data and analytics; organisations need to be wary of focusing on a technology as the only solution.
- Thinking differently from software development – Businesses often focus on what it takes to build software; working from a set of requirements and building a system that meets that need. Working with data is significantly different, and therefore requires a different kind of process.
- Lacking in the necessary people and skillsets – Lack of skills at the right levels, lack of coordination with the team and misalignment between IT and business are the most common contributors to data and analytics failures.
Data and analytics at the enterprise level is no small task. It is fraught with risks and challenges. Understanding these five common pitfalls and addressing them appropriately will greatly increase the probability of sustained success.