“If I had asked people what they wanted, they would have said faster horses.” Henry Ford
I’ve seen this quote referenced so many times with regards to data and analytics technologies and it seems so perfectly apt. With the rise of Big Data and analytics we have seen a surge of investment in the various tools and technologies – en masse. Yet, not everyone is delivering the innovative business insights they were anticipating for success. Open source technology has reduced the barriers to entry, making it all the more tempting to implement them in a “me too” style. Implementing the same tools as the rest of the crowd and trying to do it better is not likely to benefit you unless there is a clear need for your business.
During my recent research in developing the Chief Data and Analytics Officer Forum, Melbourne, I came across some of the key reasons why organisations are unable to leverage their data for innovation.
Top 5 Issues to Address:
1. Lack of an enterprise-wide strategy. In a recent post about the data disconnect, it touched on the importance of a carefully managed data analytics strategy. Data strategy must it be effectively communicated across the business and underwritten by well-developed organisational support in order for it to become an inherent part of the way an organisation operates.
2. Lacking the right skillset. People are often searching for the perfect blend of IT and business experience and there is much debate around whether that skillset should be recruited or built internally. Not having the right skillset at the right time can be fatal for an analytics project.
3. Disparate information systems. Uniting all relevant data from various legacy systems and differing technologies is a very common challenge. In order for your business insights to be meaningful, they need to be derived from one source of the truth. Crucially, data governance and data quality must underpin every data analytics project.
4. Failure to identify the business problem. A point that is always highlighted at our conferences is that you must first identify the business need and then design the analytics project. Collecting data in the hope that something meaningful will emerge that will be of use for the business is an incredibly inefficient way of operating – you need to first know what problem you are trying to solve.
5. Need for a ‘fast-fail’ analytics culture. Building a culture for a ‘fast-fail’, learn quickly and move forward environment will reap the greatest rewards. Pilot the viability of your project before scaling up. Starting small and failing fast minimises the economical loss and assures the viability of the project before you start scaling up.
The pitfalls are many but the general consensus is that an iterative approach to data analytics projects is a must. Don’t be disheartened by failure – expect it. Focus on the business problem and start asking the right questions in order to tailor your project. If Henry Ford had asked his customers about their day-to-day needs, perhaps he would have got a different answer.
You can hear more on this topic at our Chief Data & Analytics Officer Forum in Melbourne this September. Phil Wilkenden, ME Bank and Richard Mackey, Department of Immigration & Border Protection will be hosting a discussion group addressing how to understand the challenges of data and analytics projects.
Monica Mina is the Content Director for the CDAO Forum Melbourne. Monica is the organiser of the CDAO Forum Melbourne, consulting with the industry about their key challenges and trying to find exciting and innovative ways to bring people together to address those issues – the CDAO Forum APAC takes place in Sydney, Melbourne, Canberra, Singapore and Hong Kong. For enquiries, email:.