Suprisingly, data isn't usually the main reason why data projects often fall short. Data availability, volume and quality as well as a lack of the right tools and technologies can play a crucial role in failure, but the key culprit is less obvious.
Data ownership conflicts across departements, intra-company politics and a "what's in it for me" mindset are in fact the biggest roadblocks for data projects. A lack of leadership knowledge and support will also cause projects to lose momentum and fizzle out before they even deliver results, even tough these projects are rarely cancelled.
In order to develop and execute an effective data strategy, it is important to understand that data projects:
A data project that fails to gain the buy-in of the C-level will never lead to a real data-driven transformation. Company leadership must
be heavily involved in the project, leading the change by inspiring people, setting targets, giving direction and making decisions. As they are the only ones with the scope of responsibility to do so on an organization-wide scale.
The enterprise architecture of the future is lightweight. It will rely on rich data insights to make decisions and to move quickly in order to respond to a dynamic, global business environment. As a result, traditional, outdated waterfall-style IT flows to design your applications and architecture just won’t cut it.
Becoming an ‘experimental enterprise’ means transforming the way you operate on a fundamental level. This means embracing agile approaches, laying the foundation for a ‘fail fast’ technology platform and, of course, investing in the right technologies – but don’t panic, it always starts on a small scale.