Disruptive innovation—the big changes in an industry or sector that occur when someone or something turns the whole business model on its head—have huge implications. Big data is likely to be one of those disruptors. So are your big data skills disruption-ready? These following aspects will guide you to verify your readiness.
Digital security expertise is the biggest issue for businesses right now. This is, however, expected to be overtaken by analytics and big data skills within the next three years, according to an analysis by the Economist Intelligence Unit. In total, 43% of executives in the US and Europe said that these skills would be the most important within three years, compared with the current level of 38%.
Technical skills are an essential starting point
Big data requires big data handling skills. A good data science team needs to include someone skilled in data management, a good statistical modeller, and an analyst. Together those with these skills can move between exploration, visualisation and data mining, while ensuring good data governance.
Although technical skills will be important, they are not enough
Analytics and data management are of course essential to get value from big data, but on their own, they will not be sufficient. A strong analytics culture is also needed—one where evidence-based decision-making is not only valued but also expected, perhaps even taken for granted as a way of doing business.
Analysts will also need to understand the importance of business solutions
Being able to handle and analyse data is only the starting point for any analyst. It is also important to be able to translate findings and insights into business solutions, which means being able to understand the business as well as the numbers. Statisticians who can take the conversation beyond numbers to business solutions will be essential, either on their own, or as a core member of the data science team.
At its heart, any big data project is a problem waiting to be solved
This, of course, means that creative problem-solving skills will also be vital. And not just problem-solving: curiosity, and an ability to generate ideas, try things out and be willing to experiment will also be key. Perhaps this might best be described as the ability to ask the right questions and then look for answers.
IoT adds complexity, but does not really change the skills requirements
While big data is all about data, IoT-driven projects provide the additional complexity of devices and connectivity, plus the challenges of streaming and real time. But although this makes things more complex, the skills required are still the same: good, strong data management skills, together with the soft skills to communicate effectively and add business value.
‘Providing value’ is more important than ‘perfect’
IT specialists want things to be perfect. With their background of seeking ‘proof of concept’, they are reluctant to allow anything to be released more widely unless it is absolutely right. But as many people will understand, perfection is often the enemy of good, and certainly of adequate. And companies can get a lot of value out of both adequate and good.
But a drive for improvement is essential
This is not simply in the quality of the data platform, or the analytics software, but also among individuals. Technology and use cases are both evolving rapidly, and the only way for experts to keep up is to focus on continuous improvement as a way of life. Some of this will come from peer pressure and culture, but some will come simply from the nature of those involved.
Build, borrow and buy should not be considered mutually exclusive options
Instead, they can be combined into a hybrid option. For example, companies just starting out on a big data journey can ‘borrow’ consultants, even as they also ‘build’ their own skills, by having the consultants train their own staff on the job. More mature companies can ‘borrow’ particular skills or buy particular solutions for individual projects.
Citizen data scientists will be crucial in filling gaps
Citizen data scientists—those who are capable of acting as data scientists, but actually work elsewhere in the business—are an essential contributor to getting value from big data, not least because of the shortage of ‘specialist’ data scientists. Businesses need to ensure that data platforms are designed around ‘self-service’ to support this option, to give citizen data scientists the opportunity to make their contribution.