Digital transformation is, almost to the exclusion of all others, the buzzword of the last few years in technology. To become ‘digitally transformed’, almost all companies are investing in some sort of data project – data analytics, big data, AI, data science. A survey of senior executives at large corporations in the US by NewVantage Partners suggested that this could be the case for 97% of organisations.
However, these projects are not ends in themselves. Success on the journey leads to a data-driven company that understands data as a strategic asset that can inform better decision-making. However, becoming data driven requires careful planning, and the right acumen. Organisations are on this journey because they want to achieve value out of their data, but what they are attempting isn’t really about digital first and foremost. It’s actually about the necessity of transforming their business model.
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With that in mind, here are seven tips that can help businesses succeed with their big data and data science endeavours:
1. First, an organisation has to want to become data driven from a business perspective. That means that the process towards this has to be a top-down one. Without leadership alignment, it will be nearly impossible to instigate the culture shift required to truly become data-driven. This means that the first vital step is to ensure representation for data driven initiatives, as well as broader education at the leadership level.
2. The next step is to assess the skills within existing teams. Within an organisation, analytics skill can be spread through departments, and as part of a data driven journey, business leaders need to transition to a core, centralised practice to ensure consistency. This does not necessarily mean re-distributing teams, but instead uniting these individuals to create a series of best practices. In addition, internal events and hackathons can help to bring together your data professionals into one community striving in one direction to empower the business.
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3. Once there’s a community in place, organisations can look to actively shape and define best practices, as well as how different roles impact the analytic function. The goal here is to move from sporadic projects conducted under the direction of each department to instead guarantee consistency of approach across the organisation, with a common understanding of how to deliver value from data effectively.
4. As the role of analytics becomes more strategically important to the business, it becomes necessary to ensure governance increases. As part of this, business leaders need to be asking their data practitioners to ensure that initiatives meet business objectives, that there is consistency in delivery and prioritisation, as well as in the platforms and technologies used. In addition, it’s important the business leaders observe and adhere to data ethics, especially regarding sensitive or personal information.
5. With leadership bought in and a core data driven practice through the organisation, the task now becomes educating the business at large about the possibilities of analytics. Business leaders need to work with their data practitioners to teach the whole business a common language around analytics and dispel preconceptions of what analytics can and can’t. This will open up more fertile ground for working out what business questions data can and cannot help solve.
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6. As business interest and knowledge of the potential of data driven decisions grows, so does the lists of potential initiatives. Here, prioritisation becomes incredibly important. Business leaders need to focus on whether each initiative meets the following four criteria:
a. Will it add significant, measurable value?
b. Is the organisation ready to implement this programme? Do we have the right data and platform to make it work?
c. Is there actually a solution possible or is the technology still not available?
d. Is the business ready to adopt the new practices this initiative will require?
7. Now, with initiatives actively being implemented, the business needs to look to structure and measure success in a consistent way so that employees at all levels can see the data driven programme at work, rather than isolated instances of innovation. This is key for moving away from a series of data science projects to being a truly data-driven company. At the same time, businesses should look at how they track and progress data science maturity in different departments to create an on-going plan for success.
Thriving with data science is key for success in today’s market, because it presents the ability to transform quickly and efficiently based on real insight. By following these seven tips, businesses have the best chance of succeeding in their mission to become data driven – and therefore in their wider digital transformation strategy as a whole. This will result not just in a successful adoption of data science tactics, but in wider effectiveness as a smarter, more agile business that delivers better solutions to customers – a critical differentiator in 2019.
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Written by Rich Pugh, chief data scientist and co-founder, Mango Solutions
