Business analytics approaches can drive successful business performance which is something enterprises are constantly striving to achieve. Rich Pugh, Co-founder and Chief Data Scientist, Mango Solutions, discusses the importance of thriving with data science and how it can bring about success for enterprises in today’s tech industry.
Data is like the new oil. This analogy was first drawn by The Economist and in some respects, it is true. Successful businesses today run on data and, like oil, data is near-useless unless it is refined and treated in the correct way. But refining is a difficult process and with many business executives overwhelmed by the ‘hugeness’ of modern data, it’s easy to regard plug-and-play business intelligence, AI or Machine Learning solutions as a one-stop data-to-value machine.
The problem is that all too often, these tools aren’t able to deliver the value expected of them; even if the technology finds an important and relevant correlation, businesses are unsure how to act on the information effectively and understand the full context of the finding. Insight becomes an attention-grabbing statistic in a slide presentation, or potentially a one-off decision made based on a piece of information and then nothing further. It’s hard to quantify what the long-term value of this was, because the full context is missing.
What does it take to be truly data-driven?
A data-driven organisation values its data as a primary asset and constantly strives to turn data into operational acumen to drive better decision making. This is where data science comes in – or more specifically, a company-wide culture of data science. Rather than just a tool to turn data into insight, data science is a way of blending together technology, data and business awareness to extract value, not just information, from data. While 81% of senior executives interviewed for a recent EY and Nimbus Ninety report agreed that data should be at the heart of all decision making, just 31% had actually taken the step to restructure their organisation to achieve this. That leaves a huge majority of organisations who recognise the potential of data, but have yet to find a way to embed a data-driven culture within their business.
Start at the top
So where do you start? Firstly, an organisation has to want to become data-driven from a business perspective. That means that the process towards this has to be taken from the top down. 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.
The next step is to assess the skills within existing teams. Within an organisation, analytics skills 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 for example can help to bring together your data professionals into one community, striving in one direction to empower the business.
Once there’s a community in place, it’s then a case of getting these people to work towards what ‘best practice’ looks like, 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. It’s not about applying a one-size-fits-all approach, but instead fostering cohesion and solidity to ensure the team can agree about what needs to happen, when.
Engage and educate
Once you have your team of data science experts, it’s time to engage with the business as a whole. Educating the business requires the whole data science team to be confident with what analytics can achieve for the business and even more importantly, what it cannot achieve that the business might be expecting. This will then need to be communicated in a clear way – using language that the business teams will understand will help break down any preconceptions. This can be daunting and often, data science teams will find themselves faced with a huge variety of interest levels. Many who hear about the potential of data science will feel it has little bearing on their work – and discussions about its potential may go in one ear and out the other. However, there will also be people who are inspired by what data can do for them and want to get more involved. These people can be future champions for driving a data-driven culture beyond the core team.
Put it into practice
As business interest in and knowledge of the potential of data-driven decisions grows, so does the list of potential initiatives. In this regard, prioritisation becomes incredibly important. Business leaders need to focus on whether each initiative meets the following four criteria:
- Will it add significant, measurable value?
- Is the organisation ready to implement this programme? Do we have the right data and platform to make it work?
- Is there actually a solution possible or is the technology still not available?
- Is the business ready to adopt the new practices this initiative will require?
Finally, it’s about finding a way to quantify the value that the data science community now brings to the business and ensure that the success thereof becomes a repeatable part of the business process. 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.
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 building data science solutions around real business problems, in conjunction with the whole business team, organisations are more likely to see the value thanks to an ongoing culture of problem solving with data science. This will result not just in a successful adoption of data science tactics, but in wider effectiveness as a smarter, more agile organisation that delivers better solutions to customers.
