Boosting productivity with a culture of data science

2Mar - by aiuniverse - 0 - In Data Science

Source: gigabitmagazine.com

Since the dawn of time, and accelerated through several industrial revolutions, businesses and individuals have aspired to do more with less resources. However, with labour-productivity growth figures at an all-time low, perhaps, for the moment at least, organisations are not maximising the ability of current resources where they could be embracing and harnessing technological change. One of the major ‘resources’ of modern businesses, noted for its value but widely regarded as under-utilised, is data.

Boosting productivity with data is one of the biggest challenges in the modern enterprise, and it all comes down to finding a way to extract and maximise value. In the business world, value is all about achieving the maximum productivity possible. This aligns well with the central purposes of data science, which we at Mango define as “the proactive use of data and advanced analytics to drive better decision making.” That is to say, data scientists take existing data resources, and use these to create “more” using analytical techniques. These can be applied to many use cases, but driving productivity from data often falls into a number of key categories – delivering solutions to business problems, reporting in real-time and using trend data to drive future growth.

The first key of successful data science is that it is using data to solve real business problems. This means increasing integration between data and data science teams by fostering collaboration and creative discussions to help them understand what is needed and what is possible. Clarity of purpose and communication enables better understanding of what is needed, and the resources they need to get there. Meanwhile, the business team will not only be able to explain what they need, how and why, but may also know of department-specific or team-specific data sources which could aid in the creation of a new solution. This allows all teams to work more productively, with data scientists better informed about what the solution is for, and business teams more confident that the solution will work.

However, data science does not always need to be about creating detailed new solutions out of nowhere. Often, given the wealth of data that businesses hold, data scientists can use historical trend data to drive insights in future activities. This can be external facing (such as optimising strategies for engaging with customers) or internal (such as predicting the impact of different hiring patterns or organisational reshuffles). While historical data cannot offer a perfect answer to how a situation may play out in future, previous patterns can help shape models that at least offer a probable explanation of what might happen next. This helps make more informed decisions quicker and more accurately drive company performance.

Thirdly, data scientists can boost productivity both of their own teams and the organisation as a whole by implementing more real-time analytics solutions. These make use of not only historical data, but also look to proactively generate insights alongside action. While data scientists have traditionally been viewed as part of the strategic pipeline – that is, as providers of insight into weighty, considered decisions – the evolution of real-time analytics and streaming data enables data scientists to provide tools that support in a tactical role at great speed in changing conditions. This can support business productivity “on the fly” – such as when dealing with a customer during a complaints call, or with on-tap insights around successes and challenges with existing data science project implementations.

The benefits of data science for boosting productivity are hard to understate. However,  against the grain of our XaaS technology era, data science is more than just a plug-and-play solution.  The productivity of a data science team itself, and the business as a whole, relies on more than just tools or training or the right resources. Instead, it comes down to creating a culture of data science – and this is something championed from the top down. Harnessing data as a resource, and then finding a way to use it effectively within the work environment requires businesses to build a foundation of curiosity and an acceptance of the possibilities around data. Creating a thriving data science culture will be the difference between a vital productivity boom and a state of data overload.

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