How Businesses Can Use Data Science to Unlock Productivity

As we’ve learnt more about data science, it’s become increasingly clear that it could be the key to helping businesses unlock the detail in the data and, as a result, better understand productivity.

Here Mike Callender, executive chairman of the REPL Group explains that while data science is currently being used to great effect within the online retail sector, it’s yet to enter the business world at large. So, how best can businesses use data science to improve productivity within their organisation?

Step 1: Understanding data science

Firstly, it’s crucial to understand that data science is about more than just crunching big data sets. In fact, it’s a combination of smart analytics, machine learning and artificial intelligence that “bring data to life in a hypothesis-driven approach”. In the retail sector, data science is being applied to a number of areas and is being used to inform decision making for forecasting stock and category trends to sending customers vouchers based to their shopping habits. It’s also beginning to be used successfully in workforce management, which could also be beneficial within businesses more widely.

Step 2: How to gain insights from data

The amount of data a business has differs on a case by case basis, but where businesses do have relevant data, they often find it difficult to leverage the true value of the information. Publicly available data, such as weather or traffic, for instance, could be combined with existing internal data to improve forecasting by identifying any potential external impacts on the business. With millions of rows of data available, a single spreadsheet is unable to capture the whole picture and as such, businesses must find an alternative way to store and analyse data which is where data science comes into the picture.

Rather than splitting data across multiple spreadsheets, data science uses powerful computing technology to look for patterns across and throughout the data lake. This means analysis takes place using every piece of data the company has in its entirety. With millions of items of data in one place, data science will enable businesses to unlock new perspectives into productivity and generate real value from their data.

Step 3: Applying data science

Once a business has an understanding of data science and how to apply it, the next step is identifying use cases for it that will allow them to get more from data and unlock productivity within their workforce. Here are two use cases from the retail sector that could be applied to businesses more generally:

Use case 1: Workforce management

Without data science, retailers may end up with too few staff members to cope with demand or could end up overstaffed. But add detailed, automated large-scale data analysis to the operational mix and leaders will understand exactly what’s happening and prescribe a data-driven solution. One example is to use data science to understand which employees work well together, based not on manager observations but by looking for patterns where pairings are most productive and offer the best levels of customer service. The system then uses this insight to create optimal schedules that control cost while maximising productivity and customer support. This is something other businesses could also benefit from, particularly in shift-work within call centres or in factories in the manufacturing industry, for example, or even when restructuring teams. It is also something that could benefit healthcare institutions with rostering, helping them to detect who works best at what time of day and alongside which individuals.

Use case 2: Boosting profits

Data science can also be used to solve the problem of profitability by helping businesses to understand which operational activities contribute to surpluses. This then makes it possible to build a performance management framework so business owners can focus on these areas. This is achieved by reviewing the entire makeup and impact of a decision and interpreting historical data, such as sales, profit and revenue, and then looking for links that might not be obvious.

With deep learning and data analysis, data science can predict the outcomes of different decisions. When the optimal decision that will help maximise revenue and minimise cost is identified the result can be presented to leaders. By providing the evidence to support the recommendation, data-driven decisions can then be made with confidence.

The future of data science

As the retail sector has shown, data science provides intelligent insights that just can’t be gained otherwise. With the development of integrated systems that will enhance collective thinking, it’s anyone’s guess where this powerful new technology could take us in the future. As the use cases for data science become apparent, it presents an exciting opportunity for businesses to gain real value from their data and use it to further their business not only in terms of productivity but potentially also in terms of profitability.

Leave A Reply