Data Science Competency for a post-COVID Future
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Written by Rich Pugh, Chief Data Scientist, Mango Solutions (an Ascent company)

The COVID-19 pandemic disrupted supply chains and markets and brought economies around the globe to a standstill.

Overnight, governments, public sector agencies, healthcare providers and businesses needed access to timely and accurate data like never before. As a result, demand for data analytics skyrocketed as organisations strived to navigate an uncertain future.

We recently surveyed data scientists working in a variety of UK industry sectors, asking them about:

  • how their organisation’s reliance on data changed during the pandemic
  • how their teams are having to re-align their skills sets to deliver the intelligence that’s needed; and
  • top trends on the horizon as organisations pursue a data-driven post-COVID recovery.

What they told us offers some interesting insights into the fast-evolving world of data science.

Decision intelligence gets real

Our findings highlight how the sudden disruption of the COVID-19 pandemic brought the importance of data analytics sharply into focus for business leaders and decision makers across the enterprise.

Almost two-thirds (65%) of those surveyed said that demand for data analytics rose across their organisation. The top request areas for problem-solving and enabling informed strategic decisions included:

  • Immediate crisis response (51%) – risk modelling, digital scaling and strategy as organisations looked to make near-term decisions to address key operational challenges.
  • Informing financial/cost-efficiency decisions (33%).
  • Logistics/supply chain (26%).

As reliance on data became mission-critical, data scientists in some industry sectors were at the nerve centre of COVID-19 response efforts as organisations looked to solve real-life problems fast.

Data scientists are adapting their skills sets quickly

As organisations beef up their data strategy to better prepare for future disruptive events and thrive and survive in the new normal, data scientists are having to adjust to new ways of working and adapt their skills sets fast. Indeed, 49% of data scientists say their organisation is now investing in building their internal capabilities through learning and development programmes, with 38% actively recruiting to fill gaps.

Now part and parcel of the enterprise decision-making team, data scientists confirm they are having to hone their business and communication skills to ensure they are able to support business leaders across the organisation better. Indeed, an impressive 34% identified working more effectively with business stakeholders was now a top priority. With data now being used more broadly across the organisation, one-third (33%) of the data scientists confirmed that they plan to boost their own communication and business skills so they can interact more cohesively with business leaders – and collectively identify the right problems to solve for their organisation.

Top data trends for 2021

As organisations continue to push ahead with operationalising their data and analytics infrastructures to handle complex business realities, data scientists are scaling up their deployment of machine learning algorithms to automate their analytical models.

According to our poll, upskilling their machine learning (ML) skills was identified as the #1 priority for 45% of data scientists as they look to accelerate their AI and ML computations and workloads and better align decisions throughout the organisation.

Similarly, big data analytical technologies (such as Spark, Storm and Fink) was the top priority for 39% of UK data science teams, as was getting to grips with deep learning (39%) as analytics teams look to jointly leverage data and analytics ecosystems to deliver coherent stacks that facilitate the rapid contextualisation decision-makers need.

Finally, with more people across the organisation becoming increasingly dependent on data-driven decision making, data scientists are having to find new ways to present data in ways that business teams will understand.

In a bid to democratise data and support faster decision making on the front line, they’re working on increasing their skills in areas like data visualisation (27%) and modelling (23%) so they can tease out trends, opportunities and risks in an easily digestible way that makes it easy for decision-makers to consume and engage.

New opportunities on the horizon

In a post-COVID world, organisations are looking to tap into an increasing number of data sources for the critical insights they’ll need to tackle emerging challenges. In response, data scientists are having to extract and analyse data quickly – even in real-time – and in the right way. Integrating data-driven insights into the decision-making process.

In response, data scientists are having to upgrade their technical and business skills as organisations look for efficient and innovative ways to use the big data at their disposal.

In summary, the research highlights both how important it is to align central data communities in order to boost and demonstrate value across the business, while ensuring that investment in L&D programmes is fully aligned with developing trends and business objectives.

 

 

maths & statistics awareness month
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April marks Mathematics and Statistics awareness month #MathStatMonth in the USA, with the aim of increasing the level of interest in these subjects. Working in an increasingly data-driven world, the ability to harness meaningful insights from data is an essential business and requires specialised data science expertise.

Data science is the proactive use of data and advanced analytics to drive better decision making. This ‘proactive’ use of data is what distinguishes data science from traditional ‘statistical analysis’ and needs to be an active part of an organisation in search of insight, better decision making or improvement.

Data science as a career choice for maths, statistics, and science graduates

Many graduates from maths, statistics and science backgrounds are increasingly attracted by a career in data science. Our current graduate placement Student, Elizabeth tells us more about her early interest in data science and why it presents a natural career path for those interested in mathematics and statistics. “Data science combines the skills and applications of mathematics and statistics with the use of big data and innovating technology to solve a variety of problems. I’m particularly interested in providing solutions to real-world problems and communicating these results at a high level within a business”, says Elizabeth.

“Throughout my placement I have seen the application of using mathematics and statistics within data science projects in performing exploratory data analysis to creating statistical models. My personal interest is in different types of statistical models, and I am due to study Time Series and Bayesian statistics in the final year of my degree”.

Elizabeth has benefited from seeing how mathematics and statistics have been used to model complex situations and improve business decisions from the optimum timing of routine maintenance, saving unnecessary reactivity and costs to creating descriptive, diagnostic and predictive insights which delivered great value and significant return on investment during her time at Mango.

Growing demand for data science

With the demand for Data Scientists still on the rise into 2021, the pandemic has created an even more urgent need for rapid decision making, informed and supported by constantly changing data sets, backed by effective visualization (highlighted by the World Economic Forum (WEF) in July).

Rich Pugh, Mango’s Chief Data Scientist summarises, “Leaders increasingly understand the potential of using data to create smarter, leaner, more engaging organisations. As such, we are still seeing growing demand for “data scientists” who are able to turn that data into acumen in a repeatable and scalable way. As a multi-disciplinary practice, “data science” relies on the combination of “advanced analytics” and “computer science” skill – this, combined with an ability to creatively explore challenges that can be solved, is at the core of realising the value promised by data science”.

“At it’s core, data science relies on mathematics and statistical rigour to provide robust algorithms that can be relied upon to solve often-complex challenges. As interest in data science continues to grow, the work at the Royal Statistical Society becomes increasingly important – to drive the discussion around statistical governance, and the correct and ethical application of statistical routines”, Rich concludes.