The art and science of powering business predictions with data

Luke Frost our consultant managing the role
Author: Luke Frost
Posting date: 6/5/2014 11:17 AM

In the UK, London leads in forging new schemes to draw and train talent both domestically and abroad. From stepped up degrees, early childhood curriculum schemes, migration talent sourcing, and in-house training, businesses are finding creative ways to shrink the data science skills gap. One such suggestion from techUK's Big Data Hero, Alison Lowndes of Nvidia Ltd is to 'teach code as prolifically as reading to open the doors of the digital age for everyone."

IBM weighs in on the talent gap to define not only the term "data scientist", but the scope of all data science professionals. Though it may seem as though this career operates in a vacuum, it's imperative to understand that along with the analytical components - maths, statistics, logic, and programming, there is a cultural element as well. Data science is the process of applying scientific method to solve business problems.

It requires teamwork and communication skills to not only gather the data, but to be able to present it so that business executives can take that information and implement solutions.

Making Better Business Decisions with Predictive Analytics

Wish you could see into the future to grow your business? No crystal ball required. Predictive analytics can optimize your decision-making. Artificial Intelligence and machine learning are core skillsets for a data scientist, but the human element helps divine the good data from the bad.

Much like Michaelangelo's David, systematic data gathering is the raw cube of stone, which must then be chiseled into form; analyzed and presented. An art and a science, data science helps businesses make better business decisions by estimating future outcomes via predictive analytics. These predictions are often based on past performance, customer or client influence, and sometimes gut assumptions based on gathered data. 

As growth trends in data teams expand to include non-technical staff, the siloed idea of simply pushing data from query to conclusion is no longer enough. The data team should have a grasp of how the business works overall to impact strategies and revenue. Businesses know how important data is to their future growth. Effective data teams must interact with managers who can help to frame the company's larger strategy to drive insight and analysis with the right questions.

Hard and Soft Skills

Soft skills are not confined to simply being able to communicate findings. Other elements include instinct or gut reactions that the numbers may say one thing, but based on knowledge of the business, may not be the right answer. Data professionals with a healthy skepticism will know when to take a step back, revisit the information, and what they need to do or how best to present suggestions based on their findings. Collected data is not unbiased. Best practices when studying analysis are to have a list of questions ready such as the data source, worst and best case scenarios, and what must be true for a correct recommendation.

Data scientists and data teams are expected to not only have an aptitude with number crunching, but also the ability to communicate their findings. To combat this and to grow their data teams, some businesses have begun in-house training programs. Computer and IT professionals who show an aptitude for data science may be offered positions on teams to learn from degreed professionals as well as outside learning opportunities.

According to a recent techUK whitepaper, migration sourcing for talent abroad and early education curriculum schemes, as well as domestic apprenticeship and training programs, are just a few of the ways businesses are combatting their struggle to fill key roles.

Hindsight doesn't have to be 20/20. As the year 2020 approaches, advancements in big data, data analytics, machine and AI learning are powering business predictions as employers seek to fill key roles. In a look to the future, demand for data science professionals with both hard and soft skills will be at the forefront of the data revolution.

We have opportunities for Data Scientists across the Bitcoin, Retail, Fintech and Start-up spaces. Explore new ideas and experiment with big data to produce real-time solutions. Get in on the ground floor and take your career to the next level. For additional opportunities check out our current vacancies. 

Contact our UK Team at +44 208 408 6070 or email to learn more.

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Weekly News Digest: 14th - 18th June 2021

This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics. Five signs of a good data quality culture Particularly post-pandemic, we all want to know that our data is fit for purpose. In this article from the Government Data Quality Hub, they look at five ways to ensure that your data's quality is right for your's and your users’ needs. This includes: Everyone is involvedData quality is a commitment, not a taskYou know what works for your organisationYou know why quality mattersYou are proactive not reactive We know that committing to a good data quality culture is a continual process. This core advice allows us to take a step back and think about how you can understand your unique challenges and involve the right people, so you can prevent bad quality data before it damages your work. See more on this here. Analytics Insight: 5 types of artificial intelligence that will shape 2021 and beyond We really like this article from Analytics Insight that explores the future of technology, and specifically the rise in uses of artificial intelligence (AI). AI is often seen to be disruptive as there is an assumption that robots could take over and jobs are wiped out, but it’s more likely that humans and machines will work together to streamline processes across a range of industries. The different types of AI to keep an eye on include: Customised technology providerChoosy algorithmHuman-machine interactionReciprocating machinesTheory of mind We’re always excited to learn more about new technologies, click here to read more on this. KD Nuggets: Five types of thinking for a high performing data scientist In this piece KD Nuggets look at how the way our approach to problem-solving may be guided by your personal skills or the type of problem at hand. As a Data Scientist, appreciating different approaches can help you more effectively model data in the business world and communicate your results to the decision-makers. Whether this is model thinking, systems thinking, agent-based thinking, behavioural thinking, or computational thinking, taking the time to understand your approach will significantly help the way you complete the function of your role. To read the full article, see here.  TechRepublic: These 220+ courses will help you master tech skills and prep for IT certification exams We know that there is a digital skills gap. According to Boston Consulting Group, there will be tens of millions of job vacancies by 2030 that will be hard to fill because not enough workers have the required skills, many of which are in technology. One of the best ways to upgrade your skillset is to complete extra training and qualifications to ensure you’re always learning more about your market and providing yourself with the best opportunities to achieve your next career step. ITU Online has over 200 courses covering cloud deployment, cybersecurity and more. Of course, this isn’t the only way in which you can level up your skills, but it’s a good place to start! To read more about this, click here.  We've loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at    

How Will Embracing Flexible Working Help The Life Science Sector To Grow?

COVID-19 has drastically changed ways of working in the Life Science industry. Overnight, teams moved online, while new research had to be prioritised. Life Sciences were already moving towards more remote working, and the pandemic has only quickened this shift. There is no doubt these changes have fundamentally changed the Life Science sector and how professionals working in this space operate post-pandemic.  However, uncertainty still remains about the viability of remote working for the sector and there is a divide between those able to work remotely and those who need to go into ‘wet labs’. Is remote working a step too far for Life Sciences? Collaboration  2020 saw an increase in collaboration between professionals working across different areas of Life Sciences. Interestingly, organisations who may usually compete came together to share data and work towards a shared goal. Collaboration is essential in Life Sciences, yet for many, remote working reduces spontaneous teamwork and creativity.  New flexible lab spaces may be the future for Life Sciences though. RUNLABS have recently opened their first fully equipped flexible lab space in Paris for scientists and companies working in Life Sciences. This space hopes to builds on the existing collaborative approach in the industry and encourage further cooperative innovation. Efficiency  Many employees noticed a spike in employee efficiency when working remotely. By eliminating commutes and increasing flexibility, employees were able to be more productive with their time. Remote working also allowed organisations to streamline processes and reduce time spent in meetings.  However, insight from McKinsey highlights that research and development leaders estimate productivity has fallen by between 25 and 75 per cent due to remote working. Those in pharma manufacturing have reported lower levels off efficiency, as well as the potential for lower-quality outputs.  Research The pandemic forced remote trails to become a necessity, and since then, they have increased in popularity. While face-to-face research is still preferrable, remote trials can reduce costs and improve efficiencies. Indeed, on-site monitoring accounts for a significant portion of the costs of bringing a new product to market, yet this is no longer necessary in remote trials.   Not only are remote trials more cost-effective, but they can open research to a wider range of patients and can increase the communication between trial participants. Diversity Flexible working can run a risk to diversity and inclusion though. McKinsey also notes that, ‘when faced with a crisis, leaders often revert to relying on the core team of people they already know and trust. This disproportionately affects women and minorities because they are often not part of that group. Differences in perceptions and experiences of inclusion results in individuals or communities being disenfranchised, which can be devastating to careers and create a two-tiered culture.’ We know that 27 per cent of D&I leaders say their organisation have put all or most of their initiatives that embrace diversity and inclusion on hold because of the pandemic. However, remote work unlocks new hire pools and opens up the workplace to a more diverse workforce. Workers are no longer restricted by their geographical location or personal circumstances. Flexible working is an opportunity for Life Science organisations to harness a wider talent pool and increase their diversity. There is no doubt that Life Science is one of the most cutting-edge sectors globally and the pandemic has only cemented this. COVID-19 has shown the potential for remote working in life sciences, and in-person health care professional access may never return to pre-lockdown levels. But, going forward life sciences need to remember remote working is not practical for everyone nor every role. Organisations will need to consider individual wellbeing and role efficiency as they decide their next step.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 



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