The progression and importance of the chief data scientist

March 31, 2021

In previous years, employing a chief data scientist was viewed as quite a luxury. Today, the role has become a necessity, especially as more businesses continue to accelerate their digital transformation plans in challenging conditions.

The importance of data science and analytics has surged during the impacts of the pandemic as businesses recognise how data is critical for survival and continued progress. With data now influencing major decisions at the highest level, the need for senior leaders in data science has become more important.

Today, it is clear that leaders need to work beyond identifying and measuring analytics. Organisations require chief data scientists, capable of connecting executives and data science teams, an individual capable of defining strategy and executing data-focused plans. As a result, investment into chief data scientists roles has increased as they primarily focus on managing the data systems, creating a clear strategy and improving the overall quality of data used. 

A chief data scientist is regarded as having a deeper understanding of how new technology systems such as AI and Machine Learning can enhance data management. This has become more important as ML has continued to influence data quality and navigating big data concepts into real-world ML implementation. The chief data scientist is responsible for the navigation of this process, focusing on data as the primary driver for new initiatives. 

Businesses are actively engaging with their customers in new and innovative ways, generating new business models and exploring more efficient ways to launch their products. These processes all require complex data plans and need the support of an experienced and skilled data leader.

When it comes to making important decisions, senior executives are becoming more reliant on the chief data scientist. A study by the IDC discovered that nearly 60% of chief data scientists report directly to the CEO. The position has progressed significantly over the last few years in terms of value and responsibility.

For this year, the priorities of chief data scientists will be focused on discovering ways machine learning can be applied to manage the challenges related to the pandemic and economic recession. One area is identifying churn rate, understand when customers are likely to leave. This type of information requires expertise as well as different levels of technical and data science knowledge.

This year, chief data scientists will need to expand on their existing influence across their business during a critical stage in the economy. Pressure will be higher for them to discover solutions and so challenges will focus on ensuring data science teams are focused and working with the right data sets. At the same time, chief data scientists will be empowered to utilise corporate data assets to make important decisions for their business.


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Trends in technology opportunities in the UK

March 24, 2021

Despite the rising demand for cloud computing skills, the number of job opportunities available on LinkedIn declined by 57% last year.

Less than 55,000 technology industry roles were being advertising on LinkedIn near the end of last year, driven predominantly by reduced demand for cybersecurity and data analytics professionals. According to Accenture, the 57% decline during last year was sustained despite continued demand for selected skills in northern England and a significant rise in demand for cloud computing professionals.
Of the 55,000 open positions, approximately 35,000 advertised positions required professionals with skills in cloud computing, reflecting the growing trend of UK businesses shifting their workloads towards the cloud in response to the impacts of the pandemic.

Demand for professionals with skills in AI increased, rising by over 70% in six months, equating to nearly 7,000 roles. This was matched by similar rises in opportunities in positions associated with quantum computing and robotic skills.

The overall decline in technology opportunities is largely down to a reduction in listings for data analytics and cybersecurity-related roles, both experiencing just over a 50% drop throughout 2020.

Shaheen Sayed, the technology lead for the UK and Ireland at Accenture explains that while the pandemic has taken a toll on technology jobs in the UK, certain skills remain in high demand.

Businesses have accelerated their migration plans to the cloud quicker than anyone anticipated and many have enhanced their digital processes and utilised new technology available on the market. As more businesses look to hire talent in the cloud, AI and robotics, experienced professionals are discovering new skills to ensure they remain on top of the constant change in the technology industry and to enhance their marketability.

Overall demand for skilled professionals in robotics increased considerably in cities in the north last year, with a 450% rise in Newcastle, a 250% rise in Leeds and a 115% increase in Liverpool. These figures reflect that many cities are focusing on recruiting more technology talent and developing their status as key technology hubs. This is emphasised further by the government’s decision to base the headquarters of the UK National Cyber Force in the North of England, generating thousands of new technology-related opportunities in the region.

In other regions, Oxford experienced a 3,400% increase in demand for quantum computing skills, reflective of the rising scale of research projects and developments of businesses such as Oxford Quantum Circuits.

The overall decline in demand for technology skills over last year contradicts other figures that indicate a rise in employer demand for applicants with IT degrees. A study of one million job adverts by suggested that 60% of university degrees requested by recruiters were specifically related to IT and computing. Other findings from the Learning & Work Institute published data that suggested the skills challenge, with the number of young people taking IT related subjects decreasing by 40% since 2015.

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Managing the shortage of data talent in the UK

March 24, 2021

The chief data officer of Experian recently discussed the importance of managing the skills gap and how industry leaders can attract more diverse talent to the data sector.

The pandemic accelerated the level of innovation and creativity in regards to using data effectively. Combining the skills and expert knowledge, data can provide businesses with the necessary insights to respond with both accuracy and speed. What has become very clear over the last year is how important data can be in terms of creating a competitive advantage.

While many businesses want to harness data and become more informed with the value of information, the lack of appropriate skills and resources is hindering the ability to execute a successful data strategy. This has proven to be relatively challenging as we experience a data skill shortage. There are more data roles to fill than qualified candidates to take those positions, resulting in a competitive search for data talent. The situation has been exacerbated somewhat due to several leading technology businesses dominating the hiring of new and existing talent.

The report suggests that over 80% of data leaders are finding it challenging to hire talent in the sector and nearly 50% believe that a general shortage of skills is the biggest challenge in terms of delivering value within their organisation.

Studies also indicate that this shortage applies not only to general data roles but also to the handling and processing of data effectively. Organisations require data that has been carefully cleaned and is fit for purpose. Industry experts believe this is a vital area that requires more focus, ensuring data is presented in a way that can be used effectively.

The new National Data Strategy announced by the government focuses on the UK working towards establishing a leading data economy, highlighting data skills as the target area to allow this goal to be reached. How can the UK work towards this target?

On a national scale, data and digital skills need to be explored and broadened within education to tackle this shortage. The National Data Strategy provides the opportunity to explore data skills on a national scale, enabling government and industry members to determine which skillsets are missing and how to create strategic plans. Working with universities is critical and will allow courses to be created based on providing the best employment opportunities after education and give businesses the required skills to reach their desired goals.


Strengthening the UK’s workforce for the future

It’s not just about creating the right skills in education. Businesses need to play a part in future-proofing the workforce by providing people with the necessary skills to manage data effectively. Businesses must ensure they focus on developing their data capabilities with their existing workforce, rather than leaving data skills and knowledge in the hands of just a few people. Developing a culture where data knowledge and solutions are dispersed more openly will enable data specialists to focus more specifically on innovative and targeted work to raise business performance.


Focusing on diversity in data

Education in data is vitally important and increasing diversity will strengthen the progress in skills development in the industry. In a world with an increasing skills gap and a need for employees with more creativity and potential to innovate, it is even more important that policies and processes enable all types of people a fair and equal chance to progress and develop in the data industry.

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How the finance industry can manage the rapid rise of unstructured data

March 17, 2021

Unstructured data, whether it’s in raw form from news articles, reports or social content, is growing at a significant rate. Studies suggest that over 80% of all new data generated is in an unstructured format, yet only around 1% of this data is measured or applied to a business.

The sheer abundance of unstructured data is becoming a challenge for financial institutions. With a rising volume of incoming data, many businesses are struggling to understand where to start in utilising this information and transforming it into clear, actionable insights.

Decision-making potential is being lost simply because of the issue of data overload. As a consequence, may financial institutions are relying more on AI to support them in the decision-making process with unstructured data. New AI-driven tools can analyse, query and leverage unstructured data to deliver deep insights in record times. How can these tools provide value and support financial businesses to convert huge volumes of unstructured data into key decisions?

Extracting vital insights

Innovative big data analytics solutions that utilise machine learning can scan data and identify valuable sets of information. These tools enable financial businesses to discover vital insights that tend to remain hidden in unstructured data format, providing an immediate competitive edge over other businesses that are failing to utilise the power of AI.

These analytical tools can reveal new market developments, enabling teams at finance-focused companies to gain a deeper understanding of the market and make better financial decisions. HSBC recently launched an AI-driven investment index that measured unstructured data from multiple sources such as Tweets, satellite imagery, news content, or financial data. The ML-powered tool enables analysts to gain intelligent market insights considerably quicker compared to conventional methods.

Generate sentiment analysis

A ML algorithm focused on managing unstructured data can also explore sentiment analysis to gain a deeper understanding of the media’s feeling on a specific topic. Traditionally this process would involve highlighting and picking out certain words such as “great”, “poor” or “disaster”. The new process explores the context of synonyms and extracts the meanings, which is particularly important in the finance industry, where words and phrases have specific meanings. When these models are applied to news related to a specific company or sector, they generate qualitative information of the writer’s tone, informing you how positive or negative the stories are and how positive certain articles were in comparison. This is particularly useful in finance and investment, revealing certain aspects of a business that influence financial decisions, such as confidence in the market or a specific company.

Applying natural language processing and sentiment analysis tools in this format is an important way for financial businesses to generate value from huge volumes of publicly available data. According to a recent McKinsey report, quantitative funds that leveraged advanced analytics proved to perform better than others in terms of revenue.

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Anaplan launches its business planning tools with Amazon Web Services cloud

March 4, 2021

Anaplan has confirmed that it’s making its business planning and forecasting tools available on Amazon Web Services public cloud platform.

Anaplan provides an enterprise platform that can be used for a range of business planning purposes. The new offering is focused on an in-memory database and calculation system called HyperBlock that allows users to manage and analyse various data sets quickly across finance, HR, sales and other core business operations.

The announcement was accompanied by better than anticipated fiscal final quarter earning reported. One big advantage of the Anaplan platform is its excel-style functionality which makes it very accessible to many workers. The software includes various modules to support data-focused decisions across budgeting, demand, quota and workforce planning, planning and forecasting, commission calculation, financial consolidation and profitability modelling.

The Anaplan on AWS offering will combine the business planning tools of Anaplan with the scalable cloud infrastructure available at Amazon. It will allow businesses to work with large, diverse data sets and analyse different scenarios in real-time.

Ana Pinczuk, the senior VP and chief development officer at Anaplan explains that they are looking to expand the reach of the customers they serve. By integrating with AWS, Pinczuk believes the platform will be opened up to a much broader range of customers. Many of their new customers targeted by Anaplan have current relationships with cloud providers like AWS. Pinczuk believes the partnership will make it simpler for those customers to integrate their cloud-based data with its business planning software.

Users will be able to integrate services such as Amazon Simple Storage Service and Amason Redshift for data analytics specifically with the Anaplan platform. This will support customers in delivering accurate forecasts across a range of industries. Forrest Danson of Deloitte Consulting LLP believes that Anaplan on AWS will support businesses plan across various dimensions enabling them to keep momentum with continued market changes and to leverage new opportunities.

The partnership with AWS represents the second deal with a major public cloud infrastructure provider, following the announcement with Google Cloud last year. 

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Why businesses are accelerating their investment plans in Advanced Analytics

March 4, 2021

Advanced analytics is rapidly becoming a popular tool for business leaders looking to leverage the benefits of generating more insights on delivering more solid decision-making processes.

All businesses are capable of collecting data on their customers and market and even their competitors. There is a huge amount of data available with the potential to create insights if analysed in the correct manner. Having the data is, however, only the first step and will have little value without effective analysis. The latest technology developments in terms of visualisation, machine learning and more means of analytics have radically transformed and are in a much more powerful position than it was several years ago.

Advanced analytics enables businesses to add value and reliability to the decision making process. It reduces the risk and defines the probability of events or scenarios occurring, which is very valuable to a business. It combines various processes and tools that enable the data collected to be reshaped into patterns, trends, anomalies and key insights to allow decision making to be executed with more precision and accuracy than ever before.

Advanced analytics applies various techniques and processes to convert data into insights. Big data analytics utilises large pools of structured or unstructured data, examining the information to define key areas requiring further focus and analysis. Data mining involves extracting useful data from its raw form and analysing the information to determine the importance, impact and relevance of these datasets. This is where predictive analytics comes into play, enabling businesses to apply machine learning tools to existing and old data to define a prediction model that enhances business outcomes. Generally, the more data available, the more accurate predictions are for a business. While predictive analytics cannot represent exactly what will happen in the future, it provides a range and possibilities that could occur based on the decisions taken by a business.

Whether your a commercial enterprise or government organisation, advanced analytics represents a strong platform for enhanced decision making, improved investment choices and better governance plans. A simple example of advanced analytics is how it is effectively applied to online retail and other media services. Online platforms display recommended relevant items to purchase or content to digest, usually using advanced analytics to track and monitor user behaviour and determine other interest areas for the user. This information is useful to the customer and business and can result in additional revenue generation and higher engagement. With user experience being critical, advanced analytics is generating a win-win situation for both customers and businesses.

Advanced analytics can be applied to various areas of businesses. A priority is creating more efficient and smarter decisions, supporting people with understanding which areas require improvement and will ultimately increase business success. Risk is another significant area for businesses to focus on. Advanced analytics enables companies to have a clear understanding of trends and factors that influence the level of risk. This information is valuable in creating processes, policies or business plans that can support better governance and compliance.

Advanced analytics is accelerating in popularity as more businesses explore the benefits of gathering more insights and generating effective decisions. Organisations understand that data is a valuable asset but the challenge remains the same, applying affective and useful analysis continues to be a priority for delivering the results they require.

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