Big data is critical to the ongoing progression of financial services, but many businesses in the industry are failing to utilise its full potential of this industry.

Many financial businesses have harnessed significant volumes of new data as a result of adopting digital technology, but most have barely begun to gain the full value from the information available to them. According to a study by Seagate and the International Data Corporation, the average business collects and measures approximately 24% of the operational data available to it. There are several reasons for such a low figure, ranging from hesitancy regarding user privacy and regulatory compliance to challenges acquiring all the appropriate material in the necessary format for assessing data.

The first major challenge facing financial services businesses looking to make better use of their data comes back to their data ecosystem. The second challenge relates to the available talent, and the third is data management. The sheer capacity of the cloud is tackling the first challenge. Cloud-based technology has also created further possibilities for data and analytics teams, but industry experts believe that despite the advances in managing and governing data, there are still opportunities to improve the process. Most of the focus has moved from cross-functional platforms that allow businesses to make the most of their data.

In terms of the talent challenge, industry experts highlight how the sector is struggling to attract more people with the appropriate IT skills. Nick Broughton, the CIO at Novuna, believes that technology alone isn’t capable of generating all value; the industry also requires data-capable people with new and innovative ideas. Data science skills especially are critical to delivering real insights from the large volumes of data we have available. Attracting, retaining and growing internal talent around these important skills is another challenge when the demand in the market is rising.

A recent survey of financial businesses discovered that over 80% had struggled to hire data scientists, despite average annual salaries often exceeding £100,000. Over 25% of respondents suggested they didn’t have the necessary skills needed to achieve their commercial requirements. Considering these figures, the industry will need to become more flexible with employment policies and practices if it wants to attract and retain the data specialists it requires. Based on the high demand, these data professionals have the power to dictate the terms. Many people prefer to work remotely and flexible hours, so employers must be prepared to meet these demands.

Some businesses are going to greater lengths to create a reliable pipeline of potential talent, building networks with data science communities and creating special training programmes.

In terms of the other challenges, businesses are working hard to put more governance systems in place. The overall aim is to create a holistic view of their services and customers using data gathered and managed in real-time. It’s important to make tools accessible to everyone in a business, not just a handful of IT specialists. One opportunity emerging from this work is that it enables businesses to create new ways of meeting clients’ expectations. Ultimately, delivering the best customer experience is pivotal to any data-driven plan.

Creating a personalised service is one important area of development, but the potential of integrating augmented services, and combining data insights and human interactions is exciting for some businesses. For example, by using a range of data, companies can create investment signals and intelligence that managers can use to improve their relationships with clients. With the help of AI tech such as machine-learning systems, client-facing employees can determine trends that wouldn’t be possible. New tools can help customers understand their financial health and the risk profile of investment opportunities available to them.

The future is likely to involve clients using tools for experimentation. AI tools can show how investments could change over time but we find ourselves in a stage where regulation is failing to maintain pace with technological progression. Financial businesses must be cautious in their approach toward AI-enabled services, to maintain customer trust. Specialist positions such as data-ethics managers ensure an approach to AI is transparent, unbiased and capable of delivering the best outcomes for clients. Financial companies are already using chatbots and other virtual technology supported by natural language processing. Natural language processing also allows businesses to perform automated searches of various sources which can identify certain problems such as profit warnings or greenwash. Applying alternative data sources like smart sensors for climate-focused investments is likely to become more common in the future.

As the world moves toward data as a product, we need to start developing services with the data they serve. Making data services simple, secure and governable will be critical to the success of plans.

Over the past decade, two significant shifts have occurred in businesses developing and launching digital technologies. The first change relates to the consumerisation of IT. In previous studies, Gartner stated that consumerisation represented one of the major trends likely to impact the IT industry in the coming years. After the demise of the dot.com era, IT budgets declined, and companies began focusing on larger consumer IT markets. This shift altered how technology entered the marketplace, including the finance industry. 

Rather than innovation entering businesses and passing on customers, the customer market would adopt new tech before enterprises. The second significant change has been the rise of digital talent in the workforce and the demand that the corporate technology experience matches the customer-based requirements. People are less reluctant to differentiate between corporate and personal technology or depend on challenging enterprise tools when consumer software is more flexible and effective. The rise of remote working has accelerated this shift, with workers looking to take more ownership of where and how they work.

Rising customer and employee expectations

All of these changes have accelerated the expectations of results from technology for both customers and employees. In society today, customers have very little tolerance for waiting or potential disruption. Customers expect transactions and related processes to be as simple and seamless as possible and are likely deterred by a service if this isn’t the case. With higher expectations for customer experience, flexibility and rapid deployment of new services are a significant part of a leading business.

The finance industry has been impacted by the rise of fintech companies which dedicate themselves to data and are free of legacy systems and the barrier attached to established banks. Utilising modern services and products related to cloud-native technologies, fintech companies have scaled without the high IT infrastructure and development costs often linked with traditional software development. 

The consistent performance of a cloud-native environment can only happen if the data on which everything is dependent is adequately protected. Any data breaches can result in massive disruption to service. For example, a ransomware attack on Travelex disrupted the business for months, resulting in customers having no access to foreign currency and eventually drove the company into administration. In the move to integrate cloud-native technologies and modern software, practical considerations about resilience and data protection often are pushed aside. For the finance industry, with many regulations and policies, disregarding data protection should not happen. Inadequate data can spell disaster for the digital transformation plans for many fintechs. According to the Veeam Data Protection Trends Report 2022, about 90% of IT leaders within finance confessed to a protection gap between how much data they could lose after an outage and how often their data is stored.

With applications and data spread across physical, virtual and cloud environments, and given the sensitive nature of the financial information stored by fintech companies, infrastructure vulnerabilities and data breaches must be removed. The way IT supports our modern world has changed significantly. New cloud-native applications and microservices are reducing software development timeframes, enabling more innovation and focusing on meeting shifting customer demands. Implementing a data protection solution capable of working across these different environments is essential. With this, fintechs can ensure they can reactivate applications and protect their business and customers against cyber attacks.

Instead of highlighting the competitive landscape between emerging fintech and traditional banks, there is the potential to collaborate and generate more value. The fintech industry is accelerating innovation in finance industry, but many startups’ progress is hindered by strict regulations, data privacy and security concerns. Many traditional banking institutions are beginning to partner with fintech companies to expand and accelerate their digital plans.

The digital drive is forcing established banks to move from legacy systems to innovative solutions. It’s unlikely that fintech companies can take the stronghold of the market, but traditional banks will have to determine whether they decide to introduce their solutions or partner with fintech companies to maintain the edge over other competitors. If customer expectations continue to rise and demand the types of products and services offered by fintech startups, incumbent finance companies will have to find a solution to deliver new products or collaborate with those that can.

How fintech represents innovation in business

The rise of fintech has caused many finance leaders to adapt their core focus concerning data and digital platforms, utilising data and new services to enhance efficiency and security. Open-source software, SaaS and other architecture have become critical for tech and finance organisations exploring fintech services.

Technological progression and innovation are critical parts of fintech development, and they will likely continue to disrupt business activities within the financial industry. Many traditional finance businesses have been encouraged to be more creative as fintech companies promote new and innovative digital features and continue to gain further popularity across the financial industry.

Fintech represents innovation and is why many traditional finance companies struggle to maintain pace with new trends continuing to disrupt the industry. While fintech companies have the agility and a customer-focused approach to deliver more flexible solutions and a better user experience, the traditional banks have the size and experience, which translates into consumer confidence. Fintech expertise is often used by finance companies to improve and automate procedures and create detailed insights into their clients.

The range of fintech developments has expanded to payments and investing in new business models like blockchain, insurance-tech and data-driven marketing. The finance industry is experiencing a significant transformation on a large scale, predominantly due to technology: cloud computing, big data, robotics and artificial intelligence, and the shift in virtual and open banking and fintech. Product and software management, cyber security, customer experience, and data analytics are skills required not by just the fintech industry but also by any technology business.

There are many benefits that could transpire from a partnership between corporates and fintech. The integration of technology and greater access to data generate better compliance, security and lower privacy risks. Open banking enables third parties to create products and services around the offerings of a financial business. This collaboration would extend the potential of ecosystem-based finance, where banks and other finance companies can work with non-financial companies to create a seamless customer experience.

Finance integration has been a growing trend in the last year, with many finance companies looking to be providers to non-banks and non-financial groups looking to deliver a customer experience or service involving financial products as part of their offering. As the demand for rapid financial transactions has increased, banks and other financial businesses have been integrating fintech products within their standard practices. The ultimate purpose of fintech, aside from its focus on innovation, is to adopt technology to provide customers with financial requirements and deliver the best experience.
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Education and Business schools are trying to tackle the rising demands from new finance and other technology-focused market influencers.

Innovation in the tech industry has been disrupting finance for many years resulting in many of the new fintech companies becoming mainstream. Fintech venture funding has accelerated significantly in the last year and caused a spike in new job opportunities. New businesses, invest-tech companies and other neobanks are emerging in financial hubs worldwide. Studies show that leading tech innovators such as Revolut and Stripe appeal to many job seekers. For example, Revolut received over 250,000 applications during Q1 this year for 576 open positions. This number equates to well over 430 applications per job. Fintech companies like Revolut are actively looking for graduate talent worldwide.

The interest in fintech has left universities and other business schools racing to maintain a grasp of the skills and competencies required to fill these roles. Some schools are introducing specialist fintech degrees to attract and train the next-gen of fintech talent. For example, New York University Stern School of Business is launching a one year Master of Science in Fintech. The program will include data programming, blockchain, machine learning in finance and fintech leadership. Course representatives explain that the curriculum ensures people are prepared with the most relevant skills and tools to become instant disrupters in the fintech industry.

Some recruiters believe it isn’t all about finding candidates with fintech-related degrees. Some agencies prefer to recruit people based on their technical background or qualifications. For non-tech roles, people could potentially join with different skills. From a technical perspective, the skills required for new positions are evolving very quickly. Career industry specialists recommend acquiring a good understanding of blockchain, data science and cyber security as critical areas that set you apart from the competition. Continued learning and exploring the latest developments in fintech is vital to maintaining your competitive edge.

Fintech employers are also looking for technology talent in other fields like payments technology, data analytics and user experience. Finance business experts highlight that an understanding of the financial industry continues to be very important. Fintech employers are seeking professionals with a background in the traditional finance industry, including knowledge in risk analysis and business development, but have the potential to learn new skills virtually. Business schools could support the requirements in fintech by capitalising on this knowledge gap. There is an opportunity to assist people from tech backgrounds to gain a deeper understanding of the financial industry with a dedicated fintech for tech professionals programme. There are very few people in fintech that are capable of excelling in both finance and technology.

The ideal graduate is somebody that appreciates business and technology, and this can be hard to find. There is rising demand for individuals that can programme in multiple languages, but at the same time, graduates with skills in economics and user behaviours are also very important. The focus is now on shaping graduates with broader skills across these areas. Fintech businesses want graduates with strong communication skills and are capable of working confidently with other industry professionals, whether this is in finance or an engineering discipline. The single, most important attribute boils down to their ability to problem-solve. Candidates that can show critical thinking and problem-solving skills are highly desirable. Candidates may be technically capable and have the experience, but if they are difficult to manage or lack the communication skills, then this person may not be a good match for the culture of a particular company.

It’s clear that data and analytics are transforming industry competition, and while some businesses are accelerating at pace, many are yet to implement the necessary changes. In the latest McKinsey Global Survey, respondents believe that the changes data and analytics have created over the last few years have continued to grow. However, they also suggest that many businesses have reacted to this shift with one-off actions rather than implementing a long term plan.

Studies by McKinsey suggest many businesses are being relatively slow to respond to these transformations and potentially could find the gap between them and industry leaders extend further. Based on the report, companies with the most growth in revenue and earnings put a large amount of this increase down to data and analytics. Respondents from these businesses are far more likely to indicate data and analytics plans have supported an increase in earnings over the last few years.

What can other companies do to utilise data and analytics and follow others capitalising on the benefits of data and analytics? The most important factor is that these companies are implementing a long term data and analytics strategy and enforcing this as a core part of their workforce plan and culture. They ensure that high-quality data and modern technologies exist and can support further scaling.

In many industries, professionals believe data and analytics as a priority transforming the competitive landscape. 47% of respondents from the McKinsey survey believe data and analytics have changed the nature of competition in their sector over the last few years. While this may sound relatively low, it represents a 38% increase since the previous survey. When questioned about competitive changes, respondents point towards new analytics-focused businesses and the frequency of new companies emerging in this space. Despite the rise in competition, results suggest that most organisations still respond in an ad-hoc manner toward data and analytics plans.

Many industry professionals recognise that a lack of strategy for these areas will significantly impact future success. Over 20% of respondents believe having a data and analytics strategy is the number one reason for their success, an increase of 14% since the last survey by McKinsey.

While creating a strategy is essential, the survey results suggest that another vital factor driving success is delivering a data culture or creating measures that combine data and decision making. McKinsey interviewed a selection of businesses about their data culture and discovered that having employees use data consistently for decision making is critical for success.

Education is also a key factor, as developing a team with data and analytics skills is a top challenge to reaching a company’s objectives. Businesses have indicated a lack of company-wide education on data as a barrier to implementing new plans. Another aspect of creating a data culture is attracting and retaining the best talent, highlighted as a priority by employees at high-performing businesses. Similar to the previous survey, the biggest talent requirements are business users with analytic skills and a general need for more data professionals. While automation is growing, managing the data needed for these business changes is predominantly human-led.

Creating a data-driven culture requires technology that can support a business in utilising data and analytics. Establishing a solid data architecture enables companies to effectively collect and share data and ensure their employees can access and use information needed. It also allows for efficient delivery of high-level data quality, supporting data-based decision making.

The McKinsey survey suggests high-performing businesses have surpassed others in achieving their data and analytics plans and using both strategy and a solid data culture to extend the gap from other competitors. How can businesses improve the use of data and analytics and reduce the gap?

Improving the availability of data – the survey suggests how important it is to extract data from silos and place it in sophisticated analytics-based tools and allow decision-makers to have easier access to this information.

Recognising data as a product with genuine returns on investment – Business leaders often consider data as something supporting analytics and their decisions. Data should be viewed as an internal product shared across the group and integrated with performance, revenue, quality and other measures.

Be flexible toward data transformation plans – While high performing businesses have enforced a data culture, it’s critical to understand that even the best are yet to implement all of the suggested practices for data culture and have the room to go further. Rather than approaching this by attempting to tackle the gap with large-scale changes, businesses must focus on gradually evolving their data culture over time.

As CFOs explore ways to tackle the impact of inflation on margins, self-service analytics will be essential in driving employee productivity, according to a Gartner report. 

In December 2021, Gartner surveyed 400 finance leaders and discovered that over 50% saw self-service data and analytics as an essential driver of employee productivity. At least one in four saw it as vital for increasing business speed and agility. Self-service data analytics refers to technology and processes that finance users leverage with minimal involvement from IT departments. 

Alex Bant, chief of research in Gartner Finance, explains that two out of three finance leaders have raised their prices in response to inflation. Finding ways to improve productivity and efficiency rather than passing on inflationary costs to customers can create a critical long-term competitive advantage.

Advanced data and analytics and AI technologies generating high value and where investment is forecast to rise to include self-service data analytics, automated machine learning, cloud analytics, big data analytics and predictive analytics. 

Predictive analytics predicts a series of outcomes overtime or the distribution of an outcome that could occur for a specific event, using techniques like driver-based forecasting, time-series forecasting and simulation. Predictive analytics is one of the most popular use cases for finance executives automating their forecasting processes.

Bant explains that over 90% of finance leaders have increased their digital ambitions for 2022, but the same proportion is concerned about whether this development can continue due to slower growth, higher rates and the added pressure on profitability. Investment into the digital area, even as growth declines with be vital in determining the successful businesses of the future.

Big data and predictive analytics are considered critical technologies for generating higher revenue through improving products or services. Machine Learning and cloud analytics were viewed as the best solutions to improve cost efficiency.

The upcoming Gartner CFO and Finance Executive Conference will provide insights on the issues facing CFOs on June the 6th. The conference will deliver actionable insights for CFOs and their teams to support them on their digital journey and understand what makes a team successful. The Gartner Finance practice supports finance leaders meet their top priorities and delivering on vital initiatives that spread across finance and generate business impact.

Innovative technologies have made financial processes more efficient, reduced the number of errors and transformed how customers view and interact with their money. In terms of product development, measuring customer habits can generate better decisions, and businesses can create structured plans based on real-time API metrics and data to respond effectively to any changes in the market.
With the integration of additional SaaS-based applications and other services by more financial businesses, the opportunity for risk continues to increase too. The importance of data quality and security will only rise in a world where cyber-attacks become more prevalent, and a lack of compliance can be significantly detrimental to an organisation. API technology provides opportunities to innovate in the financial industry, enabling organisations a competitive advantage.

Businesses today are established on data, while finance-related organisations are established on customer trust. If the security of a customer’s money or information is compromised, the trust with the business is lost. An organisation must have 100% security of the data used in developing and implementing an API tool that needs that data to function.

Open banking services have given customers greater control over their finances. People can move and manage their money with greater security from stricter regulations. Open banking platforms are accessible from many devices, making them a simple and popular option for many individuals. The customer experience has improved significantly through open banking and provided several benefits, such as more affordable payment options and smart banking services.

Finance businesses can provide customised products and customer service based on intelligent algorithms that can predict the needs and behaviours of their customers. Consequently, any transactional data quality is dependent on how this data is initially structured. With such a variety of data collected, this can become challenging for data cleansing. Data teams will spend more time applying potentially ‘faulty’ data for analysis. It’s likely that valuable information could be lost or not considered, resulting in the need for more efficient data preparation.

Utilising Real-Time Decision-Making

The instant message-based transaction generated from APIs benefits users at all levels. These systems generate quick, standardised information based on preselected details encoded into the API. This standard structure results in less uncertainty for individuals and a more efficient test design that enables professionals to explore data quality issues.

How Open Banking APIs influence data quality

Protecting outbound data

Poor data within the API will result in operational and transactional problems by impacting reporting quality, searches and analytics. Financial professionals or anyone in a customer-focused position may have to search for specific customer accounts and, in the process, find duplicated data for the same individual. Duplicated records in online financial systems can cause discrepancies and take up unnecessary space, resulting in time and costly implications for a business. In some cases, internal reports could be on incorrect data and potentially be damaging when making strategic decisions on products or other services based on inaccurate data.

API Security Considerations

Systems may be at higher risk of malfunctioning when poor data passes through an API. Quality checks must test against data structures and avoid any potential security breaches. While many cases of API security problems are unintentional, there are intentional cyber-attacks which can leave business systems inactive for long periods.

When APIs are exposed to external systems, it’s critical that security and data measures are in place and data professionals can manage all data types. Centralised services in the finance industry committed to data quality management must collaborate with the relevant regulatory and legislative groups to understand and manage these processes.

While defining the problem of data quality in open banking is relatively simple, the solution can be challenging and ignoring the issue can result in considerable problems for a business. The rapid development of technology has made it challenging for organisations to handle customer data. Technology is one of the least regulated markets, while finance and banking are the most heavily regulated industries, and data quality is critical to security.

Any customer data shared with third-party groups via an API will always pose a possible risk. If any of this information is inaccurate, the risk of exposure will rise further. Managing this risk involves having data quality solutions that ensure the customer data collected from open banking APIs are complete and correct.

Data quality assurance within finance must incorporate data governance measures and include a centre of data quality excellence. Establishing these frameworks and plans for data quality standards in open banking data ensures these problems can be solved.

The job of a senior finance leader has never been more demanding than it is now. Today, people are consistently searching online for new products and solutions, watching new media, making purchases and sharing content and experiences on social platforms. This significant transition towards digital behaviour means the customer experience has become a top priority for businesses.

A positive customer experience is vital to enable sustained growth in business. It promotes loyalty, retains customers and enhances brand reputation. CEOs of international banks and finance companies are focused on managing volatile economic conditions by eliminating any hurdles that may influence profit margins. They understand that growing a sustainable and profitable business requires investment in digital and analytics. The majority understand that their business must adapt to remain competitive in the future but recognise that this change will inevitably require some investment. The challenge is transforming to this new model without incurring high costs.

Determining the ideal customer experience requires recognising the diverse competition, understanding the main KPIs that drive success and creating a clear strategy. Businesses are exploring new technologies that provide smart experiences for future generations.
A new wave of businesses that apply data and technology as the core of the user experience is rising. In the financial services industry, online payment platforms are launching seamless check-out and payment options that enable them to take a more dominant role in the finance world. During this process, businesses gather significant behaviour data. Based on this asset, companies have progressed into other markets such as lending and online retail, rivalling traditional bank payment services at an accelerated pace.

What senior leaders should be aware of is that smarter investment decisions to improve the customer experience and their profits are often connected to the development or use of other platforms. Platforms are generating efficient data-focused processes that enhance the customer experience and the overall costs. Implementing this process, however, can be challenging and requires a focus from a user experience perspective with support to avoid any major costs.

To increase growth plans, business leaders are exploring the ecosystem business model in platforms, offering an interconnected set of services where users can utilise differing options in a singular experience. According to a study by McKinsey, customers are supporting this shift, with over 70% saying they are open to integrated ecosystems.

As markets converge, there is an appeal to provide everything for everyone. In reality, senior leaders must focus on their core capabilities and not get too carried away with their offerings. A major challenge that appears with shifting to an embedded eco-system platform is to determine the focus of a business and what capabilities it requires within the organisation.

With the move to ecosystem platforms, lots of new data-focused opportunities and challenges are emerging. Businesses are utilising AI and big data to generate new insights and information. It’s not a simple process, but if done correctly this information can enhance predictive accuracy and generate valuable information for a business. Business leaders who successfully transform their company towards an eco-system platform often understand the power of AI and data in driving efficient customer experiences and increasing profitability. It requires thinking innovatively, being smart and having the confidence to adapt when necessary.

New data initiatives intend to deliver transparency in climate reporting.

Recently, the US Securities and Exchange Commission announced a draft measure requiring US-listed businesses to disclose climate-related risks and their greenhouse gas emissions. The ruling was announced in response to investors requesting further guidance on how to report on climate-related impacts. Some have criticised the plan believing it is creating an added burden for businesses disclosing information.
The announcement represents a significant step in financial climate reporting. Policies and regulations greatly vary from nation to nation, but the US has been lagging behind Europe in requiring companies to disclose data on their climate-related impacts. The US has preferred a market-driven approach towards reporting and disclosures, while Europe has created a taxonomy with guidance on what specifically is an environmentally sustainable activity. The differing approaches toward regulations and frameworks result in fragmentation and uncertainty for financial markets. Industry experts are urging more collaboration, and the use of a common language to support the financial industry in disclosing climate-related information.

A recent report called ‘Forging the path to international standards in sustainable finance’ explores these issues in detail. The mentioned report will launch at a virtual roundtable with financial industry leaders examining the alignment of international markets and sustainability standards.

Back in 2015, the Financial Stability Boards created the Task Force on Climate-related Financial Disclosures, providing a framework for businesses to disclose climate-related risks and opportunities. The TCFD measures have become mandatory in certain areas, and many plan to follow a similar path.

Pedro Guazo, a representative of the UN Joint Staff Pension Fund, is addressing the challenges and risks created by climate change in its investment activities. Guazo explains that the fund wants to ensure its stakeholders have complete transparency in the processes, scenarios and metrics used to integrate climate-related risks and opportunities into their investment process.

Adam Banai, executive director of the Magya Nemzeti Bank of Hungary, one of the first central banks to publish a TCFD report, emphasises the important role central banks play in encouraging the adoption of TCFD recommendations. Banai admits that financial markets in his region aren’t ready to shift towards a more sustainable way of working and believes the bank must lead the way in delivering the support and guidance needed to improve this transition.

Banaii believes that a key reason why markets aren’t willing to integrate environmental, social and governance factors into investment decisions is because of data. Data has posed the main challenge when compiling reports, especially in finance and the scope of business portfolios. The more complex and varied a portfolio, the more difficult it is to find relevant and comparable data from other businesses.
While this is difficult for larger businesses, it is even more challenging for smaller enterprises that usually lack access to resources to collect and measure data. Banai believes that with time more financial organisations will collect and share data because continued rising demand will lead to more data production and collection.

The TCFD focuses on standardising approaches toward climate-related financial reporting. The more areas that adopt its recommendations and more businesses producing TCFD reports, the more realistic it will become that we can reach net-zero. However, until relevant data becomes more available and accessible, the challenges to adopting TCFD on a large scale will continue.

Skills shortages mean that it will become challenging to fill future emerging opportunities in the data industry.

Business data continues to rise in scale and spread across more platforms, but data literacy skills struggle to maintain pace. Most companies have the equivalent of an average of six platforms of data. While leaders are reasonably assured that their teams can analyse data, many employees are not as confident. New research concerning data literacy and management highlighted this pattern in several businesses worldwide.

The 2022 State of the Data and What’s Next report from Red Hat and Starburst explore how businesses gather and manage their data. Data Literacy: The Upskilling Revolution explored what skills people require to deliver data-focused strategies compared to the viewpoint of senior members.

The data literacy report from Qlik presented several predictions concerning how data-driven work will transform leadership teams over the next few years. Nearly all of the leaders surveyed stated that they intend to create and hire for these new data-focused roles within their organisation over the coming decade.

People are demanding data literacy training, but as in many work situations, there is a disconnect between senior leaders and their employees. C-level executives believe that over half of employees are data literate, while only 11% of employees agree with this statement. Furthermore, over 50% of senior leaders are confident in their data literacy, but 45% rely on their instincts rather than data to make critical decisions.

Employees are actively looking to improve their data skills, but the report suggests that only 27% have had formal training with practical experience. Individuals in customer service, finance, marketing and sales stated the need for data literacy exceeds the amount of training available today. People are also concerned that businesses fail to see the responsibility of supporting their teams with developing these skills. According to the survey, senior leaders tend to allocate training opportunities for people working specifically in data-focused roles but fail to recognise people working in other general fields.

This way of thinking can cause certain business areas to fall far behind, like HR and Procurement. This method can also create a decline in enterprise value, with the report suggesting that companies with higher data literacy skills can achieve a considerably higher value for their organisation. The report describes the efforts at PwC UK, which trained 17,000 of its 24,000 employees in data literacy and advocated a shift in the overall mindset of data.

The latest data report from Red Hat and Starburst indicates the scale of the ongoing learning challenge with data. Businesses have an average of 4 to 6 data platforms and up to 12 individual data systems. The complexity of systems increases as an organisation spreads its data over various platforms, and the risk of security heightens too. Respondents to the survey highlighted the automation of IT and data operations as the top priority to enable data systems to work together. Aside from the increase in data spreading across more platforms, the volume of information has increased rapidly.