Technological innovation is transforming how finance professionals successfully operate within their business, creating enhanced information about how to improve the overall performance of finance teams.

The data insights collected from real-time data presented via modern user-friendly platforms is enabling CFO’s and their supporting finance teams to make critical business decisions that can really transform business performance, improve work efficiency and as a result, reduce costs incurred within a company. By utilising these tools, select businesses are moving ahead of their competitors and CFO’s are presented with a platform that allows them to deliver a more responsive and flexible finance team that is capable of measuring and predicting trends and responding quickly to any new opportunities or potential problem.

There are multiple benefits of new technology tools but it can be challenging to maintain the rate of progress and the increasing amount of data stream available to a finance team. With larger sources of data available, it is vital that teams create a structured system of business intelligence to utilise the most out of this data, enabling the company to spot any issues and opportunities. Information needs to be stored in integrated networks so teams are capable of using the data to produce quick reports and analytics that supports decisions within the business.

The demand for efficient and quick response for data presentation is driving continued changes within Business Intelligence (BI). The industry is focusing on creating data more effectively in improved dashboards. The further development of new technologies such as Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP) has resulted in further needs for integration into more systems. This demand has created what some analysts refer to as a RoBusiness Intelligence (RBI), referring to the shift of conventional BI into a new robotic phase solution that ultimately will improve overall decision making and enable users to enhance business performance through responsive and intelligent technology.

Utilising these new technologies and the insights they offer are critical for businesses to really continue to progress in the future. Business Intelligence has allowed finance professionals to use real-time data to make informed and influential decisions. Using robotic analytics will go even further, transforming the entire business chain with deeper and more informed information and intelligence. RBI systems can utilise the most innovative technologies to completely transform business performance and provide new services that can respond to market changes.

The benefits of Robotic Business Intelligence to Chief Financial Officers

With new integrated technologies available for data and analytics CFOs will have additional time to focus on strategic processes, improving business performance and assessing any potential trends that could disrupt business development.

CFO’s who can use the latest technology to analyse their business will be provided with fresh insights. Through intelligent data and insights, they will have the opportunity to transform their team and the entire business. Progressive changes in BI means technologically-minded CFO’s will have the chance to add greater value to the business by developing previous findings and creating innovative solutions for the future. Industry analysts believe that CFOs that embrace the digital technology available will be critical to providing and interpreting information that can transform and revolutionise an entire business.

The recent fine received by Google from France in regards to GDPR is a clear reminder to the big data and analytics market.

With another case involving a leading business showing the potential implications, businesses will need to focus further past consent and be capable of clearly showing they are allowed to utilise analytics and AI. Data analytics is critical to many businesses, their future development and enabling progressive innovation, but this is a significant challenge that needs to be addressed.

As Gary LaFever, the CEO at Anonos explains that in the scenario where analytics and AI cannot specifically highlight the time of data collection, businesses will not be able to rely on consent as done prior to the introduction of GDPR.

Under the new GDPR regulations, new safeguards are necessary to support both AI and analytics. To comply with the GDPR rules and essentially remain legal, companies will need to have a GDPR-compliant list of safeguards in place for the second stage of processing, covering analytics and artificial intelligence.

According to Anonos, these safeguards will need to incorporate the following steps:

-Include a balance of interest test that includes ‘functional separation’ (the options segment information value of data from the actual identity of each data subject). This reduces the negative impacts related to data subjects and ensures the legitimate interests of the data controller are not removed. There have been several legal cases involving major businesses that make it quite clear that attempting to claim a ‘legitimate interest’ in personal data is simply not enough.

-Ensure the compliance with secondary processing is in line with the original purpose for which the data was gathered.

-Prevent access as a standard to the minimum data required for each purpose for what is processed. This is referred to as Data Minimisation involving a specific level of control over data availability.

Technical safeguards to meet the requirements of GDPR were not required for lawful secondary processing using consent. This means many businesses do not have the required technology available. Essentially was previously viewed as acceptable prior to the implementation of GDPR is now no longer viewed as legal.

La Fever believes that companies do need to address and action relevant technical safeguards and introduce functional separation in an efficient way that has little impact on overall access to data, analytics and AI, enabling continued growth and innovation within the wider market.

Leading global data analytics provider Qlik has confirmed the acquisition of CrunchBot AI-driven analytics bot as well as Crunch Data’s team of artificial intelligence and solution focused professionals.

Qlik announced the deal explaining that the acquisition will extend their current augment intelligence and cognitive potential by creating additional conversational analytics, enhancing its existing platform and open APIs. Qlik has stated that their customers can now interact and analyse their data by using natural language via Qlik Sense and within other popular tools such as Skype, Slack, Salesforce Chat and Microsoft Teams.

Mike Capone, the Qlik CEO has explained to media that by bringing CrunchBot and Crunch Data into Qlike, customers will find it easier to include data more frequently into workflows, ensuring analytics is a vital part in the daily process of decision making.

Capone states that Qlik is continually seeking new methods to generate augmented intelligence for its customers and to enhance overall data literacy. According to Capone, acquisitions like this one will improve the potential of their customers and partners to utilise data and insights even further, increasing the overall value of the data and their business.

Capone refers to a recent report by Gartner which suggested that by 2020, nearly 50% of analytical queries will be performed via search, NLP or Voice, or will be automatically created. Customers at Qlik already can benefit from augmented intelligence and natural language search options. Acquiring CrunnchBot expands those tools further via a specific conversational and natural language platform. Qlik believes CrunchBot will work uniquely with Qlik, connecting the gap between visual discovery and conversational analytics.

CrunchBot has been validated by Qlik for quality, functionality and has been developed upon Qlik’s open API framework and the supporting platform. CrunchBot allows users to perform the following:

• Create questions in a conversational style via the Qlik Sense UI or through other major tools such as Slack, Skype or Salesforce Chat.
• Analyse their data and ask specific questions via voice interaction developed and integrated with other mainstream services such as Amazon Alexa.
• Gain detailed answers and insights, as well as auto-generated charts, interpretations, calculations over specific periods and predictive analysis data.
• Simple access to Qlik Sense analytics apps to measure your results further.
• Utilise Natural Language Processing (NLP) which naturally learns and measure user inquiries over a space of time, as well as Natural Language Generation (NLG) which generates insights on what is currently happening, why, and what to do next.

In contrast to other specific search services, this service between Qlik and CrunchBot will provide the best of both worlds. It will allow customers to utilise the conversational experience and then shift to the visual side for a more detailed insight and understanding of the data. Customers and partners at Qlik will see an increasing uptake in analytics and data literacy due to a more efficient and simpler way to ask questions, generate insights and make informed decisions with CrunchBot.

Crunch Data will support further development of analytics and at the same time improve results for customers. The product will be integrated into Qlik’s existing platform, enhancing the ability to generate high value and managed services. The team of professionals at Crunch Data, which includes a range of AI professionals and offshore development experts, have a strong working record of delivering successful projects.

Nish Patel, the CEO of CrunchBot and co-founder of Crunch Data recently stated that Qlik is the leader in analytics and in delivering augmented intelligence and machine learning to its customers. Patel states how excited they are to be a part of their advanced services, supporting their customers and ultimately making it simpler for people to understand their data.

Big data analytics is continuing to be a rapidly progressing industry with no indication of slowing down.

According to data provided by ResearchandMarkets.com, the global data analytics market is expected to reach a value of over $40 billion by 2023, with a combined annual growth rate of just under 30% from 2017 until 2023.

The Asia-Pacific market, in particular, is showing strong interest in the big data market. A growth forecast report from the IDC suggests that there is a strong appeal for big data in the Asia Pacific and will likely have a significant influence in future growth of the market.

The report states that revenue from big data in APAC will likely exceed $15 billion by 2022. More specifically, the Chinese market is predicted to be the market leader in the APAC region, followed by Australia.

Companies recognising the business value of analytics

Many businesses are now fully aware of how critical data analytics is in supporting companies making decisions and this realisation of the potential is driving further demand for this technology. Data from a recent SAS survey suggests that over 70% of respondents believed analytics provided valuable insights and a further 60% believed that data analytics enabled their business to continue innovating.

Many of these businesses do, however, encounter certain challenges, in particular relating to data that isn’t commonly used in a business. A study from Exasol explored companies within the UK and Germany and discovered that over 80% of respondents were unable to confirm the location of vital data. A further 55% also confirmed that data fragmentation across various locations was making it more difficult to extract all the relevant information.

Understanding big data analytics requires businesses to invest in specific resources that will ensure they meet their goal. Utilising the data in the most effective way will support the progression of the big data analytics market and at the same time tackle some of the risks connected to ‘dark data’, including security and compliance with data privacy regulations.

Furthermore, data related careers are becoming more attractive and companies are more willing to hire data professionals. The growing interest is data jobs is increasing the overall market value. Some businesses are already starting to benefit from people looking to pursue data related careers. For example, the leading budget airline, EasyJet announced a three-fold surge in the number of data science employees, with further plans to hire another 30 data scientists. According to their chief data officer, the added focus on data analytics will enable the business to control the damage that some factors can have on the airline. Big data allows Easy Jet to forecast certain changes that can influence business performance.

IoT systems are producing colossal amounts of data

IoT technology is used by many people today and this involves devices send and collecting huge amounts of data. According to a recent study, the total data produced from IoT devices will exceed 500 zettabytes each year by the end of 2019.  Many businesses are now utilising data from IoT devices to understand things that weren’t possible without connected devices. Many businesses that use IoT devices are not necessarily utilising the opportunities available from the collected data. The increase in IoT devices and the data provided will support further market success in the future. As businesses continue to explore innovative ways to use their data or look to employ data professionals, the market will continue to prosper.

Throughout 2018 Qlik continued to accelerate its focus in analytics and business intelligence by delivering innovative AI, enhancing the potential of the Qlik platform for all its users.

In November 2018, Qlik released its Qlik Sense product, introducing new machine learning systems into its cognitive engine and platform. The machine learning system enables the Qlik cognitive engine to become more intelligent over a period of time, continuing to learn via user interaction and from other feedback sources. Qlik is the first analytics enterprise to combine the potential of AI and ML with human intuition in a manner that utilises the power of a user for further discovery.
A report by Gartner suggests that augmented analytics is gaining popularity very fast. Combined with this progression, is the rise of ML automation and AI techniques to use human intelligence and rapidly transform data management, analytics and BI.

The new learning potential of the Qlik model will be used initially in Insight Advisor which was introduced back in June 2018 as part of the Qlik Sense Enterprise release. Insight Advisor can automatically generate and suggest the most suitable data and insights to research based on the data set and the user’s search criteria. The system makes highly relevant insight suggestions based on the machine learning from the user’s overall analytics interactions. The users can educate the machine by manually selecting analytics, modifying what the machine suggests and adding feedback to the system.

Moritz Schieder, the Visual Analytics Practice Lead for Deloitte Consulting explains that more of their clients are requesting for cases where they can utilise artificial intelligence. According to Schieder, the Qlik augmented intelligence approach is a perfect example of how machine learning can be used to combine data and the human decision maker by supporting data tasks, selecting the most suitable visualisations and recommending insights to the user.

Qlik’s AI and ML features are innovative because they work specifically with the Qlik associative engine, integrating the features of AI with human intuition. As the associative engine understands a user’s context and the data related, the suggested insights are far more relevant. As some industry members have explained, the Qlik service provides an extended view that enables users to discover hidden insights that were previously overlooked. Qlik provides the strengths of AI, where both machine and human sides become increasingly empowered. Elif Tutuk, the Director of Research at Qlik explains that their considerable investment into AI and machine learning is a significant factor for their users. It enables a higher ability to move more fluidly between levels of data exploration with added confidence. At the same time, the user will know the system is continuing to learn simultaneously and that each new insight makes the overall system smarter and can result in further opportunities.

Data analytics is transforming many parts of a business, with both small and large companies finding the benefits of implementing analytical systems into their organisation.

Recent research suggests that 2.5 quintillion bytes of data are produced every single data and businesses form a large portion of this figure. If companies have the capabilities to utilise this data to its best potential, it can significantly improve operational procedures and ultimately provide clear benefits to overall business performance. What is now quite clear is, data analytics is a core part of a business and a vital factor for success. In fact, investment in data analytics solutions in the UK is forecast to reach £25 billion by 2020. Whilst data analytics isn’t necessarily a new area for businesses, there are many companies that lack the knowledge of how to implement the system and how to use it appropriately. In previous times, a company would hire a dedicated analyst and data professional, but today it is more complex. To really utilise the benefits and optimise a workforce, businesses require analytics to be completely ingrained into the business.

Improving quality with analytics

As customer demands continue to evolve, producing a high-quality customer service is becoming ever more challenging. Within analytics, business intelligence and the performance of contact centres are closely related. The capability to utilise analytics for quality management within an entire business, creating improvements to operational procedures creates a competitive edge as well as improving engagement and overall performance. Analytics can help businesses to automatically measure customer engagement and receive predictive analytics to control potential challenging situations. In turn, this helps a business manage handle times, reduce call volumes and even predict problems before they arise.

Managing the potential of your agents

Analytics can support enhanced delivery of automation to manage simple processes, freeing up time for employees to focus more time engaging with their customers and requirements. The enables users to develop their own skills and focus their time on more precious interactions. This generally results in improved productivity and enhancement in customer satisfaction. On top of this, managers will have more insight into performance and trend indicators, information that identifies specific issues and skills gaps. This vital information can support training plans, highlight performance strengths or weaknesses and support managers in developing a targeted roadmap for further success.

Simplify business processes

Analytics can ensure managers are capable of discovering specific insights that enable all processes and performance metrics are connected to key objectives in real time. Businesses can successfully manage and avoid challenges that tend to occur for many businesses, such as understaffing issues and producing lengthy reports. Managers can utilise this information to select the most relevant and experienced staff to manage staffing challenges and automatically assigning work patterns based on other relevant data.

Whilst an organised office system is a necessity for most businesses, operations continue to face challenges. According to reports, standard office operations can result in productivity levels of around 50-60%. Analytics software, however, can improve overall productivity by 10 to 25%. Data analytics can manage business issues, generating real time information on operations and showing specific inefficiencies within a business. Data analytics does more than just maintain customer requirements. The insights generated from analytics enable companies to understand they are making the most out of their data to generate the best customer experience but also to improve future talent and maximise overall productivity. In the years to come, analytics will be an essential tool to support organisations in creating the best workforce, to retain employees and improve overall business performance.

After the recent Oracle OpenWorld conference in San Francisco, the business has progressed with its new Exadata project.

The Exadata project intends to make it considerably simpler for enterprises to handle data without requiring the need to manage dozens of servers, varied networks and a range of storage options.

The ultimate goal of the project is to provide Oracle customers with a singular stack that is capable of delivering the highly effective results. David Sivick, the technology initiatives manager at Wells Fargo and speaker at Oracle OpenWorld explains that the bank is now utilising 70 racks of Oracle’s Exadata. Prior to implementing these systems, Well Fargo required thousands of Dell servers to produce the same results.

Sivick explains that the new system has resulted in savings exceeding millions of dollars every year.

He explains that Wells Fargo has seen significant improvements in overall waiting times, a reduction in space required for compression and a general increase in application speeds. The main objective was to consolidate overall processes, the business was attempting to manage a range of varying systems, with different databases and memory systems.

Whilst Exadata can sometimes take longer to implement and operate, it does enable larger businesses to avoid managing multiple platforms and situations of handling technology upgrades over numerous systems. Wells Fargo now operates over 90% of its databases on Exadata, leaving the remaining systems in their existing form purely for strategic decisions or because they are due to be removed.

At this year’s Oracle OpenWorld, the conference focused a lot on the cloud credentials of the business and their vision of how the cloud would manage existing operational challenges. The cloud based strategy of Oracle is focused mainly on the provision of important business applications. At the conference, Oracle states that larger businesses are now using its Exadata products to improve speed and enhance the use of data services and reduce overall reliance on traditional systems.

Larry Ellison, the chairman of board and chief technology officer at Oracle highlighted the competitive edge of the business against Amazon Web Services, referring to Exadata-based projects and data warehousing.

Oracle intends to place itself as a leader in the transformation of cloud technology, an area that is likely to be vital in future ERP applications. The business is also very aware of the challenge facing larger businesses like Wells Fargo that require a complex migration of multiple systems into a streamlined integrated network.

Recent findings from the TDWI Best Practices report, BI and Analytics in the Age of AI and Big Data suggest that over 80% of enterprises are focusing significantly on analytics and BI as a major part of their budget for new technology and cloud-based systems. The report also suggests that over 50% of businesses believe AI, Machine Learning and Natural Language Processing are vital areas for future investment plans. Further findings also reveal that just over 40% of businesses are looking at improving user experience by automating the process of data insights.

The study emphasises that businesses are placing a strong emphasis on developing their current systems and replacing old technology and data platforms and replacing with innovative cloud-based BI and predictive analytics. Transforming Data with Intelligence (TDWI) is a global network of professionals within AI, analytics, data science and machine learning interested in progressing their development within the industry.

The TDWI report displays the top priorities for businesses based on investment levels for implementing new technologies and cloud-based systems.

SOURCE: TDWI BEST PRACTICES REPORT, BI AND ANALYTICS IN THE AGE OF AI AND BIG DATA. PUBLISHED DECEMBER 2018

The graph indicates that data warehouse and BI platforms are two of the main types of technology businesses are intending to use in 2019. What the study really highlights is that cloud based platforms are becoming the norm for businesses implementing new analytics and BI strategies. Cloud data storage facilities and data virtualisation are two other technologies that businesses indicate are of top priority when considering investment plans in analytics and BI strategies.

SOURCE: TDWI BEST PRACTICES REPORT, BI AND ANALYTICS IN THE AGE OF AI AND BIG DATA. PUBLISHED DECEMBER 2018

According to the report creating a high level of query performance, managing visualisations and having the ability to personalise dashboards and reports are key factors for delivering positive user experience. The report explains that predictive analytics and forecasting tools, ‘what if’ analysis and data searching on a report has resulted in the lowest level of satisfaction.

Over 80% of businesses are exploring their analytics and BI platforms to expand their insights and intelligence options. According to the report, Cloud based platforms, new analytics and cloud based data lakes are the top systems businesses intend to develop or use to replace current BI and analytics systems.

SOURCE: TDWI BEST PRACTICES REPORT, BI AND ANALYTICS IN THE AGE OF AI AND BIG DATA. PUBLISHED DECEMBER 2018

Systems. Aside from analytics and BI, a large proportions of businesses intend to develop and acquire Artificial Intelligence (AI) and Machine Learning (ML) platforms to enable a fully customised approach. Over 40% of businesses in the report plan to develop and purchase AI and ML tools, a figure that stands higher than other surveys related to AI integration. Over 10% of businesses plans to develop their own specific AI and ML systems.

SOURCE: TDWI BEST PRACTICES REPORT, BI AND ANALYTICS IN THE AGE OF AI AND BIG DATA. PUBLISHED DECEMBER 2018

Systems. The ability of Machine Learning to apply algorithms to large data sets and deliver insights is the main priority for most businesses. Over 50% of companies in the survey suggested this was a top factor. A further 47% of businesses believe AI and Machine Learning will improve the accuracy and overall quality of the information. An additional 40% of businesses view AI and Machine Learning systems as vital to support decision making by providing recommendations.

SOURCE: TDWI BEST PRACTICES REPORT, BI AND ANALYTICS IN THE AGE OF AI AND BIG DATA. PUBLISHED DECEMBER 2018

Real-time data analytics provides the potential to transform how professionals businesses operate.

Rather than manually collaborating information based on past events, professional groups can use real-time analytics to generate insights on what is happening exactly now. Real-time decision making is critical for managing resources, controlling profit margins, projects and most importantly improving customer experience.

Codex Recruitment provides key ways that real-time data analytics can help support professional services significantly improve their business performance.

Effectively using your resources

Businesses continue to find it challenging to allocate the right people to the right projects at the best time. Before the rise of real-time analytics, handling resources was a manual process resulting in frequent errors. Businesses would depend on an end of month report to identify the overall performance of their resources, where they may have been underperforming, where additional capital was spent, or in areas where their targets were exceeded. Real-time analytics enables businesses to have a better grasp of utilising resources. At any specific moment, a company can measure the effectiveness of a resource and determine what changes to need to be made to improve the overall process, before they result in costly impacts on business performance.

Maximising profits with analytics

Real-time analytics can enhance the potential profits of a business by improving two important operating procedures. This includes a clear understanding of hourly bill rates and overall billable utilisation, which refers to the hours billed divided against available hours. In a real-time scenario, a business can view how many hours have been billed on a specific project and if necessary, intervene if a specific project has lower billing projections. This minimises problems developing and further impacts on overall profit margins. Being capable of making changes in real time within a project lifetime is critical, rather than waiting until a project is done.

Proven and accurate forecasting

Real-time analytics enables professionals to operate and manage their business with higher confidence and predictable information. In previous years, forecasting lacked accuracy and generally involved manual processes. Real-time analytics removes the traditional, lengthy spreadsheets. Managers can quickly view areas such as services pipeline and project backlog, what has been invoiced and what needs to be paid. Analytic tools like this can create better decision making, improved forecasts and better results for a business.

Accurate details on supply and demand

The provision of real-time information means businesses can understand what resources are available for projects. Real-time analytics accurate provides the demands of clients and then determines where resources can be used to meet the demand. Businesses can have a clearer understanding of their resource pool and how to effectively manage this resourcing moving forward. In previous years, this level of detail was not available, meaning many companies relied on manual processes to manage supply and demand.

Controlling your value leakage

There always comes a time when a business will offer a service or product at a lower than intended rate. Too much discounting, however, can result in further pressure on service organisations and cause a strain on a business. Real-time analytics enables a company to keep a close check on levels of discounting in a company and the ability to create parameters to completely control value leakage. Decision makers can then manage the level of value leakage and make informed decisions on whether changes are required.

In today’s business world, companies simply don’t have the time to manually measure financial patterns or assess the impacts of business decisions. Real-time analytics is critical in supporting businesses in eliminating costly mistakes, encouraging growth and further development.

According to recent survey findings from Infosys, creating innovative business models to enhance revenue and overall profits should be considered the main priorities for data analytics.

The report highlights the importance and benefits of data analytics.

Many businesses are not implementing new technology to improve customer experience and control potential risks and inevitably progressive technology is a critical part for businesses to digitally transform.

The challenge, however, stands with the potential skills shortage facing certain industries, especially in the technology arena, which industry experts believe is holding back the true potential of data analytics. The Infosys survey analyses the expectations of businesses, the possibilities and challenges, the opportunities and how the rise of new technology will change the analytics market.

What are the opportunities and challenges facing data analytics?

Data analytics can operate across an entire business providing multiple benefits to overall business performance. The report suggests that Finance and Accounting tend to use analytics the most in business, followed by Marketing and Operations.

In regards to new technology, artificial intelligence is considered to be a focus area that created increased output when integrated with analytics. IoT and cloud technologies are other emerging technologies that provide similar opportunities.

Whilst there are numerous opportunities, the report also suggested certain challenges that face businesses across every industry that can hinder the implementation of analytics and associated tools to the maximum. One of the main challenges relates to the inadequate knowledge in combining multiple datasets and the lack of expertise in implementing the correct analysis techniques. Businesses are continuing to seek support from data technology partners to enhance their capabilities through specific analytics strategies, developing operational procedures and clearly defining a strategy to deliver and manage analytic procedures.

Satish H.C. the EVP and Head of Data Analytics at Infosys explains the data offers endless opportunities but having a strong understanding of data is critical if a business is wanting to truly implement a digital transformation.  Satish emphasises the importance of utilising the potential of data tools, referring to certain challenges such as businesses operating with systems that are incapable of sharing information, data integration problems and limited available resources and skills, Satish believes the Infosys report will support clients in their digital journey towards improving their data knowledge, enhancing their analytical potential and crucially monetising their own data.

Key points of the Infosys Report

Over 30% of respondents suggested that analytics had resulted in enhanced results for this business. This includes implementing intelligence created by improved collaboration with both internal and external stakeholders to generate a unique, personal and effective customer service.

28% of survey respondents showed interest in implementing analytics to manage risk in business. Respondents believe that predicting risk and detecting any potential errors that may affect business efficiency supports the overall decision-making process.

Creating new business models by focusing on the genuine needs of customers and providing a range of innovative services was considered the main requirements within data analytics.

A second priority for analytics was enhancing revenue and profit through a focus on improving business channels, processes and stakeholder systems.

Over 30% of respondents from the UK and Europe supported the idea of using analytics for overall experience enhancement. A similar number of respondents from Australia and New Zealand consider risk mitigation as a top priority for analytics.

UK Data Analytic Scene

Over a thousand senior technology and business members were interviewed for the Infosys report. Nearly 2 hundred of the executives were located in the UK. British respondents suggested three main areas of success stemming from data analytics. This includes; experience enhancement, risk management and creating new business models.

UK based respondents highlighted how certain business functions have utilised data analytics, pointing towards an increased integration within the Telecom, Manufacturing, Healthcare and Life Sciences industries.

Respondents highlighted certain challenges facing UK-based businesses including the integration of a range of datasets and ensuring data is ‘healthy’ and reliable were the most noteworthy. UK respondents explained that determining the right analytics tools and more importantly, the right people, with the skillets and experience as the most effective methods of controlling these challenges.