A new survey by researching firm Censuswide discovered that nearly 70% of global business and finance executives lack confidence in data. The survey of C-suite executives and F&A professionals commissioned by automation software business BlackLine discovered that under 30% of respondents are confident that the financial data they use for financial analysis and forecasting is accurate, despite a further 30% claiming they are under more pressure to present more accurate findings of performance due to the pandemic.

The study of 1,300 business leaders spanning seven markets (US, Canada, UK, Germany, France, Singapore and Australia) focused on the impact of the pandemic on a random selection of various global organisations. The results suggest that while businesses appreciate the important role financial data plays in managing business strategy and continuity, the lack of visibility and access to real-time data is impacting businesses ability to respond effectively to volatile changes in the market. When asked about the impact the pandemic has had on their business, over 40% stated that their business had become more focused on financial scenario planning and stress testing due to the ongoing impacts of Covid-19. A similar proportion also stated that F&A is becoming more popular by senior members to support scenario planning, indicating the importance of financial insights in moving towards a stage of recovery. 

The findings also suggested that nearly 30% of respondents are concerned that their F&A teams are not able to generate data quickly enough for their business to respond to the constant changes in the market. A combination of remote and office-based working conditions could make this even more complicated. Over 25% of respondents indicated that hybrid working models will make it more challenging for F&A teams to work together and suggest it could lead to potential inaccuracies in financial data.

Additionally, over a quarter of C-suite executives believe that they have little or no visibility into financial scenario planning or stress testing at their business, suggesting that some leaders could be making decisions based on inaccurate or incomplete information on the financial health of their business. This lack of visibility is hindering the trust in data used for vital financial processes and planning, particularly with C-suite leaders. Just over 50% of C-level executives are completely confident in the accuracy of their financial data, compared to 71% back in 2018. When F&A professionals were asked the same question, only 30% stated they were confident in the accuracy of their financial data, compared to 38% in 2018.

The main reason for this lack of trust was mainly due to the continued reliance on traditional spreadsheets and using dated processes that result in F&A teams with little visibility until the month-end. Some respondents pointed out that the problem has increased since 2018, suggesting the transition towards digital systems in F&A still needed work. Marc Huffman, the CEO of Blackline explains that aside from the impacts on our health and wellbeing, the pandemic has continued to impact businesses across the globe.

As things progress, businesses need to rethink and readjust their operations and ensure they are ready for possible outcomes, applying solid and reliable data to enable quick and intelligent decisions. Huffman emphasises that the businesses capable of doing this will be in a better plan to progress over the next few months. Huffman believes that many businesses are struggling with visibility and access to real-time financial data, yet there is a consensus that needs to change. The study indicates that business leaders understand the value of having reliable and accurate financial information and are ready to take action. 

The results show that the pandemic has developed a heightened urgency concerning digital transformation and added investment in technology. Over 30% of respondents surveyed stated that the developments in the last year have made people appreciate real-time access to financial data. When focusing on best practices and remaining competitive, technology that allows better management and visibility over financial data has a critical role to play. Over 30% stated that investing in data analytics will support their business in retaining a competitive edge and a similar number are exploring options of automation solutions to improve the accuracy and the reliability of their financial data.

New research from Gartner explores how FP&A teams can provide finance analytics that supports planning and decision making.

Analytically focused decision making has become critical for many businesses, particularly during the global pandemic. The key question now is which decisions should the financial planning and analysis (FP&A) team support?

A new report from Gartner suggests that analytics-driven decisions in businesses has increased, but believes that finance analytics should take priority and support this process. Leading companies have embraced a business-focused approach to finance analytics. To enable further growth requires additional financial analytics to support the decision-making process.

Gartner’s studies suggest there has been a 50% increase in analytic spending over the last three years. Gartner is calling for a shift towards finance analytics from the standard passive reporting structure to a more engaged process. As more sectors continue to improve their analytic capabilities, finance analytics governance becomes a little unclear. According to Gartner FP&A leaders are now questioning what role finance analytics should play in the wider analytics arena and how specifically FP&A teams can support these analytics.

Finance analytics reporting and support make up over 30% of spending in the finance function. Gartner emphasises that these investments in finance data do not support modern decision making and managers lack the understanding of how to use it efficiently. Gartner believes that the incorrect use of finance analytics can cost businesses as much as 1% of revenue per decision, mounting up to a major impact on business over time.

Gartner believes that to regain the real value of financial analytics, a business should focus on finance transformation. Business manager and finance teams should work together to define, develop and implement finance analytics. Gartner explains that decision-makers with clear data governance plans can deliver problem economics, collaborate more insights and drive each other’s concepts forward.

Gartner recommends businesses transform their finance analytics from a standard, passive reporting structure to a more relevant and engaging platform that encourages more discussion. This can be achieved by:

-Generating finance analytics based on scenario analysis
-Developing more dialogue and opinion in finance analytics reporting
-Improving the accessibility of finance analytics

Gartner explains that progressive companies are adjusting positions for joint finance analytics decision making. Being with focusing on a business decision first and finance analytics after. Gartner emphasises that finance analytics problem solving can be improved by predicting and planning for failure early.

The new return-to-workplace solutions, Oracle Fusion Cloud Human Capital Management provides a safe option and supports employees in adapting to a new working environment.

It’s still quite unclear when we will all embrace the new way of working after the pandemic. To enhance enterprises, while meeting the necessary safety standards, Oracle has launched what they refer to as a return-to-workplace solution called the Oracle Fusion Cloud Human Capital Management (HCM), an enhanced version to the Employee Care Package created in 2020. The service will support HR leaders in managing new workforce demands, including Covid-19 testing and vaccine tracking measures for HR teams, as well as automated support for individuals returning to the workplace.

In a recent press release, Oracle explains that the latest updates are incorporated in a new ‘Return to the Workplace Journey’ created to keep employees safe by supporting them with new processes, training and safety measures, while allowing HR leaders to measure critical workforce data, such as testing and vaccination information.

In the last week an international study discovered that despite the need for precautionary measures, nearly 50% of businesses lack a plan to introduce digital contact tracing. While employees continue to work remotely awaiting plans to return to the office, the requirements for creating on-premise locations is the sole responsibility of the employer, and more specifically the HR department.

The pandemic has forced many businesses to prioritise the health and safety of their workforce, and this has placed HR leaders at the core, responsible for adapting their teams and addressing these new challenges.

Chris Leone, the senior VP of development at Oracle Cloud HCM explains that Oracle’s new HCM promises to create what they refer to as “the human experience” at work, enhancing business agility and support new and innovative solutions. The new applications intended to provide the necessary means to promote workplace safety and support the return to the office.

The workplace journey in the HCM includes a review of new safety measures, regular wellness checks, mask and safety training, booking Covid-19 test and updating immunisation status via proof of vaccinations or self-reporting surveys.

Employees may have the capability to share vaccination status via new digital passports or electronic credentials. Oracle is working with technology and health businesses to ensure people will have access to these details when needed. Mr Leone believes that as the future of work continues to transform, it is essential that businesses are capable of making real-time informed decisions that protect their workforce.

The return to the workplace journey is the newest element to be added to the Oracle Employee Care Package, which consists of Oracle workforce health and safety, Oracle digital assistance and Oracle learning. There are also additional services available for Safe Travels Journey, Your Well Being Companion Journey, Onboarding Journey and a variety of other.

While businesses are recognising the value of data science and its ability to enhance business revenue, implementing and scaling data solutions across a business continue to be a challenge. A recent survey of data and analytics professionals suggests that developing a positive business culture with employees is a major factor that influences the success of data science. Led by DataIQ, a memberships-driven forum for the data and analytics community, the survey covered a panel of leading professionals across multiple sectors and companies in the UK.

The survey findings showed that 1 in 4 businesses believe data science will impact their top line revenue by over 10%. The results also indicated a continued challenge with company culture, suggesting a positive, model-focused culture is difficult to develop and still needs to be focused 0n. Approximately 40% of respondents want more clarity of the needs from stakeholders and a further 38% understand the necessity to train business users in data science solutions. Furthermore, another 32% believe there is a need for a more positive relationship with their stakeholders.

Nick Elprin, the CEO of Domino Data Lab believes that most businesses begin their work in data science by employing several data scientists, but ignore the importance of developing a model-driven culture that corresponds with their needs and the needs of business users. Mr Elprin believes the survey highlights the impact of not having a positive culture has on identifying proper use cases, creating expectations and generating quantifiable impacts on the business. Recognising these challenges is vital for businesses so they can create the right path and scale data science solutions successfully.

Additionally, 40% of respondents indicated that limited understanding or support for data science in business is regarded as a major challenge. The survey suggested that 1 out of 3 businesses stated that the conflicts between IT and data science remain another challenge. Even businesses that regard their adoption level of data science and analytics as advanced are not necessarily free of cultural conflict. Other findings from the survey included: 

Over half of all organisations believe they will experience an uplift of under 5%, indicating that the failure to implement data science contributes to lower expectations. 

1 out of 5 companies is experiencing a significant competitive advantage via applying data and analytic tools in their organisation. 

A total of 67% have assigned their data scientists together to form a core function, rather than dispersing them throughout the business. 

1 out of 3 organisations believes they require months to get their models into production. This needs to be considered because market changes are constantly changing and models that utilise outdated data will not generate valuable recommendations. 

1 in 10 businesses has implemented an enhanced automated monitoring model that creates proactive alerts when models are deteriorating. Data Scientists can then examine potential issues before they have any major impact on the business. 

David Reed, the Knowledge and Strategy Director at DataIQ believes that for data science to provide real value, a positive culture needs to be developed, enabling stakeholders and data science professionals to collaborate and share common goals. Mr Reed explains that the survey results suggest that this is easier said than done. 4 in 10 businesses identifying limited understanding or support for data science in their organisation as their business challenge. This presents a cycle that results in 1 in 8 businesses failing to generate a compelling use case for data science.

In the last few years, Business Intelligence has progressed into a driving force for business development, and with the impacts of Covid-19 accelerating further progress, we are likely to see BI transform into an essential part of an enterprise.

Augmented Analytics

This year we are likely to see BI and AI collaborating to develop augmented analytics and augmented data systems. With augmentation, businesses will be capable of determining data types automatically and gathering information directly from the source. With AI and BI working together, jobs will be easier and quicker to complete. Users will be able to apply natural language techniques to ask questions and generate key insights in a matter of seconds. With the support of AI, industry experts believe we will see more users with less technical experience gaining more analytical insights by 2021.

Storytelling

Storytelling or Automated insights will enable support businesses further throughout 2021. Rather than spending time understanding insights which will impact their business, innovative storytelling will emphasise specific findings. The system will monitor changes and create a narrative that is easy for the user to understand and to detect key findings. Industry experts suggest that while storytelling will have a direct impact on businesses, it will need to be integrated in a manner that enables insights to be delivered from regular business tools. Enabling storytelling to be available in this format i.e. an email or a chat will be vital in driving the progression of storytelling BI.

Self-Service BI

Embedded and Self-Service BI are anticipated to grow considerably during 2021. Embedded analytics enables end users the potential to manipulate their data, allowing a business to produce advanced analytics, rather than just traditional static reports. The progression concerning the adoption of embedded BI has grown over the years and interactive filtering will become more commonplace in the future.

With embedded BI, users will be able to generate actionable insights and business applications with analytical tools will become more integrated. Self-service BI will provide businesses with AI solutions to automate search engine reporting, insights, unified analytics and more. It has become even more important for businesses to be able to connect to all data points and have a holistic view of their business insights.

Cloud and Mobile BI

With the pandemic and surge of remote working driving cloud adoption, the connected cloud will be a major influence on BI this year. Businesses will be seeking vendors that provide cloud-based BI and other vendors that offer self-managed cloud networks or private cloud servers. Mobile BI will likely become more prevalent in 2021, enabling access to insights to work alongside the ability to work from anywhere.

The demand for cloud and AI-enabled BI is anticipated to surge, as remote working continues to be the norm for many businesses this year. Regardless of the working conditions, the benefits of BI in supporting businesses remain ahead of the curve, to predict market changes and improve services will be hard to ignore. Senior leaders should focus on investing or enhancing their digital and market intelligence plans.

Augmented Analytics – combining artificial intelligence (AI) and analytics is one of the latest innovative developments in the data analytics industry. For businesses, data analysis has progressed beyond simply hiring data scientists, to incorporating smart technology that provides clear insights that directly influence decision making, thanks to AI.

Augmented Analytics or AI-driven analytics, supports organisations in determining hard-to-find patterns in large data sets and reveals patterns and actionable insights. It utilises a combination of analytics, machine learning and natural language generation to automate data management processes and help with more complicated elements of analytics.

A study by Gartner suggests that by the end of 2024, 75% of businesses will rely on AI and generate a forecasted 5x increase in streaming data and analytics services. The potential of AI will enable businesses to enhance their internal data-driven decision making while enabling all members to have easier access to the data. AI can save data scientists, analysts and other data professionals considerable amounts of time spent on repetitive manual tasks.

AI benefits to analytics

The progression in the AI industry plays an important role in making businesses more efficient and capable with the support of automation. With the support of ML algorithms, AI can automatically measure data and reveal hidden patterns and insights that can be applied to the decision-making process. AI automates the report generation process and enables data to be easier to understand by applying Natural Language Generation. Using Natural Language technology means AI enables all members of the business to discover the information and extract important insights from data efficiently.

While traditional BI utilised rule-based systems to generate static analytical reports augment analytics uses AI techniques to automate data analysis and visualisation. Machine Learning uses the data to determine trends, patterns and relationships between different data sets. It can apply past events to make the necessary changes.

Augmented analytics can apply user queries to create answers in the text and visual formats. This process of data generation is automated and allows non-technical users to understand data and detect insights.
Business Intelligence can support better business decisions and improve ROI by simply gathering and processing information. An efficient BI system collects important data from various sources and generates actionable insights. Augmented analytics will improve BI and support businesses in several ways:

Enhance Data Preparation

Data analysts generally spend a lot of time extracting and cleaning up data. Augmented analytics eliminates the time spent on these processes by automating time-consuming tasks and generating valuable insights that can be applied for analysis.

Automated Insight Generation

Once data is ready for processing, augmented analytics can automatically generate insights. Using ML algorithms, it can automate processes and generate insights that generally take much longer to be completed by data scientists.

Efficient interaction with data

Augmented analytics will make it simpler for users to make queries and communicate with data sources. With the support of NLG, it can convert natural language into machine language and then generate useful insights in a much simpler language. This allows businesses to ask questions regarding their data and get answers in real-time.

Enable an entire business to use analytics

The ability to query data makes data much more accessible for everyone in an organisation to use analytics products. Businesses no longer necessarily require data scientists or technical professionals to use BI tools and understand their data.

The level of complexity and scale of data now being generated and used by businesses has reached a level that is simply not manageable by humans. Organisations are embracing the development of AI in analytics to manage data and improve overall processes. Augmented analytics is enabling this movement and applying it with BI platforms is allowing businesses to interpret data quicker and as a result, enhance their operation and make their data teams more effective.

Augmented Analytics – combining artificial intelligence (AI) and analytics is one of the latest innovative developments in the data analytics industry. For businesses, data analysis has progressed beyond simply hiring data scientists, to incorporating smart technology that provides clear insights that directly influence decision making, thanks to AI.

Augmented Analytics or AI-driven analytics, supports organisations in determining hard-to-find patterns in large data sets and reveals patterns and actionable insights. It utilises a combination of analytics, machine learning and natural language generation to automate data management processes and help with more complicated elements of analytics.

A study by Gartner suggests that by the end of 2024, 75% of businesses will rely on AI and generate a forecasted 5x increase in streaming data and analytics services. The potential of AI will enable businesses to enhance their internal data-driven decision making while enabling all members to have easier access to the data. AI can save data scientists, analysts and other data professionals considerable amounts of time spent on repetitive manual tasks.

AI BENEFITS TO ANALYTICS

The progression in the AI industry plays an important role in making businesses more efficient and capable with the support of automation. With the support of ML algorithms, AI can automatically measure data and reveal hidden patterns and insights that can be applied to the decision-making process. AI automates the report generation process and enables data to be easier to understand by applying Natural Language Generation. Using Natural Language technology means AI enables all members of the business to discover the information and extract important insights from data efficiently.

While traditional BI utilised rule-based systems to generate static analytical reports augment analytics uses AI techniques to automate data analysis and visualisation. Machine Learning uses the data to determine trends, patterns and relationships between different data sets. It can apply past events to make the necessary changes.

Augmented analytics can apply user queries to create answers in the text and visual formats. This process of data generation is automated and allows non-technical users to understand data and detect insights.
Business Intelligence can support better business decisions and improve ROI by simply gathering and processing information. An efficient BI system collects important data from various sources and generates actionable insights. Augmented analytics will improve BI and support businesses in several ways:

ENHANCE DATA PREPARATION

Data analysts generally spend a lot of time extracting and cleaning up data. Augmented analytics eliminates the time spent on these processes by automating time-consuming tasks and generating valuable insights that can be applied for analysis.

AUTOMATED INSIGHT GENERATION

Once data is ready for processing, augmented analytics can automatically generate insights. Using ML algorithms, it can automate processes and generate insights that generally take much longer to be completed by data scientists.

EFFICIENT INTERACTION WITH DATA

Augmented analytics will make it simpler for users to make queries and communicate with data sources. With the support of NLG, it can convert natural language into machine language and then generate useful insights in a much simpler language. This allows businesses to ask questions regarding their data and get answers in real-time.

ENABLE AN ENTIRE BUSINESS TO USE ANALYTICS

The ability to query data makes data much more accessible for everyone in an organisation to use analytics products. Businesses no longer necessarily require data scientists or technical professionals to use BI tools and understand their data.

The level of complexity and scale of data now being generated and used by businesses has reached a level that is simply not manageable by humans. Organisations are embracing the development of AI in analytics to manage data and improve overall processes. Augmented analytics is enabling this movement and applying it with BI platforms is allowing businesses to interpret data quicker and as a result, enhance their operation and make their data teams more effective.