Leading corporate performance management provider OneStream Software has gained a leading position in the 2020 IDC MarketScape for Cloud EPM Software category. 

OneStream is renowned for its detailed and flexible platform and for XF Marketplace, a service providing more than 50 solutions that can be configured and implemented to manage additional requirements without involving any further complexities. 

The latest report provides details on the significant growth at OneStream, its recent success in the EPM market and the establishment of several large enterprise accounts, including the addition of 5 customers equating to revenue of over $1M in annual recurring revenue. According to the report, OneStream provides a great deal of flexibility in terms of deployment options and a range of pricing options to meet customer requirements. The user interface is new, simplistic and enables customers to exactly see what stage they are in their workflow.

Chandan Gopal, research director for analytics and information management at IDC explains that EPM is a vital tool for executive planning and decisions. Mr Gopal states that it enables enterprises to plan and generate varied scenarios, a critical service in volatile business conditions. Modern EPM allows users to make plans across multiple business areas and determine the potential impact of various changes, enabling enterprises to make clear and strategic data-driven decisions.

The recognition of the value of EPM builds on the progress and success OneStream has achieved this year, despite the impacts of the global pandemic. This includes the addition of 60 new customers in Q3 and gaining 160% year-on-year growth in sales for the last quarter.

Tom Shea, CEO of OneStream Software, explains that this recognition as a leader in the IDC MarketScape validates their mission to deliver 100% customer success via a platform that unifies and streamlines vital financial processes. In today’s disruptive and volatile markets finance teams need to be capable of being agile. Mr Shea highlights that their business focuses on combining traditional finance processes as well as delivering the vital insights required to provide agile decision making for customers.

The IDC MarketScape study reviews EPM software vendors worldwide in 2020 via the IDC MarketScape methodology. This review includes quantitative and qualitative features of EPM applications across the markets, specifically looking at planning, budgeting and forecasting tasks, specifically related to finance.

IDC analysts generate individual vendor scores and positions through detailed surveys and interviews with the vendors, using publicly available information, as well as end-user experiences to determine an accurate and reliable assessment of each vendor’s capabilities.

The full IDC report can be viewed, here.

CFOs may be cautious to implement new technologies into a business, but support and providing insights into the benefits of integrating these systems can ensure a smoother transition.

After a very challenging year and continued disruptions to key technology projects, CFOs are emerging and preparing to invest in new projects that support the development of advanced analytics and automation systems. Findings from a recent Gartner survey suggest CFOs are increasing investment plans in data and analytics due to key reasons; Firstly, in finance, analytics can provide valuable insights and information that can directly improve overall performance. Secondly, automation technologies have proven to be highly efficient in eliminating data entry into multiple systems. Repetitive data processing is a challenge in finance, especially with the compatibility of using multiple systems.

While there is a clear trend towards automation and analytics, there continues to be some hesitation from CFOs to invest in these technologies. In the same study by Gartner, nearly 80% of CFOs stated that they had doubts about being capable of achieving their goals in advanced analytics, and a further 56% were concerned that they wouldn’t reach their goals by implementing robotic automation technologies.

The results from the Gartner study imply that there is a certain responsibility of IT professionals in providing support and reassurance to CFOs to enable businesses to implement analytics and other digital transformation tools.

One of the most important factors to ensure confidence in new systems is achieving goals and displaying returns on investment. Short-term projects that can demonstrate results will increase confidence in these technologies, enabling long-term, higher-risk projects to be considered.

In terms of finance, the team will be looking for more analytics but often can get overwhelmed by the sheer volume of the information displayed, impacting their overall vision.

These innovative tools can display key financial metrics but a lack of understanding of the operational procedures will ultimately affect the bottom line. Analytics tools can directly contribute to corporate financial success, but finance teams aren’t necessarily aware of these benefits. If IT teams can provide these insights to finance teams, the CFOs are more likely to see the value. This would make it simpler for IT teams to promote non-finance based analytics to the CFO and other members who are hesitant to implement these tools.

Robotic Process Automation (RPA) has been one of the predominant technologies for businesses and while it isn’t the only method of eliminating and automating repetitive tasks, it provides the most impactful and immediate way for finance professionals to see the benefits of automation.

Eliminating repetitive work can be achieved in multiple industries by implementing automation technologies that vary from RPA. This is exactly where IT professionals should show clear business options and the associate ROI for each technology available.

The ultimate goal in this entire process is to ensure the CFO is completely engaged and on board with big data, analytics and understands how automation works and can deliver better revenue and results for the business. There will always be a certain level of risk and uncertainty with implementing IT projects, but having the support of the CFO and other teams invested in project success is a significant step towards generated success.

Several years ago there was a lot of hype around the potential of big data, taking data that was too large for conventional data processing and applying new technologies and discovering various insights and solutions for businesses. Since 2012, the trend in big data has generally been positive but there have been challenges. Studies by Gartner research have indicated high failure rates with big data projects and how a large proportion of big data studies don’t make it to the transformational business intelligence stage.

Some of the main challenges related to big data are related to the unstructured form of the information, the overall quality and inaccuracies in the data. Generating data that represents what it is as opposed to the multiple options available can be complicated. With the growth of new technology tools available for processing and analysis, it does seem that quality may not be regarded on the same level as quantity. Data quantity has grown considerably in the last few years. It’s believed that nearly 90% of global data was generated in just the last 2 years. This significant growth is facilitating the development of data science and machine learning in global business analytics.

We are approaching what can be regarded as one of the most challenging years in history, a time when nearly all markets were disrupted by the pandemic. Many predictive systems which were developed based on analysis and forecasting of historical data have also been affected.
When looking at Big Data AI for humans and their related behaviours, attitudes and intentions, many of which are driven by subconscious decisions rather than specific clicks, overall success has been declining. Many companies are actively exploring the value of big data by researching and analysing customer transactions and customer data files.
Transactional data, for example, doesn’t provide any detail on why a customer bought something or whether it was a gift for someone. Customer data files commonly have incomplete information or data inaccuracies due to changes in the circumstances of the customer.

For the marketing and advertising industry, investment in digital advertising represents the biggest sector of advertising in North America. There have been many challenges with this growth of digital advertising. Several studies have identified the inaccuracies and unreliability of big data ad targeting models. Further reports have suggested that many of these models are based on data derived and collected without the consent of the customer or combines data sets generated with bot data. Applying this type of data into a model only means further inaccuracies with the final product.
In the finance and investment community, many businesses have been very interesting in integrating big data technology. In the investment field, big data has been renamed as ‘Alternative Data’ and includes anything from credit card transactions, social media, satellite images and web browsing. One of the most recent businesses to employ alternative data in this field was a hedge fund called Renaissance Technologies. Hedge funds have experienced similar challenges to other businesses by adopting big data systems. This includes potential data provenance risks i.e. does the procurement of data meet all necessary terms and conditions, understanding the accuracy of the data sets and general privacy risks in terms of how the data is generated.

A further study by Bloomberg has suggested that Renaissance Technologies models and returns in the last month have declined and some experts believe their models do not apply to the current environment. Industry professionals believe the system is reliant on models that are trained by historical data, another example of feeding a system with bad data and generating bad outcomes.

What is the solution?

The initial step is to ensure that data scientists consider the true accuracy, validity and compliance of all data sets being used as inputs. It’s very challenging interpreting bad data sets. Consideration needs to be made towards the variables with data sets. Humans represent more than just clicks and customers data sources are needed to understand and connect digital data with the reality of customer behaviour. If accuracy and validity are covered first, then the outcomes are likely to improve. Spending more time on these areas at the start of the project will enable data scientists to greatly improve the success rate for big data projects.

If data accuracy and validity are job one, it follows that outcomes should improve. By paying more attention to the accuracy, quality and validity of the data at the beginning of ML projects, Data Scientists may move beyond the 85% failure rate for Big Data projects.

The new deal between Barclays and Amazon highlights the importance of AI in finance.

The financial times recently highlighted the importance of the agreement between Barclay and Amazon, providing a seamless shopping and payment service for customers in Germany. The article explains the significance of the deal and the underlying race at banks and technology companies to discover techniques to utilise big data and artificial intelligence in the finance industry.

Industry experts believe the next steps in this process are key and will define the future winners in the finance industry. Barclays and Amazon are integrating their data with AI analytical tools to measure and determine credit, and predict what services customers will require next. Jes Staley, the CEO of Barclays believes the new partnership with Amazon is one of the most important moves to happen at the business in the last few years.

The potential power and capabilities of AI and in particular ‘deep learning’ offer many opportunities for the finance industry. Jack Ma, the founder of Ant and Alibaba was one of the first to appreciate the true potential of AI in finance, using customer and corporate online activity to assess and predict credit risk and generate bespoke services. 

Incorporating AI into finance can enable financial businesses to provide customers with more choice and better-designed services at more affordable pricing options. AI systems can measure credit risk allowing a company to provide cheaper loan services and if used appropriately can detect and potential cases of fraud.

There are a number of challenges that come with introducing AI but one of the biggest noted problems is that AI and machine learning applications could result in new issues of interconnectedness between financial markets and other institutions. 

The benefits of incorporating AI technology, however, are vast and are continuing to encourage further transformations in finance. The Financial Times suggests a number of measures that would support the adoption of AI in finance. Firstly, businesses moving towards these activities must incorporate regulated measures within a finance framework.  This means, key groups, central bankers and regulators must continue to maintain an oversight of fintech and be aware of new areas of business. 

Secondly, regulators and risk managers must connect all information systems. At the moment, there are very few people that really understand AI and finance. What generally happens is that there are experts in each field, separated into different teams and departments. Many industry experts believe this is a significant issue and requires a major shift in how AI and finance are integrated.

The third measure is to ensure the creative side of AI-focused services for finance incorporates a holistic overview. This means ensuring the people responsible for implementing these technologies are aware of the wider impacts. 

The final measure highlighted by the financial times refers to enhancing the engagement of AI technology, from a public and political perspective. Instead of placing all the responsibility on technology professionals, politicians and other stakeholders must be actively involved in how AI is incorporated into a finance business. The sheer progression and capabilities of AI are very exciting but equally requires a level-headed approach and a combined strategy that involves multiple stakeholders.

The pandemic has impacted businesses in many ways. Finance teams have had to focus on navigating through this period and managing the balance between customer demands and the economic realities that many of us are facing.

The transition to remote working happened quickly and businesses that were capable of reacting and implementing the necessary tools and resources have generally found the transition less impactful on business performance. Enabling communication channels to continue during this period has been a top priority. Constant dialogue with existing customers has enabled many businesses to maintain working relationships and assist with their requirements during these challenging times.

With continued economic stability and uncertainty regarding finances for most businesses, the priority has become focused on cash flow, cash forecasting and understanding new risks. Having the ability to determine potential challenges and regard all the associated mechanisms and scenarios is vital for future management plans. Managing finance, overall costs and how they relate specifically to revenue generation and customer satisfaction are all very important variables. Implementing a detailed plan that is capable of responding to fluctuations in revenue and cash flow has become an even more important element of business performance. 

Finance teams are ramping up their plans in terms of digital transformation. With the likelihood that many employees will continue to work remotely for some time, finance professionals have focused their attention on upgrading systems so they are available in the cloud and not on-premise. Upgrading technologies can enable staff to operate more efficiently and free up more time to spend on analysis, rather than processing information. 

The concern for many businesses is the threat of an additional wave and the associated impact on the economy. This would have implications on business growth, sales and investment into new channels. It’s very difficult to protect your businesses against these economic constraints, but ensuring your business is managing existing spend and budgets creates a better scenario for a business to respond to any significant economic disruption.

For finance teams and more specifically CFOs, the roles have shifted towards a more advisory position, guiding a business on future outlooks and new opportunities. CFOs are now more focused on scenario analysis and advising board members on the potential outcomes, and opportunities to create more value for the business. Right now, the focus for CFO’s lies with value creation, exploring how a business can generate more value and communicate this effectively across the wider team.

For the extended finance team, continuing to focus on new training and upskilling should remain a priority. Ensuring members of staff are continuously skilled is particularly important right now. Aside from specific industry training, ensuring employees have the opportunity to communicate their concerns and challenges is equally important. Maintaining a close check on motivation and morale cannot be forgotten during this period.

Having the capabilities to collect insights and important data concerning your business is vital for making informed decisions and managing difficult challenges that many businesses are experiencing now with the pandemic.

There continues to be a great level of uncertainty for many organisations around the world. By utilising reliable data and analytics, businesses can become more intelligent and efficient in dealing with the current situation. Businesses can harness this information to understand what path their business is taking and make informed decisions to improve the situation.

Periods of uncertainty and challenging events are always inevitable, however, we are resilient and capable of adapting to changing markets and needs. There are several key ways businesses can use data and analytics effectively to work through the recovery phases and look towards a positive future.

Data Management – In previous years it was realistic to react to new data sets when they arrived because the information would arrive at a relatively steady pace. Businesses could collect the data, assess it and respond promptly using conventional spreadsheets and other tools. Today, however, the sheer volume of data available in real-time means it has become very difficult to collect, manage and put the information to good use. A lot of businesses become overwhelmed and lack the time or ability to utilise all of this information effectively. Applying the necessary tools enables businesses to gather and measure data efficiently. This allows businesses to work through challenging times and create a strategic and stable plan to move forward with.

Some of the best and most efficient organisations are more reliant on data and analytics to manage their operations.

Measuring and identifying data patterns

There is a considerable amount of data available to businesses today, from multiple sources and often providing varied information. Collecting all of this information together, understanding it and making informed and accurate decisions for your business can be challenging. To begin with, a business should be capable of identifying the key signals specifically relevant to your organisation. This can be supported further with other unstructured data, information from customers and competitors for example.

For example, collecting unstructured information from your customers can enable a business to create a clearer understanding of their customers and the potential to identify risks, as well as discover new opportunities.

Integrating your data systems

Individual teams within a business may be observing signals and information in different ways. For example, finance teams may identify changes in revenue spend or changes to payment terms, whereas other services could identify these changes as standard account updates or individuals moving to a different position in the business. In short, information that could be regarded as indicators of required change may be interpreted differently by other teams.

It is essential to integrate these data points and determine exactly what information you are presented with. Applying a triangulation process enables businesses to make more informed decisions within a single platform. When a business is presented with varied data, it allows all teams to assess, ask questions and get more clarity on the information.

Use the information to allocate resources effectively
Every business is experiencing significant changes in resources and capacity. Data enables businesses to understand what areas will enable flexibility and areas which lack room for change.

In some cases, sales employees may have additional time due to deals being relatively slow. This may be an opportunity for a business to reallocate sales professionals to an alternative temporary role focusing more on post-sales or services. Businesses can utilise tools to monitor certain factors that will reveal where best to allocate resources. For example, with many now working remotely, the reliance on new technology has increased, and so some businesses are reallocating resources to provide customers with additional training and additional services to improve efficiency.

Right now businesses cannot operate without having the right information to make clear and strategic decisions. The effective use of data and analytics can enable businesses to understand their position and what specifically is required to determine how to improve their overall performance.