The growth of data has enabled businesses to understand large volumes of data and generate valuable insights. Companies that rely on this information to execute important decisions and continue to improve their products and services. The data industry continues to progress, and ‘dark data’ offers the ability to take it a step further.
Harnessing dark data is vital for businesses, particularly those in the fintech industry looking to grow and remain resilient. Dark data refers to information collected as a by-product of regular business activities but isn’t directly used to create additional income. The data gathered in these processes may not necessarily be the target, but additional information could still have some value.
Dark data represents the information that is pushed aside but could be vital in understanding customers and overall market conditions. This could include anything, from internal data such as email conversations and financial statements to external data sources, like customer profiles. Interpreting these data sources can enable businesses to gather new insights and intelligence that could greatly influence their decision-making process.
As the volume of data increases, so does the volume of dark data. Many businesses, however, are ignoring this source of information and the potential it could have on their overall performance.
Harnessing this data and transforming it into usefully structured information can be challenging. Automation and other emerging technologies have enabled businesses to manage this process more effectively. With further support, access to dark data sources can enable teams to gather more insights into their business and anticipate patterns and industry trends. As a result, the business will be more informed and prepared when making important business decisions. For example, a major hotel chain in Europe measured its internal dark data, which in their case was Wi-Fi usage data. The company used the data to identify and solve potential issues regarding waiting times to check-in and out and ways to improve staff allocation across the hotel. Dark data supported the business in optimising their resources, while at the same time improving the customer experience. Research by Accenture indicates that dark data analysis has assisted insurance companies in generating vital insights and resulted in a profit increase of up to 21%.
The potential benefits of dark data can be applied to the fintech industry too, enabling businesses to deliver stronger analytics and potential business opportunities. Dark data provides a means to remain competitive and profitable and this year all businesses will be focused on resilience. The difference between businesses that continue to progress and those that struggle will be closely related to their alignment with market conditions and customer expectations.
Dark data is a valuable tool in delivering resilience and provides a great opportunity for fintech to generate, informed, data-driven business decisions. Leveraging dark data within a business model can enable CFO’s to focus on generating new business opportunities and a considerable edge over other competitors.
AI and automation became essential for many business’s efforts to manage the impacts of the pandemic. How will these innovative services continue to transform business strategy this year?
At the beginning of 2020, the uptake of tools like AI and automation was increasing but at a cautious and steady rate. In just a few months, the rate of growth changed significantly. The global pandemic became a catalyst, accelerating the uptake of new services and changing business direction, including increased use of AI, Analytics and Automation.
The move to working remotely facilitated a major business change. This shift to remote work came at a time when there was a major increase in demand for additional customer service and support. As a result, AI and automation became a top priority in 2020 for many enterprises. 2020 witnessed a big increase in the use of automated chat bots, as businesses actively looked for ways to automate regular interactions or reduce the volume of manual tasks. This year we will inevitably continue to see businesses exploring ways to implement automation into their business and alleviate the pressures of customer demands and expectations. The surge of technology solutions has enabled most companies, big or small, to implement these types of services.
Another trend of 2020 that is likely to continue throughout this year is edge intelligence. Edge Intelligence refers to processes where data is collected, analysed and insights are generated close to where it is captured in a network. Today, smart edge solutions offer real-time insights by assessing data at the edge itself. Edge Intelligence is generating a lot of excitement in the industry, where AI/ML technologies come together with the cloud. Edge Intelligence enables systems to make decisions on locally generated data, instead of sending it to a centralised cloud or on-premises server. The ability to integrate AI and ML on the edge is a game-changer, according to many industry experts, because it can perform on the collected data and generate decisions before any data is moved to the cloud.
With a major shift of services to the cloud in 2020, a response that was driven more so by the impacts of the pandemic. Industry analysts believe edge intelligence will complete the shift to the cloud. Technologies such as 5G combined with artificial intelligence will only be capable of enhancing projects completed at the edge. After a year where remote working became the norm, this year seems a suitable time for edge intelligence to become more prominent.
Another trend expected throughout 2021 is the rising adoption of augmented analytics, a set of technologies that utilises machine learning to make data management and analysis simpler. For businesses, this means their workforce will be capable of effectively applying analytical tools without necessarily having to send analytics requests to data specialists. Data analysis is becoming more important in business and as a result the tools have become easier for employees to use. Technologies like this will be critical throughout 2021 to monitor and measure business performance and determine the best path and strategy to take this year.
Key trends for this year
AI Rate of Adoption
Gartner has forecasted that nearly 80% of enterprises will make a move from testing to operating AI by the end of 2024, creating a surge in streaming data and analytics infrastructure. 2021 will inevitably see many businesses make the move from testing the potential of AI, to integrating it into their performance plans.
Rise of Data Stories
Data stories will continue to become more prominent and are predicted to become the most popular way of consuming analytics by 2025. A large portion of these stories will be automated via augmented analytics. AI and ML processes are becoming more common in BI platforms. The traditional dashboard requires further manual work to determine the insights, but data stories provide the information without requiring the user to perform their analysis.
Increase in Decision Intelligence
Larger businesses will have dedicated analysts for decision intelligence, a practical service that incorporates several decision-making techniques. Decision intelligence combines conventional techniques with more advanced solutions like AI and Machine Learning.
Cloud will dominate
Public cloud services are forecast to be essential for 90% of data and analytics innovation by 2022. Cloud-based AI is forecast to reach a level five times higher by 2023 compared to 2019. This trend began before the pandemic, but the impact of Covid-19 has accelerated the rate of growth.
Integration of Data and Analytics
Gartner believes that non-analytical services to develop to incorporate analytics over the coming years. By 2023, 95% of Fortune 500 companies will include analytics governance into their wider data and analytics governance plans. By 2022, approximately 40% of machine learning development and measurement will be done with products that do not have machine learning as their primary goal. Gartner explains that analytics and BI providers are widening their data management capabilities and suggest that there will be more convergence soon.
A significantly challenging 2020 has left many questioning what will come next in the technology market. Many data and analytics experts from leading businesses such as Qlik, Cloudera and SAS have highlighted their own predictions for the year ahead.
One of the key areas and a popular discussion point is the prediction surrounding AI and Machine Learning. One area discusses how AI will continue to become more integrated with industry, more accessible, affordable and refined. Industry experts believe ML and AI will become more accessible to a wider range of businesses. While AI has largely been viewed as a tool generally applicable to data science experts, industry predictions suggest that this is changing and having these skills doesn’t necessarily mean you can’t utilise the advantages of AI. Other analysts expect the overall economics of AI to continue to improve, along with its accessibility.
Ryohei Fujimaki, founder of dotData reiterates this prediction, stating the automated machine learning will enhance AI accessibility for non-data scientists and enable AI to go beyond predictive analytics and generate valuable insights into other trends and events that may have previously been overlooked.
Analytics and the challenge of the pandemic
Many of the discussed developments for this year view Covid-19 as a significant influence on technology predictions. For example, analysts believe more organisations will transfer their infrastructure to the cloud due to Covid-19 investments in MIL will rise rapidly.
When the pandemic transformed the global economy, businesses were forced to invest quickly into BI tools and data software to try and gain an understanding of what was going on and to make standard business decisions. Many businesses are having to make significant cuts in budgets to alleviate the impacts of the pandemic and maintain their core business functions. Yet, this year’s predictions believe that many organisations will sustain or even increase their investment plans into data science to enable decisions that could be vital in the survival of their business.
The pandemic has spurred accelerated demand for AI solutions and has raised the focus on the ethical use of AI. Data and analytical experts believe the focus will shift towards measuring changes in customer behaviour in real-time to deliver vital actionable insights.
Analytics can continue to influence how we manage the impacts of the pandemic, rather than being the other way around. Buno Pati, the CEO of Inforworks refers to it as a battle against Covid-19 and gaining access to vital data about the health industry will ultimately enable a more efficient response to the pandemic. Analytics will not only play an important role in approvals for the vaccine development process but will also be vital for planning the expansion of tracking distribution, measuring side effects and the overall effectiveness.
For the finance industry, a recovery from the impact of the pandemic will enable initial testing with artificial intelligence and machine learning to become more of a normality. The implications of the pandemic required significant adaptation and many financial businesses have needed to make major transformations to meet customer expectations and ensure their core operations and procedures continue to run smoothly. This has spurred a wider interest in AI and ML technology, systems that minimise the need for manual processes, improve overall security and provides additional time for other innovative tasks. By reducing the time spent in creating an idea and generating value for a business, AI and ML have the potential to provide long-term advantages for businesses.
People’s expectations of financial services have increased and as a result, we are seeing banks transform into digitally-focused platforms, similar to technology leaders, with the capabilities to enhance their customer focus. The question is how can banks and the wider financial services industry utilise AI to its fullest potential.
Many financial services companies have already implemented AI and ML systems before the pandemic escalated. However, many organisations have experienced challenges in understanding how certain features of AI can be of benefit, and as a result, businesses didn’t necessarily yield the expected results from AI. Analysts believe this will improve over the coming months, with predictions that AI and ML deployment will become a major part of the economic recovery from the pandemic and Covid-19 has emphasised certain areas where AI should be implemented. This includes credit decisions, fraud prevention and improving the overall customer experience.
What financial services processes can be enhanced by AI?
Using automation to enhance document processing
Robotic process automation can enhance several functions, overall efficiency, speed and the accuracy of vital financial processes, resulting in considerable cost savings. One particular area that has emerged is referred to as ‘electronic know-your-customer’, a remote paperless solution that eliminates the costs associated with certain protocols such as client verification and signatures. A process that was commonly time-consuming and repetitive has been improved with more organisations embracing intelligent automation systems that are capable of managing and extracting unstructured data and other information.
Operating an NLP system (natural language processing) enabling the identification of missing data, means platforms provide highly accurate and reliable information. Overall handling time is reduced and businesses gain a competitive advantage by enhancing the overall customer experience.
Improving the efficiency of customer support
Virtual assistants are capable of responding to the needs of customers with little input from employees. The time and effort applied to inbound enquiries are greatly reduced, enabling more time for employees to focus on long-term projects that will generate further innovation and success in the business. Chatbots and other systems will inevitably become more common in the finance industry, with major businesses like JP Morgan implementing bots to enhance their overall operations and improve customer support.
Implementing analytics into risk management
Even being equipped with the right data, measuring and predicting credit and risk management processes is challenging. This is largely down to certain individuals and businesses not disclosing their ability to pay back loans. To manage this, businesses are applying AI for risk assessment, to understand the creditworthiness of individuals and businesses. Credit companies like Equifax use a combination of AI, ML and other analytical systems to measure various sources and evaluate the overall risk level and generate key insights into their customers.
Previously, lenders would rely on limited data sets, such as salaries and credit scores. AI enables businesses to consider a much broader digital footprint of customers to determine overall credit risk.
How businesses and clients interact has changed significantly in the last year, and this applies to the finance sector. Before the pandemic took effect, businesses were merely testing the waters with new technology. However, the widespread adoption of AI and ML over the last year has been spurred from the need to innovate and improve overall resilience.
The finance industry is now well aware of the benefits of AI. The early stages of a transformative process towards AI that began before the pandemic has accelerated and is quickly establishing itself as a standard in finance.
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.