Across the entire business, the predominant focus lies in implementing data-driven decision-making processes. Applying the potential of technology, advanced analytics and data, combined with human power to generate smarter decisions.

Despite the benefits, many businesses fail to deliver what they regard as a measurable value from their data. A study by Accenture recently discovered that only just over 30% of businesses report tangible and clear value from their data. 

Researchers from the MIT Sloan Management Review explained that many businesses tend to focus on finding a purpose for data available or extracting information from the information they have to hand. This results in creating answers to the wrong questions or possible misleading insights. A more effective approach to take is applying decision-driven data analytics. This involves emphasising the decision that needs to be made and then working backwards to explore what data will be best to deliver for that particular goal.

Data-driven decisions often place too much emphasis on the data but fail to consider how the information is generated and result in individuals reaching inaccurate conclusions. The second problem encountered is that people tend to ask the wrong questions. 

Key Considerations

Don’t wander off with your data. Creating a plan and initiative based on the data available can lead to focusing on the wrong questions and reinforce certain feelings within a business. Staying focused on the available, generally historical data will often result in decision-makers taking the wrong path and ultimately gaining insights into areas that don’t focus on the fundamental challenges a business may be facing.

Assign the right people. Most data plans are managed and operated by data scientists, who have a solid understanding of data processes but they may not be ideal at understanding the core problems of a business. Ensure data and analytics plans include other people who have a strong understanding of data and a familiarity with the business goals to enable an effective combination of the two.

Create a plan and succeed. Decision-makers in a business need to determine the right course of action for each challenge they face. They should work out what information is required to assess all possible options and use the resulting analysis to choose the best course of action.

Analytics and big data have proven to be valuable in supporting business insights, but people need to remain in control of the shaping of their business plans.

Businesses today are consumed by so much data, some face the challenge of accessing the information or lack the right tools to analyse their data.

The digital transformation of the finance industry has been happening for several years now. The potential to automate manual processes has enabled finance to transition from its conventional reporting processes to a more innovative forecasting and analytical model. While many businesses confirm they are applying analytics to their organisation in some shape or form, only 14% of finance businesses use large volumes of data available to generate valuable insights, according to the FSN Future of Analytics in Finance report.

Finance teams that have taken this approach to harness this information are better placed to forecast more accurately, create valuable scenarios and explore clear insights that support enhanced decision-making. The FSN report also suggested that 86% of analytics resources were not achieving the mark. The study believes that one of the main reasons for this is that many businesses are not utilising the value and insights from their data.

The survey found that ultimately it is the data that is holding many businesses back. Organisations are either overwhelmed by the sheer volume of data or are held back by the technology they are using to measure their information. According to the report, only 12% stated that they are suitably equipped to manage their data and have all the necessary resources to deliver clear, actionable insights.

The accelerated rise of new technologies available across the market has left many financial businesses struggling to maintain pace. This includes predictive analytics, artificial intelligence, machine learning, robotic process automation and more. While all of these technologies are valuable, creating success requires a holistic-based approach, and many still seem to be working towards this.

The survey indicated that over half of respondents were not capable of regularly adding new data sources to enhance business insights, and under half can make full use of non-financial data. When asked about the key features for analytical tools, many respondents placed AI and ML as top priorities, while at the bottom are some of the most important building blocks for creating an effective analytic system, including the ability to integrate multiple data sources.

The survey findings resonate with developments in the market. Many larger organisations struggle to deliver efficiency and agility in their reports, planning, budgeting and forecasting processes. This is often down to an over-reliance on spreadsheets and manual processes or using fragmented applications that a business may have outgrown or not capable of managing.

Innovative businesses are improving analytical insights by combining processes to deliver a singular system for financial results, budgets and forecasts. Businesses are taking further steps to integrate data processes into their analytics platforms on a more consistent basis. Accessing this type of information and combining it with financial data provides these businesses with clear views into key trends and indicators that enable decisions that can impact the future of a business.

Generating unified and efficient reporting combined with operational data requires the appropriate analytical systems and appropriately skilled talent. In our rapidly developing economy, having the necessary information systems capable of generating clear insights and analytics is not an option anymore but a vital part of surviving and succeeding for the future.