12 signs that you could improve your processes

Collecting and managing data from multiple sources can be a challenging task for many departments due to inconsistencies in file formats, file types or differing levels of accuracy. This process often involves the manual and time-consuming tasks of normalising, combining, and validating data, which can be risky and lead to costly errors. For example, accurate departmental sales data is crucial for determining revenue and profitability, and inconsistent data can make this task difficult. According to a survey by Experian, 88% of organisations face challenges in managing data from multiple sources, including difficulties in identifying relevant data, data quality issues, and data integration challenges. It is essential to address these challenges to ensure accurate data management and decision-making.

Data standardisation is critical to ensuring that data is consistent and reliable. Without it, inconsistencies in data can lead to errors and misunderstandings. For example, if departments use different naming conventions for products or customers, analysing data and generating accurate reports can be challenging. The lack of common standards has been identified as a significant barrier to data sharing by 80% of businesses, according to a study by the European Commission. To overcome this challenge, organisations must prioritise data standardisation to streamline processes, improve accuracy, and facilitate data sharing across the organisation. An alternative solution is to incorporate a harmonisation stage into your data processing to ensure data consistency and reliability, regardless of your industry or field.

Despite technological advancements in data processing, many professionals still rely on manual data entry, a tedious and error-prone task that can impede efficiency and accuracy. Unlike automated data entry methods, which allow for easier tracking and auditing of changes, manual data entry is slow, error-prone, and difficult to modify. In fact, according to Forbes, a staggering 88% of spreadsheets contain errors, many of which can be traced back to manual data entry. If you've ever had to review a spreadsheet and found manually typed source data, you'll understand the risks associated with manual data entry and the potentially serious consequences!

If your team spends hours on tedious tasks like combining data sources, cleaning and deduplicating data, or moving it between different parts of a process, it's time to consider automation. Automating these processes can streamline operations, reduce manual errors, and free up time for higher-value tasks. With automation, you can easily manipulate data, perform complex calculations, and generate reports. In fact, the Institute of Chartered Accountants in England and Wales reports that 71% of organisations plan to increase their use of automation and AI over the next five years, a good indicator that automation is the way forward for optimising business processes.

Spending too much time in spreadsheets can be detrimental to the productivity, accuracy and creativity of professionals. Manual data entry, lack of automation and poor data quality are all reasons for spending your waking hours buried in spreadsheets but is it time well spent? No professional entered their career wanting to be buried in a spreadsheet, they certainly didn’t spend all those hours training and doing courses only to find yourself there. But most importantly it’s stifling their creativity and ability to innovate. It's important to step back, reassess current processes, and implement new ones to optimise business operations and ensure long-term success. According to Harvard Business Review, professionals spend an average of 14 hours per week on spreadsheets, and that’s just the average!

Poor data quality and inaccurate data can lead to incorrect information and poor decision-making. Unfortunately, according to the Experian Global Data Management Survey, organisations believe that on average, 32% of their data is inaccurate in some way. This can have serious consequences, such as inaccurate management reporting and making poor decisions around the future of business lines. Incorrect financial records lead to incorrect financial statements or tax filings, hefty fines, and a damaged reputation. It's crucial to improve the process around data creation, both at source and during data processing to ensure that data is accurate, complete, and up-to-date to avoid these risks.

Without data quality checks, poor data quality or errors created during processing can go unnoticed. Let’s look at the example of customer data that contains errors such as incorrect contact information. This can lead to inaccurate insights and ineffective product development, failed marketing campaigns and lost sales. Establishing a data quality process can help address these issues and ensure that accurate data is used to make informed decisions, resulting in improved product development and marketing success. Unfortunately, many organisations don't prioritise data quality checks, as shown by a survey by Experian where only 38% of organisations reported having a formal data quality program in place. It's essential to prioritise data quality checks to avoid costly mistakes and ensure that data is accurate and reliable.

Data analysis is a vital tool for organisations to identify opportunities and make informed decisions. Without it, navigating complex business scenarios can be difficult and uncertain. However, limited data processing capabilities or inadequate access to the right tools can hinder valuable data analysis. Investing in advanced analytics and data analysis tools is essential to staying ahead of the competition. A survey by the Chartered Institute of Management Accountants found that 75% of finance teams plan to use advanced analytics in the next five years, indicating a growing recognition of the importance of data analysis in driving business success.

Trying to operate without real-time data makes it extremely challenging to make timely decisions and respond decisively to changes. For instance, medical professionals without real-time access to patient data may miss out on opportunities to provide optimal care or manage critical conditions. In a survey conducted by the British Medical Journal, 72% of healthcare professionals reported that a lack of real-time data was a significant barrier to delivering high-quality care. The ability to process and transform data quickly and accurately is a must to ensure that your management information and decision making is based on the most accurate real time data available.

In a process framework, key man dependency occurs when a company relies heavily on one or a few key employees who possess critical knowledge or skills necessary to complete a process or data task. This can be a problem when these employees leave the company, become unavailable due to illness or other reasons, or simply cannot handle the workload. Without their expertise, the company may struggle to maintain operations or face serious setbacks. This dependency can also limit opportunities for growth and innovation, as new ideas may not be implemented due to the lack of knowledge or skills within the organisation. A 2017 survey by the Chartered Institute of Internal Auditors in the UK found that 72% of organisations recognised key man dependency as a risk to their data processes. It's important for companies to identify where key man dependency exists and implement strategies to address it such as cross-training, documentation of critical processes, increasing automation and succession planning. By doing so, businesses can ensure continuity and sustainability in the face of unexpected departures or changes.

Sit down with your team and grab a whiteboard and a pen. Ask the person responsible for your most important process(es) to draw it out on the whiteboard. If a second person stands up at the whiteboard to make an edit or a debate ensues, you need to document your process! The Chartered Institute of Management Accountants say that 52% of businesses identified a lack of data governance as a barrier to successful implementation of data analytics.

When you challenge a data process, and you hear the phrase “because we have always done it that way”, you know it’s time to challenge the status quo. Challenging the status quo of data processes and how data is managed and analysed can lead to improved efficiency and better decision-making, allowing businesses to grow and thrive. According to a KPMG survey, only 10% of businesses in Europe are currently making full use of their data, indicating a need for more organisations to challenge the status quo and explore new approaches to data management.