Turning the abundance of data stored and accessed by businesses into useful insights is a skill. It comes with costs like time, money and risk, but, if done right, has the potential to drive performance and support growth for years to come.
For many businesses, especially small- to mid-sized enterprises, data analytics might seem like a complicated, unapproachable process. It may help to think of it as “management accounting 2.0” that provides an enhanced overview of business operations with greater detail and improving business performance by enabling data-driven decision making.
Instead of producing basic information from a point of sales (POS) system, data analytics uses the information stored in various systems within an organisation to draw insights that you don’t get from a standard report.
In addition to driving a future-focused approach for businesses to maximise performance and efficiency, data analytics also acts as organisational quality assurance. With automated reports and processes, it’s easy to compare and merge the point of sales and accounting datasets, while reducing the risk of human error.
Below, we’ve outlined a ‘best practice’ approach to using data analytics in your business, starting with knowing what information you need, and what sources of data you currently have to draw on.
Identifying data sources and systems
When businesses first use data analytics, they often find it difficult to recognise the right datasets and sources to use. As a first step, identify the problem your business is trying to solve, or what question you need to answer. Clarity on the issue at hand will make it easier to recognise and ignore any irrelevant information. This is particularly useful for organisations with lots of data.
It is often easier to decide which datasets are irrelevant, rather than trying to find the right ones.
It’s crucial to be aware of mistakes and misinterpretations throughout this identification process and you need to be mindful of potential biases, both in the data and among your team and use your data with caution and be sure to check and validate it. Even a tiny error can jeopardise the credibility of the entire analysis.
In addition to identifying the right data to use, your business will have to ensure the right technology systems and processes are in place to handle enterprise-level data analytics.
Cloud-based tools can be a powerful asset with lots to offer in terms of real-time, accurate information, integration capabilities and more. Once you start using cloud-based applications, you’ll be amazed at how legacy tools like spreadsheets seem time-consuming and prone to human error by comparison.
Getting the most out of your data and systems
While it’s good to have an abundance of data and the latest systems for analysis, it’s important to make sure that you’re maximising their value to the business.
Think about what you need the data to achieve. Is there a need to make real-time decisions? If not, then live data feeds can quickly become unmanageable and unnecessary.
You should try to match the frequency of data capture to your organisational and decision-making needs. For instance, you may only want to update a particular dataset on a quarterly basis according to your business plan.
It is best to isolate any inaccurate aspects of the analysis until you fully address the issue.
To get the most out of your systems and protect valuable data, you will want to ensure that your organisation has strong cybersecurity systems and processes in place.
While this certainly includes system security software, it is also vital to include employee training and monitoring. The biggest risk to data security faced by organisations is employee error.
According to the 2021 BDO and AusCert Cyber Security Survey Report, targeted phishing email attacks are one of the major causes of cyber incidents for businesses. To prevent these leaks and protect their data, businesses should reassess their cybersecurity hygiene and emphasise employee awareness.
Ensuring accuracy and data integrity
Another key step in maximising the value of your data is ensuring its integrity.
Without accurate data, analyses are completely unreliable and will not add value to your decision-making process.
Focus on fixing the highest-value datasets first to prioritise and organise seemingly unmanageable projects.
To ensure data integrity, conduct regular data cleanses by combing through and locating pockets of incorrect information to tidy up. If you find there are large swaths of inaccurate data, it may be beneficial to take a step back and re-evaluate your processes.
‘Bad’ datasets can still be useful. For example, you may be able to add a few tweaks to fix the problem. If not, you might want to review the decisions that led to the unreliable data and think about how you can change your approach in the future.
Remember, not all datasets are equally important. Their value comes down to how they are used and the perception of their functional importance.
Steps to success for data analysis
Too often, businesses dive straight into the data without formulating a plan. Before you get ahead of yourself, you will first need to understand what information you are trying to get out of your data and what you hope to achieve.
With your goals clearly outlined, the next step is to start gathering data.
Depending on your organisation and requirements, you may need live data feeds to make real-time decisions or just an annual data capture. Whatever your needs, create a plan to gather that information. Ideally, your datasets will come from multiple sources – both internal (e.g. Cloud accounting software or point of sale) and external (e.g., industry trends and third-party data sources).
As you collect the data, it is critical to start planning what you will do with the results.
Make sure that whoever is involved in the process has the capability to do something with the insights that you deliver, have discussions about what decisions the business is willing to make, and how they will be actioned. After all, there is no point in doing all the work to gather and analyse data if no action is taken.
For many businesses, data analytics may fall outside of their internal capabilities. Whether this is due to a lack of resources, knowledge or both. Seeking support from external data analytics experts can alleviate confusion and ensure you have the right systems and processes in place.
This article is a version of one first published at bdo.com.au on 5 May 2022.