• Michael Pyliotis

How to make your finance data work harder

A business can never suffer from too much information. And we agree, for the most part. When it comes to data insights, however, the challenge is understanding the difference between information, data and intelligence writes Michael Pyliotis, VP APAC, Basware.

According to the Melbourne Business School, Australian companies risk falling behind global competitors owing to poor use of data. This is not an information problem – it’s an intelligence problem. The data may be available, but it may not be easy to interpret, compare, or use.

This is especially true for startups and small businesses (SMEs). The plethora of data available means we can move away from making decisions based on gut feel. However, if it’s not bought together correctly, we also risk only having one piece of the puzzle. This inevitably leads to siloed decision-making. Only by using the right data and treating it as a map to unlock insights, can SME’s truly harness data to reach their business goals.

Data has to be trustworthy to be meaningful

When it’s used right, data insights can predict trends, improve profits and ultimately, set the future of the business. For example, data can be used in accounts payable for everything from choosing preferred suppliers and analysing consumption patterns, to determining buying channels and improving savings – but only if the right systems are in place to get meaningful and trustworthy data.

According to Experian, 89% of Australian businesses struggle to manage their data and they’re not alone. The same research reveals that “70% of businesses globally struggle to unlock data’s true potential because of a lack of control, leaving many businesses with untrusted data that undermines business innovation and customer interactions”.

Unfortunately, many startups and SMEs still struggle to select and use data solutions that will help them transform their businesses for the better. This is often due to a lack of data-literacy, a skill which is essential for building data trust.

There has to be a better way to trust and use data

Many businesses suspect their customer information is inaccurate. But what if there was a way to use customer intelligence to manage a business’s own information? What if we sought to interrogate our own systems, taking finance and procurement as an example, to predict our customer’s behaviours and offer them incentives for early settlement? Aside from the information the customer gives you directly, there are a host of other patterns that can be gleaned from all data sets.

This is something we’ve focused on heavily at Basware. In building our systems, we’ve found that the solution to the above question lies in using consistent, valid data and having the right tools in place to capture it from the very start. For data to be valid, usable and ultimately give finance and procurement the power to predict the future, it has to come from and be serviced by technologies that help, not hinder, the employees who use it to make meaningful change in their organisation.

Data doesn’t exist in isolation. Or, at least, it shouldn’t. With the right software solutions in place, data can be shared across organisations and platforms so that it works with your employees, allowing them to solve problems now, and adapt in the future. A combination of people and technology – or a mind-machine partnership, as we call it – is key to increasing collaboration between employees from every relevant department of your organisation: from those who generate data, to those who process it, to those who interpret and act upon the insights.

Data is useless unless you use it

Data is only as good as its interpretation. Once your data is trustworthy, the next step is to ensure that insights are supporting your business strategy. Take procurement, for example. At the basic level your data can help you analyse invoice cycle times and detect invoice processing exceptions. At a more advanced level, you can flag invoices that are likely to be paid late, or benchmark invoice cycle times across suppliers to set best practice.

Further to this, are the obvious benefits of freeing staff up for more value-added tasks: to move them from inputting, to analysing. This trend is already in place and Gartner predicts that by 2022, manual data management tasks will be reduced by 45% through the use of machine learning and automated service-level management.

That’s just the beginning.

Effective data use will future-proof your business: but only if the right data is in the right place. And to achieve this, you need the right tools to garner these insights.



Tuija Taivainen

+358 40 530 1351


Noora Veijalainen

+358 40 704 8239

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