CFOs: How You Should Harness Quality Data for Growth

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Beyond just being a trendy buzzword, big data has proven its effectiveness in helping CFOs make better decisions. By uncovering previously hidden patterns using big data – whether it is in customer preferences, supply chain or production line details – businesses can realize untapped revenue and hidden cost savings to drive growth.

However, while most organisations understand the importance of quality data, many still do not know how to harness it. This poses a significant problem for many companies, as the business cost of bad data can be as significant, or even more detrimental, as the benefits of leveraging quality data.

Lost in translation: common problems finance leaders face when collecting data

When it comes to analyzing financial data, finance leaders often face the issue of having way too much information and no time to go through them, or simply insufficient quality data. These scenarios can cause major issues for businesses in the long run – they can eventually erode the competitiveness of a company in the industry and impact financial bottom lines.

One of the most common problems that businesses face today is having inconsistent forms of financial data collected without proper planning or purpose in mind.

This might come to light only when, after investing money and effort on data collection for a long time, the company finally decides to use the data to make decisions. In this situation, a massive data clean-up needs to be undertaken to make the data usable.

Data engineers are required in a clean-up operation to analyse the state of the data, find the faults in the data collection pipeline and to make recommendations on how to realign processes so that each data point established can be readily used to mine the required insights.

Finance leaders should strive to acquire clear and purposeful data that will give them the ability to make sound business decisions and accurate financial projections for the company.

The devil is in the data

The repercussions of collecting bad data – or inaccurate data – can lead to catastrophic outcomes if left unaddressed or overlooked.

Companies may be bleeding unnecessary revenue from certain non-performing or outdated business functions and not even know it; company function leaders may also make the wrong business decisions by making inaccurate assumptions about their customers and what they are looking for, which can lead to wastage of resources.

Not investing time to nip bad data collection practices may also lead to company financial data becoming highly biased or skewed.

This will impact not only the company’s business decision-making process, but also its overall productivity as IT teams will need to review and rectify multiple data issues across affected departments.

Finance leaders need to be conscientious and thorough in the way they collect and interpret financial data. One wrong assumption as a result of inaccurate data can lead to dire consequences.

Bad data exposes businesses to vulnerabilities such as potential fraud and compliance issues with audit. It can cause company leaders to make poor business decisions which might result in large revenue losses for the company, tarnished reputation with customers and might even lead to the collapse of a business.

Best of both worlds: how leaders in finance and technology should collaborate to unlock the benefits of quality data

Before digital technologies revolutionized the corporate world, business functions mainly operated in silos. But with the advent of IT, business leads including those from finance and IT are increasingly connected, and need to collaborate with each other to harness the full benefits of technology and data.

ThoughtWorks, a software and digital transformation consultancy, has outlined four best practices for finance and IT leaders to best work together:

  1. Understanding financial data transformation is a combined responsibility, not just that of IT leaders. Company finance leaders need to understand that in order for IT to be able to source and build the right systems and frameworks to gather the best data, they need to constantly be in consultation with their IT leaders each step of the way.
     
  2. Keeping up-to-date on the latest financial technologies and software. IT leaders may be able to share insights into some of the new technologies and capabilities in the market but ultimately, finance leaders need to also take the lead in terms of being aware of what are the latest finance technological developments out there so that they are able to point IT leaders in the right direction.
     
  3. Defining your questions. Either be clear on the problem you are facing or the objective you’re trying to achieve with your data, then look at the variables you require in order to solve it.
     
  4. Recognizing the data journey doesn’t stop at “hello”. The finance data journey doesn’t just stop after your data platform has been built and adopted by the company. The data journey is an evolving one and it is incumbent on finance leaders to do regular check-ins with IT leaders to ensure that their financial data continues to stay relevant and can be used to drive tangible results for the company.

Towards developing relevant and scalable financial data platforms

It’s essential for finance leaders to take on a leading role in the whole financial data collection process.

Their specialized applied knowledge and understanding of how company finances work can help point company IT leaders and teams in the right direction.

Together, they can unlock valuable financial data within a company using automated digital processes. Synergy between both teams can result in the development of robust corporate financial platforms that are relevant, scalable and crucial for the business to thrive in the digital economy.


About the author

Sophia George is a Machine Learning Engineer at ThoughtWorks, a global software and digital transformation consultancy. At the firm, she helps create statistical models and machine learning algorithms for data science applications. She joined the company in 2015, and has worked on several data science projects across a range of industries including retail, financial services, automotive and aviation.

 

 

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