The importance of data is impossible to ignore/understate in the modern business climate. It’s quite simply the lifeblood of any company’s operations, as it can be used to outmanoeuvre competitors and to improve general performance.
The management of data is something that must be done effectively if you are to get the most out of this resource. An essential part of this process involves maintaining data integrity, which itself is an element of data quality. These metrics must be used to determine the reliability and trustworthiness of this information, as it is used to guide so much of a business’s strategy.
For finance teams in particular, data integrity is of the utmost importance, because it can influence the quality of crucial activities like financial reporting (which has a big impact on aspects like a company’s profitability and bottom line).
But what exactly is data integrity, and in what other ways is it important? In this blog, we explore the different types of data integrity, the repercussions of poor integrity, and how to implement an effective data integrity strategy within your finance department.
What is data integrity?
Data integrity most often refers to the correctness, timeliness, and consistency of information, as well as how complete the particular dataset is. This is something that should be continuously monitored, from the moment data is sourced and stored, until it is transferred or deleted.
In the context of business, it’s important for assessing the reliability of operational data, and therefore how confidently this can be used for aspects like analysis and decision-making. It can be used to judge the quality of sales, inventory, marketing, finance, and customer information.
Data integrity ensures the perceived information represents the true state of play. For example, if inventory information isn’t correct, it could lead to unwanted stock levels, which results in stockouts, or wasted stock/financial losses due to excess.
In finance teams, the accuracy of the data held will determine the effectiveness of key activities such as budgeting, forecasting, analysis, and bank reconciliation. Finance employees face a lot of scrutiny from stakeholders and regulatory bodies, so they can experience a significant backlash if their numbers are wrong.
Our annual Finance Trends Report found that:
- 50% of finance professionals want to provide a higher quality of data to company leadership.
- 27% believe the data in their FMS isn’t completely accurate.
- 25% want to get more involved with strategy.
This goes to show that these employees aren’t just aware of the importance of data integrity, but they also have a hunger to improve it so they can have a bigger positive impact within their business.
Why is it important for your business?
When data integrity works
There are several business benefits gained when data integrity is upheld properly:
- Improved reputation: When businesses consistently demonstrate data accuracy over a long stretch of time, they enjoy enhanced credibility among key stakeholders (such as customers and investors).
- Better strategic decision-making: When business data has been deemed to have integrity, it lays a solid foundation for confident decision-making that fuels improved performance.
- More controlled risk management: When information is reliable, it mitigates many of the financial risks companies would face if it was full of errors.
- Higher efficiency: This integrity can also help to streamline processes, as the data can be confidently used to identify operational inefficiencies and poor-performing functions.
- Greater regulatory compliance: Data accuracy is often an essential requirement for maintaining compliance with regulations related to data privacy, the declaration of financial figures, and financial transparency.
- Preferable customer outcomes: By maintaining up-to-date customer information, businesses can better understand their customers and therefore offer more personalised services and communication.
…and when it doesn’t
When data integrity isn’t upheld adequately, finance teams can experience a range of negative consequences:
- Poor financial reporting: When financial data isn’t accurate, it means the finance team’s reports won’t be representative of the truth. This will lead to a misrepresentation of company performance, meaning potentially damaging insights will be extracted from financial statements.
- Bad decision-making: Finance departments will likely make poor decisions if their data is inaccurate or incomplete. This can subsequently lead to missed opportunities and decreased profitability.
- Damaged credibility: If poor data integrity leads to incorrect/non-compliant financial reporting, it could damage the company's reputation and lead to a loss of trust from stakeholders such as customers, investors, and employees.
- Increased fraud risk: It can potentially increase the risk of fraud too, as inaccurate data may be used to cover up fraudulent activity.
- More inefficiencies: Incomplete datasets could lead to delays in decision-making, errors in transaction processing, or unnecessary manual interventions. This means that the finance department’s operations as a whole will be less efficient.
Types of data integrity
There are many pieces of the data integrity puzzle, which are all equally important when deciding what to measure. Here are some of the most well-known data integrity types:
Physical data integrity
This relates to the physical storage of data and the method of storage being used. It’s important to ensure this is functioning as intended and is safe from being compromised.
Logical data integrity
This is the accuracy of data stored in any given database or digital system. But it is more specifically linked to the way data entry occurs within the system, and ensuring it’s protected from unauthorised access or modification.
Semantic data integrity
This refers to the way information is interpreted. Of course, everyone has their own perception and will draw meaning from patterns in different ways. But businesses must put structures in place to ensure data is interpreted consistently and correctly.
Consistency data integrity
This relates to the consistency of relationships between different types of data within the system. Any kind of data dependency in the database must be maintained consistently as time goes on.
Accuracy data integrity
This data integrity type is pretty straightforward. It involves making sure any values stored are correct, that they are free from errors, and that they are being processed in the right ways.
Completeness data integrity
The final type to consider looks at the completeness of any group of data. Businesses must ensure they’re looking at the whole picture, by making sure all the data is present and accounted for (and that there are no omissions).
What data integrity isn’t
There are some terms that are often used synonymously with data integrity. But it’s important to understand the nuances between these terminologies so they can be applied correctly:
Data quality
This is perhaps the most similar to data integrity. There’s always a chance human error might occur when data is entered, or that some figures are duplicated or missing. The maintenance of data quality looks to counteract these issues.
Data security
Security is all about protecting data from unauthorised access, sabotage, theft, or loss. Companies must look to put measures in place to prevent breaches and maintain compliance with ongoing regulatory changes.
Data silos
Data may be stored in a variety of systems and used by multiple departments, which makes it difficult to integrate and analyse this information for cross-departmental purposes. Breaking down these silos is essential for consistent reporting, business-wide visibility, and getting the most out of this data.
Data governance
Establishing policies, protocols, and frameworks are essential parts of the data management process. Companies must comprehensively define roles and accountabilities around governance while ensuring standards/controls are implemented and communicated appropriately.
How to develop a data integrity process
So, what steps can finance teams take to implement an effective data integrity strategy? Start with the following:
1. Identify which data is important
A good starting point is identifying which data needs to be monitored (perhaps because it is a central KPI for business goals/objectives). In finance, this can include the stats related to purchases, sales, expenses, inventory, credit, and assets.
2. Define policies
Once you know which data is integral, you must map out the policies that ensure data integrity. These should cover data accuracy, consistency, completeness, and timeliness. And there should be widely known protocols for how data is collected, stored, and validated.
3. Assign ownership
Individuals or teams should be assigned ownership of certain groups of data. This way there is accountability and urgency around who will take actions to safeguard this dataset’s accuracy.
4. Establish controls
Controls and checks should be implemented, including data validation/verification processes, conducting regular audits, and restricting data access for unauthorised personnel.
5. Train staff
Provide the necessary training related to data best practices, so all staff have full awareness and understanding of the policies, procedures, and controls in place (as well as how to use them effectively).
6. Use technology
Utilise cutting-edge technological tools to minimise human error. In finance, this would include some form of digital accounting solution, to automate elements like data reconciliation and expense management.
7. Assess and improve
The final step is to constantly review and tweak the data integrity process. This regular evaluation helps with identifying weaknesses and making continuous improvements.
Achieving data integrity with Cloud tech
If a finance team is yet to adopt the Cloud, they should still prioritise data integrity before they facilitate a Cloud migration. But once they have made the leap, Cloud-based accounting solutions provide them with a centralised location to store all financial data.
This makes it easier to manage access and reduce the errors/inconsistencies that can occur when data is spread across many individual spreadsheets. Employees can collaborate easily to identify any discrepancies much quicker too.
Cloud technology allows finance professionals to view/update data in real time, as it is accessed remotely via the internet. This also makes the information more secure as it is stored on an offsite server. The seamless integration enjoyed with Cloud financial management makes it possible to automate much of the manual data entry, which streamlines the whole operation.
If you’re looking to prioritise data integrity within your finance function, be sure to take a look at our Cloud accounting solution, OneAdvanced Financials.