The quality of your data determines how much your company grows and increases in value. These days, more and more firms are using professional software solutions to make data-based decisions and automate their processes. However, the recommendations offered by AI and similar technologies are only as good as the information they have to work with. If you want to make prescient, strategically savvy financial decisions, you should take a close look at the quality of your data and optimize it in a structured way.
Poor data quality costs companies millions
US$12.9 million – that’s how much substandard data costs the average company every year according to a Gartner analysis from 2021. It's a huge number, but perhaps not all that surprising when you consider that strategic decisions are increasingly being made based on data and corresponding analytics applications are used in many company departments every day. Corporate performance management solutions have become tremendously important, particularly in the realm of finance.
Should your company expand into a new market? How much budget is available for marketing activities? Which salaries are sustainable? When it comes to answering questions like these, flawed data can be just as detrimental as an inept consultant. Right now, however, companies are still not giving enough priority to the quality of their data or don’t know how to optimize the vast amounts of information at their disposal.
What are the ramifications of poor data quality?
The possible effects of low-quality data are too severe for companies to ignore.
Misguided decisions
It's true that you can make complex decisions much more quickly based on data. If that data is inaccurate, though, your company may miss out on lucrative business opportunities or move in an ill-advised strategic direction – and unwittingly expose itself to significant economic risks.
Compliance risks
If identified during an audit, flawed and/or incomplete data can have legal consequences, including monetary fines. There’s also your company's reputation to consider when the word gets out, of course.
Inefficient processes
Employees can waste valuable time searching for data that doesn't exist or verifying redundant or inconsistent information. In order to automate and accelerate processes, your company needs to be able to rely on a sound basis of data.
Ultimately, poor data quality costs you money and will make your organization less successful than it could be going forward.
What are the hallmarks of high-quality data?
Poor data quality can be caused by a number of factors. If your company wants to tap into the full potential of its data to drive growth, you need to make sure your data is consistently robust. Meanwhile, the particular purpose you want to use your data for will determine how you should weigh the various quality criteria at hand.
Gartner suggests using nine criteria for data quality:
- Accuracy
- Currentness
- Accessibility
- Relevance
- Completeness
- Consistency
- Comprehensibility
- Reliability
- Uniqueness
How can companies improve their data quality?
Whether your organization is a publicly traded corporation or a medium-sized firm, one thing is certain: If you want to achieve long-term improvements in the quality of your data, you need to be willing to undergo a gradual transformation. You’ll also need an overarching data strategy, potent IT solutions, and most of all: structures, processes, and the motivation to rethink the subject of data itself.
To offer you a guide to transforming your organization in the interest of better data quality, we've put together the following seven steps:
1. Define your objectives
What are the particular departments in which you want to improve your data? What business advantages are you hoping this will offer? Analyze your company's current situation in order to identify action areas and set priorities. Establish metrics so you can gauge how your data quality initiative is progressing.
2. Assign responsibilities
If tasks are not adequately defined and assigned, data tends to be entered once and never checked again. A structured approach to managing data quality is essential. Who will be responsible for data management in the individual teams and departments at your company? Who will communicate the current standards and ensure that they're being met? Who will gather everyone's feedback and requests and take them to your IT department? Who will drive your company's data strategy across all areas?
3. Clean up your existing data
It's worth taking the time to inspect and optimize your current data, especially if you’ve been working with different silo solutions. If you eliminate redundancies and delete information that's now outdated before implementing new systems and centralizing your data storage, you’ll not only reduce the amount of space you need; you’ll also minimize the likelihood of migration errors.
4. Optimize your IT infrastructure
Think of a parent company that employs a variety of ERP, accounting, and HR systems that also see use at its subsidiaries in a wide range of user departments. The more interfaces and storage locations this requires, the greater the risk of data inconsistencies will be. If all the data involved isn’t synchronized, the different departments will be working with different versions of the truth. Improper synchronization, meanwhile, will produce redundancies and cause further confusion among employees. This is why you should make sure your data interfaces are up to date and working properly on a regular basis. Ideally, you should also establish a single source of truth for your data. This is especially important for financial functions, including those in accounting. These functions give your CFO what they need to ensure your company's success – not to mention a dependable means of submitting all the reporting required by law.
5. Establish standards and processes
Introduce binding standards of data quality in all your company's areas and define workflows that will consistently guarantee high-quality data. This starts with establishing naming conventions and continues with implementing verification processes and automating the archiving and deletion of data.
6. Offer training
When data is poorly maintained, there's rarely any malicious intent behind it. In many cases, employees simply don’t know how to use the corresponding software properly to search through existing data, modify it, and enter new information the right way. Training courses on different subjects will sharpen your employees’ data-related skills and help them acquire the specific knowledge they lack.
7. Set a positive example
Data is perhaps the most important raw material of the 21st century. When data quality is viewed as something only your IT department needs to worry about, it's difficult to maximize the potential of your information. Making proper use of data thus needs to be second nature to everyone in every area. It’s important to promote a positive data culture, raise awareness of the value that (high-quality) data can bring to employees’ day-to-day routines, and demonstrate the effective use of data from the top down. This is the only way to cultivate an environment in which data serves as a reliable guide.
