Data quality is the measure of how effectively data can accomplish its designated role.
There’s a saying in the data world: garbage in, garbage out.
What it means is that if you have bad data quality, you are going to get bad results from any data initiative you try to put in place. It doesn’t matter how expensive, all-singing and all-dancing the technology is, or how clear your goal is: if you’re fuelling it with poor-quality data, you will not get the results you want.
That means that good data quality is essential for all organisations because it ensures that the information used to make key business decisions is reliable, accurate, and complete. Any inaccurate data needs to be identified, documented and fixed to ensure that business executives, data analysts and other end users are working with good information.
What are the main attributes of good-quality data?
Having good data quality means your data is clean and accurate. There shouldn’t be any duplicate entries, missing fields, invalid terminology, and corrupted or inaccessible data. In a nutshell, data quality is a focus of 7 of the key metrics that you need to measure your data against:
Completeness: Are all the necessary data fields complete?
Uniqueness: Does only one version of your data exist, without being stored, edited or used elsewhere?
Timeliness: Is the information up to date and relevant to you? Do you have the data you need?
Validity: Is the data appropriate for a field and is it valid and approved? Is it reliable?
Accuracy: Do you have a single source of truth for your data? Is it accurate for its purpose?
Consistency: Does all data meet the same criteria?
Integrity: Do all elements of data exist across data sets and references match?
Why is data quality important?
Data is present in every business, whether you’re in the public sector, manufacturing, retail, banking, etc. Without it, business functions from sales, marketing, customer services or HR can’t operate - high-quality data is key for the smooth running of day-to-day operations. Essentially, data is made up of 1s and 0s, and it just takes one of those being in the wrong place to negatively impact the data quality.
If the quality of data is sub-optimal then:
- Your customers could be given inaccurate, erroneous or misleading information about your products and services
- You could be presenting inaccurate business performance information to key stakeholders or investors
- You could be planning and investing in your short and long-term objectives based on misleading data
- You could be holding Personal Identifiable Information (PII) data on your employees or customers which is non-representative or incomplete
These are just some examples, but every company should aim to spot data quality problems, monitor their data, and have a process or system to fix any issues found. Bad data can do more harm than no data at all. Poor data quality is always frustrating, sometimes risky, and occasionally down-right dangerous – resulting in catastrophic commercial decision-making and failure to comply with regulations. That’s why it’s key to have a thorough data quality strategy in place.
Data quality; an ongoing process
Good data quality engagement means keeping interest and interaction alive. It's about focusing on data quality even after the basic setup is done. Data quality is like a house plant, it needs to be looked after, regularly watered, repotted and dusted.
It involves:
- Reflecting on your organisation and your data
- Working out what “good” looks like for you
- Meeting and maintaining that standard
To maintain your standard, you’ll need a governance process. This includes:
- Understanding who's responsible for fixing things
- Knowing whom you need to get permission from to go in and change your values
- Considering a data forum
- Identifying whether you’ve got somebody in your organisation that is looking exclusively, if not as a major part of their role, at the quality of data.
What can good data quality help to achieve?
Trusted decision making
With good quality data, you can confidently make decisions based on reliable, accurate data and have confidence in its contribution to your business growth. Reliable and accurate foundations are essential for an organisation’s future planning and forecasting, ultimately steering your company towards sustainable success.
Increased productivity
Effective data quality management also frees up time to focus on more productive tasks than cleaning up data sets. For example, if you’re able to identify common issues, you can put processes in place or configure systems to prevent the issue from reoccurring. The less time spent correcting mistakes and looking for up-to-date information means that more time will be spent implementing and using the data.
Reduced risk
Ensuring your data is fit for purpose and can be trusted, reduces the risks and potential costs to the business. In some cases, poor quality data can result in fines if you don’t adhere to laws, and by having data quality processes in place, it keeps you adaptable for when new legislation is introduced. Even with robust data quality practices, errors can still occur but if you are observing data quality, you can usually produce an audit trail to show where in the process an error occurred and fix it.
So, how can Amplifi help with data quality?
Amplifi is the go-to consultancy for enterprise organisations that want their success to be driven by data. Beyond the technical details, we recognise how crucial good data management is to a business' real-world success. After all, the quality of your data is key to the success of your business outcomes.
We understand that every organisation is at a different point on their data quality journey. Whether you're just taking your first steps, somewhere in the middle, or experts who just need a helping hand, we've got your back.
No matter your level of data maturity, we tailor our approach to meet you right where you are. After all, it's about making your journey smoother, not fitting you into a one-size-fits-all box.
So, do you need help getting started with a data quality strategy, or ramping up what you're already doing? Our experts are always happy to chat through your organisation’s needs, so get in touch here, or have a read of our 5 Tips for Better Data Quality guide below.
