In the previous article we discussed the top 5 causes of poor data quality related to technology and how to address them.
In this part of the series we discuss the top causes of poor quality data related to people and processes, their implications and what you can do to fix them.
Data Quality Is Not All About The Technology
Ensuring good quality data requires more than just having the right technology in place.
It’s also about getting the right people to take the right actions when managing the data. Sometimes it also requires new behaviours and new ways of doing things to ensure you always have reliable data enabling you to reach your business goals.
Let’s take a look at the people and process-related issues affecting data quality.
Poor Processes
Inadequate business processes to capture data and manage its quality.
There are a lot of different business processes that can affect data quality. For instance, capturing data via different types of processes can result in data that is incomplete, outdated and irrelevant to its purpose.
For instance, look at how data entry is being handled in your organisation. Is it automated or manual? Is the process repeatable? Manual handling can be time-consuming and prone to human errors. They are all minor mistakes that can affect the quality of your data.
Another example is to look at the processes you have in place to manage data quality. Is data quality management undertaken by the business user who should own the data? This practice affects data quality and creates further delays in fixing data quality issues.
Lack Of Expertise
Non-involvement of people who really understand your data.
Do you have access to people who understand your data and what it means? Achieving better data quality also requires the support of data experts who can help with queries, design effective processes, identify poor quality data and offer solutions on how to resolve them.
Lack of expertise often leads to organisations making incorrect data quality assumptions and potential repeats of the same data quality issues.
Organisational Behaviours
Data is not seen as a strategic priority.
Your data quality also suffers when data is not treated as a business asset in your organisation. Companies facing this challenge would not have a clear understanding of:
- The value of the data within their organisation
- Why the data is needed in the first place
Data quality is also affected when there’s a disconnect between those who enter and capture data and those who manage and apply it. This often happens when the people who are directly engaged in capturing data do not see data quality as their responsibility nor do they understand the wider implications of poor data quality.
Poor Data Governance
Lack of accountability for data.
Data governance is the process of managing the data. It is crucial in supporting your organisation’s decision making, productivity and operational efficiencies by having an enterprise approach to how you manage your data
Poor data governance may include either not having any data governance at all, informal data governance where ownership is implied without specific accountabilities and enterprise approaches or where it is formal but not followed. All of these may lead to the wrong person making key decisions about the data resulting in errors, poor data quality, potential rework and ultimately the wrong decision.
Change
Data is not a priority amid all organisational changes.
When an organisation goes through a significant amount of change, data can sometimes fall down the priority list affecting data quality. It’s important to remember that effectively managing organisational changes also includes managing data and its quality. Your data has to remain reliable at all times.
Resolving The 5 Causes Of Poor Data Quality
Once you’ve figured out which of these people or process problems you need to address within your organisation, you will need to find a way that will help you consistently manage your data quality.
Here are some ways to address the above causes of poor data quality:
Develop A Data Culture
Making data quality a priority requires a shift in mindset on how data is used in your organisation. There has to be a shared purpose among employees and leadership. Everyone needs to work towards extracting the most value from your data to increase operational efficiencies and ultimately meet business objectives. Many organisations talk about treating data as an ‘asset’.
Improve Business Processes
Pay attention to the processes that govern the gathering and collection of your data, and constantly improve them. Your data steward, for example, should promote good data management practices and monitor any data quality issues. Do you currently have complex and manual processes that cause inaccurate data to creep into your database? Consider revising them to minimise errors.
It’s also important to train business users and help them understand how they can affect data quality. Identify the people accountable for the data and formalise their responsibilities.
Finally, a solid data strategy is essential in improving your processes and implementing a better way to manage them. If you need a guide to create your data strategy you can download a free copy of our Data Strategy Ebook here.
Bring In Experts To Resolve Data Quality Issues
To uphold data quality you may need an expert eye to help you identify the weak points in your data management. These specialists can help improve your process or audit and check the reliability of your data regularly.
For example, Sustainability Victoria (SV) recognised this requirement and engaged Data Agility to help them implement a robust data system. They needed to implement appropriate data quality assurance processes and improve the management of the collection, sharing and use of data related to hazardous waste.
To build the system, we utilised Data Agility’s solution delivery services including data quality, data preparation and analytical services. From this, we created a user-friendly, browser-based dashboard and projection model application that is accessible to all SV staff.
The solution provided SV with much more usable and reliable data, greatly improving their hazardous waste infrastructure planning. (Read more about it here).
What’s Next?
Measuring and managing data quality.
Now that we’ve discussed the top causes of poor quality data and how to fix them, the next part of this series will be about measuring and managing data quality. So be sure to come back for the next part of this series.
Data Agility provides you with an end-to-end data analytics solution, from strategy development to implementation in order to ensure high data quality. If you want to learn more, contact us today.
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