Over the course of the last eight years, I've interviewed countless data quality leaders and learned so much about the common mistakes and failures they've witnessed in past projects.

In this post I wanted to highlight five of the common issues and give some practical ideas for resolving them:

#1: Not connecting data priorities to business priorities

One of the biggest data quality frustrations I’ve witnessed in the business community is a lack of focus on tangible business issues. Data quality improvement invariably takes the form of a data-centric viewpoint. This focus can often alienate business people who just can’t make heads or tails of what the ‘data team’ are relaying back to them. On the flip side, data quality practitioners often pull their hair out because they can’t see why the business doesn’t ‘get it’.

To resolve this, you obviously need to ensure that your data quality goals will address real business objectives that stakeholders have a personal investment in resolving.

#2: Improving data in isolation

Many practitioners take a local view of their data landscape, perhaps focusing on certain issues in a particular database or collection of systems.

The danger of this is that you neglect the wider information chains that have a role to play in the quality of your localised data. For example, a common problem we hear of on Data Quality Pro is when local data warehouse improvements and data cleansing are done without any upstream prevention activities.

You also need to look at the broader environmental factors impacting your local data stores. For example, are there data design issues that are creating more problems? Do business workers have sufficient data quality awareness training? Are there sufficient data standards in place?

The development of an Information Chain to visualise all of these influential factors is strongly advised and it doesn’t just need to be data-centric. You can extend to include stakeholder ownership, design team influence, metadata and all manner of connecting influences on your source data.

#3: Creating an isolated data quality team

A lot of data quality projects and initiatives ultimately fail because they didn't properly integrate data quality activities into what was already happening in the organisation.

For example, instead of having a distinct data quality project to improve an ERP or data warehouse initiative, integrate your data quality tasks into their existing projects. From this, you may find that there's no need to create yet another organisational silo.

Some organisations adopt a federated delivery model for data quality improvement where they work closely with existing project teams to build capabilities internally and augment, where required, with a small nucleus of expertise.

#4: Not updating your data resource libraries

Data quality projects amass a huge amount of metadata and resource information, but the majority of this knowledge rarely makes it back into existing resources such as accurate data models, data dictionaries and other repositories.

A large number of data quality problems stem from poor documentation and a lack of training. Data quality projects can definitely help to plug the gap that years of neglecting documentation and design standards have left behind.

Make sure you’re identifying opportunities to build bridges with the various design and development communities. It’s often the first step to a constructive dialogue where you can start making some improvements in design and development standards.

#5: Lack of communication across the entire data landscape

A common theme through all of the previous points was the need for communication so I thought it was worth bringing it out as a separate point.

It’s easy to think of data quality improvement as some kind of technology-centric initiative, but you’ll soon realise it’s all about managing change and causing a cultural transformation in your organisation.

Communication is critical for achieving this. You need to create a comprehensive communication plan from the outset of the project; include all the stakeholders comprehensively and create an engagement tactic for each person that's clearly marked.

These are just five common data quality project mistakes that many of my interviewees have witnessed on Data Quality Pro in the past. What common failures or mistakes have you witnessed on past data quality projects?