5 Key Strategies To Tackle Data Quality Challenges - Download Infographic

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Data quality has emerged in the last decade as one of the biggest and most critical challenges facing all organisations. From Strategic decision making to surviving constant change; organisations are beginning to realise that the consequences and risks of making incorrect decisions is now far greater.

For many organisations, getting to the point where data quality is seen as a “non-negotiable” will take commitment, effort, and investment.

Before we jump into the 5 key strategies that will help overcome challenges in Data Quality, let’s begin with understanding why Data Quality seems so hard to get right?

There is now more data than ever before

We keep collecting and adding more and more data.
Globally data is forecasted to grow at a rate of 19.2% compounded annually.
https://www.statista.com/statistics/871513/worldwide-data-created/
It is inevitable that this will translate into more data for our organisations and systems. More data almost means we are more likely to see data quality issues.
Some industries have regulations on what can be collected, stored and for how long which can be harder to manage than we expect.

Data is dynamic

Our data keeps changing. We want it to change so we keep updating it, transforming it and shifting it around.
In terms of data quality this means that we’re dealing with a moving target. We need to be able to respond quickly because there is every possibility that by the time we get around to fixing a data issue it’s changed. Time may be a critical factor.
Platform transformations aren’t going to solve our data quality issues either! These almost always involve a data migration and if the data is not cleansed as part of that, the new platform is going to inherit poor quality data and data issues as a result.

Data can be hard to piece together

Our data comes in a variety of formats and representations which makes it challenging in itself to integrate so we get the full picture.
We tend to do a good job of integrating our core systems together but we also find disparate data sets along the way, typically tactical solutions created to fulfil a specific need. These also form part of our data eco-system and scope of data quality.
Think about how many times is your customer represented across your systems and databases? In terms of data quality, understanding your sources of truth can be powerful to validate and correct data issues.

Who can help us?

It’s not always clear to us who is involved in or is responsible for the quality of data.
Even when we find an issue it can be challenging to get it fixed:
- IT may be held hostage to the business making decisions about their data.
- The business may not appreciate the impacts of data quality issues, especially when framed in technical terms.
Data quality champions rise-up in an attempt to do the right thing and solve data quality issues. However we may become so reliant on them that they become the entire data quality department! A task with challenges they may not be well equipped to deal with.

5 Things that will help

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1. Prioritise High-Value Data

This might even seem obvious. Prioritise the highest value data for your organisation. Data quality is an investment, so make it work for you and get the highest return on your investment.

It may be difficult to get started. Understanding common drivers for data quality will help define the objectives and priorities:

Data issues that you may already know about – a particular process, product or system. These are obvious problems and usually well-defined, but they are seldom self-contained.
Externalising data, such as reporting or supply chains, to your customers or partners that is having an adverse impact. Applying quality checks when data leaves will uncover symptoms of data quality issues that happen earlier in the process.
Regulatory obligations to adhere to that may have uncovered issues that weren’t on the radar before or stricter compliance that may have been recently introduced with a focus on new areas.
Maturity – simply put, we don’t know what we don’t know but we know we will find something. This is where you would focus on your organisation’s main entities, like your customers, and start from there.

2. It’s a collaborative effort

Data quality is most effective when everyone comes together to collaborate and support each other on data quality. It’s a team sport!

The business can support by:

Defining what data quality issues are
Managing new and changing business rules
Determining the priority and impact of issues

IT can support by:

Working towards resolving the issues
Expertise in the affected systems and databases
Root cause analysis to identify systemic issues, such as software bugs

Everyone will need to play their part in process and this also involves management too. They can give data quality both focus and priority, ensuring the right structure and roles are in place to manage and support effective data quality.

3. Invest in the right technology

There are plenty of data quality platforms out there that will help you. InvestigateDQ is a proven and successful solution which we encourage you to take a look, ensure you find a solutionthat works for your organisation.

There are a few things to consider when looking for a data quality solutions:

Scalability

Data is only going to continue to grow and a solution will need to accommodate both that growth and your organisation’s changing needs over time. Consider performance and both the infrastructure and roles needed to support the solution.

Usability

Think about who the users are that you will empower to manage data quality. The best solutions will foster a collaborative effort, bringing users from different areas together to work on the same things, at the same time and towards the same objectives.

Customisation

Each of our organisations are, in some way, unique which also means that each will have their own unique data quality issues. Ensure a solution has the right level of customisation to meet those unique needs, especially when dealing with complex issues. We are not all trying to solve the exact same data quality issue.

Dedicated Solution

Data quality is not an afterthought and neither should the solution be. Ensure the solution can support the entire data quality lifecycle. Be wary of data management solutions claiming to address data quality – some will only perform one or two functions which can only take you so far.

Multiple Solutions

You will need more than one solution as data quality consists of more than data quality control. These are typically categorised as follows:

Preventative controls; where we want to ensure the data is correct at the point of inception, where it’s provided to us. An example may be an address validator for an online website where customers sign-up.
Detective controls; where we want to be able to monitor data and identify issues after the data has reached our domain (don’t forget- data is dynamic!)
Controls are needed in both areas for effective management of data quality and this will be distributed across more than one solution.

4. Be proactive

Have the right mindset to make managing data quality easier in the long run.

Addressing low and medium severity issues as they occur will prevent these from growing into high and complex issues over time that will have a material impact.

The Data Disease Effect tells us that not only will data quality issues get worse over time if left unresolved but they will also become more difficult and costly to fix.

5. Measure and report on Data Quality

If it matters then it’s worth reporting on. And data quality absolutely does matter!

Work out what key metrics are important to you and your type of organisation. It may be compliance based, financial or even reputational. Look back at the drivers for your data quality program

Timely and regular reporting is also key. We want to understand how we are tracking against our metrics with enough time to be able to intervene when required.

Effective data quality can be hard to report on – we can largely be focused on the issues. Consider reporting on the positive outcomes, such as the costs and impacts avoided by being to identify and resolve data quality issues proactively.

In conclusion

Some of these strategies can be quick to implement while others will take longer. You may have already started and are better prepared than you first thought. Consider which strategies you need to focus on. Click below to download the infographic.