Tackling data quality saves money and reduces risk. From
Government Data Quality
Hub.
Why is data quality important?
High quality data is essential for policy and decision making and
underpins your organisation’s strategic outcomes. Poor quality
data, including data that is inaccurate, incomplete, or out of
date, is data that is not fit for purpose. Poor quality data
increases risk and can cost you time and money. This article
looks at the costs and risks associated with poor quality data.
The costs of poor data quality
Poor quality data costs the government, businesses, and society
every single day. The DAMA Data Management Body of Knowledge
states that estimates differ, but experts think that
organisations spend between 10-30% of revenue on handling data
quality issues. However, there are also direct and indirect costs
associated with poor quality data that are more than just
monetary.
The direct costs of poor quality data are often seen in
short-term operational issues. We may send staff to the wrong
place, pay the wrong amount, fail to provide services correctly,
or spend extra time on verification or re-entering data.
Indirect costs caused by poor data quality can include the costs
of poor or wrong decisions or the costs of reputational damage.
These often have strategic costs or risks that can have a
longer-term, negative impact in the future.
There are costs involved in improving data quality, such as
training, monitoring, IT and planning costs. However, the
benefits of high quality data will always outweigh the costs of
poor data quality.
The impact of poor data qualityDecision
making
Indirect costs can be harder to measure. You may not immediately
realise the true cost of poor data quality. It may be a
longer-term consequence, such as a damaged reputation. Poor data
quality can weaken evidence, create mistrust, and lead to poor
decision making. This in turn can lead to poor outcomes for
society.
Evidence based decisions and policies are only as good as the
data they are based upon. Missing or duplicate data could result
in you over or undercounting and then in poor decisions being
made, leading to negative outcomes.
Reputational risk
Poor quality data also poses a reputational risk. This could
include negative media exposure and GDPR issues, with data
quality being a requirement of GDPR. Duplicated data could result
in you contacting the same person multiple times. This can lead
to feelings of frustration and mistrust, as well as wasted time
and resources.
Incorrect or missing personal information could also have
significant impact on the individual. For example, they could
miss important deadlines or not receive necessary information.
Unreliable and contradictory data can make it difficult to know
what is correct. Users may then question the accuracy of your
data, and this may create mistrust towards your organisation.
Missed opportunities
Data that is poor quality may also lead to you missing vital
opportunities, or cause failures in service provision. For
example, inaccurate or out of date data may result in an
unnecessary service provision in one area, whereas high quality
data could outline more valuable opportunities.
Poor data quality can also lead to organisations being unable to
assess their own effectiveness and whether money and resources
are used in the best way possible. High quality data can lead to
more targeted organisational strategy, better spend of public
money and increased operational effectiveness.
Planning for data quality
High quality and trustworthy data can improve efficiency, help
mitigate risk and reduce costs. Understanding the importance of
data quality and having procedures to tackle the root cause of
problems will ensure your organisation is able to use data to
make effective decisions. High quality, reliable data will enable
you to have confidence in your decisions. Analysts and policy
makers can spend more time using the data to drive insight, and
less time trying to figure out if the data is fit for purpose, or
what the consequences of poor data quality are on their results.
The cost of getting data quality right the first time is cheaper
than the costs of having poor quality data and having to fix it
further down the track. Producing high quality data requires
planning and commitment. Developing a data quality plan with
achievable and measurable goals that everyone can commit to can
be a good place to start. Remember that data quality is
everyone’s responsibility, as poor data quality can occur at any
stage of interaction with data.
Preventative measures and effective data quality management
should be embedded into your organisation. It is a continual
process, and everyone should be aware of the risks and have
procedures in place to account for data quality. Ensuring data
quality throughout all stages of the data lifecycle and being
able to identify data problems proactively and as they occur is
more beneficial and cost effective than retrospectively trying to
fix poor quality data.
The Government Data Quality Hub (DQHub) is developing tools,
guidance, and training to help you with your data quality
initiatives. You can find the Government Data
Quality Framework, tools and case studies on the DQHub website.
We also offer tailored advice and support across government.
Please contact us by emailing dqhub@ons.gov.uk.
