Data Analytics

The Impact of Poor Data Quality in 2021

What are the consequences of companies relying on poor data quality and how can it be prevented? Here we take a look at what it can do to your business.

Poor quality data can seriously harm your business. It can lead to inaccurate analysis, poor customer relations and poor business decisions.

Poor business decision making can then obviously have an adverse effect on how your business performs: here we take a look at some causes, consequences and preventative measures that can be taken to prevent poor data gathering and handling.

What Causes Poor Data Quality?

Non-Integration of Databases

Using various databases that don’t integrate with one another runs the risk of poor data entry habits such as duplicate records or having to re-key information with the resultant loss of time and effort.

Remedial action such as asking a software developer to help with systems integration so your databases work together can prove a worthwhile investment to avoid basic errors so saving time and improving productivity.

Inconsistent Data Capture Protocols

Inconsistencies in how data should be formatted and entered will affect data quality through inaccuracies. For example, customer names should have a certain format (ABC Ltd or ABC Limited) and open text fields can be used and interpreted in different ways by different users.

Poor data migration and integration

Migrating data to a new system or consolidating systems via integration as above carries inherent risks to your data: values can be irregular, missing or misplaced, and even simple spreadsheets can cause inconsistency problems. If your data isn’t clean, you’ll likely need rules implemented to change this.

Data decay

Data is always changing, it isn’t static and even over a relatively short period much of it will have altered. It’s estimated that around 40% of email users change their email addresses every two years, 15% one or more times a year, and 20% of postal addresses change annually. You should definitely keep your data up to date, otherwise it’ll become outdated and you’ll be wasting time and resources trying to communicate with people.

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The consequences of poor data quality

Loss in Revenue

You’ll lose client and prospect interest and revenue if you don’t keep them in the know of your latest products and activity through your communications not reaching them. Gartner recently found that for most companies thousands – and for large corporates millions of pounds – is lost each year due to lost productivity from poor quality data.

Inaccurate Analysis

If you’re conducting data analysis or predictive analytics with incomplete and incorrect data, you run the very real risk of being led down the wrong path. With duplications, missing fields and other anomalies in your data you’ll be wasting resources such as, for example, implementing a sales campaign based on poor data analysis.

Damaged Reputation and Fines

If you contact the same person or business multiple times unnecessarily, or are sending emails to a large number of dead addresses, you will likely cultivate a poor reputation both within the physical and digital world and appear inefficient to your actual and potential customers.

Along with the implementation of GDPR in May 2018, if you don’t manage your marketing data accurately you could be in for a hefty fine from the ICO (Information Commissioners Office).

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How can we improve our data quality?

Update or upgrade your software

As mentioned above, undertaking a programme of systems integration through upgrading your internal software can be a great way to increase your data quality. A custom designed database, or having your systems integrated, will make your process “proactive” instead of “reactive” so reducing inconsistencies in your existing and future data.

Establish Data Import Rules

Utilise a pre check system for any manual data that is collected, and train colleagues to ensure they’re consistent with it and follow some advice here.

Using a consistent format for names, job titles and so forth (for example ‘Director of Marketing’ & ‘Marketing Director’ are two different roles to a system!) is key to keeping your data clean and reducing duplications. Where possible, implement pre-populated online forms as these are more likely to capture data in the format you’re after.

Implement a Data Cleaning Routine

It’s important to keep as much of your data as possible up to date to combat data decay. Whether it’s keeping in contact with your customer base regularly, or using profile enrichment and audience insight software, developing a regular routine to ensure your data is accurate is crucial to the effectiveness of your marketing campaigns.

Accurate Information is King

Trusted, high-quality data is a core requirement for maximising business opportunities and driving sales. Unless CIOs, chief data officers and information prioritise high data quality, they won’t be able to take full advantage of new big data opportunities including data insights and marketing analytics.

The increasing torrents of detailed data being collected is a key resource for businesses of all sizes, but it’s only of use if it’s bang up to date.

Note: This post was originally posted on 9th August 2016; however it has since been updated to keep the data and accuracy of the content relevant.

Aims and Objectives of Machine Learning

The main aims and objectives of machine learning is to enable tech such as computers to ‘learn’ from existing data in order to perform certain tasks, answer questions and solve problems based on predicting the future.

In a nutshell, machines are literally trained – as opposed to being programmed – using existing data to create algorithms that can provide accurate future information. Examples include analysing buying trends and planning work schedules, or detecting fraudulent activity by, for example, comparing some new ‘evidence’ of behaviour against what has typically happened previously.

The specific aims and objectives of machine learning are to be able to act from a position of strength in terms of ‘knowledge is power’ as opposed to either not being able to detect anything (fraudulent activity as above), or not having much idea about future trends so making it hard to plan activity such as, for example, future marketing initiatives based on known customer behaviour and tastes.

The aims and objectives of machine learning in terms of improving work practices and boosting productivity can cover areas such as predicting journey times so improving transport logistics; predicting how long certain jobs and work tasks may take, so improving aspects such as work scheduling and staff deployment.

In marketing and sales areas, the aims and objectives of machine learning might be to enhance customer service through understanding what existing and potential customers require; improve how sales leads are generated and nurtured; profile actual and potential customers, and acquire detailed knowledge of a business’s market in terms of demographics and buying habits.

All this is possible through machine learning to use past and present data to inform future actions.

Machine learning isn’t the same as artificial intelligence (AI): machine learning is actually one element of AI which is the umbrella term used to cover the ways in which tech performs certain functions.

For example, in motoring the ‘lane assist’ function (where the vehicle automatically controls itself to stay in the correct lane on the road in the event of driver error) is an example of AI in action as it emulates a human activity (keeping the vehicle under control and in the correct lane) while the machine learning component is the data the machinery learnt in the first place to use as the reference to step in to control the car when required.


How do we get help to improve our data capture and integration?

Talk to a software development company or tech agency experienced in database development to explore how your current databases could be integrated to enable more efficient data capture and management. Cutting out weaknesses such as duplicates, potential keying errors, and making it easier to clean your databases is a big step in improving your data quality.

Should we be worried about GDPR?”

Not if you capture and store data correctly! GDPR replaced the old data protection legislation and is a reflection on the need to overhaul data regulations since more and more data is being collected, stored and manipulated.

You or your information officer or other relevant personnel (if appropriate) should understand the latest GDPR and other data legislation.

Should we have new purpose-built databases?

Perhaps: talk to an expert database development company who will discuss your requirements, challenges and your present set up and advise on the best way forward. It may be that systems integration involving your present databases will be appropriate, or perhaps a new ‘from the ground up’ solution would be advisable.