How data visualisation technology will deliver insight and data quality in 2024

It is commonly recognised that images are a powerful communication tool. This is summed up by the often repeated phrase that a picture is worth 1,000 words.

With the deluge of data from ‘big data’, Internet of Things (IoT) and increasingly artificial intelligence (AI) activity, having access to data visualisation technology that’s able to identify important trends and actionable insight is becoming key to business success.

Visuals based on data are so important because they can simplify complex data and reveal patterns, trends, and even issues with it, that informs effective decision making. This way you don’t have to be a data scientist to understand, make learnings from and act on the data.

Organisational decisions are usually made with various internal stakeholders and sometimes external partners. Therefore, having simple but effective visuals to get points across to stakeholders who perhaps do not have any working knowledge of the data concerned, can provide a more compelling way to inform and gain progressive inputs from all involved.

Advent of smart graphics

Data visualisation technology has come a long way in recent years. It has moved beyond the delivery of basic pie or bar charts that don’t add much value. Instead, particularly in the data quality space, it’s smart graphics highlighting specific inaccuracies and abnormalities with records across a database, and also at an individual customer level, in real-time, that’s offering significant value. This way any issues with customer contact data such as a postal or email address – which is vital for not only delivering insight on customers but effective customer relationship management (CRM) activity and a competitive advantage - can be identified and fixed in real-time.

At a glance understanding

To deliver confidence, or not, in the quality of data on your database any data quality platform you source should be able to provide in real-time a visual with an overall score on the data, and across different aspects of it, such as name quality and email address quality. The score should be from high to low, for example A+ to F.

Additionally, the data quality platform must be capable of providing at a glance visuals of any issues with the customer data, such as clear stats based graphics on the overall numbers of invalid postcodes, inconsistent names, even profanities in the data, for example - and be able to quickly take you to view them.

Customise visuals

Importantly, the visualisation part of the platform must be easy to use and versatile in allowing the creation of a wide variety of charts and graphs. This should include the ability to customise visuals, to provide a deep dive on any abnormalities with the data. As part of this the platform should have the power to create complex and insightful visualisations that drill down into specific data points. Along with highlighting inaccuracies in the data this approach supports decision making, because of the ability of such graphics to communicate decisions to stakeholders, as already mentioned above.

In the future, expect augmented reality (AR) – which overlays digital information in the real world – to play an important part in the data visualisation process. By its very nature it can provide ‘a bit of fun’ to data visualisation, and therefore help those utilising AR to more effectively interact with and make learnings from data.

Once data issues identified – correct them

Beyond providing access to smart visuals to spot any data issues, a data quality platform must also have the capability to correct any data inaccuracies in real-time – including names, addresses, email addresses, and telephone numbers, worldwide. It should ideally be able to enrich the data, for example add any missing data, such as a postcode, deduplicate data and undertake data profiling to source issues for further action. It’s those tools that are available in the cloud, as software as a service (SaaS) or on-premise, and don’t require any coding, integration, or training, which makes them easy to use and scalable. One with a single, intuitive interface provides the opportunity for data standardisation, validation, and enrichment, resulting in high-quality contact information across multiple databases. Furthermore, it’s important to recognise that ensuring your database has clean data is a big first step in support of identity verification and therefore in preventing fraud.

Conclusion

In today’s big data, IoT and AI world that’s driving increased volumes of data, it’s important to source data quality platforms and data analytics tools that offer easy to use, versatile data visualisation technology. It’s the best way for staff at all levels, along with other stakeholders, to spot any abnormalities in customer data, so they can be easily fixed, and more widely provide insight that improves decision making. Such functionality will reduce costs and deliver improved engagement with customers and stakeholders. As a result, it will help to drive growth and business success in 2024.