Guest blog: The role of data visualisation technology in delivering insight and data quality in 2024
Today, organisations are being deluged with data, which makes having the ability to identify important trends and actionable insight from data more important than ever. In a ‘big data’ world with Internet of Things (IoT) and increasingly artificial intelligence (AI) activity delivering large volumes of data, it’s access to data visualisation technology that’s set to drive business success.
After all, an image is a powerful piece of communication, which is why the phrase a picture is worth 1,000 words is commonly uttered.
Visuals based on data are vital because they can simplify complex data and reveal patterns, trends, and even issues with it, that informs effective decision making. With such images you don’t have to be a data scientist to understand, make learnings from and act on the data.
Remember, organisational decisions are usually made with various internal stakeholders and sometimes external partners. It’s only by having simple but effective visuals to get points across to stakeholders who perhaps do not have any working knowledge of the data concerned, that can provide a more compelling way to inform and gain progressive inputs from all involved.
Evolution of smart graphics
In recent years data visualisation technology 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 considerable value. This way any problems with customer contact data such as an incorrect 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.
The provision, by a data quality platform, of at a glance visuals on any issues with customer’s data is equally important. These could be graphics on the overall numbers of invalid postcodes, inconsistent names, even profanities in the data, for example.
Customise visuals
The visualisation part of the platform must be straightforward to use and versatile in allowing the creation of a wide variety of charts and graphs. The ability to customise visuals, to provide a deep dive on any abnormalities with the data, is an important part of this. Users need to have the facility within the platform 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 power of such graphics to communicate learnings and decisions to stakeholders, as already mentioned above.
Expect augmented reality (AR) – which overlays digital information in the real world – to play an important part in the data visualisation process in the future. Due to the way it works 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.
Correct highlighted data issues
Along with providing access to smart visuals to identify 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. The ability 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, is equally important. It’s those tools that are accessible 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. Additionally, it’s vital to recognise that ensuring your database has clean data is a big first step in support of ID verification and therefore in preventing fraud.
Conclusion
With increased volumes of data in today’s big data, IoT and AI world, it’s important to source data quality platforms and data analytics tools that offer easy to use, versatile data visualisation technology. Doing so will see staff at all levels, along with other stakeholders, finding it straightforward to spot any abnormalities in customer data, so they can be easily fixed, and more widely provide insight that improves decision making. It’s this functionality which will reduce costs and deliver improved engagement with customers and stakeholders; helping to drive growth and business success in 2024.
This guest blog was written by Barley Laing, UK Managing Director at Melissa. To learn more about Melissa, please visit their LinkedIn and Twitter page.
Authors
Barley Laing
Barley Laing joined Melissa in 2014 during an exciting expansion phase of the California headquartered company.