Data is everywhere - from every Wi-Fi handshake to transaction at a store, you and your devices are generating a wealth of data points that can be used by marketers to provide highly targeted, relevant insights that meet your needs.
For many organisations, the ability to take this proliferation of rich data and convert it into meaningful insights can be tricky. It’s well known that there is high demand for data literate professionals - as data evolves, how can we enable professionals to generate high quality data visualisations expected by stakeholders?
One such answer may lie in recent innovations in artificial intelligence and machine learning (AI & ML). The rise of general pre-trained transformers (GPT) and other data models may enable the data scientists of tomorrow to provide automated insights in a way that is highly relevant and accurate. Let’s discover how making data meaningful can have a transformative effect on the workplace, and how it will be crucial in the years ahead.
Making Data Meaningful
Data is often complex - whether you’re running a small store or a multinational brand, data can often be dispersed in a way that presents challenges for analysis and insights generation. In industry circles, this can often be categorised as the three or, in most modern scenarios, the five-V problem - where data can be highly variable, coming through in large volumes, high velocity, may not be easily validated for veracity, and as such, may present undiscovered value for a businesses operations.
Take, for example, the wealth of payments data available to a retail firm. It’s broadly known that in many countries, payment rates with cash have been falling in recent decades, notably due to the emergence of modern payment techniques such as the use of physical cards and mobile payment options. However, this fall in payments has a very different view across demographics - younger customers may be much more encouraged to pay with digital methods than their older counterparts, for example.
For a business that specialises in medical equipment for elderly adults, using payment data to inform what payment options should be available for customers can be incredibly insightful. This can help prevent over expenditure on payment platforms that are unlikely to be used by clientele, while also ensuring that popular payment options are not simply removed because of changing norms, rather than data.
Data can present immense value to a business - but transforming it from a raw form to usable data insights can often be time-consuming. As new data models and AI innovation transform the way we use data, it may open up a new frontier for the use of data in smaller businesses in ways previously unseen.
The Benefits and Challenges of AI Assistants
Modern applications of artificial intelligence, such as the wide-ranging capabilities of generative models, offer new ways for data scientists and engineers to take data and garner insights. In recent years, one of the major obstacles to high frequency, high quality AI power insights, identified by researchers working in data related fields, has been the lack of automated data validation available in database management systems (DBMS).
Recent innovations in generative pre-trained transformers (GPTs), such as recent announcements by market leader OpenAI, allow for data scientists and engineers to develop smart data validation systems, based on training data provided by said teams. These GPTs could potentially be revolutionary in the workplace - consider a model designed to monitor new data ingested into systems and send a notification when an error or significant variation is discovered in new data.
Where these errors may never have been discovered in previous monitoring models, GPTs present a fascinating opportunity into how data could be automated and streamlined into models to provide insights in a velocity far greater than current standards.
GPTs present their own, unique challenges however. As we continue to build the innovative platforms of tomorrow, we must be mindful that error-checking and monitoring will always be a necessary part of data cleaning. Simply leaving the automation to the robots could be a recipe for disaster - just ask the automated taxis.
Translating AI Insights With Visualisation
Visualisation can provide a powerful platform for translating the insights generated by AI into representations that are simple and meaningful for stakeholders. Data visualisation can benefit AI in a number of ways, such as:
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Allowing for interactivity. Many AI platforms currently produce insights at a text or transformed data level - with many models unable to create complex visualisations. A data scientist could use the data structures provided by an AI model to create a visual dashboard that uses the insights provided in a way that is more user-friendly and explorable.
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Efficient use of time. A data visualisation can use the AI insights to provide efficient and meaningful insights - rather than reading through a large volume of text, a data visualisation may simplify and streamline the insights to tell a data story for stakeholders, explaining a situation rather than simply providing insights.
This list is by no means exclusive - it is expected that in future, AI may have the capability to generate its own visualisations over time, similar to how platforms such as Microsoft use tools such as Copilot to provide suggested charts.
The proliferation of data in the workplace looks set to transform the way businesses use their data to inform decision making in the years ahead. Being able to wrangle and utilise that data in a way that is highly relevant and useful will be key for businesses to stay ahead of their competition. For those that are able to use emerging technologies such as GPT and modern AI, that may provide a valuable edge in today’s highly competitive industries.
Source: Story.KISSPR.com
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