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Stephanie Elmer

Published on

November 24, 2023



Our senior paid media specialist Steph Elmer attended Big Data London. What were the topics dominating the conversation? The reliance on dashboards, making the most of data and generative AI.  

Dashboards need to up their game  

Movements in data analytics helped evolve the basic dashboard. These provided an easy way to visualise data. Dashboards enable marketers to quickly identify trends and make more informed decisions in real-time. But recently, businesses have reported they feel limited in reaching their goals through data analysis.  

Dashboards need to keep evolving with business needs.   

  • They sometimes lack the ability to deep-dive into the data, leading to ineffective business or campaign decisions being made.  
  • They lack flexibility. Once created, they are not easily customised or changed without the support of someone with technical knowledge.  
  • With the amount of data and different sources increasing, it’s progressively difficult to scale and maintain dashboard accuracy.  

The Evolution of Data Intelligence  

In the modern day, the purpose of the dashboard should only be to display operational metrics for stakeholders to easily view and extract. They shouldn’t be used as decision drivers. We need to look deeper into the factors influencing the data. This will allow you to provide more informed actionable insights. Resulting in intelligent campaign recommendations to drive results.    

The rise of new platforms  

A good BI platform should:  

  • Smartly blend and clean data from multiple sources, unifying it into one model.  
  • Be powered by AI, delivering actionable insights and relevant suggestions based on your own campaign / company data.  
  • Be adaptable and customisable for each stakeholders needs.  
  • Act as the sole source of truth. 

Let generative AI be your teammate  

There’s many opinions on how an AI model can be best utilised to drive business efficiencies and decision intelligence.  

With the rise of Natural Language Processing, you can now generate AI-driven analytics without any coding knowledge. These can even be trained to individual business needs and jargons. Prompts now generate visualisations and SQL/Python code within seconds.    

Here’s some other AI use cases that were discussed:  

  • It can be trained to clean and increase the quality of your data.  
  • It’s ability to process large amounts of unstructured data and output the information into a CSV file.  
  • Combine existing customer data with AI to create predictive target audiences and automatically generate a new custom audience that is more likely to convert. I started to investigate this after the event and LinkedIn is introducing this soon.   
  • Use question prompts to ask AI to import public data sets that will assist with your analysis.  
  • Craft compelling real-time presentations, with the ability to embed dashboards.  
  • Monitor KPIs in real-time, with alerts and recommendations if performance suddenly drops.  

The list goes on.   

One of the natural fears from the development of Generative AI – will it put data analysts out of a job?   

In essence, generative AI is set to automate many repetitive and mundane tasks, allowing analysts to shift their focus to more creative and complex problem-solving. Time will be saved; their productivity will be maximised. 

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