TECH TRENDS
Resolve messy data to allow GenAI to deliver over ‘ the last mile ’
Generative AI ( GenAI ) is for the last mile . If you want to create something special that resonates with your target audience , you first need an underlying data infrastructure – and other forms of AI working under the surface – to facilitate this innovative approach . Matthew Biboud-Lubeck , General Manager EMEA from Amperity , outlines how GenAI can personalise communications at scale .
The emergence of GenAI has sparked significant excitement over the last two years . The output of GenAI tools is so impressive that investment in the technology increased fivefold in 2023 – with 36 GenAI companies hitting unicorn status . According to Bloomberg Intelligence , the market is now expected to grow rapidly and be worth US $ 1.3 trillion by 2032 .
WHILE MANY ORGANISATIONS ARE AWASH WITH DATA , IT ’ S OFTEN UNSTRUCTURED AND SILOED .
The potential is clear . But , there is also growing awareness around the practical realities of applying the technology . As businesses have scrambled to implement GenAI in various ways , they have also realised that this is not a simple plug and play solution .
For instance , we know the content creation capabilities of GenAI can be remarkable , but , in truth , the results can only ever be as good as the data it is based on . GenAI can be deployed to generate personalised customer experiences at scale – but it can only do this if brands hold accurate , comprehensive information on each individual and their preferences .
So , before any investment in GenAI can pay off , organisations must lay down an underlying data infrastructure to deliver the right information to these applications . This is one of the key reasons why almost half of business leaders say they are now actively driving forward data modernisation programmes – and this is likely to include an investment in other forms of AI .
Tackling the data challenge
While many organisations are awash with data , it ’ s often unstructured and siloed . It ’ s now more critical than ever for businesses to make this data usable . As businesses invest in their data infrastructure , we ’ re starting to see a seismic shift in the data management landscape , with many major vendors now adopting a ‘ lakehouse ’ architecture approach . This combines data lakes , which are repositories for raw data , with data warehouses , where more structured data is stored .
This , in effect , creates an architecture that allows businesses to access , read and make use of data , wherever it resides – and there are many benefits to be gained from this approach . For instance , it can save businesses a huge amount of time copying data from one system to another , as they no longer need to extract , transfer and load data to make it usable .
A lakehouse architecture also enables AI to view across multiple systems using a ‘ zerocopy ’ approach . This method allows companies
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