t cht lk of analytics is precision combined with a nuanced understanding of the business landscape – skills that are impossible to automate unless we reach some sort of a ‘ general ’ AI .
t cht lk of analytics is precision combined with a nuanced understanding of the business landscape – skills that are impossible to automate unless we reach some sort of a ‘ general ’ AI .
The second trend that is critical for business data professionals is moving towards a single umbrellalike AI system capable of integrating sales , employee , finance and product analytics into a single solution . It could bring immense business value due to cost savings ( ditching separate software ) and also help with the data democratisation efforts .
Can you elaborate on the role of Machine Learning and AI in next-generation data analytics for businesses ?
Generative AI somehow drew an artificial arbitrary line between next-gen analytics ( powered by Gen AI ) and ‘ legacy ’ AI systems ( anything that came before Gen AI ). In the public discourse around AI , people often miss the fact that the ‘ traditional ’ AI isn ’ t an outdated legacy ; Gen AI is intelligent only on the surface ; and both fields are actually complementary .
In my previous answer , I highlighted the main challenges of using Generative AI models for business data analytics . Gen AI isn ’ t , strictly speaking , intelligence – it is a stochastic technology functioning on statistical probability , which is its ultimate limitation .
Increased data availability and innovative data scraping solutions were the main drivers behind the that follow the organisation ’ s rules , while retrieval augmented generation ( RAG ) is increasingly employed as an alternative to LLM fine-tuning . RAG is based on a set of technologies , such as vector databases ( think Pinecone , Weaviate , Qdrant , etc .), frameworks ( LlamaIndex , LangChain , Chroma ), and semantic analysis and similarity search tools .
How can businesses effectively harness Big Data to gain actionable insights and drive strategic decisions ?
In today ’ s globalised digital economy , businesses don ’ t have a choice of avoiding data-driven decisions , unless they operate in a very confined local market and are of limited size . To drive competitiveness , an increasing number of businesses are collecting not only consumer data they can get from their owned channels but also publicly available information from the web for price intelligence , market research , competitor analysis , cybersecurity and other purposes .
Up to a point , businesses might try to get away without using data-backed decisions ; however , when the pace of growth increases , companies that rely on gut feeling only unavoidably start lagging behind . Unfortunately , there are no universal approaches to harnessing data effectively that would suit all companies . Any business has to start from the basics : first , define the business problem ; second , answer , very specifically , what kind of data might help to solve it . Over 75 % of data businesses collect ends up as ‘ dark data ’. Thus , deciding what data you don ’ t need is no less important than deciding what data you need .
The holy grail of analytics is precision combined with a nuanced understanding of the business landscape .
Gen AI ‘ revolution ’; however , further progress can ’ t be achieved by simply pouring in more data and computational power . Moving towards a ‘ general ’ AI , developers will have to reconsider what ‘ intelligence ’ and ‘ reasoning ’ mean . Before this happens , there ’ s little possibility that generative models will bring to data analytics something more substantial than they have already done .
Saying this , I don ’ t mean there are no methods to improve Generative AI accuracy and make it better at domain-specific tasks . A number of applications already do it . For example , guardrails sit between an LLM and users , ensuring the model provides outputs
In what ways do you envision data visualisation evolving in the context of business intelligence and analytics ?
Most data visualisation solutions today have AIpowered functionalities that provide users with a more dynamic view and enhanced accuracy . Further , AI-driven automation also allows businesses to analyse patterns and generate insights from larger and more complex datasets while freeing analysts from mundane visualisation tasks .
I believe data visualisation solutions will have to evolve towards more democratic and noob-friendly alternatives , bringing data insights beyond data teams and into sales , marketing , product and client support departments . It is hard to tell , unfortunately , when we could expect such tools to arrive . Up until now , the focus of the industry hasn ’ t been on finding the single best visualisation solution . There are many different tools available on the market , and they all have their advantages and disadvantages .
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