In the mere two or so years since AI entered the business data analytics arena, it’s had a major impact on how companies operate. Businesses have discovered that marrying AI with business intelligence (BI) results in richer, more timely, more accessible, and less costly insights.
Line-of-business users in every department, from sales and marketing to legal and financial, now have access to reliable, up-to-date data and analytics, without needing data science expertise nor hands-on assistance from analysis experts. More efficient and accurate insights drive better decision-making, forecasting, and strategic planning for all teams.
Employees can spot risks and opportunities earlier than ever, and take more effective action to mitigate the risks and maximise the opportunities. At the same time, AI-powered data underpins personalisation at scale, allowing companies to raise the bar on customer experience and keep a step ahead of customer expectations.
Now business analytics AI is moving into its next phase. Enterprises have passed the first excitement about their AI analytics tools, and they’re settling down to make the most of their capabilities. As both AI solutions and attitudes towards them start to mature, clear trends are emerging. Here’s what lies ahead for business data analytics in the AI era.
Traditional BI Is on the Way Out
Few business users will shed a tear for the demise of traditional business intelligence approaches. GenAI supports user-friendly interfaces that allow stakeholders to interact with data through naturally-worded queries. It’s being implemented in any number of apps, so that BI no longer requires switching contexts and using dedicated BI apps.
API-first architectures make it possible to embed analytics into every type of software. Users can tap into real-time insights as part of their workflows, be they for sales (CRM), procurement (ERP), marketing (CMS), supply chains (fleet management) or anywhere else.
Friction-free embedded analytics allows business users to remove barriers to data insights, leaving traditional BI looking old-fashioned and clunky. Now employees can view relevant data insights as part of their daily activities, supporting data-driven decisions, while customers enjoy smarter interactions and faster responses.
For example, Pyramid Analytics’ embedded analytics solution lodges interactive, advanced analytics directly into existing business tools, apps, and websites. End users gain comprehensible, up-to-date answers to every data question, without the need for tech expertise or help from data science teams. Just ask your business apps any data-related strategic question, using natural language with voice commands, and see your answers in the form of rich data visualizations within seconds.
Specialised Models Will Take Over
Until now, generalised AI models garnered all the attention. They deliver readable prose in seconds, which is brilliant for consumers who need help writing a birthday card. But companies are waking up to the fact that this isn’t good enough, accurate enough, or secure enough for enterprise analytics use cases.
Enter specialised models for AI analytics. These LLMs are trained on industry-specific and company-specific data for particular use cases. The benefits are a legion: they are faster and cheaper to train, provide answers more quickly, are more trustworthy and less likely to hallucinate, they understand industry jargon, and they can deliver precise, accurate responses.
Businesses in industries like healthcare, law, and finance can also bake in extra security and privacy mechanisms, so that their specialist LLMs meet stringent compliance requirements. That’s why Google is offering AutoML, a platform that invites developers to train high-quality models for specific business use cases. The solution is fast, cost-effective, and easy to use, giving organizations extra control over the data that their model consumes.
Data Quality Will Rise
AI data analysis relies on clean, trustworthy datasets both for training and fine-tuning models so that they meet your needs, and to produce accurate results. Poor-quality data can lead to bad decisions, missed revenue opportunities, and costly mistakes. AI business analytics show no mercy when you cut corners, so data quality is fast becoming a fundamental issue.
In response, we’re seeing companies double down on data governance. Many are appointing data stewards to consistently collect all the relevant data points, make sure they are cleaned and verified, and monitor their use to maintain data integrity.
Tools like Collibra are being implemented to optimise data governance workflows and processes, reconcile inconsistencies, and document the metadata that testifies to data provenance, format, and relationship to other datasets.
Data stewards glue information together in a coherent way, rather than just shoving it into a data lake, so that it’s homogenised into a single source of truth. They also monitor and standardise the metrics layer, which lies between data preprocessing and analytics. This is where business teams apply their own formulae, which, when under-managed, risks altering the weights given to different data points and distorting data quality.
Knowledge Graphs Will Guide the Way
As AI business data analytics drives the collection of ever-more datasets, it becomes necessary to be able to connect that data as well as monitor its quality and integrity. Knowledge graphs are coming into play to organise scattered data in ways that reveal new relationships between existing datasets.
They essentially offer a user-friendly roadmap that helps GenAI models and end consumers to understand the data in front of them.
With knowledge graphs serving as a semantic layer, AI business data analytics tools can deliver insights that are more valuable and meaningful. Business users receive responses that they can use immediately to make informed decisions about business challenges, helping transform data into productivity and revenue.
For example, RelationalAI turns business rules, decision systems, and data relationships into clear and actionable frameworks that allow users to put data to work. They create new connections between disconnected data sources to enable fast, repeatable decisions across the organisation.
GenAI Is Ringing the Changes for Business Data Analytics
It’s clear that the introduction of GenAI into business data analytics is sweeping out existing modes of work and driving new attitudes to every aspect of BI. From enforcing a rise in data quality to replacing traditionally isolated BI tools, and requiring new methods like knowledge graphs and specialised models, GenAI is stimulating a revolution. It’s exciting to watch the progress of new AI business data analytics solutions.
