The post-pandemic phase saw a boom in BI software investments, especially the cloud. Remote work had finally come of age. Automated data pipelines, app marketplaces, AI-ML technologies and GenAI have become trending topics.
In the future, which business intelligence trends will persist, and which ones will lose steam?
This article delves into the future of BI by discussing the key trends expected to shape this domain in 2024.
What This Article Covers
Key Takeaways
- The cloud market is expanding steadily, and hybrid and multi-cloud setups will be in demand.
- Software partner ecosystems will offer comprehensive functionality and drive innovation.
- The race to provide cloud computing tech for GenAI (generative AI) will heat up, with the first offerings ready to launch early next year.
- A significant chunk of vendor revenue will be from cloud verticals.
- Self-service BI will be everyone’s favorite ask, driving enterprises toward data literacy.
- Problem formulation will be an in-demand skill as GenAI experiments continue.
- Data privacy and security design investment will be necessary to counter AI-related risks.
- Decision intelligence engineering will spark interest in software buyers and vendors.
Top BI Trends for 2024
GenAI disrupted established ways of working and challenged data security norms. For vendors, it spells opportunity as the race to offer supporting technologies warms up.
Let’s look at how previous trends fared and what the future looks like.
1. Hybrid and multi-cloud setups will be in demand
The cost of data center operations in Europe is giving enterprises pause. Recently, X (of former Twitter fame) went on-premise, exiting the cloud.
Will it catch on?
After COVID-19, the cloud software market slowed initially, only to gain pace later.
There was a renewed interest in cloud technologies as distributed teams scrambled to do business. Additionally, organizations wanted more control and better preparedness and weren’t coy about investing in cloud systems.
Besides AWS, Microsoft and Google claim a sizable market share in cloud services. It’s open season for tech buyers, and how. Companies are investing in not one, not two, but multi-cloud setups.
Avoiding vendor lock-in and optimizing costs by choosing select providers for specific services are some benefits of this approach. Redundancy and high availability are others — you can minimize downtime significantly.
According to this Oracle Report, 98% of enterprises use or plan to use a multi-cloud infrastructure.
Among them, 45% opted for over five cloud providers, prioritizing efficiency, cost optimization, scalability and access to innovative technologies.
Future Forward
It seems unlikely many organizations will follow Twitter out of the cloud — it’s so convenient and lightweight. Plus, not everyone has the on-premise infrastructure to support their business at scale.
The cloud will continue to reign supreme in deployment options for the foreseeable future.
2. Software partner ecosystems will boost innovation and revenue
Partner ecosystems are networks of interconnected software tech companies collaborating to create, market and deliver software solutions.
Partners can include technology and solution providers, resellers, system integrators, consultants and developers. Each entity brings unique expertise and resources, creating a synergistic and dynamic environment.
As a vendor, partner integrations can help you reach new customers and markets. Interoperability and integration are hallmarks of these networks, offering opportunities for product value creation, scalability and innovation.
- Microsoft Azure is a partner ecosystem with thousands of companies offering solutions and services built on its cloud platform.
- Salesforce AppExchange is a marketplace with thousands of partner apps and integrations to extend the functionality of the vendor’s platform.
- Atlassian Marketplace offers add-ons and integrations for products like Jira and Confluence.
Among the trends in business intelligence, partner ecosystems offer a win-win situation for all stakeholders.
Customers get access to better solutions, ISVs benefit from wider reach and collaboration, and vendors expand their market share and brand reputation.
Future Forward
Sharing expertise and technology results in a dynamic environment where novel solutions and features emerge, pushing the boundaries of what technology can achieve.
- The demand for partner collaboration will drive the development of robust APIs, adherence to open standards and integration of BI tools that accommodate diverse data formats.
- Increased cloud adoption is a happy side-effect, making data accessible and democratizing usage across departments. When people work better, they work better together.
- We can expect more intelligent BI tools as the demand for BI solutions infused with artificial intelligence (AI ) will increase.
- Vendors will focus on improving the UX with intuitive interfaces and data storytelling capabilities, increasing engagement.
- Offering industry verticals will foster domain tech specialization.
- Data governance and security features are bound to get a boost with so much focus on cutting-edge technology.
All good things.
3. Early cloud computing innovators for GenAI will spice up the competition
Human biases and cognitive blockers limit testing ideas at scale in a limited time. GenAI can address these issues by synthesizing data for hypothesis testing and insight generation.
Generative AI, or large-language models (LLMs), bridge information gaps by offering a translation layer between the language model and data. They prepare and clean data after trawling through huge volumes that can slow you down.
According to Gartner, by 2026, generative AI will significantly alter 70% of the design and development effort for new web applications and mobile apps. It means massive cost savings in legacy application modernization.
But, a shortage of CPU processor chips threatens to put a spanner in the works. Supply chain issues, economic uncertainty and regional wars are slowing down hardware development.
A few players have a monopoly over the chip manufacturing market, and in the absence of competition, they aren’t producing that many units to make up for the shortfall.
Vendors are already integrating LLMs with enterprise applications. Think back to the apps you used with an AI bot in the past month.
Early innovators are ready to launch computing tech stacks that support GenAI. HPE plans to launch its GenAI-focused computing solution in Q1 of next year, and other players are following suit.
Microsoft launched its first custom AI accelerator, Maia, for OpenAI models, Bing, GitHub Copilot and ChatGPT at its Ignite conference in Nov 2023.
It remains to be seen if the high cost of CPU processor chips will keep AI innovations within the bounds of reason.
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4. Cloud BI verticals will continue to be in demand
Banking and insurance firms adopting the cloud grew to 91% in 2023.
According to Frost and Sullivan, the global healthcare cloud computing market will be worth $52.30 billion by 2026. Healthcare is one of the largest adopters of cloud computing.
A Boston Consulting Group (BCG) article states that 80% of executive-level software buyers say verticalized products have a moderate to high impact on their purchasing decisions.
Regulatory compliance, business complexity and fragmented technology stacks drive buyers to ask for tailored solutions to meet their unique needs. Many prominent vendors already offer industry-specific solutions.
Verticalization is an opportunity for sales teams to revisit their training and overall strategy. Buyers lean toward vendors that understand their industry.
Offering vertical solutions earlier in their growth journey can be lucrative for software vendors. Per the BCG article, vendors with up to $5 billion in revenue range can expect a YOY growth of 65-80% by offering vertical solutions.
For instance, a CRM software vendor can verticalize by offering solutions for financial institutions or healthcare organizations. It’s a case of the more, the merrier, as vendors enter into partnerships with third-party entities to target niche markets.
SAP Business One and Oracle HCM are fully vertical solutions.
Future Forward
Verticalization is a way to differentiate yourself, and vendors who haven’t diversified into specific industry offerings are likely to lag. We foresee increased vendor competition and more investment in vertical BI tools.
Read our Business Intelligence Market Insights article to learn more.
5. Self-service BI will drive data literacy initiatives
Self-service analytics is easier said than done, requiring data preparation and formatting behind the scenes. And the demand for data skills has exacerbated the existing skills shortage.
Failure to get returns despite analytics investments is a matter of concern. It’s why enterprises push to close the data literacy gap.
According to the State of Data Literacy 2023 report, 78% of U.S. business leaders believe data literacy to be the most critical employee skill.
Vendors are cashing in on this opportunity by offering innovative technologies that make life easier for the average knowledge user.
A semantic layer helps define the entire organization’s standard business rules and definitions.
- With Tableau 2020.2, Salesforce launched a logical layer for associating multiple tables while retaining their native level of detail using normalization.
- This year, Salesforce launched a VizQL Data Service with a user-friendly programming interface for querying and data modeling.
- Google launched a standalone metrics layer called Looker Modeler, making metrics available in Looker Studio, ThoughtSpot, Tableau and Microsoft Power BI, among others.
Enterprises are yet to warm up to the data mesh and vendor-provided metrics libraries. Aligning their data culture to a new organizational setup will take time. These two are to watch out for among BI trends.
A data mesh aims to accelerate analytics by assigning separate teams to manage and deliver byte-sized data products to consumers. A metrics library is a collection of predefined metrics to get you started quickly.
Another new trend is making natural language searches all-powerful. Type the column name, visualization or data field, and the system displays it on the UI, thanks to active metadata.
Industry experts say specialized data skills aren’t going away anytime soon.
We asked Shaku Atre, president of Atre Group Inc., about data experts’ future. Here’s her take.
If anything there will be an increase in data scientists. It is possible that it may come in additional flavors with the increase in additional features with Artificial Intelligence.
“Software is getting more sophisticated, but it is nowhere near the human brain’s inference-drawing capabilities.
“In order for the software to have that capability, it will need more data and not only more high-level data, but more granular data.
“And that is where a data scientist is absolutely needed.”
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6. Citizen data scientists will need to get creative with GenAI
Self-service BI doesn’t require a degree, and data analysis and reporting come full circle with consumers becoming power users.
Ryan Wilson, Vice President of Technology at Signal Ventures LLC, said as much.
What we’re starting to see is very business user-friendly business intelligence platforms that can be highly automated and are starting to incorporate some data science tools that don’t require a data scientist with a Ph.D. to utilize.
“This is going to lead to more and more companies incorporating data-driven business at every level of the business.
“As this happens I think we’ll start to see everyone becoming a bit of an analyst, which will start to shift the role of a dedicated analyst to running, maintaining, and extending these platforms and tools in most organizations.”
Industry experts have divergent views on whether prompt engineering will be in demand as a skill.
Future Forward
According to this Harvard Business Review article, problem-framing will be crucial to navigating the evolving AI landscape, and critical thinking skills will help maximize LLM benefits. It boils down to asking the right questions.
According to entrepreneur Mark Cuban,
Time productivity will be defined by how well you can ask the right questions to get the appropriate answers from your models.”
That being said, domain, data querying and data mapping knowledge will always be essential.
Alongside this, AI-related risk management and responsible governance will be in-focus trends in business intelligence.
7. GenAI will drive the conversation around data security.
Large-language models scrape data from the web and risk exposing sensitive data publicly. With no guardrails on AI accessing your proprietary data, GenAI challenges data security paradigms.
The reliability of AI-driven results is under the scanner. Inherent bias can skew analytical results — after all, there’s a human behind the AI model.
And AI hallucinations — flawed algorithms and incomplete data — give the technology a bad name.
In the future, users will expect organizations using AI to be transparent about access, usage, retention and protection of their data.
Regulatory compliance adds another layer of complexity.
The ‘right to be forgotten’ under Article 17 of the GDPR requires organizations to delete personal data when the user requests it. However, it might undermine the utility of machine learning models that learn from this data.
What’s a happy middle approach?
Future Forward
Meta And IBM launched the AI Alliance this year to support open and responsible AI innovation while ensuring user trust, safety and security.
Governments are taking AI-related security concerns seriously. An Executive order from the U.S. president this year laid down AI security standards to protect citizens’ privacy while promoting innovation.
Enterprises will be more likely to adopt AI models from organizations that operationalize trust, transparency and security. But that’s not all.
Enterprises can anticipate ongoing investments in AI-related privacy and security design.
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8. Automated data storytelling will augment self-service analytics.
Gartner estimates that by 2025, data stories will be the most popular way of interpreting data. And 75% of stories will have automation behind them, thanks to augmented analytics.
Automated data storytelling refers to the process of using AI-ML to transform data into compelling narratives without extensive manual intervention.
It automates data analysis, visualization, and communication, enabling anyone to extract insights and share them effectively with a broader audience.
Big data integration floods organizations with information. Automated storytelling can convey timely insights before they become yesterday’s news.
This BI trend is not the same as self-service analytics. Both differ in the level of automation and the degree of human involvement required. Self-service analytics may not be for an audience but for independent data discovery.
- Tableau, Looker and Yellowfin offer data stories modules.
- Qlik Sense Insight Advisor automatically generates visualizations on inputting a text query.
- With Domo Storyboards and Power BI, you can generate text summaries along with visualizations in response to data queries.
Watch this SalesForce video to learn how BeyondCore works with Watson Analytics for plain text insight.
In the future, we can expect to see more integration of automated data storytelling within self-service analytics platforms.
9. Decision intelligence engineering advancements hold promise for software buyers
According to Cassie Kozyrkov, former Chief Decision Scientist at Google, an inherent bias can skew decisions unless we define every consideration in advance. She calls it being data-driven rather than data-inspired.
A new BI trend, decision intelligence engineering proposes approaching decisions with a mapped-out strategy.
- What data do we need?
- Which parameters should we factor in?
- What are the possible decisions?
- What are the acceptable values for taking decision A?
- What will be the values for deciding on course B?
Nailing these points will help us avoid bias and be data-driven in the true sense.
At the enterprise level, decision cycles are getting extremely short, and wrong moves can set you back.
If you’re wondering where it sits among other analytics types, decision intelligence spans them all, including descriptive, diagnostic, predictive and prescriptive analytics.
However, it can mean different things to different people, and buyers need to be able to discern a decision intelligence application from an analytics platform with self-service capabilities.
Cindi Howson, Chief Data Strategy Officer at ThoughtSpot, sounds a note of caution.
Take a buyer-beware approach and understand if a technology provider is providing analytics to support decision-making or a true platform to operationalize decision-making.”
The more prominent players are performing independent research into this technology.
With Cassie Kozyrkov at the helm, Google launched its decision engineering lab to develop new tools, techniques and frameworks for enhancing decision-making across its operations. And Alibaba recently followed suit.
The technology is nascent, and we can expect exciting developments in the coming years.
Next Steps
Did you know AI has a massive carbon footprint? Chip manufacturing processes consume water, and training GPT-3 requires 70,000 liters of clean freshwater in Microsoft’s U.S. data centers.
Water also functions as a coolant in data centers. By 2027, the global AI-related demand for water will be over 4 billion cubic meters, which is more than what half of the U.K. consumes annually.
Unless the movers and shakers take sustainable development seriously, it may be too late. How can you contribute?
Including environmental best practices in your investment plan is an excellent way to start.
Read more about BI software trends in our Future of Business Intelligence article.
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Which business intelligence trends do you foresee creating ripples in the coming year? What are your thoughts on AI-related innovations? Let us know in the comments!
SME Contributors
Shaku Atre is president of Atre Group, Inc., New York City, NY and Santa Cruz, California, a BI and data warehousing corporation.
Atre is an acclaimed author, and her books include DataBase: Structured Techniques for Design, Performance and Management and Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications.
She’s an accomplished speaker, addressing audiences in the USA, Canada, Europe, South America, Asia and Australia on business intelligence, data warehousing, data mining, customer relationship management (CRM) and database technology.
Hundreds of her articles have been published in trade publications over the years.
Ryan Wilson is Vice President of Technology at Signal Ventures LLC. An experienced data analyst, Ryan built dataflows, dashboards, and cards for over 20 companies as a Domo consultant with Build Intelligence.
He led a team of developers and consultants in maintaining over 3200 visualizations, 2400 datasets and 700 dataflows.