According to an IDC prediction, by 2024, organizations with efficient BI tools and technologies will be at a significant advantage, with five times more reaction time to capitalize on new opportunities.

Will you be one of them? The right BI software and data maturity can get you there. This article discusses the future of business intelligence, current trends and their likely trajectory in the coming year.

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What This Article Covers

• Key Takeaways
• Top BI Trends
  • Generative AI
  • Data Governance
  • No-Frills BI Functionality
  • Cloud Cost Management
  • Automated Data Storytelling
  • Decision Intelligence
  • Reverse ETL
• Next Steps

Key Takeaways

• Generative AI technology will continue to race forward while enterprises scramble to tame it.
• Data marketplaces, active metadata and data mesh will disrupt existing data management practices.
• In the future of BI, a cost-saving mindset will drive a no-frills approach to tooling.
• Cloud cost management will drive in-house efficiency innovations and open the software market to small players willing to plug this gap.
• Automated data storytelling seems poised to overshadow self-service BI.
• Augmented analytics will expand to include decision intelligence.
• The business intelligence future will see reverse ETL technology in focus for delivering business value to customer data platforms (CDPs).

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Top Trends for 2024

1. Generative AI

Large-language models (LLMs), or generative AI (artificial intelligence) programs, are machine learning models that perform language-related tasks based on acquired patterns and knowledge. They include text generation, answering questions and producing images, music and code.

Though we’ve barely scratched the surface with LLMs, they’re currently used for automating workflows, generating SEO insights and producing easy explanations. Developers use LLMs to develop SQL code and edit, debug and optimize queries.

A Forbes Advisor survey states that 97% of business owners believe ChatGPT will help their businesses. LLMs provide personalized insights, customer relationship management, cybersecurity and fraud management.

They’re so easy to use that naysayers believe dependence on LLMs might make people lazy and less willing to acquire technical skills.

Besides, they aren’t entirely free of bias, and any calculation or coding errors are likely to get magnified in the results, so fact-checking is gaining importance.

Job loss fears are more real, but maybe we don’t need to worry. As mentioned above, trust issues persist with LLM-generated content, and human oversight is necessary, for now at least. A generative AI model is only as good as the people running it.

Looking Forward

According to Gartner, by 2027, generative AI will reduce modernization costs by 70%, thanks to LLM-generated insights on where legacy applications lag.

There’s no going back from here. The business intelligence future will have generative AI playing a pivotal role, considering the ease of use it offers.

According to this McKinsey survey, a quarter of C-suite executives admitted to personally using generative AI tools for work. Over 40% of the respondents said promising generative AI advancements spurred their organizations to consider increasing their AI investment.

Considering its potential, many market players are quick to jump on, and enterprises are finding out fast — join the generative AI club or be left out.

Philip Moyer, Global VP, AI & Business Solutions, Google Cloud, says

If leaders focus on the delta between what employees like to do and what they often have to do, they can target generative AI use cases that make workers happier and more productive.” Source

The early days of generative AI saw some companies trust generative AI models with their data and get burned, making organizations wary. LLM vendors have come in for severe criticism for not providing adequate data security.

The backlash was loud enough to prompt LLM developers — Amazon, Anthropic, Google, Inflection, Meta, Microsoft and OpenAI — to promise the Biden administration they would actively manage AI-related risks.

The generative AI model is yet to reach maturity, and we can expect a guarded approach to adoption as businesses do a cost-benefit analysis. It’s a phase of trial and error for many companies as they scramble to keep up with the competition.

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2. Data Management

Since data is the lifeblood of operations and analytics, its management is the most essential part of the data pipeline.

Data management is an umbrella term for quality management, secure data sharing and ready information access while maintaining high query performance.

Automated Data Quality Management

Maintaining data quality is much more than removing incomplete and inconsistent values, profiling and enrichment.

There is an increased demand for observability modules to handle unexpected issues and unplanned downtime in data pipelines, AWS Glue Data Quality being one such application for ETL processes. High-quality data drives confident decisions and promotes client trust.

The Data Economy

Enterprises seek third-party data to stay competitive despite changing customer preferences, economies and politics, and data marketplaces offer a legal way to enrich their data with external insights.

Data marketplaces are online stores for buying and selling data, and they’re secure — modern computing technologies enable the processing of data in its encrypted form.

Building a data marketplace requires a data catalog, metadata engine, security and data governance with well-defined processes and policies. Snowflake has a data marketplace with connectivity to over 480 data providers.

Understandably, enterprises have data quality concerns. There’s more.

Active metadata, the next-gen form of static metadata, serves datasets and fields with simple UI searches, so data security and quality must be on point at all times.

Active Metadata

Finding dashboards, queries and columns by name on the user interface are examples of active metadata in action — there’s no need to switch tabs or access a separate module.

Active metadata helps identify dataset relationships, search glossary terms, parse queries, and retrieve dataset lineage to check whether the data is correct and where it originated.

As Prukalpa Sankar says in her article on active metadata,

If (metadata) actively moves to the places where people already are, it becomes part of and adds context to a larger conversation.”

With active metadata, you can enrich the user experience by surfacing popular data assets fast, free space by purging unused data, accelerate processing and allocate computing resources dynamically.

It boils down to a common business language — the semantic layer, though recent trends show the industry has been slow to adopt this concept.

Defining metrics and mapping them to source systems is a tedious undertaking, which explains why many businesses might be unwilling to change how they work.

Data Mesh

The data mesh works on the concept of a decentralized domain-driven design (DDD) for data teams and treating data as their end product.

It proposes separate data teams for different operational and analytical domains. Each team should consist of people close to that data — managers and data experts responsible for building the data models and publishing the data for the consumers.

Though the data mesh steadily gains followers because it supports observability, many enterprises don’t have the skill set, organizational buy-in and tool maturity. Large-scale adoption of this concept might take a while to materialize.

Looking Forward

Data marketplaces give you comprehensive insights without buying additional software. The benefits of data sharing far outweigh the risks, and adhering to data privacy regulations while buying or selling third-party data will be in focus in the future of BI.

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3. No-Frills BI Functionality

Due to the current industry focus on controlling costs, companies are prioritizing essential BI attributes over shiny, new features. This shift benefits buyers seeking barebones BI solutions.

It will encourage efficient spending and increased ROI as companies will allocate resources only to what’s necessary for their specific data analysis needs.

Focusing on must-have BI features will enhance user experience by reducing complexity and simplifying tools, making them accessible to more employees. Additionally, it’ll help companies stay agile and adapt quicker to changing markets.

Looking Forward

The business intelligence future will likely see vendors focus on one platform, one solution, rather than building a system for multiple requirements. This initial fragmentation will progress to system integrations with many technologies working together.

Anticipating more system integrations, vendors will bake efficient integration options into their offerings. It’s already happening — Fivetran complemented its data integration capabilities by acquiring HVR, an enterprise data replication system.

There’s another untapped market on the horizon — vendors will diversify into offering modern data tools that support legacy systems.

4. Cloud Cost Management

The soaring costs of cloud computing and storage are extreme for enterprises, and they’re scrambling to control expenses. Did you know that Netflix spends most of its $1 million cloud-dedicated allocation on AWS storage?

Source

There is little wiggle room with high transition fees when changing providers. Leading hyperscalers are under investigation for charging exorbitant egress fees. And zero visibility into their billing methods is increasing subscriber distrust.

Future Outlook

In the future of BI, cloud cost management will be top of the mind for enterprises, and companies will continue to invest in the cloud but with more caution. And they have help.

Active metadata platforms like Atlan are helping reduce costs by optimizing data processing and removing unused data to make space.

Snowflake and Databricks are investing in developing features to reduce costs.

Snowflake announced in June 2023 that they plan to reduce storage by 7-10% with improved data compression. Databricks plans to improve response time by introducing a 12 times faster query engine and automatic ETL optimizer.

Those who can’t develop in-house may look for affordable, third-party options, and small vendors might step in to address this need. Early entrants into the data cloud efficiency market include Bluesky, Slingshot and CloudZero.

And those who can are shifting on-premise. In a recent development, X (formerly Twitter) reduced cloud costs by 60% by moving their data assets out of the cloud.

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5. Automated Data Storytelling

Less than one-third of corporate employees can use data independently. Despite self-service analytics and data democratization, you need skills like statistics and knowledge of computer sciences to study and use data.

It’s AI to the rescue again with automated recommendations, visualizations and natural language insight generation. You can generate visualizations and reports with simple text commands and ask your AI digital assistant to explain the results.

Adding common language summaries to reports and visualizations adds the human context and simplifies result interpretation.

For business users, automated data storytelling solves intuitive insights. For data experts, it accelerates analytics and data-related tasks. Additionally, AI-driven results are objective and unbiased.

Salesforce acquired the data storytelling software Narrative Science to complement Tableau’s Explain Data and Ask Data functionality. Yellowfin is another example of data storytelling software,

Here’s something to consider. Why would you need handy toolkits and intuitive drag-and -drop user actions when inputting your query generates ready visualizations and easy-to-understand explanations?

We’ll have to wait and watch if automated data storytelling replaces self-service analytics.

6. Decision Intelligence

Strategies fail because of wrong decisions based on ill-fitting KPIs or incorrect data mappings. It makes performance tracking an impossible task, and at the end of the product cycle, businesses are none the wiser.

In recent years, there’s been a stronger emphasis on augmenting the decision-making process with AI technology and predefined decision pathways.

Decision intelligence proposes designing the route the AI system should take as it decides for you — like drawing a map when you want to give someone directions to your home.

Why is it in the spotlight now?

Because it should not be an afterthought, and teams should ideally factor it in at the beginning when defining KPIs. And it combines data science with decision engineering and social sciences.

According to Gartner, decision intelligence brings “multiple traditional and advanced disciplines together to design, model, align, execute, monitor and tune decision models and processes.”

Besides AI, a semantic layer and active metadata are crucial in providing insights on demand in standard business terms.

Many software consultancies offer decision mapping, performance monitoring and creating systems of record for documenting the decision intelligence process. Additionally, AI software programs provide enterprise-level decision intelligence.

Looking Forward

The future of business intelligence looks bright, with market leaders like Google and Alibaba venturing into research by launching data intelligence labs.

According to MarketsandMarkets, the value of the decision intelligence solutions market will reach $22.7 billion in 2027.

Source

A note of caution from Cindi Howson, Chief Data Strategy Officer, ThoughtSpot, “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.”

7. Reverse ETL

The business value of your tech stack lies in using the results of your analysis to grow your business.

Reverse ETL is a data integration process different from traditional ETL in that it takes data from a warehouse and syncs it into operational and transactional systems, including CRM, marketing automation and customer support systems.

It enables different teams to access the latest data, closing the data-to-insights loop from ETL to data analytics and reverse ETL.

Reverse ETL helps organizations make warehouse data accessible and actionable, empowering teams to use it for daily operations and decision-making.

Reverse ETL systems have three objectives.

• To enable near-real-time data flow from the data warehouse to operational systems.
• To ensure decision-makers have access to up-to-date warehouse data.
• To keep the various business applications synced with the latest warehouse data.

Looking Forward

Though most tools offer either data ingestion or reverse ETL, Hevo Data provides both. In the future of BI, more software partnerships will likely provide organizations with full functionality, including data ingestion, analysis and reverse ETL.

Another trend, data activation, looks to replace reverse ETL as it involves making data usable directly within use cases from the warehouse rather than sending it to customer data platforms.

A note of caution for software buyers — reverse ETL platforms aren’t the same as data activation platforms.

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Next Steps

LLMs and data marketplaces put the spotlight again on data governance, which is always top-of-mind of enterprises, thanks to GDPR and other regulations.

Automated data storytelling and decision intelligence hold the potential for more freedom for you and me. Reverse ETL is in focus as operationalizing analytics gets due attention from enterprises keen to recover their data investments.

Artificial intelligence is a force to reckon with, and it throws up new challenges daily. As data engineers race to tame it, I wonder — who’s the master here?

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What does the future of business intelligence look like to you? Can businesses with legacy BI tools compete with AI technology? Let us know in the comments.