X (formerly Twitter) recently went on-premise, saving up to 60% in cloud storage costs. Can small and mid-sized businesses afford such drastic cost-cutting moves?

How do such trends affect your company’s software investment plans? What will the business intelligence market look like next year?

This article discusses BI trends, data security, and cost considerations and provides vendor comparison tips to help you procure a suitable BI tool.

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What This Article Covers

• Top Trends for 2024
• Benefits of BI Software
• Case Studies
• Data Security Considerations
• Vendor Comparison
• Cost and Pricing Considerations
• Next Steps

Key Takeaways

• AI automation, data sharing, self-service BI and decision intelligence will be at the forefront of enterprises’ minds.
• Getting the correct information to the end user at the right time is in focus with the data mesh.
• Exploding cloud storage costs will be on the minds of vendors and enterprises.
• Regulatory compliance, user authentication, secure metadata and permissions configuration are must-have BI features.

Top BI Trends for 2024

1. Data Management

Despite post-pandemic pangs, the BI market is set to grow. Fortune Business Insights predicts the business intelligence market size will increase to $54.27 billion by 2030.

Big data integration spawned a host of data management technologies in BI software, including data sharing. CEOs realize they risk missing out on important information if data remains siloed behind proprietary firewalls.

Source

The Data Economy

Data marketplaces connect information vendors and buyers across industries and geographical locations.

Third-party data sharing is helpful for consumer services and goods industries, government agencies, healthcare providers, banks and insurers.

Organizations realize that the benefits of data sharing far outweigh the risk of competitors outperforming them, as seen during the COVID-19 pandemic.

Snowflake has a data marketplace with connectivity to over 480 information providers. In a shared data economy, product owners and consumers win.

Data Quality Management

Confidence in data drives hundreds of business decisions daily, and quality management is behind it.

A data catalog is an organized inventory of your data assets, enabling control over who accesses them. Data searches are faster, and defining standard terms and business rules becomes possible, as does troubleshooting issues.

However, serving multiple product owners with varied needs can be too much for a single data team. A decentralized setup makes sense to early innovators willing to change how they work.

Enter the data mesh, a concept that distributes data ownership to separate teams composed of people close to specific data assets.

Another advantage of this modular setup is that it helps analyze operational and analytical data separately, which is how it should be.

But, adopting a data mesh entails a culture shift, which isn’t easy in setups with established work norms. Whether this shift will be voluntary or after organizations reach a tipping point remains to be seen.

2. AI-ML Integration

There’s no limit to what AI can do, with automated system deployment and pipeline authoring taking the grunt work out of data analytics and business intelligence.

There’s more. Automatic system scaling, server provisioning and downtime help keep costs minimal.

Generative AI is undoubtedly a handy assistant, but it’s too early to tell if it’ll impact jobs.

According to this McKinsey survey, the focus will be on upskilling rather than role shifts.

• Data and machine learning engineers and AI data scientists were the most frequently hired roles in the past year.
• AI-related software engineers are less in demand than last year, but the demand for prompt engineering skills is rising.
• Around 40% of respondents from AI-adopting organizations expected over 20% of their workforce to undergo upskilling.

Shaku Atre, keynote speaker and president of Atre Group Inc., is hopeful: There will be more data scientists as time passes.

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.

The current “catch all” term being “Artificial Intelligence” it is highly likely that additional flavors for additional functions provided may be reflected as “Data Scientist for Artificial Intelligence”, “Data Derivator for 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. People have been naïve to think that new technology is going to solve all the problems in no time. It has never happened. And, I think, it will never happen.”

The McKinsey Survey states that we can expect a decrease in workforce size in service operations in the future.

On the technology front, data scientists are working on experimenting with GenAI to close the insights-to-decision loop. More on it ahead.

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3. Cloud Cost Management

The evolution of cloud computing services saw a drop in prices due to the increasing business intelligence market size and rise of virtual machines. But inflation and supply chain disruptions played spoilsport.

While pay-as-you-go cloud models offer flexibility, the absence of charge caps and vendor lock-ins can be restrictive. Additionally, real-time data ingestion is power-intensive.

External geopolitical factors like wars, embargos and economic sanctions can drive up computing costs.

The Ukraine war forced cloud service providers to scramble for power as operating data centers in Europe became prohibitively expensive. And the situation looks set to worsen with the Israel-Gaza conflict.

The BI market will focus on cost management technologies like in-database analysis while staying performant.

4. Self-Service BI

According to Fortune Business Insights, the self-service BI software market will grow to $20.22 billion by 2030.

Does it mean total freedom from manual data work? Self-service BI is a deceptively simple term for what still needs much work in the background, considering the business intelligence market size.

Despite intuitive interfaces, toolkits and handy buttons, data cleansing is mainly manual, and you’ll require a particular data format for independent report generation.

Or you’ll need someone to prepare the reports before you can go in there and start digging.

According to Simone Sharma, Product Leader – Clinical, Revvity Signals,

Self-service analytics is most useful when an end-user can find and access data they need (data that has already been formatted to a certain model) without jumping through many hoops and propagate their already set dashboards/visuals with this newest data. That to me is the extent of self-service…”

Source

Active metadata seeks to achieve this by making values, datasets, columns and fields available via simple text searches.

Metadata need no longer rest passively in a data catalog. Instead, it can actively push data where you work.

The business intelligence market focus will be on decreasing the time of delivery of data assets to users.

5. Decision Intelligence

According to Markets and Markets, the value of the decision intelligence software market will be $22.7 billion by 2027. It’s a sizable increase from $10 billion in 2022. What does this term mean?

Gartner defined decision intelligence as a domain that “combines traditional and modern disciplines to design, model, align, execute, monitor and tune decision models and processes.”

This definition reflects a shift in the industry focus from data analysis to decision-making, the pot of gold at the end of the rainbow. That’s the endgame. So why the focus now?

Even with the best BI platforms, determining the dependencies to decide the following action can be time-consuming. A delayed decision is a lost opportunity, so reducing the decision-cycle time is essential.

With programmable AI, we can now aspire to close the insights-to-decisions gap. Decision intelligence incorporates well-defined metrics and a robust semantic layer.

And there’s more. As we shall see in an Oracle case study, it can involve defining the decision pathways for your AI systems.

You can train your BI software with the dataset relationships and condition logic. Running your data through the if-then conditions results in a decision output you can accept or reject.

Decision intelligence is an evolving trend likely to be on the watchlist of vendors and buyers.

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Benefits of BI Software

Focusing on active metadata, domain-driven design and proactive decision-making can reap rich dividends with better revenue, satisfied customers and operational efficiency.

Boost Revenue

BI software enables personalized marketing using data analytics, customer segmentation and predictive modeling.

• Informing pricing strategies with competitor analysis helps align your business approach with existing trends.
• Understanding what sells can help develop your unique selling proposition (USP).
• Identifying gaps in competitor offerings can give you innovative ideas.

Gamification, a customer engagement strategy, uses BI-driven data insights to attract users.

Coca-Cola partnered with TensorFlow to boost its loyalty program.

They used TensorFlow’s CNNs (convolutional neural networks) to capture the 14-character code under their bottle caps when consumers took a picture with their mobile phone camera.

Users scanned over 1,80,000 codes in six months, inspiring several promotional campaigns.

This BI-powered strategy exemplifies how market intelligence and technology integration drive successful customer engagement and promotional initiatives.

Gain a 360-Degree Business View

Data integration tools in the business intelligence industry combine diverse sources, eliminating silos and providing unique, consistent views.

• They analyze various data types, employing OCR (Optical Character Recognition) or computer vision for unstructured data transformation.
• Data integration provides flexibility for new data sources without significant downtime.
• Accurate, contextual data drives reliable BI reports and analyses. Access to well-prepared, integrated data enhances decision-making.
• Near-real-time data extraction delivers up-to-date information.

Modern BI solutions enhance data interpretation through visual modeling, promoting user engagement.

Improve Customer Experience

Capturing user personas requires a finger on the market’s pulse. What are users saying about your product? What do customer interactions tell you?

• Sentiment analysis, text mining, surveys, feedback forms and social media listening are valuable market mood indicators.
• Revenue analysis helps interpret buyer patterns and reduce customer churn.
• Higher conversion metrics stem from effective marketing and customer engagement and reduce the cost per acquisition.

Business intelligence helps craft personalized outreach and exclusive offers.

A BI tool optimally utilizes market data to enhance the customer experience, but scalability is necessary to manage such large data volumes.

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Streamline Financial Planning

Financial management software relies on current and historical organizational data for budgeting and forecasting.

Scenario analysis enables risk assessment, and financial key performance indicators (KPIs) let stakeholders track progress and identify deviations from fiscal goals.

Calculating actual versus budgeted spending — variance analysis — provides a realistic picture of expenses. Stakeholders can compare it with their financial objectives and pivot with market trends.

This holistic approach ensures that financial strategies are data-driven and adaptable, positioning you for agility and resilience in an ever-evolving financial landscape.

Manage Workforce

BI software for workforce management helps you define the criteria for new hires by evaluating existing employees’ skills, performance and tenure.

Aligning your workforce with organizational goals becomes easier with tailored hiring strategies, thanks to skill gap analysis with BI systems.

Following hiring success metrics across various channels can help you identify the best recruitment avenues. Similarly, tracking the time-to-hire can help uncover bottlenecks and streamline the hiring process.

The business intelligence industry supports recruitment budgeting by providing cost-per-hire metrics.

You can act to retain employees by anticipating turnover with predictive analytics and performance benchmarking.

Data analytics enables compliance tracking and risk mitigation, especially when training staff in healthcare and finance.

Facilitate Operations

BI software is critical in offering near-real-time access to operational data, fueling daily operations.

• Supply chain and manufacturing rely on linked ERP and inventory management systems to run their pipelines smoothly.
• BI tools provide activity logs and reports to enforce and track quality control.
• Anticipating product demand with predictive analytics tools can help you avoid backed-up inventories and stockouts.
• Tracking supplier metrics maintains accountability and provides value for your investment.
• Delegating daily reporting to BI software cuts down on manual work and reduces the chance of human error.

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Case Studies

Retail Management Software: Qlik Data Analytics

Urban Outfitters, facing the challenges of data silos and diverse operating styles across 650 stores, transformed its operations with Qlik’s BI market solutions.

The retailer struggled with consolidating data and achieving daily visibility. Varied time zones and pandemic-induced disruptions added to their woes.

Qlik’s Data Integration and Snowflake cloud migration streamlined data consolidation, providing a shared central repository.

With 240+ Qlik apps in production, the retailer achieved real-time analytics, replacing complex reports with highly visual dashboards.

Improved decision-making with scalable analytics facilitated quick, informed actions, enhancing store operations for Urban Outfitters.

Financial Management Software: ThoughtSpot

Modern Restaurant Concepts, a food-forward eatery chain, sought a streamlined solution to accelerate reporting and gain real-time insights for their twin brands, Modern Market Eatery and Lemonade.

Relying on manual data aggregation in Excel, their team struggled to provide timely reports. They partnered with ThoughtSpot to upgrade their data stack to the cloud with Google BigQuery, their data warehouse.

ThoughtSpot’s Modern Analytics Cloud provided intuitive, agile reporting, enabling access to daily and hourly sales and labor data.

Modern Restaurant Concepts anticipates a 50% increase in user adoption within the first year, highlighting the success of their shift to a more efficient, data-driven strategy.

Human Resources Management Software: Oracle Analytics Cloud

Oracle Analytics Cloud (OAC) stands at the forefront of decision intelligence, leveraging low-code, composable services within its analytics cloud.

An example scenario involves aiding managers in bonus payout decisions through advanced analytics.

• OAC’s Machine Learning (ML) feature, Explain, plays a pivotal role in unraveling facts and relationships within datasets.
• You can construct decision models within OAC by incorporating identified fields and values and inputting the logic for bonus calculations.
• OAC exposes these decision models through a REST API, integrating seamlessly into a decision intelligence application interface.
• It allows managers to select an employee ID and invoke the decision model, providing instant bonus amount suggestions based on robust analytics.
• By incorporating embedded analytics, a comprehensive summary of previous bonus amounts for the selected employee is readily available.

Such pathway mapping ensures a holistic view of decision-making history, empowering managers with valuable insights and enhancing the overall decision intelligence framework within the organization.

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Data Security Considerations

GDPR for EU citizen data and HIPAA for healthcare information — which security capabilities do you need in your BI software?

• Key evaluation features include data encryption and masking, audit trails, user authentication, and SSO through protocols like SAML, OpenID Connect or Shibboleth for secure access.
• Separate folders, role-based access control (RBAC) and automatic logoffs keep data secure.
• Data destruction and retention policies ensure you dispose of data assets properly.

• As discussed above, metadata and data catalogs are vital for permissions, column-level security, debugging and rule enforcement.
• The SOC (System and Organization Controls) reports assure customers and auditors of regulatory compliance.
• PCI DSS adherence is essential for organizations handling credit card information to ensure the encryption of cardholder data.

After software procurement, you will be responsible for configuring user protocols and data protection practices.

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Vendor Comparison

Define your requirements and create a vendor evaluation framework, including cost, features, scalability, support and data security.

• Calculate the total cost of ownership (TCO) by factoring in maintenance, support, training, add-ons and system integrations.
• Evaluate vendor reputation and check if they offer scalability, future growth, integration capabilities and a user-friendly interface.
• Check previous audit reports, certifications and third-party ratings while inquiring about data security.

Check for gaps in processes and policies and determine how they affect your risk exposure and compliance requirements.

It pays to include security considerations in your contract.

• Discuss the contract terms and perform an ROI (return on investment) analysis before deciding.
• List your security requirements in the contract. Include clauses for data ownership, access, encryption, storage, disposal, incident notifications and responses, liabilities and penalties.
• Implement proprietary controls to enhance systems security. Update the contract after implementation as regulatory and safety requirements change.
• Monitor vendor performance using dashboards, metrics and scorecards, give feedback and review your relationship periodically.

Read our Lean Selection Methodology article for a step-by-step software selection guide.

Cost and Pricing Considerations

Include the following costs in the TCO.

• License costs involve a one-time fee for purchasing the software and depend on the number of users and devices.
• Subscription costs include an upfront platform cost and a monthly fee per user, billed monthly or annually. They include installation, implementation and maintenance charges.
• Customization costs vary depending on the required functionality, like configurable dashboards and data elements, workflow complexity, interface changes, and reporting needs.
• Data migration costs depend on your existing software, data volume and complexity, availability of migration tools, and gaps between the current and new systems.
• Training costs cover webinars, documentation, and online and in-person upskilling.

As an example, here’s a look at Power BI pricing.

• Power BI Desktop is free to download, though report publishing is available only after signing in to the Service model.
• Power BI Service is cloud-based and offers free and paid services, including Power BI Pro and Premium Per User.
• A Power BI Pro license costs $9.99 monthly and allows report sharing.
• Its Premium version is capacity-based, offers 400 TB storage and costs $4,995 monthly with dedicated workspaces.
• The Premium Per User version costs $20/user/month and offers 100 TB storage.

Read our Power BI Pro vs Premium article for more information.

Add these questions to your list for vendor discussions.

• Do they require a minimum monthly or yearly commitment?
• Do they entertain custom quotes?
• Is a free trial available?

To give you an idea of the business intelligence market, Sisense starts at $21,000/year for five users.

Refer to our product pricing guide for details on other leading BI market products.

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Next Steps

The business intelligence market involves keeping your ear to the ground.

eCommerce launched in response to customers shifting online, and virtual fitting rooms became popular when people couldn’t step outside.

The technologies discussed above will drive BI platforms, whatever the next business trend. Being aware of their power can assist you in choosing the right system.

Ready to start your software search? Get our free comparison report to generate a scorecard of your preferred BI platforms and evaluate them by feature.

Which notable trends do you see taking hold in your industry in the coming years? Will humans be able to rein in generative AI? 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.