In legacy business decision-making, data is managed by specialists who organize relatively small datasets to extract insights, often presenting their findings in monthly reports. This can lead to decisions that are based on outdated information and gut instinct, limiting the ability to adapt and improve in real time. While all organizations face these constraints equally, the lack of timely data hinders operational efficiency and continuous growth.

Quickened Pace of Change

Organizations struggle to keep pace with accelerating shifts in customer expectations, business concepts, and societal trends. The traditional, centralized inquiry model — where executives request insights and wait weeks for answers — is insufficient. In fact, that delay is now a competitive liability.

While not every business process demands real-time data, the need for timely insights is undeniable. There is a growing demand for more up-to-date data, and companies recognize an increased need for recent insights. Delayed decision-making hampers agility and prevents businesses from maximizing value in a rapidly changing environment.

To stay competitive, companies must decentralize decision-making, shifting authority closer to those with the expertise to act. Empowering employees to make informed decisions whenever and wherever they are needed is no longer optional — it’s essential. As Harvard Business School’s Frances Frei and Anne Morriss put it, “The most effective leaders solve problems at an accelerated pace while also taking responsibility for the success and well-being of their customers, employees, and shareholders. They move fast and fix things.”

Frictionless Access to Data

To enable effective decision-making, organizations must provide quick and frictionless access to the right data at the right time. This means making data accessible to executives and every knowledge worker who needs it. In a fast-moving business environment, delays in accessing insights can mean missed opportunities and slower responses to challenges.

For knowledge workers to make informed decisions, data must be current, ensuring relevance to the situation, and accessible, meaning easy to retrieve and use. As Aptos Retail CIO Jason James stated during a recent discussion, “The speed of business and competition now requires that stakeholders have quick access to meaningful data to make decisions. Data needs to be up-to-date, relevant, and relatively easy to access.”

The good news is that big data, generative AI, and agentic AI have the potential to revolutionize data accessibility. These technologies can transform business processes by delivering insights to decision makers faster and creating more agile, responsive organizations. In a world where speed and accuracy drive success, leveraging AI-driven data solutions is no longer a luxury — it’s a necessity.

Becoming Future-Ready: Data Recency & Operational Tempo

According to the Massachusetts Institute of Technology Center for Information Systems Research (MIT-CISR), only 22% of organizations are future-ready, meaning they have industrialized their data and optimized the customer experience. This highlights a significant gap in data maturity, with most businesses still struggling to effectively harness their data for decision-making. Research reinforces these concerns, while also shedding light on data recency — how up-to-date information must be to drive effective decisions.

Data recency needs vary depending on the business function, ranging from real-time updates to quarterly refreshes. Organizations typically classify business intelligence (BI) data recency into categories such as real-time, hourly, daily, weekly, monthly, and quarterly. While real-time data is often idealized, the reality is that not all processes require it.

Aligning Data with Business Needs

Research also underscores the importance of operational tempo — data leaders must ensure that data flows and processes align with business objectives. While real-time data is valuable, its necessity is often overstated. In practice, only a small number of BI data needs are real-time, and an even smaller amount requires hourly updates. However, the demand for fresher data is growing, with many organizations reporting an increasing need for higher-recency BI data. To remain competitive, organizations must refine their data strategies, ensuring they deliver the right level of recency to meet business needs without overwhelming infrastructure or decision makers.

Challenges Affecting Data Speed & Availability

Several factors influence how quickly organizations can operate, including complexity of business processes, management approaches, and varying levels of data literacy. For data to drive effective decision-making, data-delivery processes must align recency with the specific needs of business users and processes. Without this alignment, organizations risk either overwhelming teams with unnecessary real-time data or limiting agility by relying on outdated insights.

Balancing Data Recency with Business Needs

While fresher data can be valuable, not all decisions require real-time updates. Many BI implementations still rely on monthly or daily data updates because these frequencies are sufficient for processes like financial analysis, financial-close cycles, product development, and long-term planning. Even the most data-driven organizations recognize that their recency needs range from real-time to quarterly, depending on the business function. Instead of assuming real-time updates are always beneficial, organizations should strategically evaluate where higher data recency truly adds value.

Strategic Approach to Data Recency Optimization

To optimize data recency, data leaders should:

• Analyze how different business processes rely on data at various levels of timeliness.

• Review data-delivery architectures to identify gaps where data timelines don’t align with decision-making needs.

• Architect data flows that match specific business processes and strategic objectives.

• Consider how decision makers actually use high-recency data before increasing update frequency.

Without a strategic approach, organizations risk investing in unnecessary real-time data pipelines that add cost and complexity without delivering proportional value.

Findability Vs. Data Recency

Interestingly, increasing data recency does not necessarily improve the ability to find analytic content. Organizations with easier-to-find analytical content report less need for high-recency data; this indicates that data recency and findability are separate challenges — both must be addressed independently to maximize BI effectiveness. Simply speeding up data flows won't help if users struggle to locate the right insights when they need them.

Key Insights on BI Success

Organizations with high BI success rates tend to:

• Recognize and accommodate a broad range of data recency requirements.

• Require more high-recency data (real-time and hourly) compared to organizations with less successful BI implementations.

• Acknowledge diverse and evolving recency needs, ensuring data is timely where it matters most.

Parting Words & Key Takeaways

Speed and accuracy in decision-making are now defining factors for business success. Organizations must move beyond legacy approaches, where data is siloed and decisions are made based on outdated insights. While the demand for real-time data is growing, not every process requires it — the key is aligning data recency with actual business needs. Companies that take a strategic approach — optimizing data flows and empowering employees with the right insights at the right time — will gain a competitive edge.

Key takeaways:

• Not all data needs to be real-time. Instead of defaulting to higher-frequency updates, organizations should evaluate where fresh data truly drives value.

• Findability matters as much as speed. Increasing data recency won’t help if employees can’t quickly locate and use the right insights.

• AI and automation offer powerful solutions. Leveraging AI-driven analytics can streamline data delivery and make timely insights more accessible.

Ultimately, organizations that balance speed, accessibility, and strategic data investment will be best positioned to navigate today’s fast-changing business landscape.