McKinsey analysts predict that enterprise employees will rely on data for almost every decision come 2025. If true, this development would mark a significant departure from the current business modus operandi. According to our research, only 25% of enterprise data professionals believe their organization’s decision-making process is data-backed or strategic. 

How are these two concepts – the perception of data progress and the reality of disorganization – so distinct? To answer this question, we need to understand why data-driven processes remain so elusive for many enterprises.

Business data is becoming increasingly complex. The massive volumes of data generated and consumed on a daily basis equates to over-taxed systems. By 2025, global data creation will reach 180 zettabytes, up from 147 zettabytes today and just 41 zettabytes in 2019. That represents a 23% CAGR of data creation, consumption, and duplication over the past four years. Handling this amount of data is challenging – understanding and deriving insights from it is even harder.

Furthermore, organizations must uphold high data governance standards to maintain compliance with local and global requirements. This concern, coupled with many others (including data quality, management, and integration), often distracts leaders from data’s most considerable promise: its ability to inform the right decisions at the right time. When leaders unlock this capability, they achieve data intelligence.

Let’s discuss what data intelligence looks like in a modern enterprise and how leaders can achieve it today.

The A, B and Cs of Data Intelligence

An organization’s knowledge of where, how, and when to leverage data directly relates to its level of data intelligence, a concept originated by Stewart Bond, VP of Data Intelligence and Integration Software at IDC. Data-intelligent organizations aren’t marred in the processes of data governance or quality and management; instead, they’re empowered by their data, learning and growing alongside it. These organizations have a healthy relationship with data, viewing new content and context as a helping hand, not a hindrance. Compare that to organizations swimming in data, too preoccupied with storing information to begin understanding it.

Gartner predicts that 80% of data and analytics initiatives will fail by 2027. The source of this failure? Popular but inflexible data governance models that prioritize control over their data instead of market opportunities. Simply put, data intelligence represents a better or more focused approach for most modern data environments. It emphasizes the importance of real-time insights and data in motion, empowering decision-makers to capitalize on market opportunities instead of worrying about avoiding the consequences of bad data.

Several markers exist to determine whether an organization has achieved data maturity. Data-intelligent enterprises can process vast amounts of data in real time; capture insights from this data without friction; access it through one “golden record”; and never worry about its quality or accuracy.

We can break down the critical nature of data intelligence by focusing on its ability to empower processes, decision-making abilities and people:

• Innovation: Data intelligence enables organizations to adopt innovative solutions like generative AI, machine learning (ML) and automation. Organizations with strong data management practices and an overhead understanding of their data can utilize said data into these new systems, allowing them to process accurate information and operate at peak efficiency.
  
  Conversely, organizations without a proper data organization strategy will find these technologies relatively unhelpful to decision-making processes. This is because AI is only as effective as the foundational data it is trained upon. AI and innovation are the fuel, not the vehicle. Adding rocket fuel to a minivan does not make it go faster or magically become a rocket. So, if we conceptualize data governance as the brakes, data Intelligence is the gas pedal and steering wheel – while data is the vehicle itself.

• Business intelligence: Data-intelligent organizations have superior insights into critical areas of opportunity like efficiency, cost reduction and resource optimization. They proactively mitigate cost centers, including redundant and unnecessary data. As such, they can focus solely on relevant and accurate information, which provides a much more fertile ground for advantageous decisions.
  
  For context, only 16% of organizational data in the U.S. is mission-critical. The other 84% is duplicative, unnecessary, or “dark” (aka completely uncategorized). Unnecessary data costs an organization twice – once to store and again to mislead data-driven decision-making. Organizations unlock data intelligence when proactive management and organization strategies drive down this data debt.

• Culture: Data organization isn’t a “one-and-done” solution. To cultivate data intelligence, employees and leaders must understand the strategy’s long-term importance to operations. After all, internal stakeholders are the true arbiters of organizational data, deciding its placement and applicability to important business decisions.
  
  Thankfully, data intelligence pays dividends to its practitioners by empowering them to easily access and understand information more deeply. By providing all employees with access to the same resources, data intelligence strategies eliminate silos and enable productive cross-departmental collaboration. 

To kickstart a data intelligence program, leaders must assess their approach to traditional data concerns like governance, management, and organization. Doing so is the key to unlocking competitive advantage, speed, and simplicity when making high-stakes decisions.

Achieving Data Intelligence

Organizations must conduct a data audit as part of their approach to data intelligence. This step is especially crucial for enterprises without formalized data structures or a data team/partner – however, all organizations can benefit from ongoing or regularly scheduled audits. The average organization receives data from 400 sources, with 20% of organizations drawing from 1,000-plus data sources. Understanding this disarray is the first step to correcting it.

During this process, leaders will develop an understanding of existing organizational data governance and hygiene practices. They must use this understanding to establish a formalized data management framework, using current stumbling blocks as a guiding light. For example, if an organization struggles to understand its data governance, including data provenance and meaning, it’s likely too early to consider DataOps strategies. For reference, 60% of data leaders indicate that governance remains their organization’s top priority.

These steps lay the groundwork for a robust data management strategy. However, data intelligence requires more than management. To truly unlock meaning from data in motion, leaders must deploy leading data integration and quality tools, including master data management (MDM) solutions. MDMs and other data intelligence solutions vastly improve an organization’s ability to ingest, transform, and consolidate data from various sources into a single repository, enabling data teams to improve organizational visibility, performance, and decision-making.

Organizational data literacy improves when all stakeholders maintain access to fluid and relevant data. Consider the ramifications of two departments operating on conflicting data and arriving at two completely different conclusions, misdirecting resources and spending. This scenario is not only likely but incredibly common with the current data M.O.

Data intelligence empowers everyone with the same high-quality information and is the only proactive solution to these persistent data challenges.

Fast Forward to 2025

The path toward data intelligence is paved by the promise of technologies like MDMs, AI, and automation. The rewards of adopting these tools are immense and include the ability to achieve data maturity, swiftly capitalize on emerging opportunities, mitigate risks and drive innovation through accurate, real-time insights. 

Although McKinsey’s prediction about entirely data-driven organizations may fall short of its 2025 deadline, one fact remains certain: a data-driven future awaits. And in this near future, data intelligence will become the differentiating factor between sowing a competitive advantage and falling behind.