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What is Data Intelligence? 

In the digital era, data intelligence is often likened to the new gold – an unparalleled resource that, when mined and refined, can unlock immense potential for business growth. It empowers organizations to navigate the complex business landscape with precision and foresight.

But what exactly does data intelligence mean?

Data intelligence definition

Data intelligence is about empowerment. It provides a clear understanding of data obtained through the insightful examination of metadata, revealing details about the categories, quality, origins, custodianship, modifications, and interconnections of data. Simply put, by extracting its significant characteristics, data intelligence equips you with a deep understanding of your data’s essential qualities – its who, what, where, when, and how.

For instance, you might be curious about the importance of your data for digital transformation projects, its dependability through various stages of processing, or the potential consequences it may hold if compromised and misused. Data intelligence is unlocked when an organization’s data, traditionally housed in several disparate systems, is unified in a singular platform, creating a ‘golden record’.

With this golden record, leaders can access real-time insights about essential metrics, including customer behavior, financial performance, operational efficiency, market trends, inventory levels, employee productivity, risk assessments, regulatory compliance, supply chain logistics, and competitive analysis.

DOWNLOAD: The Semarchy Data Intelligence Product Overview eBook

What’s the difference between data analytics and data intelligence?

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While they are sometimes mistakenly interchanged, these concepts represent different yet essential aspects of modern data management, each playing a unique role at various points within the data lifecycle.

Data intelligence is primarily concerned with strategically handling data as a critical business asset. Conversely, data analytics concentrates on applying data to generate insights and inform business decisions. Both elements are crucial to the current data-driven landscape, with data intelligence laying the groundwork for robust data analytics practices by ensuring that the underlying data is accurately managed and understood.

However, data intelligence extends beyond data evaluation as an isolated asset. It explores broader queries that delve into an organization’s overall data narrative: What is the purpose of our data? What is the justification for retaining it? Seeking answers to these questions enhances operational efficiency and supports numerous data intelligence applications, such as data governance and facilitating self-service analytics, among others.

Yet surprisingly, most business leaders take slight advantage of their organization’s data when developing their game plan. Semarchy recently surveyed 155 enterprise data professionals about data’s role in their organization’s business strategy and how they value data intelligence. Alarmingly, only 25% of respondents believe their organization’s strategic decisions are data-driven. Reasons for the inability to leverage data included the lack of a single source of truth and data quality management concerns, among others.

Data intelligence benefits

Some important data intelligence benefits include the ability to make adaptive decisions, create stronger data foundations, achieve operational efficiencies, utilize augmented analytics, and foster an environment of teamwork and trust. It ensures that all key stakeholders have access to vital information in real time, thus supporting data-driven decision-making within an organization.

Data intelligence equips your workforce with the means to make the best decisions at the right time by:

  • Presenting a unified and comprehensive view of data. Organizations handle vast quantities of raw information in today’s data-driven environment daily. According to Statista, global data creation and consumption are projected to exceed 180 zettabytes by next year, with corporate data at the forefront of this massive influx. Imagine scaling this proliferation of data across your entire company – consider the sheer amount of data collected by your marketing division compared to that processed by your IT team.
  • Employing data intelligence tools, such as data governance and master data management (MDM) software to merge various datasets, facilitating a holistic view of organizational health. This ‘golden record’ eliminates silos, guaranteeing that employees across various departments have access to consistent and accurate information.
  • Revealing hidden insights. While individual data sets can provide insights on specific departmental functions, a golden record provides a complete picture of enterprise efficacy. Problems in one department are often replicated in others – recognizing these commonalities is critical to reducing company-wide cost inefficiencies. By analyzing the context and relationships between diverse data streams, data intelligence tools aid in surfacing crucial insights that might go undetected in an ocean of information.
  • Enhancing data quality and governance: Data intelligence solutions actively manage data quality by pinpointing and correcting discrepancies, redundancies, and errors. These solutions pave the way for transparent data governance. Moreover, data intelligence provides insight into potential savings and resource optimization. Estimates suggest that a third of a company’s data might be redundant or trivial, and storing such data incurs significant expenses. Data intelligence empowers leaders to prioritize the retention and processing of essential data while securely discarding what’s unnecessary.

Data intelligence best practices

Realizing the full potential of data intelligence requires a thoughtful approach grounded in proven methodologies.

If you’re thinking about using data intelligence in your business, there are a few best practices to keep in mind:

  • Merge disparate sources into a unified data platform to create a comprehensive data overview or ‘golden record.’
  • Leverage diverse types of metadata, such as behavioral, technical, business, and provenance, to inform the use of data and policies.
  • Implement active metadata supported by machine learning and artificial intelligence to gain real-time insights about data use.
  • Foster a data-driven culture by nurturing data literacy, promoting data stewardship, and supplying tools and resources that facilitate collaboration and education.
  • Engage in continuous data quality management to detect and correct data irregularities, which in turn strengthens governance strategies.
  • Invest in the latest data intelligence tools and platforms that leverage AI and ML to help you make better decisions.

Finally, make sure you plan for how to use the insights generated by your data intelligence efforts to make the best possible decisions for your business.

Data intelligence examples: activated data in action

Businesses of all sizes in various sectors can employ data intelligence for strategic purposes.

Retailers can leverage it to forecast shopping patterns and decide on their inventory selection. Financial institutions can apply data intelligence to detect fraudulent activity and combat financial crimes such as money laundering, while insurance firms can utilize data intelligence to evaluate risks and determine premiums.

Data intelligence manifests in applications such as data governance, digital transformation, cloud data migration, analytics, privacy, risk, and compliance strategies. It can be particularly impactful in scenarios such as electronic patient health information management in healthcare, demographic analysis for public sector planning, supply chain efficiency in manufacturing, and customer data utilization in financial services.

Data intelligence tools to consider

Data intelligence solutions are becoming increasingly important as businesses strive to make the most of their data. They automate the process of big data processing and analysis, making it easier for businesses to get a clear picture of what’s happening within their data sets.

Tools for data intelligence include data catalogs, business glossaries, data dictionaries, profiling tools, stewardship dashboards, data lineage features, and data cataloging applications with natural language processing capabilities.

Sophisticated data intelligence software platforms integrate these tools to provide seamless access to consolidated data management processes, using on AI and machine learning to automate curation activities.

Discover the Semarchy Data Intelligence Solution

Semarchy recently launched a Data Intelligence solution designed to help organizations collaboratively govern and improve data from assets to initiatives with activated metadata. With this solution, organizations can eliminate challenges such as overwhelming data volumes, friction capturing insights, and data accuracy and quality concerns. The solution focus on simplicity and intuitive user experiences, crucial for accelerating adoption and fostering collaboration between business and IT.

Ready to find out more? Get in touch today.