Data intelligence is often summarized as turning data into actionable insights. It results from the analysis of data—usually large quantities from different sources—that yields information that can be used to support organizational decision-making. Advanced analytics that leverage artificial intelligence (AI) and machine learning (ML) are commonly used with big data to derive data intelligence.
According to International Data Corporation (IDC):
Data intelligence leverages business, technical, relational and operational metadata to provide transparency of data profiles, classification, quality, location, lineage, and context; Enabling people, processes and technology with trustworthy and reliable data.
Let’s jump in and learn:
Why Organizations Need Data Intelligence
Data is an asset, but it is not inherently valuable. If not carefully managed, data becomes problematic—expensive to store and risky if it’s accessed by unauthorized users. The value of data is what can be done with it.
Data intelligence comes from accessing and analyzing data. That is where the magic happens. Raw data is aggregated, and powerful analytic tools are used to run analysis and related queries—the results of which reveal more than the sum of the parts.
Data intelligence enables organizations to adjust strategies to meet changing requirements more quickly. It provides insight into patterns and trends to predict changes. This is how data intelligence helps organizations develop information-based ideas and plans.
The many benefits derived from data intelligence include:
- Improved operations and user experiences
Data Intelligence Use Cases
Across industries, organizations use data intelligence to achieve similar objectives.
Gain insight into customer profiles and segments:
- Understand customer behavior and characteristics.
- Group similar customer types.
- Create more targeted messaging and offers.
- Make better decisions regarding product and service development.
Better understand corporate investments:
- Give business data more context related to its investments.
- Monitor and evaluate investments.
- Forecast potential future investments.
Use real-time data to direct sales and marketing:
- Identify regional or local sales patterns.
- Track event and promotional campaign trends.
- Compare daily to historical sales history.
- Improve customer satisfaction.
Improve logistical and operational planning:
- Optimize with analysis of delivery times, anticipated weather conditions, and traffic.
- Identify optimal warehousing locations.
- Select optimal transport routes.
Improve customer experience.
- Identify purchasing preferences.
- Match products and services to needs.
- Create a user-centric customer experience.
Examples of industry segments that effectively utilize data intelligence include:
Data intelligence is used in healthcare to analyze complex patient information to optimize treatment, reduce treatment costs, and minimize wait times.
With data intelligence, data that has long been used to understand customers’ near-term purchase behavior and long-term buying patterns is exponentially more powerful. Behavior is analyzed and actions are taken in near real-time to improve customers’ experiences and increase purchases in physical stores and online for e-commerce marketplaces.
Data intelligence helps companies make energy provisioning more efficient and lowers costs by analyzing historical demand and predicting minute-by-minute, hour-by-hour energy demands.
All areas of the travel industry use data intelligence to determine times when demand is higher and to provide highly-targeted offers based on traveler patterns regionally, seasonally, and by demographics.
Educators use data intelligence to track and give teachers an in-depth, holistic view of students’ academic progress. This allows them to identify areas of interest, proficiency, and potential weaknesses to provide specialized coaching when needed and tailor their curricula to optimize each student’s experience.
Factors to Consider in Improving Data Intelligence
Big data has tremendous potential to improve data intelligence. As more data is captured, work continues to effectively and systematically extract and analyze that big data.
To realize maximum return on investment (ROI) and improve data intelligence, advanced analytics continuously evolve to support even more sophisticated data mining, big data analytics, prescriptive analytics, and predictive analytics.
When considering improving data intelligence, risks must be taken into account; it relies on data, specifically the collection of large volumes of data. Increasingly, regulations target data collection practices. Data intelligence initiatives must be mindful of regulations and how they impact data collection, storage, and processing.
Data Intelligence Components
Four key types of data analysis techniques are used to create data intelligence:
- Descriptive data analytics—What is happening?
This is the most commonly used data analysis technique. It provides a view of key metrics and measures within an organization.
- Diagnostic data analytics—Why is it happening?
This is used to drill down to identify root causes.
- Predictive data analytics—What is likely to happen?
This focuses on using predictive models to assess the likelihood of something happening in the future, forecast a quantifiable amount, or estimate a point in time at which something might occur.
- Prescriptive data analytics—What should be done?
This takes into account an understanding of what has happened, why it has happened, and various what-might-happen analyses to help identify the optimal response.
A number of data types are collected, stored, and processed by organizations for data intelligence. Each provides a different lens for viewing data intelligence insights.
Vast amounts of data are produced and gathered by organizations. The result is big data. More than a massive cache of data, big data also includes the storage of the information for use in analytics to create data intelligence.
Most big data is unstructured and gathered at high velocity from multiple sources in different formats. This data needs to be stored properly so data intelligence analysis can be performed efficiently and effectively. A big data storage system is made up of large clusters of high-capacity servers that are designed to support analytics and process vast quantities of data.
Data mining is the process of analyzing big data to identify patterns to inform data intelligence. Reports are produced and dashboards are populated to make the data intelligence easily accessible. Once analyzed, the data is assembled into categories, then stored, to enable and expedite future analysis.
The data intelligence yielded from data mining is mostly predictive; organizations can predict and follow future trends, as well as identify opportunities for optimization.
Event processing tracks and analyzes categorized data. Organizations are able to analyze patterns in real-time to derive data intelligence.
With online analytics, organizations capture and assess web data to measure online traffic and derive intelligence from it. Online analytics provides data intelligence related to:
- Brand awareness
- Customer behavior
- Customer experience
- Market research activities
- Online campaign efficacy
- Online presence
- Site performance
- Web traffic
Infosec data Intelligence
Data intelligence provides valuable insights to help infosec teams optimize and improve the efficacy of their security postures. Security teams can see what is behind every piece of data to identify potential threats and predict attack vectors. Data intelligence also improves security audits and is even used in penetration testing and incident response activities.
Make Data Intelligence a Priority
Take data to the next level by investing in the resources needed to access and analyze it. Any organization can benefit from data intelligence; from sales and marketing to IT and finance, the insights gained support better decision-making and overall optimization.
Egnyte has experts ready to answer your questions. For more than a decade, Egnyte has helped more than 17,000 customers with millions of customers worldwide.