Data Discovery in BI: Turning Data Into Actionable Insights

In today’s data-driven world, Data Discovery is a critical tool for uncovering hidden insights and enhancing decision-making within Business Intelligence (BI). How do Data Discovery and Business Intelligence work together? The two analytic tools are similar in approach but serve subtly different roles in your organization. Data Discovery has growing applications in Business Intelligence, especially when integrated effectively. In this article, we’ll explore key methods and real-world applications that every business should know to gain a competitive edge.

Quick Takeaways

• Business intelligence and data discovery both consolidate data from multiple sources
• Business intelligence is better used to generate general insights for day-to-day operations
• Data discovery is best used for in-depth analysis of specific issues
• Data discovery uses data visualization to help users more easily understand detailed analysis

The Core Functions and Benefits of Business Intelligence

Business intelligence (BI) is a process for gathering, organizing, and analyzing data with the goal of delivering actionable insights. BI lets management and other staff query the collected data, generate detailed reports, and visualize data via the use of dashboards. Companies use BI to better run their businesses on a day-to-day basis. 

How Business Intelligence Works?

Business intelligence involves the use of a variety of technologies and tools. It’s typically a five-step process that involves:

• Collecting data from various sources
• Integrating and organizing the collected data 
• Analyzing the data
• Visualizing the insights provided by the data
• Using the data for better decision-making

The first step in BI is data collection, and it can be costly. Data is typically found in a number of different systems and department silos, in a variety of file formats, both structured and unstructured. (According to multiple sources, 80% to 90% of all data is unstructured, which makes it challenging to extract value from.) 

To be of value, this disparate data must be integrated into a single data warehouse and organized in a logical fashion. The data must also be reviewed and cleansed so that it is consistent, accurate, and complete. For this stage of the process, Data quality management is essential for ensuring the necessary high data quality.

The integrated data can then be analyzed, using a variety of BI tools. Some of these tools use artificial intelligence (AI) and other advanced technologies to extract actionable insights from the data. 

To make the data more useful, it must be presented in a way that is easily understood by management and key staff. While some staff still request detailed reports, management is typically better served by a more visual presentation. Reporting from the Marketing Vision 2021 conference confirms that visual dashboards and reporting are a top priority for organizations in all industries. 

With all this information and analysis in hand, management and staff can make more informed decisions for day-to-day operations. This is the ultimate goal of business intelligence. 

Benefits of Business Intelligence

Business intelligence provides numerous benefits to organizations of all types. The benefits include:

• Provides faster insights 
• Enables easier identification of trends (thanks to data visualization)
• Enables smarter operating decisions
• Enables analytics and insights deeper in the organization, thanks to embedding analytics in business practices

What is Data Discovery and Why is It Crucial for BI?

Data discovery is a subset of business intelligence. Like BI, data discovery involves collecting data from a variety of databases, consolidating it into a single source, and using it to provide visibility into various aspects of your business. This visibility helps identify key factors and trends and uses that information to make more informed business decisions.

How Data Discovery Process Works?

Data discovery is essentially a three-step process, as follows:

1. Data Collection and Preparation: Data is gathered and prepared for use; this includes integrating data from multiple sources, cleansing the data, and presenting it in a format that is easy to query and use.
2. Data Visualization: Data is visualized through visual dashboards, charts, diagrams, and other media.
3. Advanced Analytics: Data is organized and summarized into simple reports to aid in decision-making.

To provide actionable insights, the data discovery process relies on sophisticated data analysis skills, including data modeling and guided analytics. The use of visualization techniques and intuitive reporting makes it easy for non-technical users to quickly grasp and absorb key trends and data.

A data discovery platform typically includes the following features:

• Advanced data analysis tools
• Data visualization tools (dashboards, charts, tables, etc.)
• Predictive analytics
• Automatic data identification and classification
• Full-text and metadata search
• Data quality management, including metadata management

Benefits of Data Discovery

How does the use of data discovery benefit your business? The benefits are similar to those of BI, in general, and include: 

• Ability to visualize datasets, trends, and outliers
• Provides automatic classification of data
• Offers broad access to data insights across the organization
• Enables a bigger picture view of key data
• Improves regulatory compliance and risk management

How to Use Data Discovery in Business Intelligence?

Data discovery democratizes business intelligence. By providing easy-to-grasp data visualizations, data discovery makes it easy for users at any level to better understand key business trends and take advantage of advanced analyses. 

Comparing Data Discovery and Business Intelligence

Data discovery and business intelligence serve two similar but distinct functions in an organization.

The similarities are significant. Both business intelligence and data discovery consolidate and normalize data from a variety of sources, including databases siloed in individual departments. Both BI and data discovery hep to maximize the value of your organization’s data. Both provide valuable analysis and insights

The differences are also significant:

• Business intelligence provides a more consistent flow of information to support day-to-day decision making
• Data discovery is better at providing one-off, on-demand analysis of key data and trends

When considering adoption, organizations should take into account the “single source of truth” available in BI solutions, the skill sets required for each solution, and BI solutions that integrate discovery as well traditional reporting and dashboard capabilities.

Business Intelligence(BI) VS. Data Discovery- When to Use ?

Both business intelligence and data discovery start by collecting and consolidating all available data and converting it into a standardized and usable format. From there you can choose either BI or data discovery tools, depending on what you aim to achieve.

You should employ BI tools to:

• Provide enterprise-wide reports
• Generate real-time data for use in day-to-day operations
• Identify long-term trends
• Share general information with a wide range of users

You should use data discovery tools when you want to:

• Provide tactical analysis
• Provide answers to specific business-related questions
• Dig deep into specialized data investigations
• Analyze data from unstructured sources, such as social media
• Share specific analysis with a limited group of users

In short, business intelligence provides general information useful across the enterprise, where data discovery provides detailed information based on specific analysis. They both have their places in your organization.

(The following video goes into more detail about how data discovery is changing business intelligence.)

Data Discovery: Use Cases Across Industries

Data discovery is a versatile tool that traverses multiple sectors, offering tailored solutions to meet diverse business needs. Let’s delve into a few pivotal industries where data discovery is making a significant impact.

Retail: Enhancing Strategy and Customer Experience

In the retail sector, data discovery is crucial for understanding customer preferences and refining strategies. Retailers leverage this technology to:

Analyze customer behavior for insights into buying habits.

Tailor marketing efforts to individual consumer needs.

Optimize inventory management to ensure stock levels meet demand.

These data-driven insights enable retailers to make well-informed decisions, ultimately leading to enhanced customer satisfaction and improved sales.

Finance: Strengthening Risk Management

Financial services heavily rely on data discovery to streamline operations and mitigate risks. Key applications include:

Predicting client behaviors, such as potential mortgage defaults.

Enhancing risk management frameworks by identifying compliance issues.

Facilitating strategic financial planning through robust data analysis.

By harnessing the power of data discovery, financial institutions can better anticipate market trends and safeguard against potential risks.

Healthcare: Improving Patient Outcomes

Data discovery is transformative within the healthcare industry, where it supports:

Enhancing diagnostic accuracy and treatment effectiveness.

Analyzing genomic data and clinical trial outcomes for drug development.

Advancing personalized medicine by understanding drug interactions.

These applications lead to improved patient care and accelerate medical research, driving innovation in healthcare solutions.

By applying data discovery techniques, industries can harness the power of information to innovate, streamline operations, and ultimately enhance their service offerings.

Overcoming Common Challenges in the Data Discovery Process

While data discovery offers significant advantages, businesses often encounter challenges that can hinder effective analysis. Here are some common issues and practical solutions:

1. Data Silos: Isolated data sources can impede comprehensive analysis, making it difficult to gain a holistic view of business operations.

Solution: Integrate all data into a unified platform. This approach ensures that data from various sources is accessible and can be analyzed together, fostering collaboration and better insights.

2. Data Quality: Inaccurate, incomplete, or outdated data can lead to misleading insights and poor decision-making.

Solution: Utilize AI-driven validation tools, like DataBuck, to automate data quality checks. These tools can identify discrepancies and ensure that the data used for analysis is reliable and up-to-date.

3. Managing Big Data: The sheer volume and complexity of big data can overwhelm traditional data management systems, leading to slow performance and bottlenecks.

Solution: Leverage cloud-based BI platforms that are designed to scale with your data needs. These platforms offer flexible storage and processing capabilities, enabling businesses to handle large datasets efficiently.

4. Lack of User Training: Users may struggle to navigate data discovery tools effectively without proper training, limiting their ability to extract insights.

Solution: Invest in training programs to empower users with the skills they need to utilize data discovery tools effectively. This investment can enhance data literacy across the organization.

Boost Data Discovery and BI Using DataBuck’s Solutions

Whether you need the general information provided by business intelligence or the specific answers afforded by data discovery, you need to start with high quality data. To ensure the highest possible data quality, turn to DataBuck from FirstEigen. DataBuck is a data quality management solution that can automatically validate thousands of data sets in just a few clicks. It can identify bad data and either clean it or isolate it, as well as constantly monitor new data fed into your data pipeline. When you need clean data for BI and data discovery, turn to the data quality experts at FirstEigen. 

Contact FirstEigen today to achieve better Business Intelligence and data discovery results.

Check out these articles on Data Trustability, Observability & Data Quality Management-

• 6 Key Data Quality Metrics You Should Be Tracking
• How to Scale Your Data Quality Operations with AI and ML?
• 12 Things You Can Do to Improve Data Quality
• How to Ensure Data Integrity During Cloud Migrations?