What Is Data Discovery? How to Find Data Patterns and Outliers

Imagine driving in low-visibility conditions.

You can’t see what’s ahead of you. High beam headlights don’t help. Plus, it’s raining and you have to maintain a steady speed. 

Just like how poor visibility increases the risk of accidents on the road, poor data insight increases the likelihood of catastrophes in your business. 

Traditionally, businesses relied on data scientists or analysts to sort data formats and uncover insights. This left business leaders dependent upon information technology (IT) experts to understand their own data.

Modern organizations leverage analytics platforms to absorb, manage, discover, and explore data across the business network. Data discovery helps them unlock new opportunities, improve governance frameworks, and meet regulatory compliance requirements.

The data discovery process helps non-technical business leaders understand complex data sets using visual tools. This ease of accessing business intelligence (BI) helps all stakeholders boost efficiencies and refine business decisions. Seamless knowledge discovery bridges the gap between those who prepare data for analysis and those who need to interpret data to drive business decisions.

How does data discovery work?

Data discovery isn’t a tool. It’s a process that helps you analyze patterns to meet goals and remain competitive. It relies on multiple methods such as analysis, modeling, and visual outputs. Businesses use the following data discovery categories to develop a single view of data and gain insights.

Data preparation

Data preparation involves cleaning raw data before analysis. Businesses use data preparation software to preprocess, profile, cleanse, reformat, merge, and transform data. 

The prep gathers information from internal and external sources and makes it consistent for data analysis. For example, you may have to detect null values, deduplicate data, or detect outliers to ensure data quality before analysis. 

Data preparation process

Data preparation workflow includes the following steps:

• Data collection gathers data from data warehouses, data lakes, operational systems, and other relevant sources. At this point, data scientists also examine whether the data is suitable for the analytics application objective.
• Data profiling involves exploring the data you collect. During this stage, analysts look for data patterns, anomalies, relationships, inconsistencies, and missing values.
• Data cleansing corrects errors to ensure accuracy. Businesses use data cleaning to fill missing values and fix errors and inconsistencies.
• Data structuring organizes information according to analytics requirements. For example, you might convert comma-separated values (CSV) files into formats that analytics tools can understand.
• Data enrichment involves structuring data into a unified format. This step aims to further optimize and augment data sets.
• Data validation checks data completeness, accuracy, and consistency. Validation is the last step of data preparation and stores prepared data into a repository for users.

Data preparation also includes the curation of ready-to-use data. Organizations curating data generally index, catalog, and maintain data sets and metadata. Depending on the company structure, IT and data management teams, business analysts, data scientists, and data curators participate in the curation process.

Visual analysis

This process involves data manipulation and visual presentation with interactive tools. Data visualization helps non-technical users grasp data relationships with charts, diagrams, or dashboards. 

For example, visual analysis makes it easier for marketers to understand how customers use their products so they can align their strategies accordingly. Similarly, finance teams use graphical analysis to get a 360° view of cost vs. revenue. 

Guided advanced analytics

This type of data discovery combines visual and reporting techniques to offer a holistic picture of a company’s data. 

Guided advanced analytics enables businesses to study relationships among data from different sources and evaluate the implications of the efforts. For example, companies can spot new patterns and connections to make better data-driven decisions. 

It’s a great idea for traditional businesses moving to e-commerce platforms to use guided advanced analytics to integrate existing information with web data for better strategic decisions.

Data discovery vs. business intelligence vs. data exploration

Data discovery is the process of collecting data and spotting patterns for actionable insights. The process combines data from multiple sources to help businesses see the big picture and make better decisions.

Business intelligence parses organizational activity data to help the management make data-driven decisions. BI tools combine business analytics, data visualization, data mining, performance benchmarking, and descriptive data analytics capabilities. 

Data exploration is the first step of data analysis. The data exploration process helps businesses explore data patterns, characteristics, and points of interest in an extensive data set in an unstructured way. 

Importance of data discovery

Businesses assess market landscapes before making decisions, just like you check for cars ahead and behind while switching lanes.

Data discovery platforms help you figure out how individual data points create a holistic view of your operations so you can optimize business strategies. The best part is that most data discovery systems offer visual reports and dashboards for a complete data view across disparate systems. 

Let’s look at why organizations are increasingly adopting data discovery to identify, catalog, and classify critical data. We’ll also discuss how this simplifies transparency and adherence to data governance policies. Using data discovery, your business can:

• Discover new revenue sources. Businesses fuel value creation with meaningful data insights. Using data intelligence, they can explore large volumes of data and find ways to create new products, optimize data usage, and keep data secure. Nowadays, enterprises turn to data discovery platforms to identify these opportunities and uncover new revenue streams.
• Boost data democratization efforts. Organizations with data across systems often suffer from a lack of transparency. Under these circumstances, you can use data discovery platforms, artificial intelligence (AI), and machine learning (ML) to create metadata repositories. These repositories help the entire organization familiarize itself with data locations and movement protocols.
• Minimize data handling abuses. Most companies struggle with the proverbial extra weight of customer data they collect. You can lose customer confidence when this data doesn't adhere to governance policies. Because of this risk, any digital transformation effort involving multiple data types and application workloads should leverage data discovery to ensure trustworthiness.

A significant reason why companies choose data discovery is its ability to predict patterns that affect business outcomes. Some organizations also use visual analytics platforms to solve challenges, track business key performance indicators (KPIs), and create sustainable solutions.

History of data discovery

Data discovery used to be a manual process. Companies recorded data on paper and searched through them by hand to retrieve information. Almost needless to say, data discovery became easier with the advent of computers.

One of the early examples of a data discovery platform was the SETI@home project which used the idle power from personal computers to look for extraterrestrial intelligence, hence the name Search for Extraterrestrial Intelligence (SETI). The project was released for the public in 1999. 

In the 1960s, economic analysts and statisticians referred to data discovery as data fishing because it involved data mining without a predetermined outcome, like how you fish in real life. During the 1990s, the database community started working with data mining and open-ended analysis forms, which resulted in data discovery improvements. 

Data discovery became a major academic research area with the First International Conference on Data Mining and Knowledge Discovery (KDD-95) in Montreal in 1995. Big data came next, along with machine learning algorithms. The data discovery process evolved from an academic exercise into a must-have business process in the following years. 

Today, almost all industries use data discovery to make better decisions, from financial institutions to retailers to construction management firms.

Types of data discovery

Depending on your technical know-how, you break down the data discovery process into two levels: manual and smart data discovery.

Manual data discovery is the manual process of data preparation and cleaning. Data analysts and scientists use this data discovery method to analyze and manage data efficiently. 

The manual data discovery process heavily relies on machine learning and advanced technology expertise. Before modern technologies emerged, data specialists used manual data discovery methods to map data, monitor metadata, categorize document rules, and conceptualize available information. 

Smart data discovery is a user-friendly approach that uses ML, AI, and natural language processing (NLP)  to prepare, integrate, and analyze data. Businesses use intelligent data discovery software to visualize data interactively, discover hidden patterns, and access insights faster.

Occasionally, these tools can't keep up with the amount of new data added to the backend. That's when companies turn to governed data discovery. 

Governed data discovery (GDD) is a comprehensive approach focusing on business requirements to simplify data delivery, meet IT requirements, and keep data secure. IT teams leverage GDD to ensure speedy data delivery for analysis while meeting data governance requirements. 

So, what exactly do GDD systems do? 

• They keep data safe from internal and external threats.
• They offer built-in centralized self-service to help users run and manage BI analyses.
• They provide data governance and a single version of the truth to keep teams up-to-date about the latest business insights and KPIs.

Why are organizations focusing more on GDD these days?

Consider a situation where you need unrestricted data access for faster business decisions. However, you must also simultaneously meet the IT team’s data security, integrity, and governance policies. GDD helps you and the IT team to function seamlessly with BI-enabled GDD. 

Big data discovery

Data discovery is easy to use, but limits the depth of data exploration. On the other hand, data science can be highly complex, but challenging to implement in an enterprise setting. These difficulties led to the invention of big data discovery, which helps businesses to transform raw data into insights with minimal lift.

Big data discovery tools allow businesses to manipulate many data sources more efficiently than traditional data science or analytics systems. 

Traditional analytics projects require you to prepare data before analyzing it. For example, you’d have to predict business questions, model data, gather data resources, manipulate model feeds, and build pipelines using extract, transform and load (ETL) tools before diving into data analysis. As a result, there’s a lesser focus on data analysis. 

Big data discovery efficiently addresses these problems by enabling businesses to:

• Shape and analyze data visually
• Enable users to engage with data
• Refresh or add data to new projects
• Focus on data analysis rather than preparing data up-front

Big data discovery benefits

Big data discovery helps you make sense of data, collaborate with internal and external stakeholders, and answer complex questions. Companies enjoy the following benefits while working with big data discovery.

• Better decision-making: Big data discovery solutions help businesses optimize decision-making, improve customer experience, and drive innovation based on data insights.
• Guaranteed data accessibility: Big data discovery software systems enable even companies without the expertise to access data. Anybody can interpret data with the help of necessary tools.
• Less wasted time and money: Big data discovery platforms automate different parts of the data discovery process, helping companies to save money and spend more time on process optimization based on insights.
• Fewer data risks: Companies employ big data discovery to comply with law regulations and address potential threats in real time.

Data discovery process steps

Maintaining a business results in a massive amount of data from customers, suppliers, and operations. Moreover, companies receive data from online, traditional, and social networks. Data discovery connects all this information so companies feel confident about the business decisions they make. The data discovery process includes the following steps, regardless of whether you use manual or smart techniques.

Define data requirements

The first step is collecting the necessary data, measurements, and metrics for effective analysis. Before analysis, all this data is stored in a data warehouse. 

Businesses use data integration software to gather and connect complex datasets from disparate sources. This free flow among data sources streamlines the standardization of different data formats and integrates data sources efficiently. 

Standardize, clean, and prepare data

Businesses can’t interpret raw data without cleaning and standardizing it. Data cleansing helps companies spot issues like errors, distortion, or corruption. Removing flawed information paves the way to a clean, accurate, and reliable database.

To prevent skewed results, businesses also check the measurement unit at this stage. Some companies manually re-process data to find duplicates or fill in incomplete data.

Share data

At this stage, you share clean data with authorized individuals within your team and organization. They report back with their unique perspectives after a thorough data evaluation. Collaboration helps businesses gain diverse interpretations and study different data aspects. 

Analyze, develop, and visualize data insights

Once you gather different perspectives and have a clean dataset, you’re ready to enter the visualization stage. Data analysts use various tools for analysis during this phase. 

Companies turn this analysis into charts, maps, and graphs so that non-tech stakeholders can easily understand data trends.  They can then accurately define their business goals and the steps to take to meet them.

Improve strategic decisions with insights

Now, you’re ready to act on the insights you extracted. You address patterns and trends to optimize business processes and improve operational decisions. The knowledge you gain from data discovery gives you a competitive advantage so you stay ahead within your industry.

What are data discovery tools?

Data discovery success often depends on the tools you use. However, there isn’t a one-size-fits-all platform. 

Most data discovery tools are designed to execute data preparation, visual analysis, and guided advanced analytics. Across industries, there are limitless ways for business leaders to use them to understand complex data. 

Cloud-based data discovery tools aid you in collecting information from a variety of sources, discover insights, and share them with the rest of the organization. 

Analytics software solutions

Analytics platforms or BI solutions enable businesses to make better decisions with actionable insights. You can use these tools to connect data sources, prepare the analytical environment, and empower non-expert users to find insights faster. Data analysts and scientists use these software systems to dive deep into a company’s day-to-day business activities.

Data preparation software solutions

Data preparation software systems help companies integrate, combine, and analyze data from multiple sources. Data analysts and business users leverage these platforms to combine data from disparate sources and extract actionable insights efficiently.

Some organizations combine data exchange software with analytics solutions and data preparation tools to procure third-party data without changing its meaning. Data exchange platforms use data-as-a-service (DaaS) models to help companies acquire relevant industry data and fuel data-driven decisions.

What to look for in a data discovery tool?

What should data discovery tools do for you? Keep reading to find out.

• Need limited IT support. The key goal of data discovery is to make data accessible to non-IT experts. Your data discovery tool should be able to collect, sort, and display data automatically, using a user-friendly interface.
• Offer extensive customization. An efficient data discovery tool should be able to display select parameters and provide customization based on business requirements. Customizing what you see enables business leaders to quickly get the information they need.
• Ensure easy accessibility. Data discovery is a cooperative, ongoing process. Therefore, cloud data discovery software that promotes interdepartmental collaboration and allows access from any device is essential.
• Feature advanced capabilities. Efficient data discovery tools can pull data from multiple sources and present them in digestible formats using advanced data gathering and refining capabilities.
• Provide intuitive data visualization. Consider choosing a data discovery tool that offers a complete picture of the data without compromise.

Data discovery use cases

Data discovery helps companies in many industries interpret the information they get from complex data. Using AI and machine learning, data discovery uncovers patterns and trends that businesses can use to make better decisions. Below are some examples of how different business areas leverage data discovery.

• Business planning: Data discovery is crucial for businesses looking to make decisions based on data from different business units. For example, the chief finance officer (CFO) may dive deep into each department’s historical data to evaluate past performance and allocate a better budget for the upcoming fiscal year.
• Client prospecting: To persuade your target audience, you need to understand their needs, problems, and aspirations. This is when an efficient data discovery tool comes in handy. The sales team can, for example, utilize a data discovery platform to gather and profile the relevant, available data before contacting leads.
• Social media analysis: Companies use different social media platforms to answer queries and boost customer loyalty. Real-time data discovery points these organizations toward customer behavior trends, resolve customer issues, and addresses complaints.
• Fraud prevention: Online businesses face IT threats and fraudulent activities like internal issues, phishing attempts, and data hacks all the time. Efficient data discovery systems help those companies spot data outliers and address suspicious activities before any damage is done.
• Insurance claims: The insurance industry processes thousands of claims every day. Manual processing leads to longer wait times for patients and a greater risk of errors. Insurance companies leverage data discovery platforms to collect and process insurance claim data, evaluate suspicious claims, and spot potential frauds.

Almost every industry can use data discovery to interpret complex data from different sources, uncover actionable insights, and share them with the rest of the organization.

Data discovery benefits

Data discovery is of utmost importance to enterprises with data across devices and cloud storage software. For customers, employees, and business partners to gain insights and make critical business decisions, you must identify, locate, and classify this data. 

Data discovery intends to prevent sensitive data loss and implement robust security measures as the organization dives deep into this data. Below are other benefits that you can expect from data discovery solutions. 

• Everybody can understand the data. Traditionally, business leaders needed IT and data experts to figure out data insights. Regardless of their data literacy level, all stakeholders can easily interpret data analysis with data discovery tools. This ease of understanding insights helps each business team fulfill their data needs.
• It offers a 360° view of company data. Data discovery software lets you visualize different data streams across the organization for in-depth analysis and insights. A customer-facing company can gather data from its website, social media channels, mobile application, and partners to address customer behavior.
• It improves risk management and compliance. Growing volumes of data force organizations to be proactive about identifying potential risks. Data discovery software solutions allow businesses to spot outliers, stress-test data management practices, and comply with regulations such as California Consumer Privacy Act (CCPA) and General Data Protection Regulation (GDPR).
• You can classify data accurately. Companies can’t discover insights without sorting the data they collect. Data discovery helps them run data classification based on conditions, context, and channel. They can segregate sales and marketing data to find unique customer experience insights.
• It boosts competitive advantages. Businesses trying to improve performance rely on a data-driven approach to evaluate the business landscape and make decisions. That’s why enterprises with high data volumes and velocity must check for flawed inputs. When they don't, they experience garbage in, garbage out (GIGO), the oldest computer technology adage that means incorrect results resulting from incorrect data. This is where data discovery swoops in to help businesses reduce data silos across enterprise resource planning (ERP) tools, customer relationship management (CRM) software, local databases, cloud-based systems, and spreadsheets.
• Data control happens in real-time. Through data discovery, companies can take specific actions on data based on predefined controls and contexts. This real-time data control is crucial for secure and compliant data practices.
• It facilitates actionable plan development and data recycling. Raw data is of no value unless you can extract insights from it. Businesses generally use embedded analytics and BI tools to better understand data patterns and uncover insights for business decisions. Plus, data discovery also helps you reuse old information for future assessment.

Data discovery challenges

Data discovery isn’t free from challenges, so let’s discuss common issues preventing insightful data analysis outcomes.

• Lack of data management policy results in weak data governance implementation and long-term risks such as failure to understand the business landscape. Businesses must create suitable data governance frameworks to maintain data accuracy and consistency.
• Data volume or size can impact analysis outcomes and even result in skewed results if your organization hasn’t implemented a robust data governance policy.
• Data inconsistency happens when two teams don’t work with a single source of truth. As a result, teams suffer from data silos and varied data outcomes.
• Data diversity can be equally challenging when businesses don’t use the same data format to capture or integrate data from different sources. That’s why efficient data discovery requires companies to follow re-defined data points while introducing new data points.

Data discovery best practices

When data grows at an unprecedented speed, you need to keep pace. Following these data discovery best practices helps you protect data amidst the changing cybersecurity landscape and keep sensitive data secure.

• Build a central data catalog: Track data and protect sensitive information from malicious attacks by discovering and centralizing data assets. Consider monitoring sanctioned and shadow data assets across on-premise infrastructure and multicloud environments.
• Extract metadata and identify sensitive data: You can use native connectors and representational state transfer (REST) API to extract metadata from data across warehouses, non-relational data stores, and cloud data stores. Once you discover sensitive information from all assets, you’ll better analyze its business impact.

• Catalog sensitive data and assess risk: Create a sensitive data catalog and consider using data discovery platforms with cataloging capabilities. Gathering sensitive data intelligence helps you assess data risks, concentration, and cross-border transfers. Data discovery platforms help you evaluate and mitigate data risk with overall risk scores.
• Connect data to owners, compliance, and regulations: Connecting sensitive personal data to users helps you fulfill data subject requests (DSR) with minimal lift. You should also use automated data discovery mechanisms to comply with GDPR, CCPA, and present data processing activities when needed.
• Automate when you can: Data discovery isn’t a one-off project. Users at large businesses share and move data constantly because they continually add new data sources. Plus, yesterday’s insensitive data may become sensitive today. Therefore, you should consider automating data discovery and classification to make the whole process more scalable.
• Act fast: Data discovery helps you drive security strategy with risk assessment and remediation prioritization. Deploy control as soon as you detect outliers or suspicious behavior. As a result of this fast response, you’ll be able to boost agility, prevent cyberattacks, and comply with data compliance regulations.

Implement a successful data security and compliance strategy at your organization by combining smart automation, strategic planning, and lightning-fast execution.

Take the wheel with data discovery

You can pave the way for your employees to decode data and find insights if you use the right tools for data discovery. Want to make data insight discovery even easier for your organization's non-IT experts?

Explore how non-technical users can access, visualize, understand, and leverage data with self-service BI tools.