What is Data Discovery and Why Is It Essential for Investors?

Data discovery use cases

The main use cases for data discovery are fraud detection, social media analysis, data completeness, accessibility, compliance, business relationship insights, and lead generation.

Let's take a closer look at some of the more notable data discovery use cases. 

Lead generation

By visually mapping data you are able to find insights that may not have been recognized before. Many sales and acquisition teams utilize data discovery for lead generation.

They can also combine customer data from multiple sources and generate relevant data insights that will enhance lead scoring and lead generation

Investment signals

Data discovery is essential for investors because it allows them to find new investment opportunities. In this case, data discovery revolves around finding and evaluating new startups to invest in. Investors can either conduct manual data discovery or resort to advanced data discovery solutions to generate investment signals.

Firmographic data is a great source of information for investors looking to find new companies. It allows you to easily discover new companies that might otherwise be beyond your scope of reach.

Data points such as company name, location, headcount, industry, revenue, and more open up the option to filter companies according to specific parameters and discover new investment opportunities that fit your ideal company criteria.

Remove the hassle of spending hours on end looking for new investment opportunities and let us present the required data for you. Furthermore, you won't need to worry about the validity and freshness of the company data. It's our primary goal to always keep the data fresh and accurate.

Data completeness

Today's understanding of data quality involves data completeness. Data completeness is realized during the preparation and analysis phases.

Compliance

Because of the broad analysis involved in data discovery, many businesses utilize the process to achieve data compliance with the GDPR (General Data Protection Regulation).

Data discovery methods

Similar to processes such as data normalization and competitive analysis, the data discovery process has been greatly improved by the rise of artificial intelligence and automated tools.

Let's take a look at the two methods of data discovery processes and the reason for the transition from manual to automated discovery.

Manual data discovery

Before the invention of automated data discovery tools, data specialists were required to spend countless hours manually preparing, mapping, and analyzing data.

Today, automated data discovery tools and artificial intelligence work together to speed up this process. Manual data discovery involved the monitoring of metadata and data lineage to unpack trends within datasets.

Extensive knowledge about data categorization and data lineage was required to manually map and organize data during this time.

Automated data discovery

As mentioned above, the rise of automated data discovery due to the technological advancements in automation and AI has greatly influenced the rise of intelligent data discovery as a necessary practice for long-term business success. Automated data discovery is also often known as performing smart data discovery.

Intelligent data discovery consists of data mapping specifications, data flow diagrams, data matrices, and other factors that make up a strategic data approach.

Today, AI is able to visualize and map data using machine learning algorithms in ways that were not possible before. The AI analyzes data relationships and detects patterns that can provide valuable data-driven insight and accelerate business processes in the company.

This advancement has also increased the readability of the discovery process making it a more business user-oriented process, not only suitable for data professionals.

Automation allows sales teams, acquisition experts, and other business users to find relevant data insights.

Linking data classification and discovery

Classification is a more complicated process than data discovery, as it requires additional steps not used in the discovery process. Classification assigns classification labels to data by utilizing predetermined keywords and rules.

Essentially classification involves automated large-scale data tagging, where data experts can establish how they want their data classified. Predetermined keywords and tagging rules are able to classify data across multiple platforms and support the use of networks and clouds.

From this process, businesses are able to increase their data visibility, improve security, and narrow scope.

Here is a closer look at these use cases.

Data visibility

Classification allows you to realize the potential of your datasets. By increasing data visibility, companies are able to find hidden gaps in security, lead generation, and internal organization.

For example, the AI-based labeling process involved in classification may uncover metadata not yet realized by the human eye.

Ultimately, data visibility can unlock the potential of all aspects of your data.

Security and compliance

As previously mentioned security and compliance, such as compliance with the GDPR, are significant concerns of data experts and businesses alike.

Classification of data can help point out compliance gaps and security concerns that may be subject to regulations.

For example, because classification involves the tagging and labeling of data, data scientists are able to program AI-based classification models to tag non-compliant data and security holes.

Narrowing scope

Because there are so many ways to utilize data narrowing the scope of your data is an important component of unpacking a deeper understanding of what your data is telling you.

While data discovery looks at the big picture of your data, classification is able to limit the scope and focus on the prominent aspects of your data.

This allows businesses to focus their time and efforts on the most important datasets and insights.

What are the tools for data discovery?

Because data discovery is not a one-step process as it involves different types of tasks and, as mentioned above, there is more than one approach to the process itself. There are many tools that can be used to discover data. Most of these tools are designed to perform tasks involved in data discovery, like combining data from multiple sources and identifying trends and patterns in said data.

However, there are some useful tips you should keep in mind when choosing these tools.

Firstly, flexibility is key. Look for tools that can seamlessly connect to various data sources. Next, you should consider the ease of use. For example, if needed, can non-technical users effortlessly navigate the tool's interface? The power of data discovery lies in its accessibility to all stakeholders. Additionally, scalability and performance is crucial. As your data needs grow, will the tool be able to handle the expanding volume without compromising performance? Finally, keep an eye out for tools that offer robust security features to safeguard your sensitive information.

Data discovery best practices

Implementing useful and common practices in your data discovery process can enable your organization to harness the full potential of your data, empowering more effective and insightful decision-making. Here are three best practices that you should consider if you want to enhance your data discovery process.

Focus on data quality. This involves ensuring accuracy, completeness, consistency, and relevancy of the data. Quality data leads to accurate insights and confident decision-making.

Enforce data governance. Governance strategies such as defined policies, data ownership, and usage rights will not only protect sensitive information but also lead to better compliance and data usage.

Automate the process. As mentioned above, the automated data discovery process has many advantages to it, one of the key ones being how it makes data discovery a quicker and more business-oriented process.

Summary

In all, data discovery is a powerful process that utilizes all aspects of a dataset, unlocking the potential of your data.

As more and more bytes of data are created it is important for businesses, data experts, and investors to understand that discovery and classification are necessary components of business intelligence and data security.