What is Data Discovery?
January 15, 2021
Unlocking the power of data has become more and more popular among business experts and investors. With a proven track record, data has shown to generate leads, provide insights, and predict market trends in a way never before seen. Additionally, BI-Survey projects that 80% of the data-driven industries will utilize data discovery over the next few years, meaning it is more important than ever for businesses to protect and organize their data. This is where data discovery comes into the picture.
Data discovery is a data collection process that involves gathering data from various databases, cataloging said data, and classifying the data for evaluation and analysis. The data discovery process provides security and organizational solutions, among other things. This article will explore the process of data discovery, its use cases, as well as the beneficial linkage between data discovery and data classification. Let’s start by taking a closer look at the data discovery process.
The data discovery process
Similar to organization-based analysis processes, such as data aggregation, data discovery is an ongoing process that involves detecting patterns, outliers, and errors throughout large structured and unstructured datasets. Ultimately, data discovery requires four main steps: preparation, visualization, and analysis. These steps continually work together to provide hidden insights, potential security breaches, and visual mapping.
The first step is essential for a quality data discovery process. Data preparation rearranges the data so that the visualization and analysis portion of data discovery can run more smoothly. Without preparation, the data will be too convoluted to properly uncover any hidden business insights. Data preparation essentially cleans and merges the data quality within the datasets being examined. Today, there are many types of software that provide data preparation in addition to other discovery and classification tools. These automated tools are able to erase outliers, unify data formats, detect null values, and standardize data quality.
Once all of the data points have been transformed into a consistent and readable format the data then enters the visualization stage. Visualization, also referred to as data mapping, displays the prepared data into visual formats such as charts, graphs, maps, etc. to provide business experts with broader insights. These visual guides, resulting from data mining, data preparation, and sorting, display the primary trends found in the dataset being processed. Arguably, data visualization is the most important step for data discovery, as it is a foundational aspect of AI-based business intelligence.
After the mapping and visualization process is complete, the data is then analyzed so it can be summarized and organized into a succinct readable format. Oftentimes the analysis process involves summarization in the form of descriptions. It is important to note these descriptions aren’t necessarily complete sentences that suggest consequential information. Rather, instead of a spreadsheet, analysis aims to describe the implications of visualization and provides descriptive statistics that make the data more readable and almost story-like.
Similar to other data analysis processes, data discovery is iterative. According to Baseline, nearly 90% of all data was created in the past few years, and this rate is expected to increase exponentially. For businesses, this means that in order to unleash your data’s potential this process must be repeated and built upon regularly.
Data discovery use cases
The main use cases for data discovery are fraud detection, social media analysis, data completeness (data quality), accessibility, compliance, business relationship insights, and lead generation. Let’s take a closer look at some of the more notable data discovery use cases.
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.
Today’s understanding of data quality involves data completeness. Data completeness is realized during the preparation and analysis phases.
Because of the broad analysis involved in data discovery, many businesses utilize the discovery process to comply with the GDPR (General Data Protection Regulation).
Data discovery tools
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 and the reason for the transition from manual to automated discovery.
Manual data discovery
Before the invention of automated discovery tools, data specialists were required to spend countless hours manually preparing, mapping, and analyzing data. Today, automated data discovery tools and AI 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 data discovery as a necessary practice for long-term business success. Today, AI is able to visualize and map data in ways that were not possible before. This advancement has also increased the readability of the discovery process so that sales and acquisition experts can find insights.
Linking data classification and discovery
Data 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 supports 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 data classification use cases.
Data 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. Data classification 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.
Because there are so many ways to utilize data narrowing the scope of your data is an important component to 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 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.
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 a necessary component of business intelligence and data security.
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