Your enterprise is swimming in data. But even in the era of big data, your data’s only useful if your business users can get their hands on the data they need when they need it… and then understand what it’s telling them.
Data discovery is an aspect of data management that involves collecting, evaluating, and connecting data from a variety of sources, cleaning and preparing that complex data, sharing it across the organization, and performing analytics to gain insight into business processes. Data discovery enables a dynamic understanding of your data based on how it’s ingested, stored, aggregated, and used.
And the data discovery process empowers your business users and data specialists to get the right data at the right time when they’re making important decisions for your organization.
Data discovery is closely related to data classification, e.g. usefulness, sensitivity, or security requirements. It also plays an important role in creating easy-to-digest business intelligence (BI) insights, empowering even non-technical users to help an organization derive insights and inform data-driven business decisions.
Also important: data discovery tools help organizations understand how they process, manage, maintain, and transfer sensitive data to ensure that they’re in compliance with privacy laws and regulations, including the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
Why is Data Discovery Important?
Data discovery provides domain-specific, dynamic understanding of your data from various sources based on how it’s ingested, stored, aggregated, and used by specific consumers.
It can help answer questions like:
What data set is most recent? Which data sets can be deprecated?
When was the last time a table was updated?
What is the meaning of a given field in my domain?
Who has access to this data? When was the last time this data was used? By who?
What are the upstream and downstream dependencies of this data?
Is this production-quality data?
What data matters for my domain’s business requirements?
What are my assumptions about this data, and are they being met?
In short, data discovery gives data scientists and business leaders an opportunity for data exploration and an under-the-hood look at their systems and operations. This, in turn, lets them better understand their business challenges, then overcome them by making more-effective, data-driven decisions via predictive analytics.
Beyond this, when analyzed, the raw data businesses collect about their customers, partners, operations, etc., becomes knowledge. Data discovery helps them to turn this knowledge into a competitive advantage.
Types of Data Discovery
There are three main types of data discovery, and they work in concert to uncover data insights, identify security issues, and provide data analysis via easy-to-understand visual dashboards. When completed and combined with business intelligence (BI) software, these steps result in a top-down view of a company’s data in a user-friendly format.
Data preparation is the cleaning, reformatting and merging of data from different data sources across the organization so it can be analyzed in a consistent format. Steps taken to prepare data for analysis include deduplication, deleting null values, detecting outliers, and generally ensuring only high-quality data is used for business analysis. Technological advancements now allow for much of this work to be done via artificial intelligence.
Data visualization is one of the most effective tools data and business leaders can use to turn their data into knowledge and understand what can be gained from its analysis. Most often presented in the modern enterprise via a data dashboard, visual analytics help non-technical users understand their various data and derive business insights from them.
Data analysis uses both descriptions and interactive visuals to paint a complete picture of a company’s data in a succinct and easily understandable format. Advanced analytics empower business leaders to look beyond the data itself to see the wider implications of their data discovery efforts, uncover deep insights about their organization, and ensure accuracy in crucial business decision-making.
What Are the Benefits of Data Discovery?
Data discovery provides businesses with a complete picture of their data, enabling a big-picture view of the many data streams within their enterprise, and allowing them to uncover new insights while formulating solutions to their business challenges. It also makes data analytics understandable for stakeholders across the business, regardless of their level of data literacy.
Furthermore, data discovery helps businesses identify potential threats in their data so they can be more proactive in regard to risk management and data security. And it allows companies to apply specific real-time actions to the data they collect, ensuring it is stored and analyzed in accordance with organizational and legal guidelines, and that their data governance practices are secure and compliant.
Other benefits of data discovery include:
Empowering self-service discovery and automation, allowing users to easily find and leverage data without a dedicated support team.
Leveraging machine learning to gain a bird’s eye view of your data assets as they scale, ensuring that your understanding adapts as your data evolves.
Surfacing the right information at the right time and drawing connections between data assets.
Enabling dynamic discovery and a high degree of reliability across your data infrastructure, regardless of domain.
Process for Data Discovery
Whether performing manual data discovery or using more-advanced data discovery software, the process usually boils down to five steps:
Understanding what data is needed
Locating the sources that will provide that data
Setting up a search query within the data
Determining the relevance of data sources, eliminating irrelevant data, and refining search queries
Evaluating the quality of the results
In the past few years, this process has become significantly more efficient thanks to technological advances and the emergence of more-powerful artificial intelligence algorithms. According to Gartner, “smart data discovery” — “a next-generation data discovery capability that provides business users or citizen data scientists with insights from advanced analytics” — is the latest advancement in this arena.
Schedule a demo to learn more about how data.world’s knowledge-graph-powered data catalog can enable data discovery at your organization.