Data discovery

What is data discovery?

Data discovery is the process of collecting data from various sources, categorizing them, and preparing them for analysis. Think of all the structured, unstructured data lying around in an organization. Where does the data come from and reside? Who has access to them, how they are being managed and processed? Data discovery becomes the foundation if you want to find answers to all these questions.

Through data discovery, you not only ensure data security and governance but also help data reach non-technical users in a form they could understand and act on.

Data discovery is a must for business analysts, data scientists, sales and marketing teams, product managers, and decision makers to derive actionable insights, understand markets, and gain high-level insights on business performance.

Steps involved in data discovery

Data discovery involves the following 4 to 5 steps. It’s often a cycle that the organization must repeat to extract more value and valuable information.

Data collection: this is the first step in data discovery, which means aggregating and collecting data from the identified data sources. It also means that you analyze what data sources are required for analysis. Example: transactional data, survey feedback, CRM data, etc.

Data profiling: Second step of data discovery is conducting data profiling to assess the data quality, consistency, and completeness.

Data exploration: exploration involves using visualization and analysis tools to learn about the intrinsic patterns and characteristics of data. It can also tell if there are any anomalies that fall out of range.

Data transformation: the fourth step involves transforming data into a suitable format, removing duplicates, standardizing formats, and more.

Visualization and reporting: The last stage is converting the data into visual reports for data absorption, so the team could discover insights within data. This statistical analysis part gets the story out of the data, in the form of story, description, or visual charts.

Data discovery example

Let’s assume a retail company trying to understand its audience and improve their eCommerce shopping experience. A series of data discovery steps the company performs to achieve this goal:

• Collecting data from PoS systems, eCommerce platforms, customer feedback, etc.

• Profiling to standardize the data, remove redundancies, and ensure data consistency using data profiling tools like DataRefine.

• Using visualization tools like PowerBI or Tableau for data exploration and customer journey analysis.

• Aggregation and transformation of customer data and product categories to find hidden patterns. Creating new entries with values like maximum value per customer, average time spent, etc.

• Further deepen analysis to find areas of improvement and find values like most-visited customers, churn, etc.

• Turn identified into reports and dashboards and identify the impact that can help improve eCommerce shopping experience.

Why is data discovery important?

Better decision making: data discovery improves decision making across all levels by bringing out hidden patterns that aren’t visible to naked eyes.

Increased data adoption: more users from tech and non-tech sides start using data and are able to back up their decisions with data. 

To have an end-to-end view of your data: data discovery provides 360 degree visibility of your data, bringing in all data sources in one central place.

Supports data governance: every industry has regulatory requirements to meet concerning data quality, data handling, whether it’s used correctly or not, etc.. Data discovery supports this process, helping with compliance checks and audits.

Data discovery use cases

Data discovery has many use cases across industries and functions. 

Customer personalization: analyzing customer data and segmenting that based on demographics, preferences, age group, and more will help marketing teams run personalized campaigns and increase ROI, which requires data discovery. 

Smart inventory management: data discovery is a must for any logistics and inventory units that manage inbound and outbound inventory in real-time and align that with sales and demand.

Product development: trying to find areas of improvement to develop a resilient product through observing market trends, customer feedback, and competitive analysis. Data discovery could streamline the information flow from all directions, filtering out useful insights.

Healthcare diagnostics: analyzing patient data and medical records to identify disease spread and symptom patterns for early detection and cure.

Financial forecasting: financial, transactional, and sales data discovery to forecast future sales accurately and plan operations ahead.  

Fraud detection: analyzing tons of transactions in real-time, looking for suspicious behavior to prevent fraudulent transactions.

From traditional data discovery to augmented analytics

While the above data discovery example happens with traditional data discovery cases, modern organizations are more equipped when it comes to data handling. This includes availability of self-serve analytics and augmented analytics platforms, where the business user access up-to-date information on their own, depending on their access levels. Through intuitive interfaces, they could send voice or chat comments and interact with data. Together, it fosters a data-driven environment across all levels of the company, despite the growing volumes of data.