Data discovery is a business-user driven process of extracting actionable patterns and outliers from data. It involves collection and analysis of data from various sources to gain insight from hidden patterns and trends. Data discovery tools use a variety of methods such as pivot tables, pie charts, heat maps, bar graphs and geographical maps to help users accomplish their goals.
Benefits of Data Discovery
Data discovery helps transform messy, unstructured, raw data to improve its analysis. It allows:
- Actionable insights: Data discovery takes complex data and organize it in ways which allows visualization of the information within it.
- Agility: Data discovery gathers and formats data from various sources and structures to accelerate its analysis. It thus provides analysts with the right data in the right format. All this can be done without relying on deep technical or statistical knowledge.
- Reusability: As the new set of data is collected, it needs to be cleaned, stored, and made available for future use. Data discovery allows both new and past data to be reused at scale.
- Flexibility: Data discovery can help use the same data in different ways to create unique insights. It facilitates a single version of data to be used across different teams.
By 2020, it is estimated that for every person on earth, 1.7 MB of data will be created every second.
Pitfalls in Data Discovery
There are some restrictions that may affect the process of data discovery. Some of these are:
- Poor data quality: A poor quality of data hinders the ability to deliver customer-centric value. To avoid this, planning, searching and segmenting the fields before time ensures the expected value delivery.
- Data management: Mismanaged data can introduce crashes into the data discovery process. Inaccurately collected and stored data can introduce errors into an analysis without the user’s knowledge.
- Data volume: Data can be enormously huge which can hamper the analysis. Strong data governance and capable technology can help overcome this challenge.
- Data variety: Data volume leads to increased variety of data formats that creates a challenge in consistent data. Proper technical skills to gather and clean data is necessary to analyze and consume the data for a successful data discovery process.
- Data consistency: Inconsistent data can result in inadequate decisions based on old or invalid data. It is important that data has a single version where it can be edited, extracted and analyzed on a regular basis.
Each minute, 300 new hours of video show up on YouTube. Facebook users send 31 million messages and view 2.7 million videos every minute, making a total of more than 100 terabytes.
Steps to a good data discovery process
A good data discovery involves various processes that helps in extracting meaningful visualization and insights from the data.
- Mix diverse data: Data can come from a variety of sources, both in structured and unstructured formats. The first step is to store the data in a single right place where analysis can be done. While independently stored, data should be blended and treated as one single piece.
- Normalize data: Data needs to be cleaned and structured using different techniques and tools in order to facilitate proper analysis.
- Data discovery model: A strategic approach to use the data involves collection, curation, and analysis to drive proper insights for further use. In terms of approach, data discovery model includes the use of diagrams, symbolic references, and textual information such as data mapping specifications, data matrices and data flow diagrams.
- Data visualization: A proper visualization of data can help proper communication or narrative of insights generated from the data. It can be done in various formats like heat maps, scatter plots, pie charts, bar graphs or even a simple textual presentation.
Visual analysis is an important feature for decision-makers to absorb and act on data. Furthermore, advanced data analytics provides statistical information which results in sophisticated and pattern oriented data analysis.