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How is Data Discovered? – Process

History of Data Discovery

Common Data Discovery Challenges

The data discovery cycle is a dynamic process that characterizes how organizations repeatedly improve their technique of elaborate insights drawing from data.

1. Define the Subject

  • This beginning step is to set the goal/question you are looking to respond to through data discovery very explicitly.
  • To do this one should determine those information sources that exist in the organization. This may include databases, spreadsheets, customer relationship management (CRM) systems, or even external one.

2. Data Collection

  • This step requires you then to put together these data sources.
  • It could be by harvesting data, making it a workable form, and confirming that it is in a common structure among different sources.

3. Data Cleaning and Preparation

  • Raw data frequently has erroneous inputs, inconsistency, or missing data. This category deals with the cleaning and readying of the data to ensure its exactness and safeness for analysis.
  • Techniques of data cleaning might be identifying and fixing errors, dealing with missing values and transforming data from inconsistent format to a uniform one.

4. Data Analysis and Exploration

  • This is the magical part of the whole process!Your job will be about conducting analysis of the pre-processed data which can reveal patterns, trends, and relationships that are of worth investigating.
  • At this stage, data visualization tools and statistical techniques are the most usual means of examining the data and revealing hidden trends from various perspectives.

5. Communicate Findings and Iterate

  • The next step is to decipher what the data means and then to share your interpretation with the most relevant stakeholders through a simple and concise language.
  • It might entail coming up with reports, dashboards, or presentations that enable you to cogently explain the insights you have gathered.
  • The data discovery process operates iteratively. Per the knowledge outcomes from the analysis exercise, you may need to revisit your initial research questions, restep the process, or collect new data for additional analysis.

What is Data Discovery?

Data discovery is a pivotal step in the data analysis and business intelligence process, allowing organizations to make informed decisions, achieve dynamic growth, and stay competitive in the marketplace.

Table of Content

  • What is Data Discovery?
    • Key Aspects of Data Discovery
  • Why is Data Discovery important ?
  • Categories of Data Discovery
  • History of Data Discovery
  • How is Data Discovered? – Process
    • 1. Define the Subject
    • 2. Data Collection
    • 3. Data Cleaning and Preparation
    • 4. Data Analysis and Exploration
    • 5. Communicate Findings and Iterate
  • Common Data Discovery Challenges
  • How to Overcome Common Data Discovery Challenges?
  • Data Discovery Use Cases
    • 1. Business Intelligence (BI) and Reporting
    • 2. Customer Analytics
    • 3. Fraud Detection and Security mechanisms
    • 4. Supply Chain Optimization
    • 5. Healthcare Analytics
  • Conclusion

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What is Data Discovery?

Data Discovery is the process of identifying patterns, trends, and insights within a meaningful dataset. It includes collecting data from various types of sources and then applying an advanced Data Analytical technique for identifying the patterns and themes within the collected dataset....

Why is Data Discovery important ?

Data discovery has a lot of importance for many reasons particularly in modern data driven decision, problem solving & business intelligence. The following are some key factors explaining why Data Discovery is Important:...

Categories of Data Discovery

There are two main categories of data discovery:...

History of Data Discovery

The history of Data Discovery is closely associated with the development of data analytics. In the 1970s, Business Intelligence became popular, promoting the use of data to support decision-making. The Data Warehouses were developed in the 1990s, which aimed at collecting data for proper analysis. By the time 2000s arrived, technology had developed highly efficient analytical approaches like data mining and predictive modeling. After 2010, data discovery platforms with specific functionality became popular. They offered a full solution for data preparation, visualization, and discovery. This continuation brought in the 2010s with the Big Data era, necessitating such powerful tools and techniques. Today (roughly around the mid-2010 to the present), AI and ML are revolutionary and therefore, by automating insights generation and employing intelligent processes, data discovery is becoming enhanced....

How is Data Discovered? – Process

The data discovery cycle is a dynamic process that characterizes how organizations repeatedly improve their technique of elaborate insights drawing from data....

Common Data Discovery Challenges

Data Quality and Consistency issues: Inaccuracies, inconsistencies, and incomplete data across various sources can hinder the accuracy and reliability of insights gained during the data discovery process misleading conclusions and compromised decision-making due to unreliable data. Data Security and Privacy: Ensuring compliance with data privacy regulations and securing sensitive information poses a significant challenge during data discovery, especially with the increasing focus on data protection. Data Integration Complexity : Combining and integrating diverse data sources with varying formats and structures can be complex, leading to difficulties in creating a unified view for analysis. Scalability Issues: As data volumes continue to grow exponentially, scaling up data discovery processes becomes a challenge, impacting performance and responsiveness leading to slower analysis, increased processing times, and potential system overload in handling large datasets. Lack of Standardization: Absence of standardized data formats, definitions, and terminologies across different departments or sources can create confusion and hinder effective collaboration. Limited Data Governance: Inadequate data governance practices, including the absence of clear data ownership, stewardship, and documentation, can result in uncontrolled and unmonitored data access. Technology Integration Challenges: Implementing and integrating new data discovery tools and technologies within existing IT infrastructure can be challenging, leading to compatibility issues and disruptions....

How to Overcome Common Data Discovery Challenges?

Overcoming common data discovery challenges with the modern data stack helps implementing effective strategies to navigate and extract insights from vast and complex datasets. One key challenge is the sheer volume of data generated, requiring organizations to adopt advanced data discovery tools and technologies that can efficiently sift through and analyze large datasets. Some of the Modern data discovery tools features that effectively address the challenges associated with data discovery:...

Data Discovery Use Cases

Data discovery empowers organizations across various industries to unearth valuable insights, make informed decisions, and boost overall efficiency. Here’s a closer look at some prominent use cases:...

Conclusion

Therefore, data discovery is a crucial step in the broader process of data analysis & business intelligence. It helps organizations or businesses to make informed decisions, making them dynamic, also leading to continuous personal growth and development which is required by an organization to compete well in the marketplace. That is why data discovery is so important....

Data Discovery- FAQs

What are the methods of data discovery?...

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#AI-ML-DS #Data Analysis

History of Data Discovery

Common Data Discovery Challenges