Top 250 Data Management Interview Questions and Answers

Structured data is organized and easily searchable, while unstructured data lacks a predefined format.

• Structured data is organized into rows and columns, like a database.

• Unstructured data includes text documents, images, videos, and social media posts.

• Structured data is easier to analyze and query, while unstructured data requires more advanced techniques like natural language processing.

• Examples of structured data include customer information in a CRM system, sales data in a...read more

The six data quality dimensions are accuracy, completeness, consistency, timeliness, validity, and uniqueness.

• Accuracy - data is correct and free from errors

• Completeness - data is whole and not missing any parts

• Consistency - data is uniform and follows the same format

• Timeliness - data is up-to-date and relevant

• Validity - data conforms to defined rules and constraints

• Uniqueness - data is distinct and not duplicated

I am using the Extract, Transform, Load (ETL) methodology and implementing it via Syniti.

• I am extracting data from various sources such as databases, files, and applications.

• I am transforming the data to meet the requirements of the target system or database.

• I am loading the transformed data into the target system using Syniti's data integration tools.

• For example, I may be using Syniti Data Replication to replicate data from one database to another in real-time.

Processing semi structured data involves extracting and organizing information from data that does not fit neatly into a traditional database structure.

• Use tools like Apache Spark or Hadoop for processing semi structured data

• Utilize techniques like data parsing, data cleaning, and data transformation

• Consider using NoSQL databases like MongoDB for storing semi structured data

• Examples include processing JSON, XML, or log files

Data migration is the process of transferring data from one system to another.

• It involves identifying the data to be migrated

• Mapping the data to the new system's format

• Extracting the data from the old system

• Transforming the data to fit the new system

• Loading the data into the new system

• Verifying the accuracy of the migrated data

IT assets records can be managed in a database by implementing a comprehensive asset management system.

• Create a centralized database to store all IT asset records

• Develop a standardized naming convention for assets

• Assign unique identifiers to each asset

• Record detailed information about each asset, including specifications, purchase date, warranty details, and location

• Implement a system for tracking asset movements and changes

• Regularly update and maintain the database to ensure...read more

Data backup of laptop/pc can be done using external hard drives, cloud storage, or backup software.

• Use an external hard drive to manually backup important files

• Use cloud storage services like Google Drive or Dropbox to backup files online

• Use backup software like Acronis True Image or EaseUS Todo Backup to automate the backup process

• Create a backup schedule to ensure regular backups are performed

• Test the backup to ensure it can be restored in case of data loss

Data entry involves inputting information into a computer system or database.

• Ensure accuracy and precision in entering data

• Use appropriate software or tools for data entry

• Organize data in a systematic manner

• Verify data for errors before finalizing entry

MDM stands for Master Data Management, which is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.

• MDM helps in ensuring data consistency and accuracy across the organization.

• It involves creating and managing a central repository of master data, such as customer, product, and employee information.

• MDM helps in improving data quality, reducing data redundancy, and streamlining data sharing.

• It en...read more

Master data refers to the core data entities of an organization that are used across multiple applications and business processes.

• Master data is the foundation of an organization's data management strategy

• It includes data such as customer information, product information, and financial data

• Master data is typically stored in a centralized repository and is used by multiple systems and applications

• It is critical for ensuring data consistency and accuracy across the organization...read more

Data retrieval steps in Informatica ETL process

• Identify the source data to be extracted

• Create source and target connections in Informatica

• Design mappings to extract, transform, and load data

• Use transformations like Filter, Joiner, Lookup, etc.

• Run the ETL job to retrieve data from source to target

Data validation processes ensure data accuracy and consistency.

• Performing range checks to ensure data falls within expected values

• Checking for data type consistency (e.g. ensuring a field is always a number)

• Validating data against predefined rules or constraints

• Identifying and handling missing or duplicate data

• Implementing data cleansing techniques to improve data quality

Handling big data involves collecting, storing, analyzing, and interpreting large volumes of data to derive insights and make informed decisions.

• Utilize data management tools like Hadoop, Spark, or SQL databases

• Implement data cleaning and preprocessing techniques to ensure data quality

• Use data visualization tools like Tableau or Power BI to present findings

• Apply statistical analysis and machine learning algorithms for predictive modeling

• Ensure data security and compliance wit...read more

Data lake is a vast pool of raw data while data warehouse is a structured repository for processed data.

• Data lake stores raw, unstructured data in its native format

• Data warehouse stores structured, processed data for easy analysis

• Data lake is used for exploratory analysis and big data processing

• Data warehouse is used for business intelligence and reporting

• Data lake allows for storing large amounts of data at low cost

• Data warehouse provides fast query performance for specific ...read more

I support multiple backup tools depending on the client's requirements.

• I have experience with Windows Server Backup

• I am familiar with third-party tools like Veeam and Backup Exec

• I can also work with cloud-based backup solutions like Azure Backup

• I always ensure that backups are tested and verified for data integrity

Data warehouse (DWH) is structured and optimized for querying and analysis, while data lake is a vast repository for storing raw data in its native format.

• DWH is used for structured data and is optimized for querying and analysis.

• Data lake stores raw data in its native format, allowing for more flexibility and scalability.

• DWH is typically used for business intelligence and reporting purposes.

• Data lake is suitable for storing large volumes of unstructured data like logs, image...read more

Data cleaning issues can be overcome by implementing automated processes, setting clear data quality standards, and regularly monitoring data quality.

• Implement automated data cleaning processes using tools like Python pandas or SQL queries

• Set clear data quality standards and guidelines for data entry to prevent errors

• Regularly monitor data quality and conduct audits to identify and correct any issues

• Utilize data validation techniques to ensure accuracy and consistency of data...read more

Data cleansing is the process of detecting and correcting errors or inconsistencies in data to improve its quality.

• Identifying and removing duplicate entries

• Correcting spelling mistakes and formatting errors

• Standardizing data formats and values

• Handling missing or incomplete data

• Ensuring data is accurate and up-to-date

I have used various data sources including databases, APIs, logs, and files.

• Databases (SQL, NoSQL)

• APIs (REST, SOAP)

• Logs (system logs, application logs)

• Files (CSV, JSON, XML)

Structured data is organized and easily searchable, while unstructured data lacks a predefined format and is harder to analyze.

• Structured data is organized into a predefined format, such as tables or databases.

• Unstructured data does not have a specific format and includes text documents, images, videos, etc.

• Structured data is easily searchable and can be analyzed using traditional methods.

• Unstructured data requires advanced techniques like natural language processing to extra...read more

Json data normalization involves structuring data to eliminate redundancy and improve efficiency.

• Identify repeating groups of data

• Create separate tables for each group

• Establish relationships between tables using foreign keys

• Eliminate redundant data by referencing shared values

I manage data storage by organizing files, utilizing cloud storage, and implementing data backup systems.

• Organize files in a systematic manner for easy retrieval

• Utilize cloud storage services for secure and scalable storage solutions

• Implement data backup systems to prevent data loss in case of emergencies

SCD stands for Slowly Changing Dimensions and lookups in Informatica are used to perform data transformations by looking up data from a reference table.

• SCD is used to track changes to dimension data over time.

• There are three types of SCD - Type 1, Type 2, and Type 3.

• Lookups in Informatica are used to perform data transformations by looking up data from a reference table.

• Lookups can be connected to different types of sources like flat files, databases, etc.

• Example: In a Type 2...read more

Data flows through networks in packets, following a specific path determined by routing protocols and switches.

• Data is broken down into packets before being transmitted over a network.

• Each packet contains information such as source and destination addresses.

• Routing protocols determine the best path for packets to reach their destination.

• Switches forward packets based on MAC addresses.

• Data flows through different network devices like routers, switches, and firewalls.

The phases of CDM are data collection, data cleaning, data analysis, and data interpretation.

• Data collection involves gathering relevant data from various sources.

• Data cleaning involves removing errors, inconsistencies, and outliers from the collected data.

• Data analysis involves applying statistical methods and techniques to analyze the cleaned data.

• Data interpretation involves drawing meaningful conclusions and insights from the analyzed data.

Utilize data management tools, prioritize data based on relevance, and implement efficient data processing techniques.

• Utilize data management tools such as databases, data warehouses, and data lakes to efficiently store and organize large volumes of data.

• Prioritize data based on relevance to the research project or analysis to focus on key insights and reduce processing time.

• Implement efficient data processing techniques such as parallel processing, data compression, and inde...read more

Handling large datasets involves optimizing storage, processing, and analysis techniques.

• Use distributed computing frameworks like Hadoop or Spark to process data in parallel.

• Utilize data compression techniques to reduce storage requirements.

• Implement indexing and partitioning strategies to improve query performance.

• Consider using cloud-based storage and computing resources for scalability.

• Use sampling techniques to work with subsets of data for initial analysis.

Backup strategies are plans and procedures put in place to protect data in case of loss or corruption.

• Regularly scheduled backups to ensure data is up to date

• Offsite backups to protect against physical damage or theft

• Incremental backups to save storage space and time

• Automated backups to reduce human error

• Testing backups to ensure they can be restored successfully

Incremental refresh is a process of updating only new or changed data in a dataset.

• Identify the key columns that can be used to track changes in the data

• Use date or timestamp columns to filter out new or updated records

• Implement a process to regularly check for new data and update the dataset accordingly

Incremental data loading is the process of adding new data to an existing dataset without reloading all the data.

• It involves identifying new data since the last update

• Only the new data is added to the existing dataset

• Helps in reducing processing time and resource usage

• Commonly used in data warehousing and ETL processes

To reduce data size, I would use techniques like data compression, data aggregation, and data summarization.

• Utilize data compression techniques such as ZIP or GZIP to reduce file size

• Aggregate data by grouping similar data points together

• Summarize data by creating averages, totals, or other statistical measures

• Remove unnecessary columns or rows from the dataset

• Use data deduplication to eliminate duplicate entries

CSV flow is a process of importing and exporting data in CSV format.

• CSV stands for Comma Separated Values

• Data is organized in rows and columns

• CSV files can be opened in Excel or any text editor

• Example: Importing customer data from a CSV file into a database

• Example: Exporting sales data from a database to a CSV file

I handle an average of 50 collection files per month.

• On average, I handle 50 collection files per month.

• The number of files may vary depending on the month and client needs.

• I prioritize timely and accurate collection of debts.

• I maintain detailed records of all collection activities.

• Examples of files include credit card debts, medical bills, and utility bills.

There are different types of backups, including full, incremental, and differential backups.

• Full backup: A complete backup of all data and files.

• Incremental backup: Only backs up the changes made since the last backup.

• Differential backup: Backs up all changes made since the last full backup.

• Scheduled backups: Regularly scheduled backups to ensure data is protected.

• Offsite backups: Storing backups in a separate location for disaster recovery.

• Cloud backups: Backing up data to r...read more

MDM (Master Data Management) is used in various industries for managing and integrating data from multiple sources.

• MDM helps organizations maintain a single, accurate, and consistent view of their data across different systems and applications.

• In healthcare, MDM can be used to ensure accurate patient records and facilitate interoperability between different healthcare providers.

• In retail, MDM can help manage product information, pricing, and inventory across multiple channels...read more

MDM tools are used for managing and governing master data across an organization.

• MDM tools help in creating a single, reliable source of master data.

• They enable data integration and synchronization across multiple systems.

• MDM tools provide data quality management and data governance capabilities.

• Examples of MDM tools include Informatica MDM, IBM InfoSphere MDM, and SAP Master Data Governance.

Data mapping involves linking data fields from one source to another, ensuring data accuracy and consistency.

• Experience in identifying data sources and destinations

• Ability to create data mapping documents

• Knowledge of data transformation and validation processes

• Experience with tools like Excel, SQL, or data mapping software

• Ensuring data integrity and quality throughout the mapping process

MDM activities are crucial for effective procurement management.

• MDM activities ensure data accuracy and consistency across all systems.

• They help in identifying and eliminating duplicate or outdated data.

• MDM activities also enable better decision-making and cost savings.

• Examples of MDM activities include data cleansing, data governance, and data integration.

• MDM activities require collaboration between IT and procurement teams.

A list of questions related to Informatica MDM and its processes.

• Automerging jobs in Informatica MDM

• Defining trust settings for base objects

• Loading data into Siperian Hub

• Match rules and tokenization in Informatica MDM

• Data loading stages and components of Informatica Hub Console

Data Warehouse design involves structuring data for efficient querying and analysis.

• Identify business requirements and data sources

• Design dimensional model with facts and dimensions

• Implement ETL processes to load data into the warehouse

• Optimize queries for performance

• Consider scalability and data governance

Clinical data management involves several phases including data collection, processing, analysis, and reporting.

• Data collection involves gathering information from various sources such as electronic health records, case report forms, and laboratory results.

• Data processing includes cleaning, organizing, and transforming the collected data into a usable format for analysis.

• Data analysis involves applying statistical methods and algorithms to extract meaningful insights from the...read more

Common types of backups include full, incremental, differential, and snapshot backups.

• Full backup: A complete copy of all data in the system.

• Incremental backup: Only backs up data that has changed since the last backup.

• Differential backup: Backs up all changes since the last full backup.

• Snapshot backup: Captures the state of the system at a specific point in time.

Yes, I am familiar with backups.

• I understand the importance of regular backups to prevent data loss.

• I am experienced in setting up and managing backup systems.

• I am knowledgeable about different types of backups, such as full, incremental, and differential backups.

• I am familiar with backup software and tools, such as Veeam, Acronis, and Backup Exec.

• I am aware of best practices for backup storage and retention, including offsite backups and disaster recovery plans.

Plan data migration by assessing current data, creating a migration plan, testing the migration process, and executing the migration.

• Assess the current data in both data centres to determine the scope of migration

• Create a detailed migration plan outlining the steps, timeline, resources, and potential risks

• Test the migration process in a controlled environment to identify and address any issues

• Execute the migration according to the plan, monitoring progress and ensuring data i...read more

Implementing a Data Governance framework involves defining policies, procedures, and roles to manage data assets.

• Identify stakeholders and their roles in data governance

• Define policies and procedures for data management

• Establish data quality standards and metrics

• Implement data security and privacy measures

• Create a data catalog and inventory

• Monitor and enforce compliance with data governance policies

• Continuously review and improve the data governance framework

DM is required for effective management of resources, decision making, and achieving organizational goals.

• DM helps in setting goals and objectives for the organization

• It helps in allocating resources effectively

• It aids in making informed decisions based on data and analysis

• DM ensures that the organization is moving towards its goals and objectives

• It helps in identifying and addressing problems and challenges

• For example, a retail store manager may use DM to decide on the best ...read more

We use a combination of on-site and off-site backups with regular testing and verification.

• We use a mix of physical and cloud-based backups to ensure redundancy.

• We perform regular backups on a daily, weekly, and monthly basis depending on the criticality of the data.

• We conduct periodic testing and verification of backups to ensure data integrity and recoverability.

• We have a disaster recovery plan in place that includes backup and recovery procedures.

• We ensure that backups are...read more