Data Management
Data Management
Data Management
Data management is the practice of managing data for access, integration, and visualization for the benefits of an organization. Managing data effectively requires having a data strategy and reliable methods to access, integrate, cleanse, govern, store and prepare data for analytics. As enterprises grow, data is collected and created from many sources – operational and transactional systems, scanners, sensors, smart devices, social media, video, and text. However, the value of data is not based on its source, quality, or usability.
“Data, the foundation for predictive and prescriptive analytics”
Historically, the data was created and accessed for operational purposes, however, in the past 2 decades the value of data is recognized in its ability to provide analytics for reporting and decision making. Here are some types of data and databases in use:
What do we offer?
Data Visualization - Ability to access and visualize data across platform and applications from any source (databases, emails, social media feeds, notes, etc.) which can be utilized for efficient business processes and reporting requirements (i.e., data studio)
Data Preparation - Combining, cleansing and transforming data to make it usable for data analytics needs is the important task. Data preparation automation tools help cleaning the data and our analysts can help fix the source of dirty data (i.e., Dataprep).
Data Integration - Process of combining data to make it more useful for data warehouse (DWH) for analytics and reporting needs. With data integration tools you can automate this process (ETL - extract, transform, load, ELT - Extract, load and transform)
Data Quality - Practice of ensuring data is correct, usable and can be relied upon is data quality. As the data is created, accessed and shared across the organization, it is important to assess and validate the data quality.
Data Governance - Logicbulls work with our clients to develop a framework for data governance across people, process and tools/technologies. Further using data governance tools, you can define checkpoints, rules and toll gates to ensure the data integrity and data management.
Augmented Data Management - Our experience in cloud and traditional on-premises hosting environments provides added advantange when it comes to artificial intelligence or machine learning techniques to make processes like data quality, metadata management and data integration self-configuring and self-tuning (i.e., dataprep, bigquery)
Data Management Use Cases:
Let us know what data management problems you are facing and what is your end-game with data management, analytics and reporting.