Data Management

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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:

Raw data - emails, text files, notes, manual scans and other direct source of data from end users
Batch processing and extract, transform, load (ETL).
Structured query language (SQL) and relational database management systems (RDBMSs)
Not-only SQL (NoSQL) and nonrelational databases
Enterprise data warehouses, data lakes and data fabrics and BigData
Data catalogs, metadata management and data lineage
Cloud computing and event stream processing (data streaming)

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:

Artificial intelligence (AI) and machine learning (ML) -
Enterprise processes rely on AI, which is the science of learning systems to simulate human tasks through learning and automation. For example, AI and ML techniques are often used in making supply chain decisions, distribution issues in retail. With AI and ML, it’s more important than ever to have well-managed data that you understand and trust – because if bad data feeds algorithms that adapt based on what they learn, mistakes can multiply quickly.
Internet of Things (IoT) -
The data published by remote sensors embedded in IoT devices is often referred to as streaming data. Data streaming, or event stream processing, involves analyzing real-time data on the fly. This is accomplished by applying logic to the data, recognizing patterns in the data and filtering it for multiple uses as it flows into an organization. Our IoT team can work with your team to develop IoT architecture for data ingestion, data processing, data visualization and reporting
Data fabric and integration layer -
The term data fabric describes an organization’s open data landscape – where vast amounts and types of data are managed, processed, stored and analyzed, using a variety of methods. The integration layer plays an important role in the data fabric. Like a business glossary, the integration layer is a way to link data to commonly defined business terms used across the organization.
Data management and open source -
Open source refers to a computing program or infrastructure in which the source code is publicly available for use and modification by a community of users. Using open source can speed development efforts and reduce costs. And data professionals can thrive if they can work in the programming language and environment of their choice.
Data federation -
Data federation is a special kind of virtual data integration that lets you look at combined data from multiple sources without needing to move and store the combined view in a new location. So, you can access combined data exactly when you request it. Unlike ETL and ELT tools that show a snapshot at a point in time, data federation generates results based on what the data sources look like at the time of the request. This gives a timelier and potentially more accurate view of the information.

Let us know what data management problems you are facing and what is your end-game with data management, analytics and reporting.

Testimonials

How our services bring about success

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