Data management tools

Together, information systems and data management help tackle the challenges of Big Data, real-time analytics, data modeling, and the overall smart use of information. The emergence of large, diverse, and publicly available data poses the challenge of using the best methods that lead to the effective use of information systems, which is imperative to develop a holistic view of information.

Information systems and data management govern the two chief elements that are the pillars upon which they are built: data and information. In a previous article , we discussed the key differences between data and information and the unique connection they share. In essence, data is meaningless, but once it is processed, interpreted, formatted, and organized, it becomes information as value is extracted with a specific goal and purpose. Massive amounts of data require a powerful data management and information system in place in order to process and analyze it to glean value and derive information that leads to smarter business decisions.

Here, we describe some of the fundamental management approaches that exemplify the role of information systems in data management. The practice of orchestrating data integration across the cloud to deliver data management functions such as backup, disaster recovery, archival, search, analytics, and more, in a single, unified, run-anywhere cloud platform.

Approach used to define and manage critical data in a unified way to provide a single point of reference and avoid redundancy on an organizational level. Mastered data may include reference data and analytical data that supports decision-making. Derived from master data management, reference data management defines permissible values that can be used by other fields.

Reference data can be internally or externally mandated. Extract, transform, load ETL is the procedure of copying data from one or multiple sources into a destination system, a data warehouse, which presents data in a different format that is ideal for high-performance analysis. Data integration combines technical and business processes to combine data from varied sources to transform it into meaningful and useful information.

The practice of performing advanced data analytics to process selected data from big data sources and data warehouses to present it in a visual context and help convey the significance of said data. In essence, these tools are heterogeneous multi-platform management systems that harmonize data. Oracle Data Management Suite: Comprehensive platform that delivers a suite of solutions that enable users to build, deploy, and manage data-driven projects by delivering consolidated, consistent, and authoritative master data across an enterprise and distributes this information to all operational and analytical applications.

It enables data governance and quality, policy compliance, repeatable business processes, cross-functional collaboration, and change awareness throughout the enterprise. SAP Data Management : Integrated technology platform that uses a single point to access all data, whether transactional, analytical, structured, or unstructured, across on-premise and cloud-based solutions.

It provides access to metadata management tools to enable an intelligent data management process by taking advantage of the cloud benefits, which include low cost of ownership, elasticity, serverless principles, high availability, resilience, and autonomous behavior. IBM Infosphere Master Data Management Server : A comprehensive tool that helps manage enterprise data to present it into a single trusted view and deliver analytic capabilities.

It includes a security system, transaction control, multi-domain support, event management and data quality analysis. It manages all aspects of critical enterprise data, regardless of system or model, and delivers actionable insights, instant business value alignment, and compliance with data governance, rules and policies across an enterprise.

IBM Infosphere orchestrates data throughout the complete information lifecycle. Data can be organized in models, it can be updated by creating rules, and it can include access controls to authorize who updates the data. It provides service-oriented architecture endpoints using Windows Communication Foundation WCF and it implements a hub architecture using MDS to create centralized and synchronized data sources to reduce data redundancies across systems.

Dell Boomi: Enterprise-grade platform that is unified and versatile, leveraging all the advantages of the cloud. The platform is designed to provide ease of use and high productivity by:.

Talend: Single, open platform for data integration, data management, enterprise application integration, data quality, cloud storage, and Big Data across cloud and on-premise environments. It helps transform data into business insights to help companies make real-time decisions and become data-driven. Tableau: Interactive data visualization solution that helps users see and understand data.

It helps simplify raw data into an easily understandable format for smart data analysis. Visualizations are created in the form of dashboards and worksheets through its key features that include data blending, real-time analysis, and data collaboration. Amazon Web Services - Data Lakes and Analytics: Integrated suite of services that provide the necessary solutions to build and manage a data lake for analytics.

AWS-powered data lakes are capable of handling the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights.

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This cookie is installed by Google Analytics. This cookie is set by Snitcher B. Used by Google DoubleClick and stores information about how the user uses the website and any other advertisement before visiting the website. This cookie is set by doubleclick. Here are a few salient features of Ataccama ONE:. Data Visualization Tools allow you to view your data in a pictorial format like graphs and charts , which makes it easier to draw coherent insights from it thus simplifying the analytical process.

Tableau is a BI platform that helps people see and understand data with a belief that Data Analysis should focus on asking the right questions to extract meaningful insights with built-in visual practices. Salient features of Tableau are as follows:. Looker is another Cloud-based visualization and analytics platform.

This allows you to share actionable insights in real-time. Here are a few salient features of Looker that set it apart:. For Looker, the pricing depends on the scale of deployment and the number of users. For more details on the pricing, you would have to get a quote from Looker. Microsoft Power BI provides interactive visualizations and Business Intelligence capabilities with a simple interface, designed to be used by analysts and data scientists alike.

Here are a few salient features of Microsoft Power BI:. This article so far has given an outlook of some of the best Data Management Tools out there, their features, and pricing.

But Data Management is not without its challenges either. The Data Management challenges usually stem from the ever-increasing volume of data. This article helps you grasp the concept of Data Management and why is it important for enterprises to have a solid Data Management Strategy in place. It then shows you the different types of Data Management Tools available in the market, along with a few key features that set them apart from the rest.

In the end, the decision to leverage one or more of these tools lies with you depending on your requirements. Extracting complex data from a diverse set of data sources can be a challenging task and this is where Hevo saves the day!

Hevo is fully automated and hence does not require you to code. Description: data provides integrated capabilities for database management and data analytics.

Backups to Amazon S3 are continuous, incremental and automatic. Redshift also includes Redshift Spectrum, allowing users to directly run SQL queries against large volumes of unstructured data without transforming. Description: Ataccama ONE is a comprehensive master data management product that offers an intriguing list of capabilities for many use cases. The solution offers a machine learning-centric user interface, as well as a data processing engine that is responsible for data transformations, evaluating business rules, and matching and merging rules.

The platform supports any data, domain, and a variety of integrations. Description: Cloudera provides a data storage and processing platform based on the Apache Hadoop ecosystem, as well as a proprietary system and data management tools for design, deployment, operations and production management.

Cloudera acquired Hortonworks in October It followed that up with a buy of San Mateo-based big data analytics provider Arcadia Data last September. It describes the structure of a piece of data, its relationship to other data, and its origin, format, and use. The solution serves as a searchable repository for users who need to understand how and where data is stored and how it can be used.

Users can also document roles and responsibilities and utilize workflows to define and map data. Collibra is unique because the product was built with business end-users in mind. Description: Commvault is well-known in the backup and disaster recovery marketplace, performing as one of the top solution providers. The company also offers a cloud data management product that allows organizations to manage data via on-prem and cloud deployments.

Users can fully manage data across files, applications, databases, hypervisors, and clouds including Amazon Web Services, Microsoft Azure, Google Cloud, and Oracle Cloud. Description: Druva Phoenix offers data availability and governance functionality for virtual machines and physical servers. Its cloud-centric approach is unique and combines high-performance, scalable backup, disaster recovery, archival, and analytics. The product can be deployed quickly at sites located around the world while also aligning with regional data storage regulations.

Phoenix can also be managed from a central location to provide full control over server backups and data composition. Description: Google offers a fully-managed enterprise data warehouse for analytics via its BigQuery product. The solution is serverless and enables organizations to analyze any data by creating a logical data warehouse over managed, columnar storage, and data from object storage and spreadsheets.