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Data Management

18 Best Data Management Tools

by Svitla Team

August 16, 2019
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What is data management?

Almost every aspect of our lives generates data. Nowadays, data drives everything and has become a critical and valuable resource for any organization as they receive it as pouring rain from many diverse sources. But what makes data valuable? The value of data stems from the insights that can be discovered by manipulating it to extract the potential that lies within.

And that’s where data management comes into the picture.

Data management, as defined by Dataversity,  is “a comprehensive collection of practices, concepts, procedures, processes, and a wide range of accompanying systems that allow for an organization to gain control of its data resources. Data Management as an overall practice is involved with the entire lifecycle of a given data asset from its original creation point to its final retirement, how it progresses and changes throughout its lifetime through the internal (and external) data streams of an enterprise.” 

In essence, data management aims to facilitate the optimization of data used to propel decision-making processes in an organization by collecting, maintaining, and using data in a secure, efficient, and cost-effective fashion.

Data management covers the following operations: 

  • Create, access, and update data across diverse data tiers.
  • Store data across clouds and on-premise.
  • Use data across apps, analytics, and algorithms.
  • Provide high-availability and disaster recovery.
  • Secure data and provide privacy.
  • Archive and destroy data based on retention rules and compliance requirements. 

Data management is interwoven with the following practices and concepts:

  • Data access: Ability to harness and collect information wherever it is stored. 
  • Data quality: End-to-end practice of ensuring data is accurate and usable for its desired purpose. It helps achieve better, cleaner data, regardless of volume or type.
  • Data preparation: Practice of preparing data for analytics and reporting.
  • Data integration: Steps taken to combine different types of data.
  • Data federation: Virtual data integration that enables the visualization of combined data from multiple sources without having to move and store the combined view to a new location.
  • Data governance: Rules and decisions that help manage data to secure alignment between the data strategy and the business strategy. It enables the effective management of all critical data assets regardless of size, type, or location.
  • Master data management (MDM): Practice of defining, unifying, and managing all common and essential data in a centralized hub.
  • Data streaming: Practice of analyzing data while it’s in motion. It allows users to filter, cleanse, and correct fast-moving data before it is stored to gain instant, tangible results in real-time and from a single interface.

Role of information systems in data management

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.

Cloud 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.

Master data management

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.

Reference data management

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.

ETL and data integration

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.

Data analytics and visualization

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.

Based on these 5 approaches to data management, let’s list the best data management tools available.

Data management tools

Data Management is as successful as the tools used to store, analyze, process, and discover value in an organization’s data. In essence, these tools are heterogeneous multi-platform management systems that harmonize data.

The most widely used data management tools belong to the industry’s biggest software groups whose experience guarantees a high degree of performance, security, efficiency, effectiveness, elimination of data redundancy, and privacy that is necessary for companies that are leaving the entire organization’s information in the care of external vendors.

Here’s a list of the most prominent data management tools on the market.

1. Oracle Data Management Suite

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.

2. SAP Data Management

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.

3. IBM Infosphere Master Data Management Server

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.

4. Microsoft Master Data Services

Microsoft Master Data Services: Platform that includes a suite of services that enables users to manage a master set of an organization’s data. 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 enables users to develop MDM solutions that are built on top of an SQL Server database technology for back-end processing. 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.

  • Microsoft Azure Data Factory: It is a hybrid data integration service that simplifies ETL at scale and is specifically designed for all data integration needs and skill levels. With its rich visual environment, users can easily construct ETL and ETL processes in a code-free fashion by integrating data sources from more than 80 natively-built and maintenance-free connectors.
  • Microsoft SQL Server SSIS: Microsoft SQL Server Integration Services (SSIS) is a platform for building enterprise-level data integration and data transformations solutions. It solves complex business problems by copying or downloading files, loading data warehouses, cleaning and mining data, and managing SQL Server objects and data. Additionally, it extracts and transforms data from a wide variety of sources such as XML data files, flat files, and relational data sources, and then loads the data into one or more destinations. The platform includes a rich set of built-in tasks and transformations, graphical tools for building packages, and the Integration Services Catalog database to store, run, and manage packages. Last but not least, it allows users to leverage the graphical Integration Services tools to create solutions without writing a single line of code.
  • Microsoft Power BI: Business analytics service that delivers insights to enable fast, informed decisions. It helps transform data into compelling visuals that can be shared on any device to visually explore and analyze data, on-premises and in the cloud, all in one view. Additionally, it enables collaboration through customized dashboards and interactive reports, and it scales easily with built-in governance and security.

5. Dell Boomi

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:

  • Connecting all applications and data sources across a hybrid IT landscape.
  • Synchronizing and enriching data through a centralized data hub.
  • Achieving interoperability between internal systems and external partners.
  • Exposing underlying data as APIs to deliver scalable and secure, real-time interactions.
  • Transforming manual processes into automated processes with flexible business logic and workflow capabilities. 

6. Talend

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.

7. Tableau

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.

8. Amazon Web Services - Data Lakes and Analytics

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. AWS provides a comprehensive set of services to move, store, and analyze data.

9. Google Cloud - Big Data analytics

Google Cloud - Big Data analytics: Solution platform that offers a broad set of tools for cloud-based data management, as well as a workflow manager to tie components together such as BigQuery for tabular data storage, Cloud BigTable for NoSQL database-style storage, Cloud Pub and Cloud Data Transfer for data intake, ML Engine for advanced analysis via machine learning and artificial intelligence, Data Studio for GUI-based analysis and dashboard construction, Cloud Datalab for code-based data science, and connections to BI tools such as Tableau, Looker, Chartio, Domo, and more.

There are also a number of emerging data management tools from relatively small vendors that are worth mentioning: 

10. Looker BI

Looker BI: Business intelligence software and Big Data analytics platform that helps users explore, analyze, and share real-time business analytics easily. It captures and analyzes data from multiple sources to help make data-driven decisions.

11. Chartio

Chartio: Cloud-based business intelligence and analytics solution that helps users analyze data from business applications. It offers interactive drag-and-drop capabilities to create, filter, and share dashboards, SQL mode to communicate with databases, data layering, visualizations and charts, and data blending to combine disparate data sources and glean actionable insights quickly.

12. Alooma

Alooma: Enterprise-scale data integration platform with exceptional built-in ETL tools. Alooma offers a strong focus on rapid pipeline construction, data quality monitoring, error handling, protection against data loss, flexibility to write scripts to monitor, clean, and move data as needed, and much more.

13. Panoply

Panoply: Cloud-native automated data warehouse that makes it easy to integrate and manage an organization’s data. It has an extensive array of native data connectors, it provides automated data ingestion and scaling of multi-node databases, it includes an intuitive management dashboard and an in-browser SQL editor, and much more.

14. Blendo

Blendo: Cloud-based ETL solution and data integration service that connects to numerous data sources and automates the collection, detection, and preparation of data using an optimal relational schema.

15. Informatica Powercenter

Informatica Powercenter: On-premise ETL tool that provides seamless connectivity and integration with all types of data sources, an automated data validation, advanced data transformation that supports non-relational data, and a metadata-driven management approach.

16. Informatica MDM Reference 360

Informatica MDM Reference 360: Cloud-based platform that has an end-to-end approach with embedded data integration, data quality, process management, and more.

17. Collibra

Collibra: Tool that automates workflows to create new code sets and performs accurate data mapping to eliminate barriers to data access.

18. Profisee

Profisee: It manages master data by standardizing, cleaning, and matching source data. It enforces business processes to enable data stewards to master data with feedback from analytics.

Data management challenges and evolution

Data management challenges stem from the increasing proliferation of data. Some examples of what organizations face include:

  • Extracting value that solves a specific problem. It’s easy to collect data, but making sense of it is what really matters. One of the challenges of data management is that organizations are only starting to realize that the concepts of data management and data analytics go hand in hand in extracting value from data most effectively. 
  • Unclear goals and objectives. A prominent challenge associated with data management is the lack of clarity around what answers are sought from processing data. If there’s not a clear goal for acquiring the right data and analyzing it to glean actionable insights and intelligence to facilitate data-driven business decisions, then the resources of the data management system are not being fully exploited. 
  • Not utilizing data management to its full extent. With vast quantities of data pouring in, companies find it challenging to fully understand the amount of data they have, where it is, how to use, and what to do with it.
  • Meeting compliance requirements. Compliance regulations are changing constantly and organizations must quickly and regularly review their data and practices to make sure everything is in line with the new or updated requirements.
  • Multiple data storage options. Data is stored across multiple platforms, including data warehouses, unstructured data lakes, knowledge bases, and more, which makes it hard to analyze since it is not in a single format or in a single repository. Data must be transformed into a unified shape, format, or model to allow numerous analyses.

With these challenges in mind, it’s important for organizations to create comprehensive data management strategies that have concise and clear objectives that serve specific business goals. Ultimately the goal is to reduce the need for manual data management and work towards a new data management technology, the autonomous database.

Over the last couple of years, the infrastructure of data management has continuously evolved and is now moving heavily towards the cloud, which results in a more managed and fully integrated data stack. The future is cloud-oriented as these platforms help with robust data management strategies in terms of data ingestion, loading, transformation, optimization, and visualization, all in a centralized, unified system. 

Cloud-based data warehouses are powerful enough to connect directly to data sources, manage data loading, clean and prepare data via natural language processing and machine learning, and apply the necessary modifications to make it ready for analysis.

Another signifier of the evolution of data management is its new role as business capital. As organizations continue to establish how valuable data assets are in identifying trends, making decisions, and taking action before competitors, data has moved up in the value chain, which brings new implications for competitive strategy and the future of computing. Data is now at a point where it is central and mission-critical for any organization, regardless of size or type.

Conclusion

At Svitla Systems, we have numerous years of experience under our belt with evident successful results for our clients in regards to comprehensive information systems and data management. Thus, we understand the intricacies of constructively managing data and dealing with the numerous emerging and well-established tools available in the technology ecosystem to effectively harness the inherent value of data and derive valuable insights and intelligence from it.

We’ve said it before and we’ll say it again, the future is data-driven, so it is critical and vital to build, develop, and deploy a comprehensive data management strategy that addresses your company’s needs in terms of gaining value from data capital.

With experts in our ranks who are clearly accomplished in an arsenal of data management tools, we are sure to be your one-stop partner solution to orchestrate and realize your data management strategies. For more information about how we can help you with your data management projects, contact us and we will be glad to give you more details.

by Svitla Team
August 16, 2019

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