An efficient toolkit can make capturing, transforming, and analyzing data easier. Therefore, data managers want reliable technologies to optimize the workflows concerning data acquisition, analytics, and protection. This post will list the top data management tools alongside their pros, cons, and suitability based on popular use cases.
What is Data Management?
Data management oversees technologies, strategies, and operations encompassing enterprise intelligence resources. So, a data manager is the professional responsible for handling conflicts or risks affecting source selection and data quality assurance.
Cost-effectiveness and cybersecurity are also essential considerations in a robust data management strategy. After all, a company’s data needs keep increasing while the rise of modern cybercriminal activities threatens business confidentiality and stakeholder privacy. As a result, most corporations utilize data governance solutions, empowering managers to enhance resilience to data leaks and corporate espionage.
Furthermore, data management can address data quality issues through automated techniques. Quality parameters, like relevance and accuracy, are integral to evaluating whether the current data strategy is adequate for longer.
Data managers must possess remarkable leadership, communication, and technology skills. They need these to coordinate engineers, architects, analysts, cybersecurity specialists, and departmental chief executives. They can develop more comprehensive workflows by adopting novel database management system (DBMS) platforms featuring multi-cloud environments and vast interoperability.
What are Data Management Tools?
Data management tools range from coding languages that help establish relationships between data objects and independent databases to allow specific query responses. They provide mathematical, conditional, contextual, and quality-related features. Besides, the best data management tools can support automation through integrated commands or third-party application programming interfaces (APIs).
Types of Data Management Tools
1| Relational Database Management System (RDBMS)
RDBMS is the most popular approach, involving tabulated data and enabling user-friendly database commands. It is scalable, and several software programs utilize this approach to provide flexible data retrieval options.
2| Distributed DBMS
A distributed database management system leverages multiple physical storage locations and relies on networks to update interdependent datasets. Later, a central program will ensure version consistency based on incremental database modifications.
3| Hierarchical DBMS
A hierarchical DBMS uses a leveled database management strategy leading to a tree-like dependency. This method defines top-down or bottom-up connections across multiple databases. The parent-child definition helps develop smaller databases serving specific requirements while preserving data integrity to facilitate ease of retrieval.
4| Object-oriented Programming (OOP)
OOP replaces the tabulated approach to database management with an object-driven technique that assigns properties to data objects. Unlike RDBMS, identifying and solving data quality issues can be easier in object-oriented programming since the relational approach is secondary or absent. Moreover, reusing code helps boost productivity and reduce database operations’ complexity.
Data Management Tools
Corporations, universities, and governments procure many types of data management tools, such as Oracle, Microsoft, Google, IBM, Looker, Talend, and SAP program suites. An ideal DBMS facility might streamline data enrichment and cloud computing integration. It must support advanced protection facilities and data visualization services. Some tools are suitable for big data analytics, while others can be unique, enabling custom extract, transform, load (ETL) pipeline development.
Top 10 Data Management Tools
1| IBM Data Studio
IBM Data Studio lets your team develop and debug data-centric applications. It enhances the Eclipse framework and structured query language (SQL) components in your company’s in-house DBMS tools.
Therefore, IBM Data Studio will be more efficient in administrating routines and tuning query tasks. This data management tool has two components. A web console monitors the system's health while the client assists you in the development work.
2| SAP Datasphere
SAP Datasphere automates semantic definition usage to transform heterogeneous data into business-relevant formats. It also operates based on cloud computing and hybrid technological workflows. Native data management tools for integration, access regulation, system monitoring, cataloging, and extensive import-export are available. This ecosystem is compatible with Google Cloud, DataRobot, Collibra, Confluent, and Databricks.
Its previous iteration, SAP Data Warehouse Cloud, needed comprehensive catalog and semantics control. Therefore, SAP Datasphere has become the newer and better version.
3| Oracle Enterprise Data Management (EDM)
Oracle EDM empowers organizations to respond to data quality and querying troubles. It ensures consistent standards across master data management and robust business intelligence tools. You can use Oracle EDM to migrate your DBMS applications to a cloud computing environment.
Some popular use cases of its data management tools are connecting disparate data sources, importing hierarchies, synchronizing APIs, and visualizing data.
4| Google Cloud
Google Cloud offers multiple database management features, like AlloyDB AI, which embraces generative artificial intelligence (Gen AI) to enhance DBMS capabilities. Its Spanner service is suitable for gaming and supply chain management companies. Meanwhile, Cloud SQL helps streamline customer relationship management activities.
Bare Metal for Oracle lets you migrate data in legacy systems and data centers. Likewise, Google Cloud has Bigtable, a NoSQL database service. Finally, Memorystore will help meet your extensive storage requirements and save time since it is a managed service.
5| Microsoft Azure
Azure enables agile data operations for all stakeholders. Its performance enhancements range from cloud-based data migration to AI-powered applications. Simultaneously, you can migrate your SQL databases with fewer coding hurdles using Microsoft Azure.
ASP.NET web apps, SAP programs, and software testing are frequent components of an enterprise data management strategy that benefit from Azure. While MS Sentinel will enhance governance through secure operations, Azure DevOps will improve collaboration, code export, and delivery.
6| Informatica PowerCenter
It helps develop data warehouses and ETL pipelines for organizations worldwide. Informatica PowerCenter can modernize data extraction, source selection, transformation, and analytics loading. Its components include client programs, servers, and repositories.
Your workflow managers can use it alongside the best data management tools for data integration involving business-to-business (B2B) data transfer. It features vital features like change data capture or CDC that increase governance effectiveness.
7| Amazon Web Services (AWS)
AWS supports resource optimization to reduce data operations costs. Several data consulting teams rely on Amazon Web Services for over 200 cloud-powered features. Besides, AWS helps corporations, startups, and governments benefit from agile and innovative database management approaches.
It is one of the top data management tools with a significant client base in many nations and industries. You get purpose-built databases to fulfill a specific requirement in risk management, software development, business intelligence, customer relations, and data governance.
8| Teradata
Teradata boasts comprehensive risk mitigation for data governance officers. It suits data unification, flexible analytics, and AI integration initiatives for DBMS modernization. Teradata also offers data connectors for Google Cloud, AWS, Dell Technologies, Dataiku, and Accenture.
It provides VantageCloud and a serverless engine alongside data fabric platforms. Additionally, data engineers, managers, and professionals overseeing transactional record-keeping tasks can leverage Teradata to streamline governance and automate analytics.
9| Collibra
Collibra’s data intelligence platform ensures continuous data quality improvement with governance, analytics, lineage, and AI capabilities. Its core offerings include a flexible operating model, enterprise data coverage, and a user-friendly workflow engine. In addition to usage analytics and holistic reports, you get ad hoc extensions.
As a result, it is ideal for data lifecycle management. Companies can access more than 100 integrations for Collibra. Consider the examples of Apache, Amazon Redshift, Kafka, Cloudera, Looker, and Azure Data Factory integrations.
10| TIBCO Platform
TIBCO facilitates real-time insight extraction and simplifies data access. It can regulate master data and reference datasets. TIBCO EBX belongs to the top agile data management tools for master data enrichment.
It can provide interfaces to coordinate data operations involving multiple data sources, such as cloud platforms, edge computing tools, and on-premises data centers. TIBCO also deploys cloud applications through Kubernetes-based tools. Data managers get BusinessWorks and Flago presets to begin new projects on this platform.
Features of Data Management Tools
Each enterprise must select a DBMS or switch to another data partner when its business strategy changes. After all, data processing needs evolve due to regulatory transitions, stakeholder demands, ease of use concerns, data volume increments, and business mergers.
Therefore, the best data management tools comprise the following functionalities.
1| Framework Compliance Monitoring
Policymakers, customers, employees, investors, and suppliers worry about corporate data usage practices. Some demand privacy-first engineering, while others ask for reasonable transparency disclosures. Every reputed brand must attentively listen to stakeholders’ concerns to thrive in today’s hypercompetitive markets.
A data management framework offers reliable logic for computing systems to identify, prevent, and rectify non-compliance risks. So, all types of data management tools must provide compliance-related monitoring or assessment features.
2| Data Observability and Quality Assurance
Data observability or quality management helps check the health of your datasets across all organizational units. If a platform provides data observability features, it must support real-time troubleshooting of quality issues.
Incomplete records, outdated values, and corrupted data objects undermine the business value of enterprise data. After all, these flaws skew the analytical program’s insights and reporting activities, indicating that immediately finding and addressing them is crucial.
3| Multi-Cloud Support
Trusted data management tools include data connectors, which move data assets between multiple cloud computing environments without manually creating import-export jobs.
This facility protects your employees and company stakeholders from vendor lock-ins. It also reduces data loss risks due to platform-to-platform compatibility complications.
4| Automated Governance Optimization
Modern DBMS ecosystems feature advanced user authentication, encrypted communication channels, version control, CDC, and accountability management. Therefore, brands can investigate controversial events, generate transparent documentation, and combat data manipulation.
Automating tasks to implement a data governance framework is essential in the banking, financial services, and insurance (BFSI) industry. Other companies must also embrace precision across data operations to prevent fraud, corporate espionage, insider trading, and intellectual property theft.
Conclusion
You want your organization’s data leaders to encourage their teams to develop new skills. To accelerate innovation, you must find the best combination of available data management strategies and tools. At the same time, watch out for novel compliance requirements. Choosing an inadequate data partner risks losing your brand reputation and hurting data quality standards.
Thankfully, global enterprises like SAP, Microsoft, Oracle, Teradata, and IBM have maintained an impressive track record of empowering data managers in many sectors. However, business leaders must consider the financial and technological aspects before implementing multi-platform DBMS approaches. If required, seek expert guidance to maximize your returns using the best data management tools.
SG Analytics, recognized by the Financial Times as one of APAC’s fastest-growing firms, is a prominent insights and analytics company specializing in data-centric research and contextual analytics. Operating globally across the US, UK, Poland, Switzerland, and India, we expertly guide data from inception to transform it into invaluable insights using our knowledge-driven ecosystem, results-focused solutions, and advanced technology platform. Our distinguished clientele, including Fortune 500 giants, attests to our mastery of harnessing data with purpose and merging content and context to overcome business challenges. With our Brand Promise of “Life’s Possible,” we consistently deliver enduring value, ensuring the utmost client delight.
A leading enterprise in data management solutions, SG Analytics employs cutting-edge DBMS tools and strategies to augment global organizations’ data storage, governance, analytics, and visualization capabilities. Contact us today for robust data quality assurance and advanced AI integrations.
About SG Analytics
SG Analytics is an industry-leading global insights and analytics firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media and entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company and has a team of over 1100 employees and has presence across the U.S.A, the U.K., Switzerland, Canada, and India.
Apart from being recognized by reputed firms such as Analytics India Magazine, Everest Group, and ISG, SG Analytics has been recently awarded as the top ESG consultancy of the year 2022 and Idea Awards 2023 by Entrepreneur India in the “Best Use of Data” category.
