Data just keeps getting bigger and more diverse. Separating the core from the noise is getting harder and harder. According to Dataversity: “Data Management 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.“

This process involves:

  • creating, updating and storing data locally or in the cloud
  • data protection privacy policy compliance and backup recovery
  • using data for analytics, etc.

It can empower you by transforming all your data into insight, answer your questions, and act at the moment.

Data management approaches

Data management helps you solve big data, real-time analytics, or data modeling problems. Applying best practices leads to the efficient use of information systems, which is essential for developing a holistic view of information. There are 5 approaches to data management.

  1. Cloud data management deals with organizing your data into a single cloud platform that works anywhere.
  2. Master data management organizes critical data to avoid redundancy.
  3. Reference data management arranges the company’s data in multiple copies containing sometimes inconsistent and conflicting information.
  4. ETL and data integration combines data from different sources to transform it into meaningful and useful information.
  5. Data analytics and visualization are responsible for the presentation of data in a form that ensures the most effective work.

Based on these 5 approaches, we have listed some data management tools.

Data management tools

Here is a list of 9 data management tools on the market owned by major vendors. Let’s have a look at their features.

#1. SAP Data Management 

It is a unify intelligent data platform that is open and enterprise-ready to meet your daily needs. This tool uses a single point of access to all data, whether transactional, analytic, structured or unstructured, in on-premises and cloud solutions. It provides access to metadata management tools to enable intelligent data management by taking advantage of the benefits of cloud computing, which include:

  • low cost
  • elasticity
  • serverless principles
  • high availability 
  • resilience
  • autonomous behavior

This tool helps to build a data management process in a corporation, but this platform will work for their corporate business apps as well.

#2. IBM Infosphere Master Data Management Server 

IBM Master Management is designed specifically to help you know, trust and use your data throughout the whole information life cycle. MDM empowers you with trusted master data at the 360-degree view to drive better business insights and transformation. The solution is built on the flexibility of an open data platform with best-in-class security and governance to create a modern analytics enterprise foundation that meets the demands of your business on-premises, on cloud and hybrid at any scale. This tool provides secure, governed and self-advice access to trusted data across the enterprise for experimentation and collaboration.

  • Users can provision and operationalize data with a graph-based exploration of Master data, Transactional data and Hadoop to drive insights and analytics. 
  • With built-in consent management they can capture content to facilitate governance and adhere to regular compliance requirements. 
  • They can even optimize master data environments with agile and intuitive dashboards to proactively manage data and respond to changes.

MDM provides clean, consistent and timely information for big data projects.

#3. Microsoft Master Data Services 

This tool works with SQL Server (all supported versions) only on Windows. Managing an organization’s core dataset includes: 

  • organizing data in models
  • updating data according to the created rules
  • access control
  • centralization and synchronization of data to avoid redundancy

The MDM project mainly involves the assessment and restructuring of internal business processes. A successfully implemented MDM solution results in reliable, centralized data available for analysis and leading to better business decisions.

#4. Dell Boomi

It is a cloud-native, unified, open, intelligent enterprise platform that makes it easy to connect data from different applications. The scope of applications includes cloud analytics and deep learning. With Boomi you can: 

  • improve end-to-end efficiency (eliminate time-consuming manual data entry and delight customers with accurate, timely order fulfillment)
  • automate lead-to-cash workflows (streamline with real-time data exchange and simplify syncs of accounts, orders, and products)
  • enhance visibility and results (on-demand clarity into customer data enhancing interactions and timely insights for actionable reporting and forecasting)
  • build on integration success (start quickly, build efficiently, grow confidently and extend with the Boomi unified platform: Connectors, Workflow, APIs, Data and more)

#5. Talend 

Talend is a software integration platform that provides solutions for data integration, data quality, data management, data preparation and big data. Plus, it is the only ETL tool with all plugins that integrate seamlessly with the big data ecosystem. According to Gartner, Talend is in the magic quadrant of leaders for data integration tools.

This tool is very cloud-centric as data-organization. Its primary pattern involves the data-lake layer trying to bring data into the cloud in its native format using AWS S3 buckets. A data lake on its own is useless. Data governance is critical. It’s important to make sure that data is well understood, well looked after, the right people have got the data. Talend acts as a catalogue, transformation and data-preparation technology.

#6. Tableau 

It is an interactive analytics system that allows in the shortest possible time to conduct a deep and versatile analysis of large amounts of information and does not require training for business users and costly implementation. 

Advantages of Tableau:

  • data processing of any format – from Excel to Oracle
  • quick install (90 seconds)
  • doesn’t require long-term implementation
  • availability of ready-made industry solutions
  • high speed of obtaining results
  • low cost
  • intuitive interface
  • create any report in just a few steps
  • reduced data analysis time
  • wide possibilities of information visualization
  • all levels of report complexity – from the simplest to trend analysis and correlation

#7. Google Cloud – Big Data analytics

This tool is a whole platform for working with data in the cloud and also integrates several featured products:

  • BigQuery. Serverless data warehouse
  • Dataflow. Batch and stream data processing
  • Dataprocs. Managed Apache Spark and Apache Hadoop
  • Cloud BigTable. NoSQL database-style storage
  • Cloud Pub and Cloud Data Transfer. Receiving data
  • ML Engine. Advanced analysis with machine learning and artificial intelligence 
  • Data Studio. Analysis based on a graphical interface and building a dashboard
  • Cloud Datalab. Code-driven data analysis and connection to BI tools, like Tableau, Looker, Chartio, Domo, etc.

#8. Amazon Web Services – Data Lakes and Analytics 

This tool allows you to quickly get the necessary answers as a result of the analysis of the entire data set.

  • Data storage. Execute SQL queries and complex analytic queries on structured and unstructured data without having to move it.
  • Big data processing. Simple and fast processing of large amounts of data for their structuring and study, as well as for joint work with it.
  • Real-time analysis. Collecting, processing and analyzing streaming data as it enters the data lake and real-time response.
  • Operational analytics. Search, exploration, filtering, aggregation and visualization of data in near real time, for tasks such as application monitoring, analysis of logs and history of website navigation.

#9. Oracle Data Management Suite

Thanks to this tool, users can create, deploy, and manage data-driven projects across the enterprise. The program consolidates and structures this data in advance. 

  • Data on-boarding. On-board product data from multiple systems and trading partners.
  • Data sharing. Share relevant, timely and complete product data to all consuming systems.
  • Data quality. Ensure data is appropriately classified, standardized and validated.
  • Data enrichment and governance. Establish and enforce policies for the creation, update and retirement of product data.

Conclusion

Innovation is driven by data, therefore, it is very important to create the right data management system that meets the needs of your company. It is not so important whether it is a Tableau or an Oracle product, the main thing is that your data was useful to you. Stay tuned for our new posts!