"Information is the oil of the 21st century, and analytics is the combustion engine." – Peter Sondergaard

As global data continues to grow exponentially, data management has become an increasingly essential but resource-draining and challenging task. In many cases, organizations find that their data pipelines are outdated, unmalleable, and rigid (i.e. not easily unified), and the analytical reports are not accurate enough to risk data-driven decision making.

Experian released its annual global data management benchmark report, which surveyed over 1,100 data practitioners and business leaders. The objective was to review international data quality and management trends. The findings echoed the difficulties mentioned, and three main challenges emerged:

  •  Shortage of trusted data and meaningful insights
  •  Data debt dragging down ROI of critical initiatives
  •  Lack of data literacy and data skills within organizations

Big data is the backbone of modern business, but without proper management, it is rendered useless. That is where data management tools are invaluable. They can mitigate many of the difficulties listed above, but not all are equal and are best suited to specific environments and use cases.

Table of Contents

  1. What is Data Management?
  2. The Importance of Efficient Data Management
  3. Data Management Risks and Challenges
  4. Types Of Data Management Tools
  5. Best Data Management Tools
  6. Integrate.io For Your Data Management Toolkit

What is Data Management?

Data management refers to a comprehensive collection of practices, concepts, procedures, processes, and a wide range of accompanying systems that allow an organization to control its data resources. The role of data management spans a given data asset's entire lifecycle from its original creation point to its final retirement.

According to DAMA International, the organization for data management professionals, data management is; "the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise."

Data Management covers ten disciplines, as seen in the DAMA Wheel diagram below, with Data Governance at the core. Check out our post on Data Governance if you want the specifics of this sub-branch.

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The Importance of Efficient Data Management

Inadequate data management can sap organizations with incompatible data silos, inconsistent data sets, and data quality problems, which hinder their ability to run business intelligence (BI) and analytics applications or even lead to misleading analytical reports. 

Treating and managing data as a resource is an essential function of information management. Accurate and relevant data is the source of valuable information. The efficient management of data makes it possible to leverage data-driven decision-making to its maximum potential. It is clear that business success depends not only on the possession of resources but also the efficient utilization and application of resources.

Information is a resource and requires a similar management treatment (people, money, land, and equipment). One must apply resource management techniques, like planning, costing, budgeting, evaluating, to the firm's information resources, and assigning the position for information resources management responsibilities to more senior levels within the organization.

Data Management Risks and Challenges

The risks and challenges associated with data management, the organization’s associated threat, and operational models should push you in the direction of the tooling you want to implement based on their offerings.

Data Governance

High-profile data leaks such as the Cambridge Analytica scandal are, unfortunately, rife in the current world. Consequently, data governance has become a critical priority for data executives. According to a Ponemon study, the average cost of a data breach is $3.86 million, and the average price for a stolen record containing sensitive information is approx $148 per record. It's no longer enough to manage one's data records effectively. It's now essential for the business to govern who can access and use data assets. Your data management toolkit should provide a framework that helps ensure the privacy and information security controls are automated and in place when a user is moving or has access to data.


As mentioned earlier, compliance and legal requirements add complexity (and expense) to all data management processes. For example, European companies have to comply with (GDPR) General Data Protection Regulation. US healthcare data is affected by the Health Insurance Portability and Accountability Act (HIPAA). Companies using third-party IT services require auditing procedures like Service Organization Control 2 (SOC 2). One such data management tool should ideally provide features to reduce the burden. For example, automated data retention policies that evaluate privacy and legal potentialities against economics and need-to-know concerns in the context of data retention time, archival rules, data formats, and the authorized means storage, access, and encryption.

Cost Management 

Often, the process of storing, archiving, analyzing, reporting, and managing big data is very costly. Small and medium enterprises can sometimes find it challenging to balance the setup, migration, and overhauling expenses with future revenue generation promises. Tools should have transparent costing procedures with technical support for transitional onboarding.

Types Of Data Management Tools

One can employ a vast array of tools and techniques as part of the data management process. The following is a short excerpt before we dive into the best data management tools.

Data Integration

One of the most fundamental requirements of a data ingestion framework, modern cloud data warehouses have the processing capability to manage write operations on large data sets efficiently. Cloud data warehouses are very fast at processing data which has led some companies to abandon the traditional ETL (Extract, Transform, Load) solution for the more modern ELT (Extract, Load, Transform). ELT sacrifices the transformation staging area in favor of speedier data ingestion and greater agility. However, because ELT sends raw, unprepared data directly to the warehouse and relies on the data warehouse to carry out the transformations post-loading, it can run up afoul of compliance standards and cause processing costs to skyrocket. This is why many are now option for the hybrid option of ETLT, which provides the best of both worlds. For more information about this cutting edge solution, check out our post on the topic.

Data Analytics and Visualization

An emerging field concerned with analyzing, modeling, and visualizing complex high-dimensional data, Data Analysis does the number crunching, and Data Visualization transforms numbers into actionable insights for business management. Refer to our post on humanizing big data for more information on data visualization strategies.

Master Data Management 

MDM tracks essential company-wide data points, known as master data, and provides insights related to company operations, clients, and goals. These platforms utilize information from multiple domains and departments and pinpoint the organization's core data.

Big Data Management

It is common to deploy big data management systems in the cloud, using object storage such as Amazon Simple Storage Service (S3). NoSQL databases have become very popular in big data deployments as they can store and manage various data types. Traditionally, relational databases such as MySQL or PostgreSQL have been the standard. However, modern applications must have the ability to handle massive amounts of unstructured, semi-structured, and even polymorphic data in real-time. Open source is the norm, i.e., Hadoop, a distributed processing framework with a file system that runs across clusters of commodity servers (HBase database, Spark processing engine, Kafka, Flink, and Storm stream processing). 

Best Data Management Tools

The following is a list of some of the best data management tools out there, which you can add to your organization’s toolkit.

Oracle Enterprise Data Management Cloud

Oracle Enables any company to manage application-specific business viewpoints, govern changes across them, share and map data sets to accelerate cloud deployment, and build an authoritative system of reference. It allows one to:

  • Rationalize differences across business perspectives by providing the ability to compare alternate business perspectives, lists, or hierarchies, in a side-by-side layout.
  • Connect disparate enterprise applications using universal adapters and simple wizards.
  • Manage changes to enterprise master data elements with transparent request mechanisms


In short, Tableau is a visual analytics engine that makes it easier to create interactive visual analytics and dashboards, allowing non-technical analysts and end-users to convert data into understandable, interactive graphics. Some of our favorite features: 

  • The data highlighter is an excellent data exploration tool, allowing one to quickly search for and visually locate – i.e., using a bright yellow highlighter – specific data. It does it without changing the context of the data.
  • Tableau makes it easy to collaborate with other users and instantly share data in the form of visualizations, sheets, dashboards, etc. in real-time. It is seamless and secure.
  • Trend lines within Tableau are highly intuitive. These best fit lines predict a trend's continuation, identifying a correlation between two variables by mapping both in unison.


Databricks is essentially an implementation of Apache Spark on Azure. With fully managed Spark clusters, it processes large workloads of data and helps in data engineering, data exploring, and visualizing data using Machine learning. Databricks provides seamless integration with many Azure services such as Blob Storage, Data Lake Store, SQL Database, and BI tools like Power BI, Tableau, etc. It is an excellent collaboration platform letting data professionals share clusters and workspaces, which leads to higher productivity.

  • Databricks provides a familiar and intuitive environment. Even though Spark-based, it is possible to use widely known programming languages such as Python, R, and SQL. These languages are converted in the backend through APIs to interact with Spark.
  • Databricks' ability to facilitate collaboration is invaluable. One can initiate deployment from notebooks with a few simple UI interactions - not to mention an optimized in-built scheduler.


Segment can collect and send data to various places, including Zendesk, Optimizely, and Google Analytics, to name a few. Also useful for sending data to several databases and integrating with multiple marketing tools.

  • Protocols within Segment prevents the infection of bad data, built to automate and scale the data quality best practices (Align, Validate, Enforce, Transform)
  • Documentation is incredibly transparent. It's easy to get started, track methods, and understand data from each source and destination; the platform also offers support through an email ticketing system. 
  • Personas is a personalization platform that enables one to create unified customer profiles while also building and enriching audiences and activate audiences across marketing tools. The platform allows one to identify groups of users ("audiences") based on behavior or relational metrics calculated from the data and send them to Destinations for identity resolution.


DreamFactory is an API automation and management gateway capable of generating robust, documented APIs for almost twenty databases, including Microsoft SQL Server in addition to file storage, caching, e-mail, 3rd party APIs, and SOAP services. It is possible to integrate multiple APIs into a modular and centralized public-facing platform while retaining the functionality of disparate API extensions with thousands of pre-existing users across various contexts.

  • A scripting interface that supports four scripting languages and 3rd party libraries are also supported - business logic integration allows for data validation, masking, and transformation.
  • API keys and role-based access controls restrict endpoint and method access. DreamFactory supports many user authentication options, such as SSO authentication and stateless scaling with JSON Web Tokens (JWT), OpenID Connect, OAuth/LDAP/Active Directory integration, and Okta. 
  • Excellent SQL support, including automatic REST endpoints for data, schema, functions, and stored procedures and API parameters for complex filters, pagination, sorting, multi-table joins, calculations, aggregation, and more.

Integrate.io For Your Data Management Toolkit

Integrate.io is an industry-leading data integration platform with a robust feature set built from the ground up to fit a wide range of ETL use cases. 

Want to learn how Integrate.io can help enable your ETL needs? Get in touch with our excellent customer support team to chat about your big data goals and requirements and request an Integrate.io platform pilot.