Data Management tools are used to develop and monitor practices, as well as organize, process, and analyze an organization’s data. These tools are designed to arrange and harmonize data, and should provide a high degree of efficiency and effectiveness.

Data Management tools also support privacy, security, and the elimination of data redundancy. Effective Data Management uses a combination of software tools and best practices to control and organize data resources effectively.

Organizations doing business today need Data Management tools that provide an effective way to manage their data. These tools are often part of a Data Management platform, or are available on a cloud. Some Data Management tools are open source.

Data Management platforms containing these tools should perform tasks such as data cleansing, ETL, data consolidation, and more. Businesses often have problems translating data coming in from different sources, and with different formats. They can also have problems with scaling. An intelligent Data Management strategy can protect an organization from becoming an environment of chaos and confusion.

It may be preferable to use a platform containing a variety of tools. These platforms may be more convenient and provide tools that are more “user-friendly.” Knowing which tools are needed to operate a specific business is necessary when selecting a platform. For example, the Data Management tools used by an online retail business are different from those used by an educational website. The two organizations would be using different Data Management platforms.

The Data Management Tools

Below is a list of basic Data Management tools and their descriptions. Many have open-source options, and some tools have commercial on-premise options. As stated before, very often these tools are part of a larger Data Management platform, or tied to the cloud. If an organization is using a platform that is missing a tool or two, downloading an open-source tool might be a good solution.

• Data Cleansing Tools: These support the process of finding inaccurate, corrupt, and irrelevant data, and correcting it. This process has also been called “data scrubbing” and “data cleaning.” In terms of research and analytics, this is a critical phase for projects. It boosts the reliability and value of an organization’s data. (OpenRefine is a free, open source, downloadable cleansing tool.)

Common problems with data include misplaced entries, typographical errors, and missing values. In some situations, data cleansing must have certain values, and these values need to be corrected or filled in. In other situations, duplicated data must be removed to eliminate confusion. Data containing these kinds of inconsistencies and mistakes is called “dirty data.”

• Data Integration Tools: These perform data cleansing, mapping, and transformation. Data integration tools support analytics by aligning and merging data. They consolidate data from a variety of sources into a single storage area. The data consolidation tool (or feature) should support automated data collection from a variety of systems and formats (COBOL, PDF, etc.). It helps turn raw data into useful information that promotes faster and better decision-making. On-premise and open source data integration tools are available.

These tools help to understand and retain customers, and support collaboration between departments. They also reduce project timelines by using automated development. The process typically uses four layers of technology: an ETL data pipeline, data sources, business intelligence (BI) tools, and a data warehouse destination.

• ETL (Extract, Transform, and Load) Tools: These expedite the process of data consolidation. They automate the extract, transform, and load process, and can copy data within minutes after being initiated. They “extract” structured and unstructured data, or raw data, and consolidate it into a repository. The transformation process includes cleansing, standardization, and deduplication.

The last step of the ETL process is downloading the transformed data. It can be downloaded all at once (called a “full load”) or it can be downloaded at scheduled intervals (called “incremental loads”). (On-premise and open source ETL tools.)

Missing information can lead to missed opportunities. Decision-making that is based on inaccurate data often leads to undesirable outcomes. Guiding a business through the Great Material Continuum (also known as the “Great River” for Star Trek fans) requires reliable information, which data integration tools can help provide. Having all the pertinent information available supports new opportunities and makes decision-making much easier.

• Scalability as a Tool: It allows a computer system to increase or decrease its performance in response to the constantly changing needs of applications and system processing demands. For example, a system with a growing number of users needs a database that can increase its processing power to keep up with the increased demands. Businesses experiencing rapid growth need to give special attention to scalability. (Open source considerations.)

• Data Backup and Disaster Recovery: The purpose of a backup is to store a copy of the data so it can be recovered after a system failure. Data backup and disaster recovery tools/features are necessary for easy access and retrieval of data after a system goes down.

Additionally, it should support the easy modification of data, or regular upgrades without downtime or disruption. A proper backup should be saved in a separate system, protecting the backed-up data if the primary system fails.

• Cloud Data Management Tool: These allow organizations to manage their multi-cloud (both on-premise and public clouds) services and resources. Cloud Data Management of the cloud includes everything from Data Governance to life cycle management to automation.

Comparing Data Management Software Platforms

There are a large number of articles online with titles along the lines of “The Best … 6, 12,  20 Data Management Tools.” These articles typically describe not the actual tools, but the supposedly best platforms “containing” Data Management tools.

Data Management platforms provide Data Management tools, and store important data (customer information, mobile identifiers, cookie IDs). These types of tools also help marketers and advertisers develop an understanding of their customer’s preferences and shopping patterns. Data Management platforms (DMPs) can unify data and break down silos. They bring large amounts of data together, creating a single platform, and providing a more cohesive perspective of a business’ customers. Below is a list of some Data Management platforms:

• Salesforce DMP: Useful to marketers looking to collect, unify, and use data taken from multiple sources. This platform uses artificial intelligence and machine learning to provide researchers with customer data profiles and assists in engaging existing customers and helps in finding potential customers.

• Talend Platform: Has some tools that are open source. Their platform is designed for Data Management, data integration, enterprise application integration, cloud storage, and data Daa quality, across their cloud and for on-premise environments. The Talend platform helps to transform data into business intelligence and make decisions in real-time.

• Lotame DMP: Offers information from different sources, ranging from emails to social websites to CRM tools and much more. It supports standard features, and also provides access to a fully automated suite of tools. Lotame is designed for publishers, marketers, and digital agencies. It is good for increasing audience engagement and for unifying data.

• Cloudera: Provides one of the most complete DMPs available today, Cloudera offers a high degree of scalability, performance, data quality, and data integrity. This platform includes a variety of useful features, such as cluster management, alert management, monitoring, and diagnostics.

• Oracle Data Management Suite: Delivers a suite of tools that allows users to create, deploy, and manage projects. It delivers consistent, consolidated master data and distributes the information to all analytical and operational applications. It supports Data Governance, policy compliance, and change awareness within an organization.

• SAS DMP: Very useful for gathering the data of legacy systems and uses Hadoop (an open source framework used to store data and run applications). This platform allows users to update their data, alter processes, and perform analytics. (Warning — this can be expensive.)

• Snowflake DMP: A unique platform offering Data Management “as-a-service” and supporting multi-cloud strategies. Users can take advantage of its high-speed analytics process. Snowflake has no infrastructure to manage and is very easy to use.

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