The Best Data Management Tools & 5 Ways Of Thinking About Data Management

Data management isn't straightforward. It demands careful supervision from the moment the data is created until it's retired. When data is managed properly, you can mitigate risk while enhancing data usability and quality.

Companies often run into problems when working with data from disparate sources and scaling. Whether it's issues related to data duplication, isolation, or complex management issues, a robust data management strategy that is supported by the right tools can get you over any potential hurdle that lies ahead.

What is Data Management?

At its most basic level, data management (DM) describes the process of collecting, storing, and using data efficiently, securely, and cost-effectively. The primary objective here is to connect and pipe in data from different sources and make critical business decisions. However, as we generate more and more data in every aspect of our lives, DM can become increasingly challenging.

Effective data management is a combination of best practices, concepts, processes, procedures, and an extensive collection of tools that help enterprises control and manage their data resources effectively. In other words, it's a multiplatform heterogeneous process that involves various tools and objectives to achieve centralized data coherence. It's a process that is followed throughout the lifecycle of any data asset. 

Tools essential to effective data management fall into these general categories:

  1. Cloud data management
  2. ETL and data integration 
  3. Data transformation
  4. Master data management
  5. Reference data management
  6. Data analytics and visualization

Below we cover several great tools from each of these categories, both to help you understand each category and to move closer to selecting the best data management tool for your needs. 

Best cloud data management tools

Cloud data management tools help organizations integrate and manage data across multi-cloud environments. This approach allows companies with large volumes of data to store, sift through, analyze, and routinely manage their data entirely in the cloud. 

Data management tools boast functions like archiving, backup, disaster and recovery, archival, search, analytics, and more. While giants like Amazon and Google have dominated this niche, many smaller companies have entered the market to offer tools for customers with data needs of all sizes.

1. Panoply

Panoply is an ETL tool and a cloud-native data warehouse that makes data integration and management effortless. It is highly user-friendly and accommodates teams with varying skill sets, including business users.

Key features include: 

  • An extensive selection of native data connectors that enable easy, one-click data ingestion
  • An intuitive dashboard that takes the guesswork out of data management and budgeting
  • Automated scaling of multi-node databases for low-maintenance data warehousing 
  • In-browser SQL editor for data analysis and querying 
  • Connections to common data visualization and analysis suites such as Tableau, Looker, Power BI, and more

TL;DR: it's an excellent turn-key business intelligence solution for SMBs who want to derive the most value from their data at a fraction of the cost. 

Panoply price: a free trial is available; see all pricing options.

2. Amazon Web Services

Amazon Web Services (AWS) offers an ever-expanding set of tools you can put together into an effective cloud data management stack. If you're already on AWS and are generating massive amounts of data, this might be the right cloud data management tool for you.  

Key services include: 

  • Amazon Athena for SQL-based data analytics 
  • Amazon S3 for temporary and intermediate storage 
  • Amazon Glacier for long-term backup and storage
  • AWS Glue for building data catalogs to categorize, search, and query your data 
  • Amazon QuickSight for dashboard construction and data visualization 
  • Amazon Redshift for data warehousing 
  • Separate billing for each spun up service so that costs depend on the extent of utilization 

TL;DR: it's a useful tool for large enterprises that generate oceans of data and have the technical prowess to manage it. However, costs can quickly add up, so careful planning is necessary. 

AWS price: varies and depends on your implementation.

3. Microsoft Azure

Microsoft Azure offers a variety of options when it comes to setting up a cloud-based data management system. It also comes with various analytics tools that can be used on your Azure-stored data. Like AWS, Azure also accommodates multiple databases or data warehouse styles and provides a great set of tools for managing them. 

Key services include: 

  • Standard SQL databases and VM-based SQL servers 
  • Blob storage
  • NoSQL-style table storage options
  • Private cloud deployments
  • Azure Data Explorer for real-time analysis of very large streaming unprocessed data sets 
  • Easy integration with Panoply for ELT/ETL services

TL;DR: as these tools are cloud-based, you're good to go without any implementation headaches. However, there's a learning curve if you're not familiar with the Azure environment.  

Azure price: varies and depends on your implementation.

4. Google Cloud 

Like Amazon and Azure, the Google Cloud Platform also offers a wide array of cloud-based data management tools. It also provides a useful workflow manager that's leveraged to tie-up different components together. 

Key Google Cloud components include: 

  • BigQuery for tabular data storage and BigQuery analytics for SQL-style queries
  • Cloud BigTable for NoSQL database-style storage
  • Cloud Pub/Sub and Cloud Data Transfer for data intake (Google Cloud can also connect with a variety of other data sources)
  • ML Engine for more advanced analyses using ML and AI 
  • Data Studio for dashboard construction and GUI-based analysis 
  • Cloud Datalab for code-based data science 
  • Connections to standard BI tools like Charito, Domo, Looker, Tableau, and more

TL;DR: if you're already on Google Cloud and are working with vast amounts of data, this would be an easy addition, but even highly technical users will have to contend with a steep learning curve.

Google Cloud price: varies and depends on your implementation.

Best ETL & data integration tools

ETL and data integration tools move data from a source to a destination. If Different tools offer different degrees of flexibility in managing the extract-transform-load process (e.g., ETL vs. ELT), so be mindful of your business needs when vetting them.

Modern ETL tools also vary widely in terms of how you can work with your data. Some tools offer visual interfaces, others provide point-and-click integration, while still others require a more robust knowledge of code.

1. Informatica PowerCenter

Informatica PowerCenter is an on-premise ETL tool. Informatica PowerCenter offers the following key features:

  • Seamless connectivity and integration with all types of data sources using out-of-the-box connectors
  • Automated data validation via script-free automated audit 
  • Advanced data transformations including non-relational data, XML, JSON, PDF, Microsoft Office, and IoT data
  • Metadata-driven management that provides graphical views of data flows, impact and lineage

TL;DR: In a world of cloud platforms, Informatica PowerCenter is an on-prem holdout that could be exactly what companies bound by complex regulatory concerns need.

Informatica PowerCenter price: available upon request.

2. Stitch Data

Stitch Data is a cloud-based ETL platform. Stitch offers the following key features:

  • Pre-integrated with dozens of data sources on and off the cloud, moves data into Amazon Redshift, S3, BigQuery, Panoply, PostgreSQL, and more
  • Easy scheduling for data replication
  • Error handling and alerting with automated resolution when possible
  • API and JSON framework, letting you push data into a data warehouse programmatically
  • Managed cloud service with automatic scaling and enterprise-grade SLAs

TL;DR: Stitch offers a wide array of integrations as well as a number of community-sourced connectors via its open source Segment platform, making it a highly popular choice.

Stitch price: starts at $100/month, based on data size 

3. Fivetran

Fivetran is a fully-managed data pipeline with a web interface that integrates data from SaaS services and databases into a single data warehouse. Fivetran's key features include: 

  • Provides direct integration and sends data over a direct secure connection using a sophisticated caching layer

  • Caching layer helps to move data from one point to another without ever storing a copy on the application server

  • Fivetran does not impose any data limit

  • Can be used to centralize a company’s data and integrate all sources to determine Key Performance Indicators (KPIs) across an entire organization

TL;DR: Fivetran is big and only likely to get bigger given its recent valuation. It's known for being a bit more complex than Stitch, but the real make-or-break is whether or not it has the connectors you need.

Fivetran price: Starts at $1/credit; pricing model is based on Monthly Active Rows.

4. Blendo

Blendo is another cloud-based ETL and data integration service, with the following key features:

  • Connects to numerous data sources with a few clicks, moves data to Amazon Redshift, Panoply, PostgreSQL, MS SQL Server, and more
  • Loads and synchronizes historical data from cloud services
  • Load data periodically or at selected frequencies from different data sources
  • Automated collection, detection and preparation of data using optimal relational schema

TL;DR: Often praised for its service, Blendo is a solid choice but may lack some critical integrations.

Blendo price: starts at $150/month, depends on number and types of integrations as well as data volume. 

5. Microsoft SQL Server SSIS

Microsoft offers SSIS, a graphical interface for managing ETL using MS SQL Server. Key features include: 

  • Easy-to-use interface allows users to deploy integrated data warehousing solutions without having to get involved with writing much—or any—code
  • Graphical interface allows for easy drag-and-drop ETL for multiple data types and warehouse destinations, including non-MS DBs
  • Great solution for a team with a mix of technical skill levels, as it’s equally effective for ETL adepts and point-and-click types alike

TL;DR: If you're working with SQL Server, SSIS is an obvious option. However, it does require coding skill for some operations, which could be a problem for less technical teams.

SSIS price: $0.450/hour

6. Azure Data Factory

In addition to SQL Server SSIS, Microsoft’s on-premise ETL solution, the company also offers Azure Data Factory (ADF), an ETL tool for their cloud-based Azure platform. Key features of ADF: 

  • ETL pipelines in ADF are built in a graphical interface, allowing for low-code use
  • Wide variety of data connectors for easy data ingestion
  • Full support for loading data into Azure data warehouses

TL;DR: Azure Data Factory is a more user-friendly option than SQL Server SSIS that could be ideal for companies looking for an on-premise ETL option.

Azure Data Factory price: $1 for 1,000 runs.

7. Talend

Talend open source data integration software products provide software to integrate, cleanse, mask and profile data. Key features of Talend offerings include: 

  • GUI that enables managing a large number of source systems using standard connectors
  • Master Data Management (MDM) functionality
  •  Single, consistent and accurate view of key enterprise data

TL;DR: Tons of reliable connectors make Talend a favorite among its users, but it does require some expertise to manage well.

Talend price:  $1,170/user monthly or $12,000 annually.

8. Alooma

Alooma offers an enterprise-scale data integration platform with great ETL tools built in. Some key features of Alooma offerings: 

  • Strong focus on rapid pipeline construction, data quality monitoring and error handling
  • Protection against loss or corruption of data in a potentially error-prone ETL process
  • Flexibility to intervene and write your own scripts to monitor, clean and move your data as needed
  • Designed for enterprise-scale operations

TL;DR: If you've got massive amounts of data, Alooma could be a good option. However, user complaints about difficult debugging that could be a dealbreaker.

Alooma price: available upon request. 

Best data transformation tools

Data transformation tools enable businesses to change data formats through automation. It's a critical step in the data integration process where both structured and unstructured data from disparate sources are migrated and automatically transformed within minutes.

It's crucial because any misstep could lead to incompatibility and data loss. So when choosing a data transformation tool, it's essential to pick one that offers transformation, cleansing, and enrichment without data loss.

1. Dataform

Dataform enables collaboration on SQL pipelines in BigQuery. This fully managed data transformation platform helps organizations effectively handle different cloud data warehouse processes. 

Key features include:

  • Helping teams write SQL workflows in a collaborative IDE 
  • Offering built-in version control and integration with Github
  • Allowing data teams to write data quality tests and unit tests and set alerts whenever it fails to ensure that your data is always reliable
  • Publishing tables and quickly automate complex SQL workflows
  • Creating a centralized repository for data definitions across your company, documenting your data, and discovering datasets in a data catalog
  • Running schedules to ensure your data is always up to date

TL;DR: Dataform is best suited for medium to large enterprises with a team of data analysts and engineers. As it's a highly technical tool, it's not an option for business users. 

Dataform price: varies and depends on the number of users and features used. A free version is available.

2. dbt

dbt (data build tool) is a SQL-based data transformation tool that initiates data transformation by writing SELECT statements. Built to streamline data analytics and engineering workflows, you have to write models that reflect your core business logic. 

Key features include: 

  • An SQL-based tool with straightforward workflows for building modular transformation and data modeling flows 
  • Automated data quality management with a robust testing system 
  • Streamlined analytics code deployment using environments, package management, and continuous integration
  • Easy data documentation with auto-generated DAGs, easy data annotation, cataloging, and reusable macros 
  • Data snapshotting for better historical data analysis 

TL;DR: it's open-source and highly customizable. It's SQL-based and only the "T" in ETL, so you'll need other tools to work with it.

DBT price: $50 per developer seat, per month, or custom pricing for enterprise teams, and a free basic tier for developers is also available.

3. Airflow

Airflow is a popular open-source data infrastructure tool originally developed at Airbnb. Although it doesn't actually do any data processing, Airflow helps schedule, organize, and monitor ETL processes using Python. 

Key features include: 

  • Directed Acyclic Graphs (DAGs) that allow its scheduler to spread your tasks across an array of workers without having to define precise parent-child relationships between data flows
  • Handy web-based UI for managing and editing your DAGs 
  • Nice set of tools that makes it easy to perform "DAG surgery" from the command line
  • Highly extensible and scalable

TL;DR: if you already have a team of Python coders on hand, you're good to go. The CI/CD can be tricky, and there's no native support for Windows.

Airflow price: is free and open source.

4. Luigi

Like Airflow, Luigi is also an open-source solution, but Spotify developed it. This Python-based tool makes the management of long-running batch processes easier. It can handle tasks that go far beyond the scope of ETL, but it does ETL pretty well too. 

Key features include: 

  • Python codebase, making it easy to build efficient data pipelines, especially ones that deal with long-running batch processes 
  • Easy dependency resolution
  • A web interface that allows users to visualize tasks for workflow management 
  • Conceptual similarity to GNU Make, but isn't only for Hadoop 
  • Atomic file system operations ensure that your pipelines will never crash with partial data 

TL;DR: it's a good option for enterprises with Python coders, but unlike Airflow, not much development is going on right now in the Luigi ecosystem.

Luigi price: is free and open source.

Best master data management tools

Master Data Management (MDM) tools aim to manage the central and master data of a business. These include customer data, employee data, operations data, regulatory data, and more. 

MDM tools help you with data cleansing, centralization, transaction control, key mapping, and multidomain support. You can also use these tools for information distribution and global synchronization across different locations.

1. Dell Boomi

Dell Boomi Master Data Hub is an enterprise-grade platform that leverages the cloud to maximum effect. Cohesive and versatile, it helps organizations effectively manage a variety of application and data sources across hybrid cloud environments. 

Key features include:

  • Helping companies achieve interoperability between external partners and internal systems
  • Defining models via low-code and visual experiences and synchronizing and enriching data using the centralized hub
  • Deploying data models and identifying which source systems to interact with
  • Onboarding system records into a consolidated repository and automatically merging similar records
  • Enabling data stewarding and alerting teams to resolve duplicates and data entry issues
  • Governing data with real-time bidirectional process flows across silos

TL;DR: it's a tool that accommodates non-techy business users, but Boomi doesn't come cheap.

Dell Boomi Master Data Hub price: follows a customized pricing model, and a 30-day free trial is available.

2. Profisee

Profisee Master Data Management helps enterprises manage master data by cleaning, standardizing, and matching source data. You can enforce business processes and empower data stewards to master data leveraging feedback from analytics, including governance and progress measurements. 

Key features include:

  • Stewardship and governance to enable "data stewards" within the organization to manage master data with feedback from analytics
  • Golden record management to standardize, clean, and match source data without any coding
  • Event management to detect data changes and to distribute events to subscribing systems
  • Integrator to federate master data for global enterprises, with real-time Bi-directional integration
  • Enterprise workflow to enforce business processes across the organization and let administrators manage data steward performance
  • SDK to enable the integration of custom applications

TL;DR: Profisee MDM comes with a user-friendly and intuitive UI, but you still have to contend with a learning curve. It's best suited for regulated industries like finance, healthcare, and insurance. 

Profisee price: available upon request.

3. SAP NetWeaver

SAP NetWeaver MDM is a component of the NetWeaver development platform. It enables swift and flexible design, implementation, and execution of new business strategies and processes. If you're already working with SAP products like mySAP suite, it's relatively easy to integrate data about your people and processes. 

Key features include:

  • Automatic extraction of master data from all major SAP applications
  • Distribution of master data to targets and loading of master data from other sources
  • Integration of data using business content like repository structures, validation rules, and inbound and outbound mappings
  • Enabling programmatic data integration via APIs and web services

TL;DR: it's a powerful solution that comes with many features. But you'll need to have the necessary skill sets to get the most out of this solution.

SAP NetWeaver pricing: available upon request.

4. Semarchy xDM

The Semarchy xDM platform is a popular platform among leading brands in Europe and North America. Also known as Semarchy Intelligent MDM, the tool helps companies overcome data governance challenges. Companies can leverage xDM's material design, as well as AI and ML protocols for data enrichment, data quality, and data stewardship. 

Key features include:

  • Data modeling, quality, and version control that isn’t Hadoop dependent but is optimized for Oracle
  • Updates and changes being tracked and propagated using metadata, allowing for iterative and "evolutionary" data management
  • A step-by-step UI that is customizable to specific business roles (i.e., analysts, operations, etc.)

TL;DR: it's a reliable solution leveraged by large enterprises to overcome data governance challenges. However, you'll need some skills and experience to use it.

Semarchy xDM price: available upon request.

5. TIBCO MDM

TIBCO is a leading MDM solution that's popular among industries like banking, energy, insurance, government agencies, and more. It's an excellent tool for companies that require multidomain management, workflow visualization, relationship mapping, and more. 

Key features include: 

  • Flexibility and scalability for multidomain applications
  • An independent, distributed master data layer
  • A customizable no-code visual interface that can be built and adapted to suit various business roles
  • Match and merge and highly rated data versioning support
  • SOAP Web services support

TL;DR: TIBCO MDM is best suited for large enterprises who want to manage different data types in a centralized location. It's not very intuitive and can be quite a challenge for beginners and new users.

Tibco MDM price: available upon request.

6. Ataccama ONE

Ataccama ONE is a highly automated data management and governance tool that can be run on-premise, in the cloud, or a hybrid setup. This AI-powered platform is ready for mission-critical deployments and integrates and unifies data governance, data quality, and master data management.

Key features include: 

  • Data discovery and profiling
  • Machine learning-based data curation, cleaning, and classification
  • Automated metadata discovery and project configuration
  • Domain agnostic but is pre-configured with pre-built rules for MDM for typical domains such as customer, contact, and product
  • Connectors for a wide range of data types and sources
  • GDPR ready systems for those operating in Europe
  • Full data audit history with a detailed log of applied business rules and transformations 

TL;DR: Acama ONE provides all your data management tools in one centralized location, but users report that it can be quite buggy with each update.

Ataccama ONE price: available upon request, and a free trial is available.

7. Stibo STEP

Stibo Systems is probably the oldest company on this list. Founded in 1795 in Denmark, Stibo has undoubtedly come a long way since its origins as a printing company. Stibo STEP is more of a recent addition from the company's data management arm. It provides a high level of automation, merging, and centralized data across channels like products, suppliers, customers, and location information. 

STEP is popular among enterprises in industries like finance, manufacturing, travel, and hospitality. Furthermore, its automated data and language translation feature makes it highly suitable for multinational operations. 

Key features include:

  • Customizable branding with an intuitive STEP Workbench UI
  • A holistic multidomain MDM platform 
  • Automated review and approval of digital assets
  • Enterprise-level master data management

TL;DR: Stibo STEP is perfect for multinationals who require cross-channel consistency, but you'll need the necessary expertise to use it.

Stibo STEP price: available upon request.

Best reference data management tools

Reference data management is a subset of master data management used for classification and defining permissible values used by other fields, both internally and externally. These can be anything from zip codes, country codes, measurement units, currency, products and pricing, and so on. 

It's crucial to use robust tools to manage this type of data as it serves as a reference point for a number of systems. Poor reference data management can lead to operational inefficiencies, poor governance, and incorrect reporting and analytics.

1. Collibra

Collibra's Data Governance offering comes with reference data tools that help analysts, data scientists, stewards, and business users by automating workflows to create new code sets. It also performs accurate data mapping to remove barriers to seamless data access.

Key features include:

  • Automated workflows and generation of new code and code sets
  • Engagement in precise data mapping
  • Delivery of codes and code sets to users in a friendly manner
  • Comparison of data from different sections of the company

TL;DR: feature-rich, but pricing can be confusing, and implementation costs can come as a surprise to some customers.

Collibra price: available upon request.

2. Magnitude

Magnitude Reference Data Management is equipped to integrate various domains into a single model and enables the support of cross-domain relationships. Smart algorithms help minimize manual stewardship through automated matching, harmonization, and survivorship.  

Key features include:

  • Multidomain modeling to support business structures from code lists to multi-path, self-referencing hierarchies
  • Automation for governance and control over reference data objects and load processes
  • Invalid data being consistently routed to data stewards for rectification and improvement
  • Mapping of global to local, external to internal, and specific to general mapping without disruption to existing elements
  • Governance to enable customizable workflows to control business processes related to reference data
  • Model-based security controls that allow users to view, add, or update
  • Time variance to help users change models, subjects, attributes, and associations and retrieve any previous version of the object

TL;DR: Magnitude is a business user-friendly tool, but it can get complicated once you start integrating data from multiple sources.

Magnitude Reference Data Management price: available upon request.

 

3. Informatica MDM Reference 360

Informatica MDM Reference 360 is a cloud-based tool that provides an end-to-end approach with embedded data quality, data integration, process management, and more. As it's fully-cloud-based, you can improve performance and scalability without much effort.

Key features include:

  • Self-service master data management and workflows designed for business users
  • Merging and cross-referencing of data from new types and sources
  • Configurable, user-friendly dashboards that help control the quality of reference data sets 

TL;DR: Informatica MDM Reference 360 helps users quickly implement rules and make changes, but there can be a steep learning curve. 

Informatica MDM Reference 360 price: available upon request.

4. Reltio Cloud

Reltio Cloud is a graph-based master data management tool that is equipped with reference data management tools. Reltio is built on graph databases to provide maximum flexibility in scaling data stores and defining clear relationships between the data in your repository. 

Reltio can be used to manage mission-critical data and win in the experience economy. Reltio Connected Customer 360, built on cloud-native, big data architecture featuring graph technology and machine learning, is at the heart of customer experiences. This approach enables hyper-personalization, accelerated real-time operations, and simplifies compliance, and all at scale.

Key features include: 

  • Cloud-native big data architecture with graph technology and ML
  • Support of both structured and unstructured data at scale
  • User-friendly interface that enables hyper-personalization
  • Easy integration with existing MDM tools and other data sources
  • An always-available cloud platform that makes zero-downtime upgrades possible

TL;DR: it's an excellent tool for Fortune 500 companies focused on delivering enhanced customer experiences, but you'll have to contend with a steep learning curve.

Reltio Cloud price: available upon request with a free trial option.

Best analytics & visualization tools

From machine learning-enabled notebooks to drag-and-drop dashboards, analytics and visualization tools are designed to help you derive insights from your data. 

While all the options on this list offer some degree of data visualization, tools vary in the customizability of your data viz. These tools also offer a range of query options from SQL-first to drag-and-drop.

1. Tableau

Tableau is a BI platform available both on the cloud and as downloadable software, with the following key features:

  • Easily connects to data sources
  • Allows easy access to visualizations for teams, partners and clients
  • Enables unlimited data exploration with interactive dashboards
  • Creates “dashboard starters," actionable dashboards setup in minutes with data from popular web applications
  • Creates interactive maps automatically

TL;DR: Tableau is great for businesses that are seriously into data viz but that also want the ease of drag-and-drop analysis. 

Tableau price: starts at $70 per user per month.

2. Cumul.io

Cumul.io is a cloud-based embedded analytics and data visualization platform. Cumul.io has the following key features:
  • Intuitive drag-and-drop interface for creating dashboards
  • Secure and white labeled embedding into your SaaS platform, web application, or online portal
  • Connect to databases or data warehouses with just a few clicks, no server installation required
  • A flexible API to fully automate dashboard creation, hyper-customize dashboards, or set up interactivity between your app & embedded dashboards

TL;DR: Cumul.io is a top-notch option for companies looking to offer embedded analytics, especially if they want customer-facing dashboards up and running quickly.

Cumul.io price: $995 - $2,700 per month for full embedded capability or white-labeling, with plans ranging from 100-1000 monthly active viewers.

3. Looker

Looker is another cloud-based analytics and visualization platform, with the following key features:

  • Define metrics once using LookML, Looker’s simple data modeling language, and Looker writes SQL queries to answer any question on those metrics
  • Make data beautiful with easy-to-read dashboards that allow users to drill in and explore
  • Connect directly to databases, with no extracts or software to download
  • Open access to dashboards and reports to everyone, not just analysts or data scientists

TL;DR: Looker is ideal for companies that prefer downstream control of their data model and business logic.

Looker price: available upon request.

4. Metabase

Metabase offers a user-friendly, open source interface for connecting and analyzing your data. As a data visualization tool, it offers: 

  • Hundreds of native data connectors
  • Easy setup and access for your entire team
  • Large range of different data visualization options to choose from
  • White labeled embedding (premium embedding tier and up)
  • Auditing and data permission management (enterprise tier and above)

TL;DR: Metabase's low cost can help companies get started with analytics and visualization, but may fall short as a long-term solution.

Metabase price: Metabase is free and open source, so its free tier offers a range of features that will be suitable for most users. Paid plans start at $100/month.

5. Microsoft Power BI 

Power BI, Microsoft’s offering in the business analytics space, is designed to be useful for business analysts and data scientists alike. Main features: 

  • Simple drag-and-drop interface that is designed to be familiar to users of MS Excel
  • Power BI is a no-code platform, and offers both desktop and web clients
  • Library of pre-built connectors
  • Performance tends to lag on very large data sets—look elsewhere for big data solutions

TL;DR: Power BI is popular for a reason: it's easy to use thanks to its Excel-like interface that lowers the barrier to entry for non-analysts.

Microsoft Power BI price: $9.99 per user per month.

6. Mode Analytics

Mode Analytics offers a web-based data analytics suite aimed at data scientists and analysts, with a focus on collaboration and sharing. Some of Mode’s key features: 

  • Designed to make it especially easy to connect your data sources to their built-in SQL editor and visualization platform
  • Mode can pipe the results of your SQL queries directly into an R or Pandas dataframe in a Mode-native notebook
  • Share the data, analysis and resulting dashboard all through Mode
  • Mode is a good choice for a team of data scientists who want to be able to collaborate more easily

TL;DR: Mode is ideal for teams that need to support both analysts looking to use SQL, r, or Python and business users who need easy dashboarding.

Mode Analytics price: available upon request. 

7. ClicData

If you need an all-in-one tool, ClicData could be a good fit. Its primary features include:

  • Native  integrations with many common data sources
  • Automated data transformation to clean and normalize your data
  • Drag-and-drop dashboarding for interactive cloud-based dashboards that can be automatically refreshed
  • Ability to set up alerts and share dashboards via email as PDFs, images, or interactive links

TL;DR: ClicData could work well for companies that prefer to work with a single vendor for all their data needs. When evaluating cost, pay close attention to what pricing tier your data sources will land you in.

ClicData price: starts at $71 per month for an annual contract.

Towards integrated data management

There's no replacement for managing business processes around structured data in large organizations, but cloud-based platforms can help with data management strategy. For example, they can support the treatment and preparation of raw data, data ingestion, loading, transformation, optimization, and visualization, all automatically in a single system. 

For example, Panoply's cloud data platform can connect directly to data sources, manage data loading, and automatically transform your data into clean tables that are ready for analysis. Tools that provide an integrated big data stack take us one step closer to a truly holistic data management concept.

At Panoply, we believe in simple and robust data management. Although Panoply was developed to work well for data engineers that simply don't have the bandwidth to manage everything on their own, analysts can also be successful.

Learn how Panoply's built-in integrations and DBA-free storage makes it easy to manage your data.
Get a Demo
Read more in:
Share this post:

Work smarter, better, and faster with monthly tips and how-tos.