This screen shows the responses to a single question in The Data Management Survey. You can choose the question in the dropdown box in the top left corner of the report. The filters to the left of the screen can be used to modify the chart.
N = 907
The KPI Dashboard allows you to compare product results by criteria (KPIs). For each KPI, values are shown for all the products in the peer group selected in the peer group filter. The peer group average is displayed as a gray bar. All products displayed above the gray bar are above average in the peer group, and those below the gray bar are below average. At the top of the screen, you can choose which KPI and peer group to analyze.
This report shows a summary of the KPI results for a single product. Simply select a peer group and product in the filters at the top of the screen. The product filter will only display the products in the selected peer group. The peer group filter will only display the peer groups in which the selected product appears.
This screen allows you to make your own short list of products and compare them directly against each other based on multiple criteria. First select a peer group that you want as the basis for the product comparison in the filter. Then select the relevant criteria (KPIs). Multiple KPIs can be selected in this filter. Finally select the products you wish to compare.
The Data Management Survey Analyzer web app is an interactive tool that enables you to perform your own custom analysis of The Data Management Survey data. The app contains results from all the questions asked in The Data Management Survey 23. Product-related results based on a series of important criteria (known here as KPIs – Key Performance Indicators) are also available to view.
| KPI | Short explanation | |
|---|---|---|
| Demographics: Country | Country | |
| Demographics: Department | Which department do you work in? | |
| Demographics: Industry | Industry sector | |
| Demographics: Number of administrators | Number of people involved in the general administration of the data management product | |
| Demographics: Number of developers | Number of people involved in developing and managing data processes/models/content | |
| Demographics: Number of product users | Number of data management product users | |
| Demographics: Number of employees | Number of employees | |
| Demographics: Percentage of product users | Percentage of product users in company | |
| Demographics: Position | Position in company | |
| Demographics: Region | Region | |
| Selection: Chosen product | Which data management product would you like to review? | |
| Selection: Reasons to buy | Why was the product chosen? | |
| Product: Extent of usage | To what extent is your data management product used in the company? | |
| Product: Recommendation | Would you recommend others to buy your data management product? | |
| Product: Scenarios | For which scenarios/application areas do you use your data management tool? | |
| Product: Satisfaction | To what degree are you satisfied with your data management tool? | |
| Product: Usage problems | What, if any, are the most serious problems in the use of your product? | |
| Features: Adaptability | How would you rate the adaptability of your product? | |
| Features: Automation | How would you rate the automation of your product? | |
| Features: Connectivity | How would you rate the connectivity of your product? | |
| Features: Development efficiency | How would you rate the development efficiency of your product? | |
| Features: Functionality | How would you rate the functionality of your product? | |
| Features: Performance | How would you rate the performance of your product? | |
| Features: Platform reliability | How would you rate the platform reliability of your product? | |
| Features: Price to value | How would you rate the price to value of your product? | |
| Features: Product enhancement | How would you rate the frequency and quantity of innovative, helpful product updates? | |
| Features: Support quality | How would you rate the support quality of your product? | |
| Features: Time to market | How would you rate the time to market of your product? | |
| Features: Usability | How would you rate the usability of your product? | |
| Non-product-related questions: Decision-making in the company | How are decisions made in your company and what role do data and empirical values play? | |
| Non-product-related questions: Trends | Which data & analytics trends do you think have the strongest impact on data management investments? | |
| Non-product-related questions: Acquiring new technologies/software tools | To what extent have investments in acquiring new technologies/software tools changed over the past 12 months? | |
| Non-product-related questions: AI & advanced analytics initiatives | To what extent have investments in AI & advanced analytics initiatives changed over the past 12 months? | |
| Non-product-related questions: BI initiatives | To what extent have investments in BI initiatives changed over the past 12 months? | |
| Non-product-related questions: Create and occupy new data & analytics roles | To what extent have investments in creating and occupying new data & analytics roles changed over the past 12 months? | |
| Non-product-related questions: Data & analytics competence building | To what extent have investments in data & analytics competence building changed over the past 12 months? | |
| Non-product-related questions: Data & analytics strategy development | To what extent have investments in data & analytics strategy development changed over the past 12 months? | |
| Non-product-related questions: Data governance & data quality initiatives | To what extent have investments in data governance & data quality initiatives changed over the past 12 months? | |
| Non-product-related questions: Data security & privacy initiatives | To what extent have investments in data security & privacy initiatives changed over the past 12 months? | |
| Non-product-related questions: Employment of external service providers | To what extent have investments in the employment of external service providers in the area of data & analytics changed over the past 12 months? | |
| Non-product-related questions: Internal marketing for data & analytics | To what extent have investments in internal marketing for data & analytics changed over the past 12 months? | |
| Non-product-related questions: Metadata initiatives | To what extent have investments in metadata initiatives changed over the past 12 months? | |
| Non-product-related questions: Migration to the cloud | To what extent have investments in the migration to the cloud/cloud infrastructure changed over the past 12 months? | |
| Non-product-related questions: Modernizing data & analytics architectures | To what extent have investments in modernizing data & analytics architectures changed over the past 12 months? | |
| Non-product-related questions: Planning & forecasting initiatives | To what extent have investments in planning & forecasting initiatives changed over the past 12 months? | |
| Non-product-related questions: Simplify data access | To what extent have investments in simplifying data access for business users and make it more flexible changed over the past 12 months? | |
| Non-product-related questions: Strategic identification and generation of new data | To what extent have investments in the strategic and connection/generation of new data changed over the past 12 months? | |
| Non-product-related questions: Support communities and collaboration | To what extent have investments in the support communities or cross-departmental collaboration with data changed over the past 12 months? |
Technologies that provide data warehouse capabilities as a service in the cloud.
Analytical database products prepare, store and provide data for analytical purposes.
Platforms that help to build up and utilize data knowledge effectively and efficiently utilizing automated processes, e.g., for linking and analyzing a wide variety of metadata from distributed metadata sources.
Products in this peer group support data-driven or requirements-driven data warehouse design and implementation. They mainly focus on the simplification and automation of data integration and data modeling tasks.
Data pipelining products take a modern approach to data integration and support more than one data integration pattern. A pattern can be data interaction, data integration, data preparation or even data orchestration in order to get data connected and to make it usable for any business purpose.
Business software generalists have a broad product portfolio including most (or all) types of enterprise software for a variety of business requirements (e.g., ERP, BI, DM).
Data governance products help to control, develop, monitor and secure data to make it usable for business needs. They do not manipulate data. Instead, they focus on managing and leveraging metadata such as data catalogs.
| Product | Number of answers |
|---|---|
| Alation Data Catalog | 31 |
| Amazon RedShift | 27 |
| AnalyticsCreator | 28 |
| Collibra DI Cloud | 27 |
| dataspot. | 30 |
| Exasol | 33 |
| Google BigQuery | 36 |
| Informatica EDC | 32 |
| Informatica PowerCen. | 27 |
| MS Azure Data Factory | 30 |
| MS Azure Synapse | 28 |
| MS SQL Server | 98 |
| MS SSIS | 31 |
| Oracle Data Integrator | 32 |
| Oracle Database | 32 |
| Qlik Data Integration | 30 |
| SAP BW/4HANA | 45 |
| SAP DI | 30 |
| SAP DWC | 33 |
| Snowflake CDP | 32 |
| Synabi D-QUANTUM | 39 |
| Talend DI | 32 |
| TimeXtender | 30 |
| Aggregated KPIs | Root KPIs |
|---|---|
| Customer Satisfaction | Price to Value |
| Time to Market | |
| Recommendation | |
| Product Satisfaction | |
| Support Quality | |
| Customer Experience | Performance |
| Platform Reliability | |
| Development Efficiency | |
| Usability | |
| Innovation | Product Enhancement |
| Automation | |
| Technical Capability | Connectivity |
| Functionality | |
| Adaptability |
| KPI | Short explanation |
|---|---|
| Price to Value | This KPI is based on how users rate their tool in terms of price-to-value ratio. |
| Time to Market | This KPI is based on how users rate their tool in terms of the time it takes to implement new use cases and changes. |
| Recommendation | This KPI is based on the proportion of users that say they would recommend the product to others. |
| Product Satisfaction | This KPI is based on the proportion of users that say they are satisfied with their product. |
| Support Quality | This KPI is based on how users rate their tool in terms of support quality. |
| Customer Satisfaction | This KPI combines the Price to Value, Time to Market, Recommendation, Product Satisfaction and Support Quality KPIs. |
| Performance | This KPI is based on how users rate their tool in terms of performance. |
| Platform Reliability | This KPI is based on how users rate their tool in terms of platform reliability. |
| Development Efficiency | This KPI is based on how users rate their tool in terms of development efficiency. |
| Usability | This KPI is based on how users rate their tool in terms of usability. |
| Customer Experience | This KPI combines the Performance, Platform Reliability, Developer Efficiency and Usability KPIs. |
| Product Enhancement | This KPI is based on how users rate their tool in terms of product enhancements. |
| Automation | This KPI is based on how users rate their tool in terms of its support for the automation of recurring processes. |
| Innovation | This KPI combines the Product Enhancement and Automation KPIs. |
| Connectivity | This KPI is based on how users rate their tool in terms of connectivity to data sources/targets and interfaces to integrate in ecosystems. |
| Functionality | This KPI is based on how users rate their tool in terms of functionality. |
| Adaptability | This KPI is based on how users rate their tool in terms of adaptability. |
| Technical Capability | This KPI combines the Connectivity, Functionality and Adaptability KPIs. |