Why is efficient data management so important?
The ability to use data is crucial, even vital. It is critical to a transformation process that will enable a company to successfully position itself and assert itself in a highly dynamic, data-driven world. A basic prerequisite for exercising such an ability is properly functioning data management.
By data management we mean all conceptual, methodical, technical and organizational measures that serve to efficiently manage, protect and profitably use data. These include disciplines such as data governance, data architecture, data quality, master data management, data warehousing and data security.
The image of data management – formerly a hidden process in the dark basements of the IT department – has changed dramatically in recent years. The professional and technical relevance of data management is constantly increasing, especially when it comes to making trustworthy and correct data usable for BI and analytics. As a result, the relevance of data management in corporate strategy is increasing. Data has become a strategic asset. And a strategic asset must be efficiently maintained.
In the second edition of The Data Management Survey, we have extended our focus beyond BI to cover data & analytics and all tools that help to monetize data. This embraces BI, data management and analytics capabilities in operational systems, exploratory environments (e.g., data lakes) for advanced analytics as well as a holistic view of company data with regard to strategic tasks (e.g., data strategy). Each of these areas has its own specifics in terms of data types, technologies, methods and architectures as well as required skills and personnel resources.
Data management tasks for BI and data warehousing focus on data integration, data preparation and data modeling as well as data storage and provision for reporting, analysis and planning. Today’s requirements for classic BI architectures and technologies are to be able to keep pace with ever more dynamic requirements and growing complexity in the area of data processing and systems. Often there is no way around fundamental modernization.
In this article you will discover:
- the best data management software tools as rated by users (Data Management User Review Matrix) and
- the most important lessons learned from surveying over 700 respondents about data management software usage and selection.
The Data Management Survey: Head-to-Head Tool Comparison
The Data Management Survey: The User Comparison
To make the comparison and search for the best tool for your company easier, this article sets out to provide you with a balanced view of what users of data management software – as well as BARC analysts – have to say about the leading data management products on the market.
By combining our in-depth knowledge of current data management software tools with our large database of user reviews of data management software, we aim to guide you through the first steps to finding the best tool for your company.
We believe a combined view of user feedback and in-depth analyst perspective is necessary in order to fully appreciate how data integration, data warehouse automation software tools and analytical databases compare against each other, and to understand which are the best ones for your company.
Strengths and weaknesses of the products are graphically presented and BARC’s analysts highlight interesting findings in their commentaries. Find out why specialists such as AnalyticsCreator, Exasol, InfoZoom/IZDQ and Snowflake were rated better than generalists such as Microsoft, Oracle and SAP. Find out which data integration tools real-world users believe will really help you to reach your goals quickly and efficiently.
The Data Management User Review Matrix rates software based on product satisfaction (x-axis), innovation power (y-axis), functionality (color) and support quality (circle size). To see the comparison tool in action, please select a peer group.
The Data Management Software User Review Matrix
Who should read the study?
In this highly dynamic world, internal and external influences require increasingly rapid and well-judged reactions. This can only be mastered if we have our systems under control. This study offers interesting insights, especially for members of data, BI or analytics competency centers, data experts, architects and people who prepare data for corporate management. It is designed to give you some orientation in this competitive software market and provide you with valuable guidance for upcoming tool selection projects or stress tests.
Not all data management tools for data & analytics are alike. They focus on specific topics such as analytical databases, ETL, data warehouse automation or cover a variety of functionality in a broad portfolio. This is why we use peer groups to compare products.
The peer groups are primarily based on the various activities involved in analytical projects and the different user groups. They take into account how customers say they use the software, which can vary substantially from product to product. But we also include the experience and judgment of BARC analysts in deciding on the groupings.
Peer groups are simply a guide to the reader to help make data management tools easier to compare and to show why individual products return such disparate results. They are not intended to be a judgment of the quality of the tools.
The point to the peer groups is to make sure that the comparisons between the data management tools make sense. The products are grouped together as we would expect them to appear in a software selection shortlist.
To make a proper choice, buyers should first segment the market into the tool types that fit their requirements. The peer groups are intended to help with this task.
The KPIs
The interaction of several architectural and technical factors, the concept of application use and the fulfillment of set expectations form the foundation of product satisfaction. This KPI provides an indication of whether – and in what quality – the performance promise has been fulfilled and how conveniently the tool can be used. The KPI goes beyond specific functionality and evaluates the product as a whole.
Efficiency in data management can be significantly improved by using innovative technologies such as AI. The development and user adoption of new, useful features as well as a robust, well thought-out and transparent vendor roadmap are important indicators for companies wanting to leverage the tool in the best and most efficient way in the medium to long term.
In order to perform various data management tasks, the requisite functionality must be available to users. This KPI is an indicator of the efficiency and effort with which tasks can be implemented, based on the scope and completeness of the functionality offered with the product.
Product support from the vendor is a key determinant of project success. There can be a big difference between the level of support services offered and the quality of the actual support provided. This KPI helps buyers to understand how helpful the software vendor’s customer support really is.
The KPI rules
The peer groups
Analytical database products prepare, store and provide data for analytical purposes.
Data management products are tools that help to connect, transport, transform, prepare and enrich, monitor and protect data.
Data warehouse technologies prepare, store and provide data for data warehousing purposes.
Data warehousing automation products cover 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.
ETL products (including ELT) connect, extract, transform and load data from various source systems to a target system for data warehousing purposes.
Global vendors have a sales and marketing reach through subsidiaries and/or partners which gives them a truly global presence. They are present worldwide and their products are used all around the world.