Why is an efficient data management so important?
The ability to use data for the benefit of the business 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 and profitably use data. These include disciplines such as data governance, data architecture, data quality, master data management, data warehousing and business intelligence as well as data security and a few more (see DAMA Framework at dama.org).
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 corporate strategy is increasing.
In this first study, we focus on data management for BI and data warehousing. However, the scope of data management is far greater and covers other data environments in the company in addition to BI. This includes operational systems, explorative environments for advanced analytics as well as a holistic view of company data with regard to strategic tasks (e.g., digitalization). Each of these areas has its own specifics regarding data types, technologies, methods, 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 a fundamental modernization.
In this article you will find out:
- which are the best data management software tools as rated by users (Data Management User Review Matrix) and
- the most important lessons learned from surveying almost 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 2150 Datavault Builder and AnalyticsCreator 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. And explore the development of SAP BW via SAP BW on HANA to SAP BW/4HANA from the user perspective.
The Data Management User Review Matrix rates software based on the product satisfaction (x-axis), performance (y-axis), data governance (color) and developer efficiency (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 a BICC, BI 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 BI 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 KPI rules
The peer groups
Analytical database products prepare, store and provide data for analytical purposes.
Data warehouse automation products cover the data-driven or requirements-driven data warehouse design and implementation. Data warehouse 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 connect, extract, transform and load data from various source systems to a target system for analytical 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.