About DataBench Project
Organisations rely on evidence from the Benchmarking domain to provide answers on how their processes are performing. There is extensive information on how and why to perform technical benchmarks for the specific management and analytics processes, but there is a lack of objective, evidence-based methods to measure the correlation between Big Data Technology (BDT) benchmarks and an organisation’s business benchmarks and demonstrate return on investment (ROI).
The DataBench project addresses this significant gap in the current benchmarking community’s activities, by providing certifiable benchmarks and evaluation schemes of BDT performance of high business impact and industrial significance.
DataBench will provide the community with important results including…
Including a complete set of metrics for BDT assessment.
Assessing the European and industrial significance of the BDT examined by the project.
A tool to connect and evaluate external initiatives.
Providing guidelines to the use of the project’s results, Framework & Toolbox, describing metrics implementation and benchmarks.
Based on existing efforts in big data benchmarking and enabling inclusion of new benchmarks that could arise in the future, the DataBench Toolbox provides a unique environment to search, select and deploy big data benchmarking tools, giving the possibility to generate unified technical metrics and, most importantly, going the extra mile and derive business KPIs for your organization.
Deliverable name: Data Collection Plan
Actual Delivery Date: 2018-08-31
This deliverable presents the plan of the data collection activities executed as part of WP4. The general goal of WP4 is to evaluate the impact of BDT (Big Data Technology) on business performance in key use cases adopting advanced big data and analytics technologies. The work in WP4 is based upon a case study approach. From a methodological standpoint, the case study analysis is inherently in-depth and bottom up, and, as such, it represents a natural complement to the extensive and top down research instruments adopted in WP2. In the framework of the DataBench project, case studies are considered necessary since the relationship between technical choices in organisations and the business KPIs these organisations have established for themselves is complex and difficult to model, as well as to measure. There is a lack of empirical evidence and benchmarks of the business benefits that can be achieved using BDTs, despite the general agreement that they provide a high level of innovation and potential to impact business. Our goal in WP4 is to help fill this gap by providing evidence of the business benefits of BDTs within the general framework of the DataBench project. This deliverable explains the methodology that will be used for the case study analysis, including the classification and selection of the case studies, the recruitment, piloting and analysis phases.
Deliverable name: DataBench Architecture
Actual Delivery Date: 2018-07-07
This document provides an overview of the DataBench Toolbox Architecture and main functional elements. The DataBench Toolbox aims to be an umbrella framework for big data benchmarking based on existing efforts in the community. It will include features to reuse existing big data benchmarks into a common framework, and will help users to search,select, download, execute and get a set of technical and business indicators out of the benchmarks’ results. The Toolbox is therefore one of the main building blocks of the project and the main interaction point with the users of benchmarking tools. This document provides the architectural foundations and main elements of the tooling support to be used by big data benchmarking practitioners. In this sense, the document gives an overview of the different elements of DataBench ecosystem to contextualize the significance of the Toolbox, as well as details about the different components of the Toolbox identified so far, and hints about their potential implementation.This document is the first deliverable related to the DataBench Toolbox. Updates to the architecture will be provided as integral part of the different releases of the Toolbox expected in the DataBench WP3 lifecycle.
Deliverable name: Project Web Portal and Dissemination Materials
Actual Delivery Date: 2018-06-30
At the heart of the DataBench project is the goal to design a benchmarking process helping European organizations developing Big Data Technologies to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance. DataBench will investigate existing Big Data benchmarking tools and projects, identify the main gaps, provide a robust set of metrics to compare technical results coming from those tools. It will provide a framework to associate those technical results with the economic processes that are imperative to a company. It will provide a robust set of benchmarks to assess which tools respond best and provide the most pertinent information for organisation’s economic planning and respond to their current and emerging industrial needs. It will provide a software tool which the industrial and research community users can leverage to do this evaluation. DataBench will interact with the Big Data PPP ICT14 and 15 projects to give access to this tool and framework to leverage the Big Data benchmarking investment so far carried out in the benchmarking community, contributing to the success of the BDV-PPP. The project envisions continuous interaction with the leading BDT suppliers and international industrial benchmarking user communities and has a strong relationship with the BDV cPPP. This deliverable presents the initial output of the dissemination and communication activities of DataBench. This includes the development and management of the project web portal, and the production of a set of dissemination materials for online publication and distribution at events.
Deliverable name: Project Fiche
Actual Delivery Date: 2018-04-05
At the heart of the DataBench project is the goal to design a benchmarking process helping European organisations developing Big Data Technologies to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance.
Deliverable name: Dissemination and Liaison Plan
Actual Delivery Date: 2018-03-31
DataBench has the potential to become a key element of the Big Data Value PublicPrivate Partnership, since it will contribute to measure the real impact of the investments made by industry and the European Commission in this partnership. It will support the benchmarking of Big Data Technologies, therefore helping to understand the progress with respect to the state of the art but will also relate technical performance indicators with business indicators, establishing a bridge that has never been built before. The challenges are however important. Among them, DataBench will require tight collaboration with a number of players and communities. Working hand-in-hand with benchmarking communities as well as with pilots and use cases (ideally coming from PPP projects) is key. They will be contributors but also validators of the so called DataBench ToolBox, main product to be produced by the project. Along the three years duration of DataBench we will have to gain the credibility and recognition of those communities, since -in most cases- they will be the first adopters/users of the DataBench outcomes. WP6 will support DataBench in creating the connections and engaging with those communities, will define and will implement a dissemination and communication strategy and will pave the path towards the self-sustainability of the results beyond the project duration. This deliverable describes the initial steps in that endeavour by sharing the Dissemination and Liaison Plan. The document identifies specifically: (1) target communities/audience, (2) Phases of the dissemination strategy, (3) tools, channels and mechanisms that will be used and (4) Key Performance Indicators.
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