DOCSLIB.ORG

The New Analytics Lifecycle Accelerating Insight to Drive Innovation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The New Analytics Lifecycle Accelerating Insight to Drive Innovation The New Analytics Lifecycle Accelerating Insight to Drive Innovation Dave Wells November 2017 The New Analytics Lifecycle About the Author Dave Wells is an advisory consultant, educator, and research analyst dedicated to building meaningful connections along the path from data to business impact. He works at the intersection of information and business, driving value through analytics, business intelligence, and innovation. With nearly five decades of combined experience in information management and business management, Dave has a unique perspective about the connections of business, information, data, and technology. Knowledge sharing and skills building are Dave’s passions, carried out through consulting, speaking, teaching, research, and writing. He is a continuous learner—fascinated with understanding how we think—and a student and practitioner of systems thinking, critical thinking, design thinking, divergent thinking, and innovation. About Eckerson Group Eckerson Group is a research and consulting firm that helps business and analytics leaders use data and technology to drive better insights and actions. Through its reports and advisory services, the firm helps companies maximize their investments in data and analytics. Its researchers and consultants each have more than 20 years of experience in the field and are uniquely qualified to help business and technical leaders succeed with business intelligence, analytics, data management, data governance, performance management, and data science. About This Report This report is sponsored by Alteryx, Amazon Web Services and Tableau. © Eckerson Group 2017 www.eckerson.com 2 The New Analytics Lifecycle Executive Summary Analytics is at the forefront of modern business management. The role of analytics for informed decision making is well known, and much attention is given to the value of insight. The power of analytics, however, stretches far beyond insight. The next generation of analytics sees insight as only the first step—the spark that ignites imagination, ideation, and inspiration—on a path to innovation. Analytics-driven innovation is a game changer, but getting there demands change. We must rethink analytics processes, compress the analytics lifecycle, and apply the right technologies in the right ways to achieve fast, scalable, accessible, and collaborative analytics. When frequency of insights matches or exceeds frequency of questions and uncertainties, we’ve achieved fast analytics. When analytics capacity dynamically adapts to expanding data volumes and growing workloads, we have scalable analytics. When data analysts from non-technical line-of-business people to highly skilled data scientists can meet their own needs, we have accessible self-service analytics. When analytics drives communication, conversation, common understanding, and shared insights, we have collaborative analytics. Fast, scalable, accessible, and collaborative—these are the keys to analytics-driven innovation. The Age of Innovation The Demand for Innovation Innovation is an essential business capability. The leading companies of today continuously innovate and adapt their business models, not simply responding to change but in many instances driving change. Looking to the future, the surviving companies of the next generation must embrace innovation as a core competency. Amazon and Google are well known examples of serial innovators repeatedly reshaping markets, products, and consumer expectations. Considering the fate of companies who failed to innovate—Blockbuster, Xerox, Blackberry, and others—it is clear that innovation is a critical part of survival in today’s business environment. Successful companies understand the importance of innovation. Successful innovators understand the very strong connection between analytics and innovation. © Eckerson Group 2017 www.eckerson.com 3 The New Analytics Lifecycle Insight Drives Innovation Analytics and insight form the bridge from data to innovation. Innovation isn’t something magical that only occurs through miraculous bursts of exceptional creativity or the amazing capabilities of a lone genius. Innovation is a process that begins with insight—the ability to see deep inside markets, problems, and opportunities. Insight fuels ideas, imagination, and inspiration that are key ingredients of innovation. (See figure 1.) Figure 1. Moving from Insight to Innovation Analytics is an essential component. Don’t underestimate the important role of analytics for becoming an innovative organization. Analytics and insight form the bridge from data to innovation. The Modern Analytics Organization Embracing and Enabling Self-Service The world of analytics changed radically with the introduction of self-service data technologies—BI and visualization, followed quickly by data preparation, and then today’s self-service analytics tools. Code-free analysis tools became line-of-business tools of choice for data analysis and data visualization. © Eckerson Group 2017 www.eckerson.com 4 The New Analytics Lifecycle Self-service analytics grew from demand. Business people want and need faster analytics than is practical with an IT-centric approach. The demand for data, reporting, and analytics has grown and typical IT departments simply can’t keep up with growing needs. But the drivers of self-service technology go well beyond speed of delivery, including the following: Adaptability. Analytics needs to be fast but also adaptable. Every answer brings new questions that frequently demand new data. Adapting to rapidly evolving needs throughout the processes of discovery and analysis is an important aspect of self-service. Adapting to data infrastructure is also an important feature in today’s complex data management world where data may be accessed from on-premises, cloud, and web sources. Affordability. Analytics needs to be ROI-friendly. Many analytics projects are one-off efforts that don’t justify the cost of a typical IT project. The investment needs to yield results quickly, and in a way that effectively empowers all users. For a typical line-of-business analyst the user experience needs to be similar to that of working in Excel, but with advanced analysis, spatial, and visualization capabilities. Autonomy. Business analysts, data scientists, and functional data analysts want their analytics processes to be local and “in my control” with freedom to explore, discover, and change direction, as needed. Self-service data and analysis are mainstream practices. You can’t put the genie back in the bottle. Self-service BI and visualization tools were quickly followed by self-service data preparation tools and data catalogs, further advancing the capabilities of line-of-business analysts. Self-service brings challenges, scalability, governance, IT support, and more—but you can’t put the genie back in the bottle. Self-service data and analysis are mainstream practices that every analytics organization must embrace. Modernizing Data Infrastructure As the demand for fast and abundant data continues to grow, enterprise data infrastructure is evolving. The data warehouse of the past struggles to keep pace with today’s needs. The data warehouse needs to be modernized. Cloud data warehousing has many advantages but value is only realized when the data is readily accessible to all who need it. © Eckerson Group 2017 www.eckerson.com 5 The New Analytics Lifecycle Cloud data warehousing is becoming increasingly popular with IT and data management organizations. Migrating to the cloud offers many advantages including scalability, elasticity, managed infrastructure, rapid deployment, and fast processing. However, cloud data warehousing investments will only return value when the data is readily accessible to all who need it. Ideally, self-service tools make it easy to access data from any location and to blend data from multiple locations. Self-service technologies must adapt as data infrastructure evolves. Relieving the Burden on IT IT organizations can’t (and probably shouldn’t) provide all of the data, reporting, and analysis that is needed by the ever-increasing numbers of people who use data. Embracing self-service unburdens IT organizations by shifting part of the onslaught of analysis and reporting requests from IT work queues to line-of-business projects. The obvious benefit is shrinking the backlog of unfilled requests by moving the workload. But technical organizations also gain advantage directly from the self-service tools. IT analysts and developers using modern data preparation tools can prepare data for business use faster, more efficiently, and with fewer errors and oversights than doing it the old way. Analysts, developers, and report writers who use the most current reporting and analysis tools do their work faster with less coding, greater agility, and enhanced collaboration capabilities. Today’s most advanced analytics technologies also help IT operations personnel to deploy, monitor, and manage analytic models and applications. Building an Analytics Community Data analysis is pervasive in business today. It happens at all levels from non-technical line-of-business analysts to highly specialized statisticians and data scientists. The reach of analytics is even broader and deeper, touching everyone from C-level to front-line operations staff. Analytics technology helps to build the analytics community with features for collaborative data exploration, repeatable and shared workflows, and managed deployment and operations. Fostering analytics culture and becoming an analytics-driven
Load more

Recommended publications
  • A Platform for Networked Business Analytics BUSINESS INTELLIGENCE
    BROCHURE A platform for networked business analytics BUSINESS INTELLIGENCE Infor® Birst's unique networked business analytics technology enables centralized and decentralized teams to work collaboratively by unifying Leveraging Birst with existing IT-managed enterprise data with user-owned data. Birst automates the enterprise BI platforms process of preparing data and adds an adaptive user experience for business users that works across any device. Birst networked business analytics technology also enables customers to This white paper will explain: leverage and extend their investment in ■ Birst’s primary design principles existing legacy business intelligence (BI) solutions. With the ability to directly connect ■ How Infor Birst® provides a complete unified data and analytics platform to Oracle Business Intelligence Enterprise ■ The key elements of Birst’s cloud architecture Edition (OBIEE) semantic layer, via ODBC, ■ An overview of Birst security and reliability. Birst can map the existing logical schema directly into Birst’s logical model, enabling Birst to join this Enterprise Data Tier with other data in the analytics fabric. Birst can also map to existing Business Objects Universes via web services and Microsoft Analysis Services Cubes and Hyperion Essbase cubes via MDX and extend those schemas, enabling true self-service for all users in the enterprise. 61% of Birst’s surveyed reference customers use Birst as their only analytics and BI standard.1 infor.com Contents Agile, governed analytics Birst high-performance in the era of
    [Show full text]
  • I Am a Data Engineer, Specializing in Data Analytics and Business Intelligence, with More Than 8+ Years of Experience and a Wide Range of Skills in the Industry
    Dan Storms [email protected] linkedin.com/in/danstorms1 | 1-503-521-6477 About me I am a data engineer, specializing in data analytics and business intelligence, with more than 8+ years of experience and a wide range of skills in the industry. I have a passion for leveraging technology to help businesses meet their needs and better serve their customers. I bring a deep understanding of business analysis, data architecture, and data modeling with a focus in developing reporting solutions to drive business insights and actions. I believe continuous learning is critical to remaining relevant in technology and have completed degrees in both Finance and Business Information Systems. I have also completed certifications in AWS and Snowflake. Born and raised in Oregon and currently living in Taiwan, I enjoy any opportunity to explore the outdoors and am passionate about travel and exploring the world. Past Employers NIKE Personal Abilities Hard work and honesty forms my beliefs Critical Thinker Problem Solver Logical Visioner Deep experience in business Developed dashboards using Developed data pipelines using analysis, data architecture, and Tableau and PowerBI Apache Airflow, Apache Spark, data modeling and Snowflake Work Ethic Loyalty and Persistence Persuasive Leader Migrated Nike Digital Global Ops Experience streaming and Led analysis and development of BI to Snowflake transforming application data in KPIs in both healthcare and Nike Kafka and Snowflake digital Education & Credentials I believe that personal education never ends Bachelor
    [Show full text]
  • Magic Quadrant for Business Intelligence and Analytics Platforms Published: 16 February 2017 ID: G00301340 Analyst(S): Rita L
    2/21/2017 Gartner Reprint (https://www.gartner.com/home) LICENSED FOR DISTRIBUTION Magic Quadrant for Business Intelligence and Analytics Platforms Published: 16 February 2017 ID: G00301340 Analyst(s): Rita L. Sallam, Cindi Howson, Carlie J. Idoine, Thomas W. Oestreich, James Laurence Richardson, Joao Tapadinhas Summary The business intelligence and analytics platform market's shift from IT-led reporting to modern business-led analytics is now mainstream. Data and analytics leaders face countless choices: from traditional BI vendors that have closed feature gaps and innovated, to disruptors continuing to execute. Strategic Planning Assumptions By 2020, smart, governed, Hadoop/Spark-, search- and visual-based data discovery capabilities will converge into a single set of next-generation data discovery capabilities as components of modern BI and analytics platforms. By 2021, the number of users of modern BI and analytics platforms that are differentiated by smart data discovery capabilities will grow at twice the rate of those that are not, and will deliver twice the business value. By 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms. By 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be autogenerated. By 2020, organizations that offer users access to a curated catalog of internal and external data will realize twice the business value from analytics investments than those that do not. Through 2020, the number of citizen data scientists will grow five times faster than the number of data scientists. Market Definition/Description Visual-based data discovery, a defining feature of the modern business intelligence (BI) platform, began in around 2004 and has since transformed the market and new buying trends away from IT- centric system of record (SOR) reporting to business-centric agile analytics.
    [Show full text]
  • Improving Data Preparation for Business Analytics Applying Technologies and Methods for Establishing Trusted Data Assets for More Productive Users
    BEST PRACTICES REPORT Q3 2016 Improving Data Preparation for Business Analytics Applying Technologies and Methods for Establishing Trusted Data Assets for More Productive Users By David Stodder Research Sponsors Research Sponsors Alation, Inc. Alteryx, Inc. Attivio, Inc. Datameer Looker Paxata Pentaho, a Hitachi Group Company RedPoint Global SAP / Intel SAS Talend Trifacta Trillium Software Waterline Data BEST PRACTICES REPORT Q3 2016 Improving Data Table of Contents Research Methodology and Demographics 3 Preparation for Executive Summary 4 Business Analytics Why Data Preparation Matters 5 Better Integration, Business Definition Capture, Self-Service, and Applying Technologies and Methods for Data Governance. 6 Establishing Trusted Data Assets for Sidebar – The Elements of Data Preparation: More Productive Users Definitions and Objectives 8 By David Stodder Find, Collect, and Deliver the Right Data . 8 Know the Data and Build a Knowledge Base. 8 Improve, Integrate, and Transform the Data . .9 Share and Reuse Data and Knowledge about the Data . 9 Govern and Steward the Data . 9 Satisfaction with the Current State of Data Preparation 10 Spreadsheets Remain a Major Factor in Data Preparation . 10 Interest in Improving Data Preparation . 11 IT Responsibility, CoEs, and Interest in Self-Service . 13 Attributes of Effective Data Preparation 15 Data Catalog: Shared Resource for Making Data Preparation Effective . 17 Overcoming Data Preparation Challenges 19 Addressing Time Loss and Inefficiency . 21 IT Responsiveness to Data Preparation Requests . 22 Data Volume and Variety: Addressing Integration Challenges . 22 Disconnect between Data Preparation and Analytics Processes . 24 Self-Service Data Preparation Objectives 25 Increasing Self-Service Data Preparation . 27 Self-Service Data Integration and Catalog Development .
    [Show full text]
  • Automating Exploratory Data Analysis Via Machine Learning: an Overview
    Tutorials SIGMOD ’20, June 14–19, 2020, Portland, OR, USA Automating Exploratory Data Analysis via Machine Learning: An Overview Tova Milo Amit Somech Tel Aviv University Tel Aviv University [email protected] [email protected] ABSTRACT up-close, better understand its nature and characteristics, Exploratory Data Analysis (EDA) is an important initial step and extract preliminary insights from it. Typically, EDA is for any knowledge discovery process, in which data scien- done by interactively applying various analysis operations tists interactively explore unfamiliar datasets by issuing a (such as filtering, aggregation, and visualization) – the user sequence of analysis operations (e.g. filter, aggregation, and examines the results of each operation, and decides if and visualization). Since EDA is long known as a difficult task, which operation to employ next. requiring profound analytical skills, experience, and domain EDA is long known to be a complex, time consuming pro- knowledge, a plethora of systems have been devised over cess, especially for non-expert users. Therefore, numerous the last decade in order to facilitate EDA. lines of work were devised to facilitate the process, in multi- In particular, advancements in machine learning research ple dimensions [10, 24, 25, 31, 42, 46]: First, simplified EDA 1 have created exciting opportunities, not only for better fa- interfaces (e.g., [25], Tableau ) allow non-programmers to cilitating EDA, but to fully automate the process. In this effectively explore datasets without knowing scripting lan- tutorial, we review recent lines of work for automating EDA. guages or SQL. Second, numerous solutions (e.g., [6, 14, 22]) Starting from recommender systems for suggesting a single were devised to improve the interactivity of EDA, by re- exploratory action, going through kNN-based classifiers and ducing the running times of exploratory operations, and active-learning methods for predicting users’ interestingness displaying preliminary sketches of their results.
    [Show full text]
  • The Essential Guide to Data Preparation
    E-BOOK THE ESSENTIAL GUIDE TO DATA PREPARATION From exploring to wrangling, prep your way to better insights 6 BILLION 26 HOURS 8 HOURS “ “ HOURS are wasted in spreadsheets per week per week are spent repeating the same data tasks per year are spent working spreadsheets — “The State of Self-Service Data Preparation and Analysis Using Spreadsheets,” IDC CONTENTS 3 Setting Up for Success: 8 DATA PROFILING What’s the Big Deal About Data Prep? Spot Bad Data 4 From Exploring to Wrangling: 9 ETL (EXTRACT- The Major Steps of Data Prep TRANSFORM-LOAD) Aggregate Data LOOKING FOR 5 DATA EXPLORATION Discover Surprises 10 DATA WRANGLING A SMARTER Make Data Digestible WAY TO DO DATA PREP? 6 DATA CLEANSING 11 Faster, Smarter Insights: Eliminate Errors The Case for Analytic Process Automation Data preparation in most organizations is time-intensive and repetitive, leaving The Alteryx APA Platform: precious little time for analysis. But there’s DATA BLENDING 7 12 Why Use Alteryx for Data Prep? a way to get to better insights, faster. Join Datasets We’ll walk you through it step by step. SETTING UP FOR SUCCESS: THE IMPORTANCE OF DATA PREP WHAT’S BUT in a typical organization, data winds up living in silos, where it can’t fulfill its potential, and in spreadsheets, where it’s manipulated by hand. Silos and THE BIG DEAL manual preparation processes are like a ten-mile obstacle course lying between you and the insights that should be driving your business. ABOUT DATA If your organization is struggling with this lag time, you’re in good company, as 69% of businesses say they aren’t yet data-driven1 — but having other people PREPARATION? with you in a sinking boat doesn’t make it more fun to drown.
    [Show full text]
  • Data Blending for Dummies, Alteryx Special Edition
    These materials are © 2015 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited. Data Blending Alteryx Special Edition by Michael Wessler, OCP & CISSP These materials are © 2015 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited. Data Blending For Dummies®, Alteryx Special Edition Published by John Wiley & Sons, Inc. 111 River St. Hoboken, NJ 07030-5774 www.wiley.com Copyright © 2015 by John Wiley & Sons, Inc., Hoboken, New Jersey No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions. Trademarks: Wiley, the Wiley logo, For Dummies, the Dummies Man logo, A Reference for the Rest of Us!, The Dummies Way, Dummies.com, Making Everything Easier, and related trade dress are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries, and may not be used without written permission. Alteryx is a registered trade- mark of Alteryx, Inc. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc., is not associated with any product or vendor mentioned in this book.
    [Show full text]
  • International Journal of Soft Computing and Engineering
    International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-2, July 2019 Implementation and Effective Utilization of Analytical Tools and Techniques in Knowledge Management Shivakumar. R knowledge is the knowledge that individuals in organizations Abstract: Knowledge management is a multidisciplinary know that they have and are conscious of it. approach to achieve organizational objectives by making best use The crucial element in any Knowledge Management of knowledge and information resources. Analytics is the process system is to ensure that tacit knowledge is captured and of systematic analysis of the past data/statistics and trying to converted to explicit knowledge. Moreover, it has been predict the future trends or data with various tools and techniques. With the advancement of technology and increasing competition, hypothesized that even explicit knowledge needs to be companies are prone to make better use of the information and converted into information that is meaningful and useful. analytics to sustain their position in the marketplace. Software as After all, mere data is not useful and only when data is a Service (SaaS) has become an important trend in organizations transformed into information and codified as knowledge is it in addition to that of the usual Excel and Google sheets analytics. useful. In this study, comparative analysis has been done between SPSS In today’s rapidly changing business world, it is critical for & Google Sheets Techniques and also Google data studio with business leaders to have the right insight and data to make the Tableau, Power BI & Google Sheets for data visualization right calls at the right time.
    [Show full text]
  • User's Guide for Oracle Data Visualization
    Oracle® Fusion Middleware User's Guide for Oracle Data Visualization 12c (12.2.1.4.0) E91572-02 September 2020 Oracle Fusion Middleware User's Guide for Oracle Data Visualization, 12c (12.2.1.4.0) E91572-02 Copyright © 2015, 2020, Oracle and/or its affiliates. Primary Author: Hemala Vivek Contributing Authors: Nick Fry, Christine Jacobs, Rosie Harvey, Stefanie Rhone Contributors: Oracle Business Intelligence development, product management, and quality assurance teams This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or "commercial computer software documentation" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations.
    [Show full text]
  • The Landscape of R Packages for Automated Exploratory Data Analysis by Mateusz Staniak and Przemysław Biecek
    CONTRIBUTED RESEARCH ARTICLE 1 The Landscape of R Packages for Automated Exploratory Data Analysis by Mateusz Staniak and Przemysław Biecek Abstract The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering. There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). We explore the features of fifteen popular R packages to identify the parts of the analysis that can be effectively automated with the current tools and to point out new directions for further autoEDA development. Introduction With the advent of tools for automated model training (autoML), building predictive models is becoming easier, more accessible and faster than ever. Tools for R such as mlrMBO (Bischl et al., 2017), parsnip (Kuhn and Vaughan, 2019); tools for python such as TPOT (Olson et al., 2016), auto-sklearn (Feurer et al., 2015), autoKeras (Jin et al., 2018) or tools for other languages such as H2O Driverless AI (H2O.ai, 2019; Cook, 2016) and autoWeka (Kotthoff et al., 2017) supports fully- or semi-automated feature engineering and selection, model tuning and training of predictive models. Yet, model building is always preceded by a phase of understanding the problem, understanding of a domain and exploration of a data set.
    [Show full text]
  • Data3sixty® Analyze
    Data3Sixty® Analyze The Challenge The laws of supply and demand are different when it comes to data. There’s certainly no shortage of data among organizations, and the demand for the analytical insights that data can reveal has never been stronger. But the challenge lies in how quickly that raw data can be transformed into actionable and reliable business intelligence. Traditional extract, transform and load (ETL) technologies are often overly technical and cumbersome tools that drain money, time and resources, and can turn data preparation into a months-long project. And in today’s hyper-competitive marketplace, speed matters. You can’t have sluggish data preparation standing between you and timely insights. Data3Sixty® Analyze Data3Sixty Analyze provides you with the ability to process and transform data and to rapidly build data-rich, analytically complex applications, improving productivity and dramatically lowering overall costs. It allows you to access virtually any data source—from legacy systems to NoSQL platforms—and easily acquire and cleanse that data in a fraction of the time. Users can create a comprehensive source for analysis without incurring the infrastructure costs and time required by traditional data warehouses and ETL. Our approach enables data analysts, data engineers, data scientists, and other users to visually collaborate as they acquire, prepare, and analyze data from disparate sources and deliver these applications to data visualization tools, direct to end users, or back to operational systems. How it Works Data3Sixty Feature Overview Data3Sixty Analyze automates data blending and cleansing, as native functions allow users to profile, aggregate, correlate and transform selected data.
    [Show full text]
  • Introduction
    Introduction 1 Keboola Connection A new generation platform for fast and transparent data integration in the cloud. Unique ability 10x Agile to instantly faster deployment of integrate, blend implementation data science and enrich than the best applications; fast thousands of existing solutions iteration data sources available 2 Keboola Connection Overview ● All your business data in one platform — ETL (Extract, Transform, Load) ● Easy framework for pulling data from over 170,000 data sources ● Automated, transparent and completely secure ● Appstore for data science applications ready to deploy; apps can be created by 3rd parties ● Scalability on AWS cloud infrastructure ● Data democratisation, giving access to the right people to the right data at the right time 3 Keboola Connection Value ● Connecting your data without worrying about maintenance; everything easily accessible on demand in a unified format. ● Plugging in new data sources is fast and easy; no programming is required. ● Planning and monitoring sequences of tasks of any complexity. You don’t have to touch a thing. ● Marketplace for data science application in case you don’t have a full time data scientist. ● Using the right muscle for each job. From MySQL to Redshift and SQL to R, we cover you. Pay for what you really use. #goCloud ● Delivering data and insights across the organisation, targeted to fit everyone’s business needs and goals. 3 Trends That Matter 30% of enterprise access to 35% 60% broadly based big data shortfall of increase in operating will be via intermediary data-savvy workers margin of an average data broker services, needed to make serving context to sense of big data retailer if using big data business decisions by by 2018.
    [Show full text]