DOCSLIB.ORG

Using Alteryx with Microsoft Azure SQL Data Warehouse Moving Massive Datasets Causes Problems

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Using Alteryx with Microsoft Azure SQL Data Warehouse Moving Massive Datasets Causes Problems In-Database Blending for Big Data Preparation Using Alteryx with Microsoft Azure SQL Data Warehouse Moving Massive Datasets Causes Problems When you need to work with large datasets, it is best not to move the data out of its location because: It’s Resource Intensive Moving large datasets saps network resources and clogs internal networks It’s Time Consuming The file transfer can take hours and even days It Can Affect the Chain of Custody Moving data takes it out of systems that track data location and ownership In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial So, how can data analysts reduce the time it takes to work with massive datasets and speed up their time to insight? By blending and preparing datasets in Microsoft Azure SQL Data Warehouse with Alteryx! In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial How Can Analysts Unleash the Power of Microsoft Azure SQL Data Warehouse? Many organizations are turning to the cloud as the most agile way to store and manage the massive volumes of data that drive today’s business decisions. A powerful solution for this new approach is Azure SQL Data Warehouse, a cloud-based data warehouse. Maximizing its benefits requires an equally agile approach to using the data stored within. This is why so many data analysts are combining Azure SQL Data Warehouse with Alteryx, a leader in self-service data analytics. The result: quick, effective insights from cloud data—a critical business advantage. In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial Processing Power Made Simple, Scalable and Secure It’s clear why organizations are using Microsoft Azure SQL Data Warehouse as their cloud solution: It’s Simple It’s Powerful It’s Scalable It’s Secure Deploy a petabyte- Leverage best in-class, Perform independent Harness industry- scale data warehouse massively parallel scaling of compute and leading, built-in security within seconds processing ability storage in seconds In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial In-Database Blending From Alteryx In many cases, Microsoft Azure SQL Data Warehouse is set up as a repository for massive amounts of data. In-database processing enables blending and analysis of large sets of data without moving the data out of Azure SQL Data Warehouse. This can provide significant performance improvements and deeper insights over traditional approaches, which require data to be moved into a separate environment for processing or force a user to only leverage a subset of the entire dataset. • Use the full computing power of the Data Warehouse to manipulate data • Convert an Alteryx workflow into a series of SQL commands that are executed in the Data Warehouse In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial Blending Alteryx can stream data from outside sources—including structured data from cloud sources, such as Salesforce.com; External Data on-premise databases, such as SAP; or desktop files, such as spreadsheets—into Microsoft Azure SQL Data Warehouse for in-database preparation and blending of the data. This helps analysts: • Gain even more context around key business challenges • Access the most up-to-date information • Take advantage of Azure SQL Data Warehouse processing power to perform data blending and get fast results In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial In-Database Tools = Maximum Convenience Alteryx offers a wide range of data prep, blending, and analysis tools to create the analytic datasets needed for solving specific business challenges. This includes 12 in-database tools to make the most of data residing in Microsoft Azure SQL Data Warehouse. Key in-database tools include: Connect In-DB Tool: Select In-DB Tool: Data Stream In Tool: Join In-DB Tool: Data Stream Out Tool: Connect to Azure Eliminate unnecessary Stream data into Azure Combine data streams Feed downstream SQL Data Warehouse fields or rename key fields SQL Data Warehouse based on common fields analytic processes or other databases, from a variety of such as SQL Server external sources In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial How It Works: Blending Data in Microsoft Azure SQL DataWarehouse 1. The user creates a data workflow by selecting tools using the Alteryx drag-and-drop interface. 2. These selections are translated into SQL commands, which are understood by Azure SQL Data Warehouse. 3. Azure SQL Data Warehouse performs the specified blending commands. L Paired with Azure SQL Data Warehouse, Alteryx can perform this process on years’ R worth of clickstreams, customer data, and even social media and customer support This workflow shows two calls—massive amounts of data brought together and analyzed for a better picture of tables/datasets being the customer purchase cycle. joined using the Alteryx in- database join function In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial Example: A sales analyst needs to filter transactions from Direct connection to Azure SQL Filter In-DB and Summarize In-DB Data Warehouse is made with the Formula In-DB tools counts the number billions of store transaction Connect In-DB tool to access more than are used to remove of records. records and retain them 10 billion lines of store transaction data. outliers and noisy data in Microsoft Azure SQL Data Warehouse as the approved and blended data source for the organization. The clean data is retained in Azure SQL Data Warehouse using the Write In-DB tool, ready for advanced analytics or visualization. In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial How It Works: Shaping Data Without Moving It To Cut Excess Data via the Alteryx Workflow: Use the Select tool to remove columns or rows of unnecessary data from the clickstream dataset. The original data is unaffected, and the new joined dataset is now much more agile and focused. Using the same drag-and-drop interface plus Microsoft Azure SQL Data Warehouse processing power, Alteryx can perform other data cleansing and shaping functions, such as: • Removing null values or calculating values • Reordering or renaming columns Select In-DB Tool This makes the data easier to analyze and convey to other analytic processes. In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial Applying Advanced Analytics In-Database Alteryx provides more than 55 predictive tools and 15 spatial tools to perform advanced predictive and geospatial analytics. In various combinations, these tools can be used for insights such as: • Predicting customer churn • Determining key service attributes, such as on-time delivery, in-stock/out-of-stock products and lowest price • Identifying the most profitable location for a retail outlet In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial Example: Here, a sales analyst pre- filters data, integrates A direct connection is made to Microsoft Azure SQL Data Warehouse using the Connect In-DB Tool. CRM data, and uses Purchase history (5,000 SKUs) is then filtered in Azure predictive score modeling SQL Data Warehouse with the Filter In-DB tool. to determine geographic impact on high-volume product purchases. CRM data is streamed into The new dataset is returned to desktop Azure SQL Data Warehouse with the using the Data Stream Out tool. Data Stream In tool, then joined with Location and predictive score modeling SKU data using the Join In-DB tool. is done in-memory with Alteryx. In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial Making the Results Available for Further Analysis Once they create a dataset, analysts have many options for analysis, including: • Keeping it in Microsoft Azure SQL Data Warehouse for analytic access • Saving the results in-database in a format ready for a visualization engine • Using the results in Alteryx for advanced geospatial analytics Alteryx supports visualization tools, such as Microsoft Power BI, and can output results in the appropriate file format, either directly launching these tools or by storing the results back in Azure SQL Data Warehouse for direct access using the integration of Azure SQL Data Warehouse with Power BI. Alteryx can also shape and trim data to make it ready for access by new analytics processes. In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial Microsoft Azure SQL Data Warehouse and Alteryx: A Powerful, Scalable Analytics Solution The combination of Azure SQL Data Warehouse and Alteryx delivers deeper business insights in hours—not weeks—for better, more agile decision-making. Data Analysts are Empowered to: • Take full advantage of Azure SQL Data Warehouse for cloud-based processing power, scalable compute, and decoupled storage • Blend data from the Data Warehouse with data from multiple sources through Alteryx’s intuitive workflow • Generate perfect datasets quickly for better advanced analytics • Apply advanced analytics, such as predictive and geo-spatial analytics, in a simple drag-and-drop interface In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial Learn More About How Alteryx and Microsoft Azure SQL Data Warehouse Provide Code-Free Blending of Big Datasets Download a 14-Day FREE Trial of Alteryx www.alteryx.com/trial » See how Azure SQL Data Warehouse and Alteryx work together Microsoft/Alteryx partner page » In-Database Blending for Big Data Preparation Download a FREE trial: alteryx.com/trial.
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]
  • 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]
  • 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]
  • Cloud Computing and Business Intelligence Market Study
    April 6, 2017 Dresner Advisory Services, LLC 2017 Edition Cloud Computing and Business Intelligence Market Study Wisdom of Crowds® Series Licensed to Domo 2017 Cloud Computing and Business Intelligence Market Study Disclaimer: This report is for informational purposes only. You should make vendor and product selections based on multiple information sources, face-to-face meetings, customer reference checking, product demonstrations, and proof-of-concept applications. The information contained in this Wisdom of Crowds® Market Study report is a summary of the opinions expressed in the online responses of individuals that chose to respond to our online questionnaire and does not represent a scientific sampling of any kind. Dresner Advisory Services, LLC shall not be liable for the content of this report, the study results, or for any damages incurred or alleged to be incurred by any of the companies included in the report as a result of the report’s content. Reproduction and distribution of this publication in any form without prior written permission is forbidden. COPYRIGHT 2017 DRESNER ADVISORY SERVICES, LLC Page | 2 2017 Cloud Computing and Business Intelligence Market Study Definitions Business Intelligence Defined Business intelligence (BI) is “knowledge gained through the access and analysis of business information.” Business Intelligence tools and technologies include query and reporting, OLAP (online analytical processing), data mining and advanced analytics, end-user tools for ad hoc query and analysis, and dashboards for performance monitoring. Howard Dresner, The Performance Management Revolution: Business Results Through Insight and Action (John Wiley & Sons, 2007) Cloud Deployment Models Defined Private Cloud: The cloud infrastructure is provisioned for exclusive use by a single organization comprising multiple consumers (e.g., business units).
    [Show full text]
  • Modernize Your Data Ecosystem for Accelerated Insight NTT DATA Intelligent Data Services
    FACT SHEET | DIGITAL Modernize Your Data Ecosystem for Accelerated Insight NTT DATA Intelligent Data Services Is your organization limited in the types of data analytics it can produce? Is your budget Benefits: overwhelmed by the expense of ETL (extract, transform, load) activities? Does every new insight require months of project execution time? • Accelerate time to value across data and NTT DATA Intelligent Data Services (IDS) helps you unlock insights from any data. intelligence initiatives We provide a flexible set of services that easily integrate with your existing analytics • Integrate big data, investments to create an end-to-end data ecosystem. IDS addresses a full spectrum of IoT, natural language use cases for the latest technological advancements — including business intelligence, processing, machine big data, fast data, internet of things (IoT) sensing, machine learning and deep learning and deep learning — all within a single user-friendly platform. learning into a unified platform What’s more, you can choose any leading cloud provider to host our platform, including • Make cognitive Amazon Web Services (AWS), Microsoft Azure and Google Cloud, utilize NTT DATA’s solutioning easy private cloud services or implement hybrid cloud for a custom, combined solution. • Provide flexibility and easy adaptability to meet organizational needs for Let NTT DATA modernize your data ecosystem and make it easy to use digital transformation Business success today relies on your ability to take quick and informed action on the data available within your organization. This involves harnessing various types of data from a growing number of data sources and managing it to build a unified digital intelligence ecosystem.
    [Show full text]
  • Data Wrangling: the New Focus for Financial Institutions for Customer & Marketing Analytics
    SOLUTION BRIEF Data Wrangling: The New Focus for Financial Institutions for Customer & Marketing Analytics In banking, as with all industries, there are two ways to maximize profits—increase Research has shown that 85% revenue or decrease costs. Using data and analytics to better understand your of analysts’ time is spent hunting customers is one way to do both. for and gathering information, A decade ago financials institutions were at the vanguard of customer analytics. While other with only 15% of their time industries were limited, at best, to the biased data from customer loyalty programs, banks left to perform the kinds of analytical had it all—virtually everything their customers did within the four walls of the bank was at functions they were hired to do. the fingertips of the banking analyst. https://www.clearpeak.com/data- But now, all the low-hanging fruits have been picked. Any bank worth its salt has a decent driven-culture-let-analysts-analyze/ customer profitability model and a Pareto chart to go with it. A/B testing for the effectiveness ClearPeak Consulting of single campaigns and targeting models for specific products have been optimized. And April 28, 2016 all banks have developed sophisticated attrition and churn models. (Although most would probably admit that they’ve struggled to successfully productionalize one into a real-time customer retention program.) At the same time, other industries’ customer analytics capabilities have caught up, and with them customers’ expectations have increased. Customers are now used to the service that they receive from sophisticated data-driven companies like Amazon, Uber and Airbnb and they expect no less from their banks.
    [Show full text]