Oracle Machine Learning Overview and From Oracle Data Professional to Oracle ...Charlie Berger
DBAs spend too time with routine tasks leaving little time for innovation. Autonomous Databases free data professionals to extract more value from data. Oracle Machine Learning, in Autonomous Database, “moves the algorithms; not the data” for 100% in-database processing. Data professionals perform many supporting tasks for “data scientists”, typically 80% of the work. Come learn an evolutionary path for Oracle data professionals to leverage domain knowledge and data skills and add machine learning. See how to build and deploy predictive models inside the Database. Using examples, demos and sharing experiences, Charlie will show you how to discover new insights, make predictions and become an “Oracle Data Scientist” in just 6 weeks!
Oracle Database House Party_Oracle Machine Learning to Pick a Good Inexpensiv...Charlie Berger
Ever seeking to use Oracle Converged Database technology with embedded machine learning algorithms to solve important problems of the day, our speakers will demonstrate how to use Oracle Machine Learning, Oracle Data Miner, a SQL Developer extension, APEX and ORDS REST Services to analyze wine data from Kaggle and pick a wine that is likely to be good (greater than 90 points) yet inexpensive (< $20). We will start with SQL Developer to import our data, explore it and build and apply machine learning models using Oracle Machine Learning, and then deploy the machine learning model in production applications using ORDS/REST services. Come see how much you can do today using Oracle’s Converged “AI” Database.
Expand a Data warehouse with Hadoop and Big Datajdijcks
After investing years in the data warehouse, are you now supposed to start over? Nope. This session discusses how to leverage Hadoop and big data technologies to augment the data warehouse with new data, new capabilities and new business models.
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEASandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning with examples, AutoML for training models and this ends with an example of how to predict fraud , to determining shopping patterns to Wine picking and different algorithms as an example and also how to predict workload for your databases. We will also use OML in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automatically
AUSOUG - NZOUG-GroundBreakers-Jun 2019 - AI and Machine LearningSandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. This presentation takes a view on our current state of Diagnostic methodology in the Autonomous Database Cloud services and how do we process this data to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. Some of the use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. We will cover techniques to analyze database issues using Machine learning techniques like Kmeans , TFIDF, Random Forests, and z-scores to predict if a spike in the CPU is a normal or abnormal spike. We will also talk about RNN’s with LSTM/GRU as some of the applications of how to predict faults before they happen. Some of the other use cases are to use convolution filters to determine maintenance windows within the database workloads, determine best times to do database backups, security anomaly timelines and many others. This is a production service and this can be used if you have a customer SR/defect today. The service is much more extensive inside the Oracle Autonomous Database Cloud. This presentation will accompany several examples with how to apply these techniques, machine learning knowledge is preferred but not a prerequisite
Oracle Machine Learning Overview and From Oracle Data Professional to Oracle ...Charlie Berger
DBAs spend too time with routine tasks leaving little time for innovation. Autonomous Databases free data professionals to extract more value from data. Oracle Machine Learning, in Autonomous Database, “moves the algorithms; not the data” for 100% in-database processing. Data professionals perform many supporting tasks for “data scientists”, typically 80% of the work. Come learn an evolutionary path for Oracle data professionals to leverage domain knowledge and data skills and add machine learning. See how to build and deploy predictive models inside the Database. Using examples, demos and sharing experiences, Charlie will show you how to discover new insights, make predictions and become an “Oracle Data Scientist” in just 6 weeks!
Oracle Database House Party_Oracle Machine Learning to Pick a Good Inexpensiv...Charlie Berger
Ever seeking to use Oracle Converged Database technology with embedded machine learning algorithms to solve important problems of the day, our speakers will demonstrate how to use Oracle Machine Learning, Oracle Data Miner, a SQL Developer extension, APEX and ORDS REST Services to analyze wine data from Kaggle and pick a wine that is likely to be good (greater than 90 points) yet inexpensive (< $20). We will start with SQL Developer to import our data, explore it and build and apply machine learning models using Oracle Machine Learning, and then deploy the machine learning model in production applications using ORDS/REST services. Come see how much you can do today using Oracle’s Converged “AI” Database.
Expand a Data warehouse with Hadoop and Big Datajdijcks
After investing years in the data warehouse, are you now supposed to start over? Nope. This session discusses how to leverage Hadoop and big data technologies to augment the data warehouse with new data, new capabilities and new business models.
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEASandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning with examples, AutoML for training models and this ends with an example of how to predict fraud , to determining shopping patterns to Wine picking and different algorithms as an example and also how to predict workload for your databases. We will also use OML in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automatically
AUSOUG - NZOUG-GroundBreakers-Jun 2019 - AI and Machine LearningSandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. This presentation takes a view on our current state of Diagnostic methodology in the Autonomous Database Cloud services and how do we process this data to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. Some of the use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. We will cover techniques to analyze database issues using Machine learning techniques like Kmeans , TFIDF, Random Forests, and z-scores to predict if a spike in the CPU is a normal or abnormal spike. We will also talk about RNN’s with LSTM/GRU as some of the applications of how to predict faults before they happen. Some of the other use cases are to use convolution filters to determine maintenance windows within the database workloads, determine best times to do database backups, security anomaly timelines and many others. This is a production service and this can be used if you have a customer SR/defect today. The service is much more extensive inside the Oracle Autonomous Database Cloud. This presentation will accompany several examples with how to apply these techniques, machine learning knowledge is preferred but not a prerequisite
DBCS Office Hours - Modernization through MigrationTammy Bednar
Speakers:
Kiran Tailor - Cloud Migration Director, Oracle
Kevin Lief – Partnership and Alliances Manager - (EMEA), Advanced
Modernisation of mainframe and other legacy systems allows organizations to capitalise on existing assets as they move toward more agile, cost-effective and open technology environments. Do you have legacy applications and databases that you could modernise with Oracle, allowing you to apply cutting edge technologies, like machine learning, or BI for deeper insights about customers or products? Come to this webcast to learn about all this and how Advanced can help to get you on the path to modernisation.
AskTOM Office Hours offers free, open Q&A sessions with Oracle Database experts. Join us to get answers to all your questions about Oracle Database Cloud Service.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Set of product roadmap + capabilities slides from Oracle Data Integration Product Management, and thoughts on data integration on big data implementations by Mark Rittman (Independent Analyst)
Oracle's BigData solutions consist of a number of new products and solutions to support customers looking to gain maximum business value from data sets such as weblogs, social media feeds, smart meters, sensors and other devices that generate massive volumes of data (commonly defined as ‘Big Data’) that isn’t readily accessible in enterprise data warehouses and business intelligence applications today.
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...Sandesh Rao
We are entering a new era in the database with the introduction of the Oracle Autonomous Database. AI and Machine Learning are center stage to most projects and assist in making complex decisions which was not possible before. Most data science projects don’t get beyond the data scientist and rarely operationalize their predictive models. there are new toolsets and methods available everyday which make this an extremely dynamic space. There are different categories of users who want to use the algorithms , the toolsets but don't know where to start. Whether you are a data scientist who wants to play with data and build your own models or make use of the database features with the built in models or use the specific AI services within a specific vertical such as Insurance or Healthcare . We will take a glimpse at Oracle's Machine Learning Zeppelin-based notebooks for Oracle Autonomous Data Warehouse Cloud to how Oracle uses AIOps and Applied Machine learning for its own operations and the Oracle AI Platform Cloud Service to provided an all rounded view of what Oracle is upto in this space
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
Hortonworks Oracle Big Data Integration Hortonworks
Slides from joint Hortonworks and Oracle webinar on November 11, 2014. Covers the Modern Data Architecture with Apache Hadoop and Oracle Data Integration products.
Database@Home - Data Driven : Loading, Indexing, and Searching with Text and ...Tammy Bednar
This session will cover loading large JSON datasets into Oracle Database 19c, indexing the content and providing a RESTful search interface - all using Oracle Cloud features.
Tame Big Data with Oracle Data IntegrationMichael Rainey
In this session, Oracle Product Management covers how Oracle Data Integrator and Oracle GoldenGate are vital to big data initiatives across the enterprise, providing the movement, translation, and transformation of information and data not only heterogeneously but also in big data environments. Through a metadata-focused approach for cataloging, defining, and reusing big data technologies such as Hive, Hadoop Distributed File System (HDFS), HBase, Sqoop, Pig, Oracle Loader for Hadoop, Oracle SQL Connector for Hadoop Distributed File System, and additional big data projects, Oracle Data Integrator bridges the gap in the ability to unify data across these systems and helps deliver timely and trusted data to analytic and decision support platforms.
Co-presented with Alex Kotopoulis at Oracle OpenWorld 2014.
Oracle Cloud : Big Data Use Cases and ArchitectureRiccardo Romani
Oracle Itay Systems Presales Team presents : Big Data in any flavor, on-prem, public cloud and cloud at customer.
Presentation done at Digital Transformation event - February 2017
Oracle Data Integration overview, vision and roadmap. Covers GoldenGate, Data Integrator (ODI), Data Quality (EDQ), Metadata Management (MM) and Big Data Preparation (BDP)
"Changing Role of the DBA" Skills to Have, to Obtain & to Nurture - Updated 2...Markus Michalewicz
The ever-changing IT industry requires DBA's to keep their skills up-to-date. This presentation discusses skills that any DBA should have, but also those that any DBA should obtain and nurture regardless of which new technology is entering the (Gartner) hype cycle. The first ever version of this deck was presented during Sangam18 under the title "(Oracle) DBA Skills to Have, to Obtain and to Nurture" and used in other occasions during 2019. It was subsequently enhanced to a more generic 2019 version, which included an outlook for 2020! This edition of the presentation maintains the generic character, but has been updated to reflect unprecedented changes in 2020 and to cover the latest Oracle technology, to provide a 3-year comparison as well as trends analysis.
Note that the link on slide 25 in the subtitle should have been: https://go.oracle.com/DBA
Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive AnalyticsMark Rittman
This is a session for Oracle DBAs and devs that looks at the cutting edge big data techs like Spark, Kafka etc, and through demos shows how Hadoop is now a a real-time platform for fast analytics, data integration and predictive modeling
DBCS Office Hours - Modernization through MigrationTammy Bednar
Speakers:
Kiran Tailor - Cloud Migration Director, Oracle
Kevin Lief – Partnership and Alliances Manager - (EMEA), Advanced
Modernisation of mainframe and other legacy systems allows organizations to capitalise on existing assets as they move toward more agile, cost-effective and open technology environments. Do you have legacy applications and databases that you could modernise with Oracle, allowing you to apply cutting edge technologies, like machine learning, or BI for deeper insights about customers or products? Come to this webcast to learn about all this and how Advanced can help to get you on the path to modernisation.
AskTOM Office Hours offers free, open Q&A sessions with Oracle Database experts. Join us to get answers to all your questions about Oracle Database Cloud Service.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Set of product roadmap + capabilities slides from Oracle Data Integration Product Management, and thoughts on data integration on big data implementations by Mark Rittman (Independent Analyst)
Oracle's BigData solutions consist of a number of new products and solutions to support customers looking to gain maximum business value from data sets such as weblogs, social media feeds, smart meters, sensors and other devices that generate massive volumes of data (commonly defined as ‘Big Data’) that isn’t readily accessible in enterprise data warehouses and business intelligence applications today.
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...Sandesh Rao
We are entering a new era in the database with the introduction of the Oracle Autonomous Database. AI and Machine Learning are center stage to most projects and assist in making complex decisions which was not possible before. Most data science projects don’t get beyond the data scientist and rarely operationalize their predictive models. there are new toolsets and methods available everyday which make this an extremely dynamic space. There are different categories of users who want to use the algorithms , the toolsets but don't know where to start. Whether you are a data scientist who wants to play with data and build your own models or make use of the database features with the built in models or use the specific AI services within a specific vertical such as Insurance or Healthcare . We will take a glimpse at Oracle's Machine Learning Zeppelin-based notebooks for Oracle Autonomous Data Warehouse Cloud to how Oracle uses AIOps and Applied Machine learning for its own operations and the Oracle AI Platform Cloud Service to provided an all rounded view of what Oracle is upto in this space
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
Hortonworks Oracle Big Data Integration Hortonworks
Slides from joint Hortonworks and Oracle webinar on November 11, 2014. Covers the Modern Data Architecture with Apache Hadoop and Oracle Data Integration products.
Database@Home - Data Driven : Loading, Indexing, and Searching with Text and ...Tammy Bednar
This session will cover loading large JSON datasets into Oracle Database 19c, indexing the content and providing a RESTful search interface - all using Oracle Cloud features.
Tame Big Data with Oracle Data IntegrationMichael Rainey
In this session, Oracle Product Management covers how Oracle Data Integrator and Oracle GoldenGate are vital to big data initiatives across the enterprise, providing the movement, translation, and transformation of information and data not only heterogeneously but also in big data environments. Through a metadata-focused approach for cataloging, defining, and reusing big data technologies such as Hive, Hadoop Distributed File System (HDFS), HBase, Sqoop, Pig, Oracle Loader for Hadoop, Oracle SQL Connector for Hadoop Distributed File System, and additional big data projects, Oracle Data Integrator bridges the gap in the ability to unify data across these systems and helps deliver timely and trusted data to analytic and decision support platforms.
Co-presented with Alex Kotopoulis at Oracle OpenWorld 2014.
Oracle Cloud : Big Data Use Cases and ArchitectureRiccardo Romani
Oracle Itay Systems Presales Team presents : Big Data in any flavor, on-prem, public cloud and cloud at customer.
Presentation done at Digital Transformation event - February 2017
Oracle Data Integration overview, vision and roadmap. Covers GoldenGate, Data Integrator (ODI), Data Quality (EDQ), Metadata Management (MM) and Big Data Preparation (BDP)
"Changing Role of the DBA" Skills to Have, to Obtain & to Nurture - Updated 2...Markus Michalewicz
The ever-changing IT industry requires DBA's to keep their skills up-to-date. This presentation discusses skills that any DBA should have, but also those that any DBA should obtain and nurture regardless of which new technology is entering the (Gartner) hype cycle. The first ever version of this deck was presented during Sangam18 under the title "(Oracle) DBA Skills to Have, to Obtain and to Nurture" and used in other occasions during 2019. It was subsequently enhanced to a more generic 2019 version, which included an outlook for 2020! This edition of the presentation maintains the generic character, but has been updated to reflect unprecedented changes in 2020 and to cover the latest Oracle technology, to provide a 3-year comparison as well as trends analysis.
Note that the link on slide 25 in the subtitle should have been: https://go.oracle.com/DBA
Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive AnalyticsMark Rittman
This is a session for Oracle DBAs and devs that looks at the cutting edge big data techs like Spark, Kafka etc, and through demos shows how Hadoop is now a a real-time platform for fast analytics, data integration and predictive modeling
Fulfilling Real-Time Analytics on Oracle BI Applications PlatformPerficient, Inc.
Are you interested in learning how to leverage your investment in Oracle BI Applications and perform real-time analytics? Do you need to see updates as they occur in real-time with no latency? This session covers how Oracle Business Intelligence Applications can be easily customized to fulfill real-time reporting requirements by leveraging the Oracle BI Apps architecture. It provides an overview of Oracle BI Applications, walks through the design steps to fulfill real-time capabilities, and addresses architectural questions around mitigating the impact on business applications.
Oracle BI Hybrid BI : Mode 1 + Mode 2, Cloud + On-Premise Business AnalyticsMark Rittman
Presented at the UKOUG Business Analytics SIG Meeting in April 2016, addresses the question as to whether enterprise BI tools such as OBIEE12c are relevant in the world of Gartner BiModal Mode 1 + Mode 2 analytics, and Hybrid cloud/on-premise deployments
Profiling of Engagers and Converters with Audience Analytics and Look-alike M...Datacratic
Join Datacratic for the Profiling of Engagers and Converters with Audience Analytics and Look-alike Modeling discussion at the conference. How much are you able to learn about your current email and site converters? Do you have a way to extract learned attributes of your best audiences to guide and optimize your audience profile and personas? In this session, we will do deep dive into audience analytics capabilities that will help you discover new audiences and drive additional scale for digital marketing programs.
Using MySQL Enterprise Monitor for Continuous Performance ImprovementMark Matthews
MySQL Enterprise Monitor is built from the ground up to support DevOps DBAs and developers. From five scenarios based on real-world issues encountered by customers, learn how you can use the power features of query analysis and statistical visualization in MySQL Enterprise Monitor to diagnose and fix MySQL performance problems. Then learn how to apply these features in a continuous fashion as a valuable addition to your DevOps toolbox.
Nida event oracle business analytics 1 sep2016BAINIDA
Oracle Business Analytics ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Oracle Openworld Presentation with Paul Kent (SAS) on Big Data Appliance and ...jdijcks
Learn about the benefits of Oracle Big Data Appliance and how it can drive business value underneath applications and tools. This includes a section by Paul Kent, VP Big Data SAS describing how SAS runs well on Oracle Engineered Systems and on Oracle Big Data Appliance specifically.
Oracle Database Lifecycle Management using Enterprise Manager 12c (Release 4)
Learn about the features from Provisioning to Compliance and Everything in between you need to maintain Database environments in the regular or private database cloud.
Iterative Discovery and Analysis: Workflow / Activity and Capability ModelJoe Lamantia
Models of the workflow and capabilities necessary for iterative discovery and analysis. Identifies the two primary cycles - Insight / Discovery, and Modeling - making up analysis workflow. Maps the deep structure of discovery and analysis activity using the Language of Discovery. Identifies core and enhancing capabilities necessary for analysis.
A practical introduction to Oracle NoSQL Database - OOW2014Anuj Sahni
Not familiar with Oracle NoSQL Database yet? This great product introduction session discusses the primary functionality included with the product as well as integration with other Oracle products. It includes a live demo that illustrates installation and configuration as well as data modeling and sample NoSQL application development.
Apresentação do Novo Serviço de Gerenciamento da Oracle na Nuvem contendo as funções de APM (Application Performance Monitor, Log Analytics e IT Analytics
OUG Scotland 2014 - NoSQL and MySQL - The best of both worldsAndrew Morgan
Understand how you can get the benefits you're looking for from NoSQL data stores without sacrificing the power and flexibility of the world's most popular open source database - MySQL.
Fast Data Overview for Data Science Maryland MeetupC. Scyphers
An overview of Open Source Fast Data platforms (Spark, Kafka, HBase, Impala, Apex, H20, Druid, Flink, Storm, Samza, ElasticSearch, Lucene, Solr, SMACK, PANCAKE)
Introducing Cloudera Navigator Optimizer: Offload Assessments and Active Data...Cloudera, Inc.
Cloudera Enterprise can be used as an adaptive, high-performance analytic database, complementing existing data warehouses by relieving the pressure of growing numbers of ETL jobs and BI analytics. But where do you get started when developing your offload strategy? How can you identify which workloads are the best fit for which system? And once you’re up and running, how can you constantly adapt to Hadoop’s changing data needs?
Cloudera Navigator Optimizer eases the path for moving the right workloads to Hadoop and then actively manages data allowing you to take advantage of Hadoop’s benefits. Now generally available with the recent release of Cloudera 5.8 and a unique part of Cloudera’s analytic database solution, Navigator Optimizer gives you the workload visibility and assessments to build a predictable offload plan, adapt to evolving data and workload demands, and optimize query performance for Hadoop technologies
3 Things to Learn:
Join Ewa Ding, Senior Product Manager at Cloudera, as she discusses:
-An overview of Cloudera Navigator Optimizer and its key features
-A live demo and key use cases of this web-based tool
-What’s next for active data optimization in Hadoop
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.