How to Stay Ahead of a Crisis, and What to Do if You Can't

Imagine you’re a luxury brand releasing a product to a rapt and loyal audience. But instead of receiving rave reviews, your product incites controversy—and social chatter swells around that negative impression. Suddenly, the brand is making headlines that threaten its reputation and could alienate customers. How can you implemen
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Spotlight

Today, modern computing techniques are capable of easily storing, processing and analyzing large amounts of and a variety of data. These techniques could correlate diverse datasets in order to create individual’s profiles, to gain insights, and to offer new digital services. Among others, three technologies that could play a major role in gaining these capabilities are Cloud computing, Internet of Things (IoT) and Big data. Although developed independently, the integration of these three technologies (referred to as Smart ICT) has accelerated the growth of data-driven applications and has unleashed numerous opportunities for businesses, individuals and the society at large [1].

OTHER ON-DEMAND WEBINARS

How to Handle NoSQL with a Relational Database

MariaDB

It can be difficult to scale out relational databases and provide more schema flexibility, thus the rise of NoSQL. However, you shouldn’t have to sacrifice data integrity and transactions in order to scale out on commodity hardware and support semi-structured data. By using an RDBMS with built-in sharding and JSON support, you don’t have to. You get the scalability and flexibility of a NoSQL database along with the consistency and reliability of a relational database – and the ability to mix and match relational and JSON data. In this webinar, we’ll explain how MariaDB Platform can be deployed as a NoSQL database by using the Spider storage engine and built-in SQL functions for JSON. In addition, we’ll discuss how you can access relational data as JSON documents, and how to enforce data integrity if a relational data model is extended with JSON
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The Modern Toolkit for Process Excellence

info.minitab.com

See in action, using a real-world use case from manufacturing, the latest toolkit for Process Excellence, powerful analytics and machine learning.How to use the Minitab toolkit to manage projects and analyse data following the Lean Six Sigma DMAIC structured approach.Discover how to power and digitalise your improvement projects and data analysis with best practice methods combined with intuitive tools.Explore how to solve problems and suggest innovations with whatever data you currently have available.Includes helpful examples of the capabilities and uses for Minitab Statistical Software, Companion by Minitab for project execution and tracking Continuous Improvement, and Salford Predictive Modeler Software for predictive analytics and machine learning.
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COMPETE WITH THE GIANTS 7 ELEMENTS OF A DATA STRATEGY

analytics8.com

For most companies, data is viewed as a problem instead of an asset. Data is often stuck in systems that dont talk to each other, manual processes affect data quality, and analytics tools arent providing clear insights. But those companies who use their data to drive business strategy are out-performing their competitors. To be more competitive in any industry, you must take advantage of the ever-growing amount of available data and that starts with a Data Strategy. A documented roadmap that clearly defines company goals and the specifics on how to get there will put you on the path towards data driven decision making.
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The evolution of analytics: From BI to AI

AVORA

Analytics have come a long way – from data warehousing and manual report building to centralised data in the cloud and machine learning driven analytics. Business Intelligence (BI) tools were revolutionary when they arrived: they made analytics more accessible to the wider business users. Their speedy and powerful analytics provided insights that businesses only dreamt off in the past. But as technology advances, so has the analytics world evolved.
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Spotlight

Today, modern computing techniques are capable of easily storing, processing and analyzing large amounts of and a variety of data. These techniques could correlate diverse datasets in order to create individual’s profiles, to gain insights, and to offer new digital services. Among others, three technologies that could play a major role in gaining these capabilities are Cloud computing, Internet of Things (IoT) and Big data. Although developed independently, the integration of these three technologies (referred to as Smart ICT) has accelerated the growth of data-driven applications and has unleashed numerous opportunities for businesses, individuals and the society at large [1].

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