Sat.Jun 16, 2018 - Fri.Jun 22, 2018

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Getting To Trusted Data Via AI, Machine Learning And Blockchain

Bruno Aziza

Establishing trust in data is critical. Organizations are now employing AI, Machine Learning, Blockchain to ensure data reliability and integrity.

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4 steps for running a machine learning pilot project

IBM Big Data Hub

Running a machine learning pilot project is a great early step on the road to full adoption. To get started, you’ll need to build a cross-functional team of business analysts, engineers, data scientists and key stakeholders. From there, the process looks a lot like the scientific method taught in school.

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Cassandra vs. HBase: twins or just strangers with similar looks?

ScienceSoft

Similar at first glance, Cassandra and HBase actually are quite different in terms of architecture, performance and data models. What are these differences and how do they influence the tasks that HBase and Cassandra perform? It’s all here.

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Brittleness and incremental improvement

DMBS2

Every system — computer or otherwise — needs to deal with possibilities of damage or error. If it does this well, it may be regarded as “robust”, “mature(d), “strengthened”, or simply “improved” * Otherwise, it can reasonably be called “brittle” *It’s also common to use the word “harden(ed)” But I think that’s a poor choice, as brittle things are often also hard. 0.

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How To Get Promoted In Product Management

Speaker: John Mansour

If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.

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IDG Contributor Network: The eye-opening new world of alternative investor data

CIO Business Intelligence

Investors, whether they be day traders at home or managers of large hedge funds, are data hungry. They pore over earnings reports and company filings and jump to read news alerts. If they’re on the super-sophisticated side, they may be using data models. And if they’re on the cutting edge, they may be using new “alternative” data sets to inform their decisions.

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Adding MongoDB to the IBM enterprise database ecosystem

IBM Big Data Hub

The modern data landscape demands more than one type of database. That’s IBM has rolled out JSON-document-based databases in Db2 and Cloudant, as well as partnered with select database providers to offer developer-focused database services through the IBM Compose platform.

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Exploring the Current State of Embedded Analytics

DataRobot Blog

by Jen Underwood. Embedded analytics is everywhere. From consumer gadgets, intelligent things, and applications to the rapidly expanding Everything as a Service (XaaS) subscription economy, analytics has been ubiquitously embedded into all areas. Read More.

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What AI Means to a Retailer Dedicated to Customer Experience

Birst BI

Retailers are focused more than ever on quickly adjusting to changing customer preferences and demand. Specialty’s Café and Bakery is a great example of a retailer that is using data to drive decisions related to product development and selection, inventories, staffing, and more to attract and keep customers. For example, retailers rely on business intelligence (BI) tools to predict future demand for products around known factors such as special events or holidays.

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Real-time business value comes from real-time data

IBM Big Data Hub

Data is business. The pace at which an organization can process data improves its ability to react to business events in real time.

IT 81
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Self-Serve Data Preparation for YOUR Users

Smarten

Self-Serve Data Prep Should be Just That – Self-Serve! Self-serve has many meanings. You can pump your own gas, you can serve yourself at a buffet, and sometimes you can even do your own data preparation. You will notice that I said ‘sometimes’ That is because you have to choose the right tool if you want to really participate in self-serve data preparation.

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Navigating the Future: Generative AI, Application Analytics, and Data

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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Brittleness, Murphy?s Law, and single-impetus failures

DMBS2

In my initial post on brittleness I suggested that a typical process is: Build something brittle. Strengthen it over time. In many engineering scenarios, a fuller description could be: Design something that works in the base cases. Anticipate edge cases and sources of error, and design for them too. Implement the design. Discover which edge cases and error sources you failed to consider.

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Teaming on data: IBM and Hortonworks broaden relationship

IBM Big Data Hub

Data is driving business. And as volumes climb with no end in sight, companies have a decision to make: harness and extract insight from that data, or watch your competitors do it as they pass you by.

IT 66
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Data democratization is driving self-service analytics

IBM Big Data Hub

Data democratization allows data to be accessed across the organization and empowers individuals to use the data in their decision making and gain critical business insights. Data democratization is fast becoming a game changer as it’s moving towards a user centric micro-services based architecture.

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What is the Holt-Winters Forecasting Algorithm and How Can it be Used for Enterprise Analysis?

Smarten

This article provides a brief explanation of the Holt-Winters Forecasting model and its application in the business environment. What is the Holt-Winters Forecasting Algorithm? The Holt-Winters algorithm is used for forecasting and It is a time-series forecasting method. Time series forecasting methods are used to extract and analyze data and statistics and characterize results to more accurately predict the future based on historical data.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.