Bruno Aziza

JUNE 17, 2018

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

Machine Learning 111

Machine Learning 111

Birst BI

JUNE 18, 2018

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.

Slice and Dice 76

Slice and Dice Machine Learning Sales Data-driven 76

IBM Big Data Hub

JUNE 21, 2018

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.

Machine Learning 74

Machine Learning 74

ScienceSoft

JUNE 20, 2018

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.

Modeling 74

Modeling 74

Advertiser: ZoomInfo

AI adoption is reshaping sales and marketing. But is it delivering real results? We surveyed 1,000+ GTM professionals to find out. The data is clear: AI users report 47% higher productivity and an average of 12 hours saved per week. But leaders say mainstream AI tools still fall short on accuracy and business impact. Download the full report today to see how AI is being used — and where go-to-market professionals think there are gaps and opportunities.

Sales

DMBS2

JUNE 20, 2018

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.

Cost-Benefit 48

Cost-Benefit Software Enterprise Technology 48

CIO Business Intelligence

JUNE 22, 2018

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.

Reporting 40

Reporting Modeling Management IT 40

Smarten

JUNE 20, 2018

In this article, we will focus on the identification and exploration of data patterns and the trends that data reveals. The business can use this information for forecasting and planning, and to test theories and strategies. Let’s look at the various methods of trend and pattern analysis in more detail so we can better understand the various techniques.

Forecasting 40

Forecasting Statistics Business Intelligence Predictive Modeling 40

DataRobot Blog

JUNE 19, 2018

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.

Analytics 40

Analytics 40

DMBS2

JUNE 20, 2018

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.

Uncertainty 52

Uncertainty Software Management Analytics 52

IBM Big Data Hub

JUNE 18, 2018

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

IT 70

IT 70

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m

Testing

Smarten

JUNE 19, 2018

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.

Data Warehouse 40

Data Warehouse Marketing IT Analytics 40

IBM Big Data Hub

JUNE 18, 2018

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 59

IT 59

Smarten

JUNE 21, 2018

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.

Forecasting 40

Forecasting Enterprise IT Predictive Modeling 40

IBM Big Data Hub

JUNE 20, 2018

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.

Analytics 58

Analytics 58

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Strategy