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7 Top Competitive Intelligence Blogs Read in 2016

Cooperative Intelligence Blog

Templates for Win/Loss Analysis – The Win Loss request I get asked the most often is, “Can you share your Win Loss templates?” I break down Win Loss analysis questions into 4 buckets: relationship health, company reputation, product/service attributes, and servicing issues. These topics are timeless.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

Datapine Blog

quintillion bytes of data being produced on a daily basis and the wide range of online data analysis tools in the market, the use of data and analytics has never been more accessible. The data quality analysis metrics of complete and accurate data are imperative to this step. 6) Data Quality Metrics Examples. With a shocking 2.5

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Machine Learning Problems: The Easy Parts

Contify

The matrix is then transformed into a similarity matrix using cosine multiplication. The rows of this matrix are the document ids and columns are values that represent how similar they are. The next challenge is to cluster this large similarity matrix and look for similar document. Similar Meaning Words.

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Machine Learning Problems: The Easy Parts

Contify

The matrix is then transformed into a similarity matrix using cosine multiplication. The rows of this matrix are the document ids and columns are values that represent how similar they are. The next challenge is to cluster this large similarity matrix and look for similar document. Similar Meaning Words.

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Handling Outliers Using Smarten Assisted Predictive Modelling!

Elegant BI

Outliers, also referred to as anomaly, exception, irregularity, deviation, oddity, arise in data analysis when the data records differ dramatically from the other observations. Dealing with Outliers: A secret ingredient for success of data analysis. Any kind of analysis initiates by looking upon the data. Outlier, an Outsider!

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Top 10 Analytics And Business Intelligence Trends For 2020

Datapine Blog

Today, most companies understand the impact of data quality on analysis and further decision-making processes and hence choose to implement a data quality management (DQM) policy, department, or techniques. DQM is indeed reckoned as the key factor in ensuring efficient data analysis, as it is the basis from where all the rest starts from.

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US Computer Hardware Industry Embracing Competition-Charged Business Landscape

ArchIntel

In her 18 years at Qualcomm, Goulart also held the position of director of research and analysis, a role she held for three years and five months. He describes himself on LinkedIn as an engineering management professional with extensive experience in forming and leading competitive analysis processes and teams.