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Quality Control Tips for Data Collection with Drone Surveying

Smart Data Collective

Here at Smart Data Collective, we never cease to be amazed about the advances in data analytics. We have been publishing content on data analytics since 2008, but surprising new discoveries in big data are still made every year. One of the biggest trends shaping the future of data analytics is drone surveying.

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Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

Smart Data Collective

It takes a lot of split-testing and data collection to optimize your strategy to approach these types of conversion rates. Companies with an in-depth understanding of data analytics will have more successful Amazon PPC marketing strategies. However, it is important to make sure the data is reliable.

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Automate Your Yardi Real Estate Data Collection and Management

Jet Global

Having accurate data is crucial to this process, but finance teams struggle to easily access and connect with data. Improve data quality. Δ The post Automate Your Yardi Real Estate Data Collection and Management appeared first on insightsoftware. Near real-time information is vital to: Save time.

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The Future of AI: High Quality, Human Powered Data

Smart Data Collective

How Artificial Intelligence is Impacting Data Quality. Artificial intelligence has the potential to combat human error by taking up the tasking responsibilities associated with the analysis, drilling, and dissection of large volumes of data. Data quality is crucial in the age of artificial intelligence.

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Supply Chain Planning Maturity – How Do You Compare to Peers?

Time allocated to data collection: Data quality is a considerable pain point. How much time do teams spend on data vs. creative decision-making and discussion? The use of scenario analyses: How widespread is the use of scenarios prior to and during planning meetings?

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science.

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The unreasonable importance of data preparation

O'Reilly on Data

Beyond the autonomous driving example described, the “garbage in” side of the equation can take many forms—for example, incorrectly entered data, poorly packaged data, and data collected incorrectly, more of which we’ll address below. The model and the data specification become more important than the code.