data quality


For AI to Change Business, It Needs to Be Fueled with Quality Data

@machinelearnbot

While progress was slow during the first few decades, AI advancement has rapidly accelerated during the last decade. But, before companies or people can obtain the numerous improvements AI promises to deliver, they must first start with good quality, clean data. Recently, I had the opportunity to interview Nicholas Piette and Jean-Michel Franco from Talend, which is one of the leading big data and cloud integration company. Nicholas Piette added that ensure data quality is an absolutely necessary prerequisite for all companies looking to implement AI.


How Can Machine Learning Affect Your Organizational Data Strategy? - DATAVERSITY

@machinelearnbot

The current enterprise data, thanks to advanced data technologies, is now collected, organized, and deposited in multi-layered, analytics platforms, which makes overall data handling and Data Management strategies more complex than ever. The article titled Three Forces Driving Enterprise Data Strategy in 2017 describes how the mountains of transactional data from sensor-driven networks and unstructured data emerging from mobile and social platforms have necessitated consistent data usage practices, which in other words, means Data Governance. When the Data Quality, Data Security, Data Governance, Data Stewardship, and Data Sharing strategies in an organization are transparent and solid, then only Machine Learning algorithms can succeed in delivering the intended business outcomes. The blog post titled Machine Learning Impacts Data Quality Matching indicates that automation can vastly improve the data matching process in Machine Learning systems.


For AI to Change Business, It Needs to Be Fueled with Quality Data

#artificialintelligence

While progress was slow during the first few decades, AI advancement has rapidly accelerated during the last decade. But, before companies or people can obtain the numerous improvements AI promises to deliver, they must first start with good quality, clean data. Recently, I had the opportunity to interview Nicholas Piette and Jean-Michel Franco from Talend, which is one of the leading big data and cloud integration company. Nicholas Piette added that ensure data quality is an absolutely necessary prerequisite for all companies looking to implement AI.


For AI to Change Business, It Needs to Be Fueled with Quality Data - Talend

@machinelearnbot

While progress was slow during the first few decades, AI advancement has rapidly accelerated during the last decade. But, before companies or people can obtain the numerous improvements AI promises to deliver, they must first start with good quality, clean data. Recently, I had the opportunity to interview Nicholas Piette and Jean-Michel Franco from Talend, which is one of the leading big data and cloud integration company. Nicholas Piette added that ensure data quality is an absolutely necessary prerequisite for all companies looking to implement AI.


Data Quality in the era of A.I. – Towards Data Science – Medium

#artificialintelligence

As the director of datamine decision support systems, I've delivered more than 80 data-intensive projects -- including data warehousing, data integration, business intelligence, content performance and predictive models -- across several industries and high-profile corporations. In most of the cases, data quality issues explain limited trust in data from corporate users, waste of resources or even poor decisions: consider a team of analysts trying to figure out if an outlier is a critical business discovery or an unknown/ poorly handled data issue; even worse, consider real-time decisions being made by a system not able to identify and handle poor data which accidentally (or even intentionally) had been fed into the process. A modern data-intensive project, typically involves data streams, complex ETL processes, post-processing logic, and a range of analytical or cognitive components. The Data Quality Reference store should also be accessible via interactive reporting and standardized dashboards -- to empower process owners and data analysts to understand the data, the process, trends and issues.


Preparing for the Challenge of Artificial Intelligence

#artificialintelligence

Freed from human-dictated logic, modern AI systems use multi-layered neural networks to store and categorize information in their own ways, and find their own "organic" ways of generalizing from examples, finding relationships, categorizing data and finding patterns. Poor data quality or training can result in biased outcomes -- essentially, a poorly educated computer that will not be a good problem solver going forward. Address the black box: The black box nature of AI systems is not simply an interesting feature; rather, it creates a set of novel issues in terms of risk allocation. In addition, modern AI systems may create insights that present acute sensitivity concerns, and AI functionalities may create new relationships among data owners.


Data Cleansing and Exploration for the New Era with Optimus

@machinelearnbot

Data scientists, data analysts, business analyst, owners of a data driven company, what do they have in common? Right now with the emergence of Big Data, Machine Learning, Deep Learning and Artificial Intelligence (The New Era as I call it) almost every company or entrepreneur wants to create a solution that uses data to predict or analyze. With Optimus we are launching an easy to use, easy to deploy to production, and open source framework to clean and analyze data in a parallel fashion using state of the art technologies. You can detect outliers and erase them, impute missing data using machine learning, clean special characters in your data set, move and update your columns with our data wrangling tools, make beautiful plots to share your discoveries and much more!


Data The Currency Gold and Diamond of the Future

#artificialintelligence

Taking cues from it and enabling IoT, Big Data Technologies and Data Science in collecting, refining and generating analytics will surely help to deliver better governance to the citizens. Recent advancements in the Blockchain technology can address this challenge and improve the delivery of public services. However, we will need data classification, data cleaning, data integration, data transformation and data reduction before we move on to performing analytics over that data. History is waiting to be written, we have to grab this moment with both of our hands and craft it in such a manner that we can become a leader in future technologies and governmental systems.


AI gets down to business

#artificialintelligence

Already, many of the 2017 CIO 100 leaders are piloting AI and machine learning projects, taking a do-it-yourself approach to building predictive models and open platforms, working with consultants, or taking advantage of new AI-infused capabilities increasingly popping up in core enterprise systems like ERP and CRM. While AI isn't exactly a newcomer -- it's been around for at least a couple of decades -- the technology has taken off this year for a number of reasons: Relatively cheap access to cloud-based computing and storage horsepower; unlimited troves of data; and new tools that make it more accessible for mere mortals, not just research scientists, to develop complex algorithms, notes David Schubmehl, research director for cognitive and AI systems at IDC. "It's really the idea that programs or applications can self-program to improve and learn and make recommendations and make predictions." Read ahead to learn how six 2017 CIO 100 leaders are transforming their enterprises to capitalize on AI and machine learning.


Data Cleaning and Wrangling With R

@machinelearnbot

To do this, we define our data frame as follows: dataframe -data.frame(ID,Age) Often times, it is necessary to combine two variables from different datasets similar to how VLOOKUP is used in Excel to join two variables based on certain criteria. For instance, suppose that we wish to link the Date variable in the sales dataset with the Age and Country variables in the customers dataset – with the ID variable being the common link. Therefore, we do as follows: mergeinfo -merge(mydata[, c("ID", "Sales")],mydata2[, c("ID", "Age", "Country")]) Upon doing this, we see that a new dataset is formed in R joining our chosen variables: Suppose that we now wish to calculate the number of days between the current date and the date of sale as listed in the sales file. For instance, we can first format our date as follows: date_converted -format(Date, format "%Y-%m-%d %H:%M:%S") new_date_variable -as.POSIXct(date_converted) seconds -diff(new_date_variable,1) When we define our seconds variable, it will now give us the difference between two dates in seconds.