data quality

R packages for summarising data – part 2


This does only work on numeric variables, but the summary it produces is extremely comprehensive. Certainly every summary statistic I normally look at for numeric data is shown somewhere in the above! The output it produces is very clear in terms of human readability, although it doesn't work directly with kable, nor does it produce tidy data if you wished to use the results downstream. One concern I had regards its reporting of missing data. The above output suggests that my data$score field has 58 entries, and 0 missing data. In reality, the dataframe contains 64 entries and has 6 records with a missing (NA) score.

Five Steps For Re-tooling Your Organization With Machine Learning Technologies


Let's say you decide to build a new house. Not only do you have to buy the materials, but you also have to hire the skilled talent who can get the job done. That is a lesson many CIOs around the world are learning about their plans to implement machine learning technologies that are able to analyze and improve performance without direct human intervention. Despite investing in machine learning, a new survey from ServiceNow indicates that most CIOs do not have the talent, data quality and budgets to fully leverage the technology. If your organization is embarking on the machine learning journey (and it should be), there are five steps CIOs must take to maximize the value of their investment.

Machine Learning is Fascinating but rides on Data Quality


Very often they are referred interchangeably but Machine learning (ML) is actually is a type of artificial intelligence (AI) and it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves.

Unlock the Value: From Data Quality to Artificial Intelligence - InformationWeek


A data analytics program can be an engine that fuels digital transformation projects and operations, helps you better engage with customers, and uncovers insights that lead to that next revenue stream. These programs have become of strategic value to organizations and essential components of digital transformation efforts.

5 Disruptions to Marketing, Part 5: Artificial Intelligence (2018 Update) - Chief Marketing Technologist


This is the final part 5 of a five-part series, providing an update on the 5 Disruptions to Marketing as 2018 marches forward. If you have not yet read Part 1: Digital Transformation, Part 2: Microservices & APIs, Part 3: Vertical Competition, or Part 4: Digital Everything, you might want to start there.

7 Habits CPG Brands Should Adopt in 2018 to Reverse the Digital Curse


All digital transformation and personalization efforts would fail if data underneath is of poor quality, siloed and delayed. Using machine learning within modern data management platform not only helps determine and improve data quality but also enriches the data with relevant insights and provides intelligent recommended actions for data quality and operational improvements. For example, if you are running a campaign for a major product launch, you can eliminate consumer profiles with low data quality (DQ) scores.

How APIs, Edge Computing, and AI Will Evolve in 2018 - DZone AI


If you've spent any time reading the round-up of 2018 technology predictions, you've likely seen artificial intelligence (AI) highlighted in nearly every one. The reason for this is that AI has a seemingly limitless number of applications and use cases for the enterprise. In fact, according to Gartner, over 85% of customer interactions will be managed without a human by 2020. While AI is definitely a hot topic to watch in 2018, there are also a few other tech areas that will have equally exciting momentum and just as big an impact on the enterprise in the year ahead. Following we'll take a deeper look at what some of those will be and how they might shape 2018.

Here come all the AI deployments; Now how do we manage AI? ZDNet


How will enterprises manage artificial intelligence deployments when most managers and executives don't understand the underlying models, data science, or technology? That question is almost haunting. And if you want another theme toss in cloud computing, which is the enabler for AI. Sure, we know AI is a bit hyped. And, yes, we may not quite know the underlying details of AI and what it means for our businesses.

Here come all the AI deployments; Now how do we manage AI?


How will enterprises manage artificial intelligence deployments when most managers and executives don't understand the underlying models, data science or technology?