09/25/2016 Do you require Metadata to enable Governance in organisation? While Metadata management is making strides in the financial industry assisting realisation of Benefits; a popular question in the Governance industry is “How Governance and Metadata should work in tandem to realize the benefits?” While there is a need to manage and govern metadata, it in itself, enables Governance in the organisation. Metadata enables policy associated with Data Sourcing, Data Usage, Data Security and Privacy, Risk, Architecture and Data Quality. A clear definition of Data
While Metadata management is making strides in the financial industry assisting realisation of Benefits; a popular question in the Governance industry is “How Governance and Metadata should work in tandem to realize the benefits?” While there is a need to manage and govern metadata, it in itself, enables Governance in the organisation. Metadata enables policy associated with Data Sourcing, Data Usage, Data Security and Privacy, Risk, Architecture and Data Quality. A clear definition of Data
12/18/2018 Blog 1 of 5: Finding the Right Data Required for Machine Learning (#ML) The first Data Quality challenge is most often the acquisition of right data for ML Enterprise Use cases. Wrong Data - Even though the business objective is clear, data scientists may not be able to find the right data to use as inputs to the ML service/algorithm to achieve the desired outcomes. As any data scientist will tell you, developing the model is less complex than understanding and approaching the problem/use-case the right way. Identifying appropriate data can be a significant
The first Data Quality challenge is most often the acquisition of right data for ML Enterprise Use cases. Wrong Data - Even though the business objective is clear, data scientists may not be able to find the right data to use as inputs to the ML service/algorithm to achieve the desired outcomes. As any data scientist will tell you, developing the model is less complex than understanding and approaching the problem/use-case the right way. Identifying appropriate data can be a significant
11/21/2018 Data Management to make Artificial Intelligence work Data Quality is Important to AI Clean Data is a crucial need to get an outcome from Machine Learning capabilities. Scale and diversity in data is also another important aspect. How accurate is the data to give a usable outcome - is a major question? Accuracy What is easy to access - are the machine-learning services and algorithms, but data is still the prime constituent of AI. The basic predictive efficiency of AI models is defined by diversity, scale and quality of input data. Coverage |
Data Quality is Important to AI Clean Data is a crucial need to get an outcome from Machine Learning capabilities. Scale and diversity in data is also another important aspect. How accurate is the data to give a usable outcome - is a major question? Accuracy What is easy to access - are the machine-learning services and algorithms, but data is still the prime constituent of AI. The basic predictive efficiency of AI models is defined by diversity, scale and quality of input data. Coverage |
11/18/2018 Data Quality Dimensions: No longer a Mystery New Ideas Into Data Quality Dimensions, Never Before Revealed There ought to be a thorough understanding of which data ought to be analyzed in availability of abaundant data. Another reason big data is often connected with poor data quality is due to unstructured social media. High-quality data is a crucial success factor in enterprisewide programs like ERM. First, they must be collect and analyzed. Data is raw, unorganized facts that will need to get processed. Data also must be volume
New Ideas Into Data Quality Dimensions, Never Before Revealed There ought to be a thorough understanding of which data ought to be analyzed in availability of abaundant data. Another reason big data is often connected with poor data quality is due to unstructured social media. High-quality data is a crucial success factor in enterprisewide programs like ERM. First, they must be collect and analyzed. Data is raw, unorganized facts that will need to get processed. Data also must be volume
11/18/2018 The Key to Successful Data Quality Dimensions While it might appear intuitive, once we get right down to it, data quality can be a tough notion to define with any precision. However Data quality dimensions framework, is vital. Of course, it always depends on what you want to do with the data that calls the priority of data dimensions
While it might appear intuitive, once we get right down to it, data quality can be a tough notion to define with any precision. However Data quality dimensions framework, is vital. Of course, it always depends on what you want to do with the data that calls the priority of data dimensions
11/15/2018 Data Governance Deliverables, Outcomes and Benefits A benefit is commonly described as “an outcome of change that is seen as positive by a stakeholder”. How come we don’t think of a cook you have employed as providing you with a service? The value of this service is just not to satisfy your hunger but also to provide you with rich nutrition, to deliver food on time, and to keep the risk of food poisoning at bay. Value is realized only when it is monitored and measured. Data Governance is an oversight on data management activities to ensure
A benefit is commonly described as “an outcome of change that is seen as positive by a stakeholder”. How come we don’t think of a cook you have employed as providing you with a service? The value of this service is just not to satisfy your hunger but also to provide you with rich nutrition, to deliver food on time, and to keep the risk of food poisoning at bay. Value is realized only when it is monitored and measured. Data Governance is an oversight on data management activities to ensure