Data blending is important because it allows data analysts
to access data from all the relevant data sources: Big Data,
the cloud, social media, third-party data providers, in-house
databases, department data stores, and more. Historically,
the challenge of data analysts has been accessing this data
and then cleansing and preparing the data for analysis. These
stages of access, cleansing, and preparing data are complex
and time intensive. Easy-to-use software tools that reduce the
burden of this data preparation and turn data blending into an
asset greatly empower the data analyst to become more effective and open new opportunities to the business.
The focus of this book is how data blending is used and what
it can provide the data analyst working to support business
decision makers. I identify what features to look for in data
blending tools and how to successfully deploy these tools and
data blending within your business.
Data blending is important because it allows data analysts
to access data from all the relevant data sources: Big Data,
the cloud, social media, third-party data providers, in-house
databases, department data stores, and more. Historically,
the challenge of data analysts has been accessing this data
and then cleansing and preparing the data for analysis. These
stages of access, cleansing, and preparing data are complex
and time intensive. Easy-to-use software tools that reduce the
burden of this data preparation and turn data blending into an
asset greatly empower the data analyst to become more effective and open new opportunities to the business.
The focus of this book is how data blending is used and what
it can provide the data analyst working to support business
decision makers. I identify what features to look for in data
blending tools and how to successfully deploy these tools and
data blending within your business.
