Lies, Damn Lies, and Data Visualization
Predictive Analytics in Healthcare: It's Not Happening
Predictive Analytics Key Component of Customer Experience Management
Predictive Analytics in Higher Education
Managing Data Quality-Tools Alone are Not Enough
Laura Sebastian-Coleman, Ph.D. Chief Data Office, Data Quality Director, Prudential Financial
Procure Iq: Reimagining How You Purchase Transportation
Tim Gagnon,Vice President Of Analytics And Data Science, C.H. Robinson
Evolution Of Cloud Technology Data Technology Platforms
Jason Gellings, Director Of Bi And Analytics At Roehl Transport
Achieving Big Data Roi Through Data Science
Anjna Kumar, Vice President, Data & Analytics
Thank you for Subscribing to CIO Applications Weekly Brief
Why is Predictive Analytics Vital for Digitization?
Ironically, the problem isn't that businesses don't have access to the data they want; it's that they are unable to cope with the demands for data transformation.
Fremont, CA: Using data to acquire meaningful insights into what consumers desire is increasingly becoming a common company optimization technique rather than a competitive advantage. Because of the digitalization of business and the 24/7 nature of a connected society, there is now more data accessible to anticipate the future than ever before.
Before you embark on your next data-driven effort, consider the following:
• First, determine which data types to use first.
As tempting as it is to go all-in with both structured and unstructured data from the start, a more prudent approach is to start small with structured Extract, Transform, Load (ETL) data pilot projects, and once the ROI in terms of delivered insights has been established, scale up accordingly and leverage the power of unstructured data via Extract, Load, Transform (ELT) processes.
• Determine ahead of time who might be in charge of data transformation and governance.
While some smaller businesses may be able to delegate the task to a Chief Data Officer, it is typically necessary to assemble a dedicated team to develop data-driven projects and upskill the workforce as needed.
• Determine how the user will measure data quality for both inputs and outputs, and convey to stakeholders the benefit of investing time and money in digital transformation.
Everyone in the organization must know why analytics programs need to get tweaked in order to be genuinely effective, as well as the role that digital transformation plays in lowering the likelihood of incorrect findings. Decision-makers who see the worth of their existing data and understand the necessity of leveraging that data to generate actionable insights will be able to forecast the future and flourish in a competitive economy.