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
Six Helpful Suggestions for Tackling Predictive Analytics Challenges
Like most software or technologies, predictive analytics can pose hurdles or problems for businesses, often overlooked until the technology fails to deliver the desired outcomes.
Fremont, CA: Identifying your goals and objectives will assist you in determining which software is ideal for your company. This will help you align your aims and keep this goal in mind while you ensure that efforts are made to carry out the strategy. It is necessary to conduct research and gather the appropriate data sources.
Because risk management is frequently a tiny department, getting permission for significant acquisitions like data or predictive analytics tools can be difficult. To address this, risk managers should set aside funds for data analytics by calculating the system's ROI (Return on Investment) and presenting a compelling business case for the benefits that can be realized.
Inputted Data of Poor Quality
If the data entered is incorrect, it will alter the output, making it unreliable. Manual data entry errors are a significant cause of this, which can have significant repercussions if the analysis influences decisions. To combat this, make sure some data inputs are automatically generated, such as drop-down fields.
Insufficient Organizational Support
Without the help and support of the enterprise, data analytics will be ineffective. This applies to everyone in the organization, from risk managers to data submitters. Therefore, risk management should be emphasized in all elements of the company and education on the system's benefits.
It is critical that everyone on the team understands the capabilities of the technology and how to use them. They can widen their roles by assisting in developing your team's talents. You can also make sure that analytic capabilities are present during the hiring process and have an easy-to-use analysis system. It also means that anyone can use the system regardless of skill level.
Switching from traditional data analysis methodologies may cause some employees anxiety or worry. As a result, businesses must define and specify how predictive analytics can help them streamline their operations and individual responsibilities.