The use of data science is proliferating in the present situation across the board. But still, most companies continue to let only data scientists use data. Instead the knowledge of the same, needs to be more democratically divided, as is the requirement of the digital age. Democratization of data science knowledge will help the entire organization stay tuned to the norms of the present requirements. There are three ways in which sharing of knowledge has to take place. They are- sharing of data tools, spreading data responsibility and spreading data skills. Data tools need to be shared so that all employees can contribute to the data warehousing, as is done successfully at Airbnb where anyone can contribute to its Knowledge Repository. Sharing tools will help the interaction among the employees. It will also lead to the use of data more readily during decision-making as all understand at least the fundamentals of the same. Sharing of responsibility helps in quicker execution of projects. In this manner, departmental problems can be solved quicker instead of relying on a central repository.


Uploaded Date:10 November 2018

The healthcare industry is trying to innovate by using data to better understand the symptoms and needs of patients. But the data warehousing presently taking place for the most part is presenting an incomplete picture because the vast majority of it gets captured at the point of care. The authentic data collection will be when retrieved during the normal processes of like, while eating, resting or working. For this the use of trackable devices and business analytics has been proposed. Companies such as Optum Analytics are at the forefront of this innovation. Healthcare professionals are often skeptical of much innovation due to the high stakes. But physician burnout is one thing that can be reduced substantially by this partnership between such analytics and device companies with healthcare operators.


Uploaded Date:09 November 2018

Some of the best practices have been identified for customer data management. First of all, one needs to avoid excess data centralization. This is because excess concentration at one place could lead to formation of silos. On the other hand, much fragmentation of the same will lead to problems during data warehousing. This will further compound the matters when trying to deduce insights from incomplete data. Specific focus now has to be maintained on data governance. Established standards are also available for the same. There must also be clear goals established on what kind of data is to be collected. Otherwise, teams end up collecting reams of it, a lot of it of little strategic value. Data management involves first of the validation of the data to be collected, stored or disbursed. The unnecessary bits need to be cleansed out. One needs to monitor the quality of the data collected, else the business intelligence the warehouse is supposed to provide will be compromised. Data security and subsequent privacy are aspects customers are deeply concerned about at present. Access to sensitive information in particular needs to be restricted.


Uploaded Date:05 November 2018

Precision cancer medicine has been around for a while as an idea, but is not getting the right execution. It requires great quantities of data warehousing to be performed on individual patients to provide precise personalized medical advice. What is missing is collaboration between medical researchers, computer scientists and clinicians. Medical schools have traditionally been kept apart from hospitals. One exception is proving to be the Icahn School of Medicine at Mount Sinai, New York. Advanced computer analytics is being used here as several different strands of the medical system can now collaborate together. High technology needs greater integration with medical research. This will help fight diseases that spring from multiple genetic drivers and not merely histology.


Uploaded Date:23 October 2018

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