Data: The Hidden Treasure for Better Decision Making

HR Tech Outlook | Tuesday, October 29, 2019

Leaders in organizations need an active data strategy to make effective decisions, empower people, and drive change.

Fremont, CA:  Leaders have a very dynamic role to play in an organization, and decision-making is one of the most significant tasks they have to undertake. Thus, leaders must incorporate data and analytics in their decision making rather than taking a shot in the dark based on intuition and bias. The role of data in decision making makes it a critical asset, and it must be managed, protected, and leveraged appropriately while democratizing it for the entire organization. CIOs, CDOs, and IT leaders need an active data strategy and execution plan essential to empower people and drive change. However, data strategies need skill sets, best practices, partnerships, regulations, and technologies. 

The marketing units of organizations capitalize on digital marketing tools to reach prospects, and nurture leads. To interpret the data, marketing executives painstakingly integrate data into a multitude of spreadsheets and later analyze the results.  It is a manual, error-prone process which needs to be replaced by interactive data visualization solution to help the executives automate data integration and provide access to internal data sources. CIOs must be flexible enough to recognize the areas of improvement in the decision-making process.  

Data visualization platforms such as Tableau and Microsoft PowerBI have empowered organizations to develop intuitive dashboards. However, the dashboards implemented miss on many data visualization best practices. Even when dashboards are easy to use, training executives to leverage analytics and training programs to educate decision-makers on the underlying data are still required. 

Implementation of agile practices in analytics and machine learning initiatives is crucial because the inherent discovery process for analyzing data often doesn’t yield a straightforward answer. The discovery process pinpoints missing data, new ways to segment the analysis, data quality issues, and additional discovery questions. The deployment of a data visualization dashboard may result in the realization that alternative and complementary views are needed to tell the story. The execution of a machine learning algorithm may result in the realization that another algorithm may work better. Thus, applying agile data practices enables feedback loops required for experimentation.  The backlog of the learnings shed light on the discovery, and implementation options are worth prioritizing.

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