“Data is the new oil,” so goes the new adage. But data analytics, and especially HR dashboards, should prescribe action, not merely summarize trends. Most data projects fail to lend credibility because they are overly descriptive and fraught with excessive human biases. To make something happen from data, the key is to know the criteria to finding pattern in data, that motivates compelling and immediately actionable insights. It all boils down to setting the right thresholds and alert mechanisms for actions. Learn how to do it . . . from this one-day workshop
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Learn how to do it . . . from this one-day ... - mrc-asia.com of case and presentation of data available (actual case done by MRC) 1300- 1700 1.6 Participants to critique on productivity
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“Data is the new oil,” so goes the new adage. But data analytics, and especially HR dashboards, should prescribe action, not merely summarize trends.
Most data projects fail to lend credibility because they are overly descriptive and fraught with excessive human biases. To make something happen from data, the key is to know the criteria to finding pattern in data, that motivates compelling and immediately actionable insights. It all boils down to setting the right thresholds and alert mechanisms for actions.
Learn how to do it . . . from this one-day workshop
HR DASHBOARD WORKSHOP HR
DAS
HBOA
RD W
ORKS
HOP
HR D
ASHB
OARD
WOR
KSHO
PHR DASHBOARD WORKSHOP
PRESCRIPTIVE HR DASHBOARDS - MOTIVATING ACTIONS FROM DATA
AGEN
DA
OBJEC
TIVE
Purpose of this Workshop
Define and reflect on current popular mea-sures for their ROI of human capital in-vestment, taking into account productivity (cost, headcount and financial return, etc) and effectiveness (retention of talent, suc-cession, etc) influences.
Principles
Evaluate the current variety of data avail-able and their interoperability and avail-ability among HRIS systems, excel sheets or employee platforms. Build dashboards in both Excel and PowerBI. Explore Mo-bile-enabled real-time analytics.
Tools
Create a system of HR metrics in conjunc-tion with agreed measures to be presented in the form of a dashboard to support deci-sion making.
Measures
Embed alert mechanism that signals breach of desired threshold range or cases of heightened change. Using internal, ex-ternal, historical, and forecast data to mo-tivate actions.
Alerts
HR DASHBOARD WORKSHOP HR
DAS
HBOA
RD W
ORKS
HOP
HR D
ASHB
OARD
WOR
KSHO
PHR DASHBOARD WORKSHOP
PRESCRIPTIVE HR DASHBOARDS - MOTIVATING ACTIONS FROM DATA PRESCRIPTIVE HR DASHBOARDS - MOTIVATING ACTIONS FROM DATA
AGEN
DA
1. We are specialists in prediction. We are moving ahead of the curve towards predictive and prescriptive analytics. Equipped with state-of-the-art machine-learning expertise, our Ivy-league trainers can contrast human versus machine generated predictive insights across the whole spectrum of analytical methods.
2. We believe in the “mobile” wave. We go for the latest visualisation tools that is mobile-enabled, and that operates fast-enough to claim the coveted real-time advantage to support businesses at the speed of light!
3. We are pragmatic in our approach. We use examples from real project work in Asia, sanitised to ensure the privacy of our clients.
WHY MRC?
Session Timing
1.1 Define criteria for metrics selection
0900-12001.2 Data assessment & modelling
1.3 Threshold and alert mechanism recommendation
1.4 Dashboard & visualizer prototyping
Lunch 1200 - 1300
1.5
Customised Casework - Building a prescriptive dashboard
Explanation of case and presentation of data available (actual case done by MRC)
1300- 1700
1.6
Participants to critique on productivity measures used in company and present solutions based on internal and external comparisons. External data is based on MRC AI market sensor (workforce and business productivity metrics such as revenue per employee)
1.7
MRC to share actual case answers and round up principles, showing how inferential statistics, descriptive statistics, and machine learning pre-dictions can be used in dashboards.
1.8Round up: Participants to recap on what to measure, and how to look at historical internal data and external data to set thresholds.