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HR Metrics Database
Presentation to PERF
HR Metrics Database
• Objectives• Progress to date • Examples • Lessons Learnt and Next Steps
Objectives
• Collect data across London Boroughs more efficiently and effectively
• More emphasis on using the data than generating reports
• Benchmarking tool with ability to drilldown into detail
• Turn standard London Councils workforce metrics into simple scorecard
Progress so far • Visits to Waltham Forest & Haringey over the
summer & collected sets of data • Defined file layouts and built data transformation
scripts • Data loaded into MS Access – one row per
person per period• Standard formulas applied • Transformed into a datamart “OLAP” cube using
Cognos Transformer
Progress so far (2)
• Presentation to Workforce Intelligence group • Revised File layouts & documentation • Reviewed use of software for “reporting layer”• Agreed two additional partner boroughs• Current line-up – Sutton, Haringey, Waltham
Forest, RBKC & Merton
Measures & Dimensions • Dimensions• Time • Organisation • Gender • Ethnicity • Age Band• Length of Service • Salary Band • Employee Group
• Measures • FTE / Headcount • Gross Pay (Total / Avg.)• Average Age • Sick Days • Sickness Cost • Sick Days per FTE• Turnover Rate • Starter / Leaver Count
0.00%
4.00%
8.00%
12.00%
16.00%
20.00%
24.00%
28.00%
Below 10K10K to 15K
15K to 20K20K to 25K
25K to 30K30K to 35K
35K to 40K40K to 45K
45K to 50K50K to 60K
60K to 70K70K to 80K
80K to 90K90K to 100K
Over100K2008/Jul
HeadcountLegend
All Boroughs
Waltham ForestHaringeyMerton
0.00%
4.00%
8.00%
12.00%
16.00%
20.00%
Under 2020 to 25
25 to 3030 to 35
35 to 4040 to 45
45 to 5050 to 55
55 to 6060 to 65
65 and over2008/Jul
Headcount by Age BandLegend
Average
Waltham ForestHaringeyMerton
Age Profile by Salary BandBelow10K
10K to15K
15K to20K
20K to25K
25K to30K
30K to35K
35K to40K
40K to45K
45K to50K
50K to60K
60K to70K
70K to80K
80K to90K
90K to100K
Over100K
Under 20
20 to 25
25 to 30
30 to 35
35 to 40
40 to 45
45 to 50
50 to 55
55 to 60
60 to 65
65 and over
AGE_BAND
ByPeriod
62 13 12 2 0 0 0 0 0 0 0 0 0 0 0
94 54 92 83 41 5 1 0 0 0 0 0 0 0 0
63 56 105 201 213 71 28 6 3 0 0 0 0 0 0
70 87 101 164 176 98 66 26 10 4 1 1 0 0 0
89 121 117 206 239 114 95 50 24 10 3 2 1 1 0
110 196 165 280 270 189 124 75 34 21 13 5 5 1 2
105 208 184 263 237 162 112 98 42 25 12 6 3 1 4
83 202 179 201 178 143 100 86 42 33 11 7 5 1 3
65 180 177 206 133 99 76 69 24 20 6 5 2 0 2
56 118 133 116 59 49 24 20 6 6 1 1 1 0 1
36 30 25 17 11 6 1 1 0 1 0 0 0 0 0
834 1264 1289 1739 1557 936 625 430 185 119 47 28 18 5 12
Total
95
400
801
860
1155
1594
1560
1353
1130
646
153
9750
Age Profile by Ethnicity Black White Mixed Asian Other Unknown
Under 20
20 to 25
25 to 30
30 to 35
35 to 40
40 to 45
45 to 50
50 to 55
55 to 60
60 to 65
65 and over
AGE_BAND
ByPeriod
20.18% 30.52% 8.01% 20.52% 2.55% 18.22%
23.16% 39.09% 5.79% 11.62% 2.68% 17.65%
23.82% 45.14% 4.03% 12.68% 3.25% 11.08%
27.56% 43.87% 3.85% 11.37% 2.95% 10.39%
34.17% 42.16% 3.40% 8.90% 2.16% 9.22%
36.68% 45.15% 2.42% 5.30% 2.57% 7.88%
33.31% 49.46% 1.84% 5.56% 2.47% 7.36%
24.05% 59.83% 1.51% 5.81% 2.78% 6.03%
16.83% 67.23% 0.79% 6.78% 2.35% 6.02%
15.19% 69.19% 1.00% 4.38% 2.32% 7.91%
15.54% 56.95% 0.86% 3.15% 2.94% 20.56%
27.45% 51.34% 2.46% 7.46% 2.59% 8.70%
Age Profile by Ethnicity
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
Black White Mixed Asian Other Unknown
Age profile by Ethnicity LegendBy Period.Under 20By Period.20 to 25By Period.25 to 30By Period.30 to 35By Period.35 to 40By Period.40 to 45By Period.45 to 50By Period.50 to 55By Period.55 to 60By Period.60 to 65By Period.65 and over
Lessons Learnt \ Next Steps • Be clearer (and less flexible) regarding the data
supplied by partner councils • Add Absence reasons, top 5% flag and
occupational groups• More checking & testing • Fully document the “transformation” process • Collect data from more boroughs• Look at more options for software for reporting
layer – (looking at Pentaho, Jinfonet & Excel)
The Challenge…
• Management Information will be the lifeblood of workforce planning
• We need to see the possibilities of what it can provide. Spend more time using the data than designing reports.
• We need to be aiming to make the information more accessible for line managers
Any Questions?
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