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?