The value of information lies in its use. This is as true for official statistics as it is for any other data.
Data gathered by Statistics SA is used in policy formulation, evaluation and monitoring of development initiatives, and measuring socioeconomic change. For example, the SA Reserve Bank's monetary policy committee carefully analyses CPIX inflation (which excludes mortgages), one of the suite of price indices, when it sets the repo rate.
In other examples, data is integrated to form indicators to measure progress on achieving the UN's millennium development goals, the government's programme of action and the accelerated and shared growth initiative for South Africa.
The key role players in policy making watch and analyse the data carefully. Reserve Bank governor Tito Mboweni and his team keep an eagle eye on shifts in CPIX - not just in terms of the actual number (+4.8 percent for the metropolitan area, year on year, between February 2006 and February 2007), but on the detail as well.
For example, the Reserve Bank team would have noticed that this annual increase of 4.8 percent in the CPIX is mainly due to relatively large annual contributions in the price indices for food (+2.1 percentage points); housing, excluding interest rates on mortgage bonds (+0.6 percentage point); medical care and health expenses (+0.5 percentage point); transport (+0.4 percentage point); fuel and power (+0.3 percentage point); household operation (+0.3 percentage point); and education (+0.3 percentage point).
The team would have noticed that these annual increases are slightly counteracted by an annual decrease in the price index for clothing and footwear (-0.3 percentage point).
Those making policy and measuring the impact of its implementation are required to explore the detail.
Take, for example, the latest data on electricity generated and available for distribution, released by Stats SA early in April. Changes in consumption are generally viewed as an important indicator of economic activity, at least in the formal sector.
Data for February 2007 reported an overall increase in consumption of electricity of 5.1 percent compared with February 2006. However, this single figure for South Africa as a whole should not satisfy those who make policy and measure its effects.
At the very least, the figure needs to be disaggregated to provide consumption in the provinces, which do not necessarily follow the same trend reported for South Africa as a whole.
Provincial data shows year-on-year increases reported for the Eastern Cape (+24.2 percent), Northern Cape (+19.1 percent), Gauteng (+9.6 percent), Mpumalanga (+8.4 percent) and Western Cape (+6.1 percent). Less electricity was distributed to KwaZulu-Natal (-2.2 percent) and Limpopo (-6.2 percent).
The policy maker needs to ask why Eastern Cape consumption of electricity has increased so rapidly compared with the other provinces and the country as a whole. What does it mean that the rate of increase in Northern Cape is much higher than in Gauteng and Western Cape What are the implications for the year-on-year decrease in consumption in KwaZulu-Natal and Limpopo?
Each series released by Stats SA needs to be analysed in this way.
A single figure for unemployment is an interesting indicator. According to the latest data, released at the end of March, the unemployment rate fell from 26.7 percent in September 2005 to 25.5 percent in September 2006.
Further investigation shows this is the result of higher employment in the year to September 2006, coupled with a decline in the number of people in unemployment.
A little more mining of the data shows that the number of employed people has risen steadily since September 2001, with employment growth strong in the two years to September 2006 when more than 1 million additional people were employed. Overall, from September 2001 to September 2006, the cumulative employment gains in the labour market totalled 1.6 million.
Even this detail is not enough. Policy makers and analysts need to use the data to disaggregate further, and explore what these figures mean for employment by industry and occupation, for the formal and informal sectors. They need to look at unemployment trends by province, by population group, by level of education and by gender.
Similar ways of using data can be explored in sectoral series. The latest data on production and sales in manufacturing and mining, released on April 11 and 12, respectively, showed that seasonally adjusted manufacturing production for the three months to the end of February 2007 increased by 2.5 percent compared with the previous three months; and that total mining production for the three months to February 2007 increased by 5.8 percent compared with the same period in 2006.
Deeper questions must be asked. What was the contribution of the 10 different manufacturing divisions What were the differences between the production of gold and non-gold minerals What was the contribution of the platinum group of minerals?
Yesterday's release of data for retail trade sales will be carefully interrogated by analysts. Retail trade sales at constant (2000) prices, for the three months to February 2007, showed an increase of 8 percent compared with the three months to February 2006.
But policy makers will extract further value when they examine the contribution of different retailer types to the percentage change in trade sales, or total sales according to type of dealer.
If a careful reading of the data raises questions of importance for policy and its implementation, then the data has value.
However, when those who formulate policy and monitor its impact do not use the data to raise these sorts of questions, then the value of the data is compromised.