Using data analysis to facilitate achieving the DTI 2013 BEE Codes



The 2013 Department of Trade and Industry (DTI) Codes are complex and meeting them will be challenging. For management to make informed decisions and measure progress in achieving transformational goals at business-unit level there must be one version of the truth that is accurate and up-to-date.

The New Codes have raised the broad-based black economic empowerment (B-BBEE) bar for all larger South African organisations: most believe that if they had to conform to the Codes now their BBBEE level would drop at least 2 points for example from 4 to 6

And yet the codes come into force now.

Make your spend data more visible

Most organisations measure their BBBEE scores reactively, a process fraught with manual intervention and checking (many organisations employ auditors and BBBEE experts to verify their scores prior to a score audit).The resulting lack of productivity, increased internal and external cost and increased level of proactive management hampers organisations achieving their transformation strategy and diverts resources away from their core activities.

What is required is real-time BBBEE data analysis, which covers all the pillars of any BBBEE sector code and gathers transactional data, or data at the ultimate granularity, and automates the process of collecting information, providing head office and business units with scorecards to:
• set targets;
• perform ‘what-if’ analyses to determine what must be done to fulfil these targets; and
• measure progress on a month-by-month basis against these targets.


Image1_PI_ESDscorecard.pngThe image along side is an example organisation’s current scores for preferential procurement compared with the DTI Codes (‘Actual %’ and ‘Actual Score’), while the ‘Forecast %’ and ‘Forecast Score’ show the effect of transferring R80-million spend from non-compliant suppliers to suppliers with an Exempted Micro Enterprise (EME) levels of 4. The overall score increases from 6.48 to 10.80.


Image2_PI_NonCompliantSpend.pngBut how can an organisation use such an analysis to recognise suppliers who are not sufficiently compliant and relocate spend, for instance, to those that have an EME or Qualifying Small Enterprise (QSE) level of 1 to 4? Within a matter of minutes a dashboard view can be created that identifies the suppliers that are not compliant and the divisions that use them, as shown in the image alongside.


Image3_PI_Level3_4suppliers.pngA further dashboard view (image alongside) shows spend with suppliers with a BBBEE level of 3 or 4, the divisions that use them and whether the suppliers are classified according to the generic codes (GEN), as EMEs or QSEs.

The next step is drilling further down into the data to see which of the suppliers could be used by other divisions, for example. Alternatively, the organisation can engage with non-compliant suppliers to verify their current BBBEE status and amend their records. As the scores are incorporated into the data, the dashboard views will automatically update to reflect the correct scores and the effect on the organisation’s overall BBBEE score.

The flexibility of the analysis will be particularly useful as suppliers grapple with accommodating the new DTI Codes and maintaining their current BBBEE status. If an organisation has a small group of suppliers providing a high proportion of goods and services it will be essential to understand these suppliers’ new scores. With regular updates of suppliers’ adjusted scores and the available ‘what if’ facilities, management can model what the supplier performance may look like and how this will affect the overall Supplier Performance (SP) score.


Image4_PI_EmploymentEquity.pngThe analysis would enable users to drill down into the detail to identify, for instance, smaller suppliers who would benefit from enterprise development spend; and staff members with the right skills, qualifications and track record to manage and groom into management positions, etc., notes the image alongside

The scorecard on the previous image shows the effect on employment equity where 50 new black middle managers are appointed (as 50 white middle managers retire) together with 50 black women middle managers. The score goes up from 8.67 to 10.94. The Venn diagram on the previous image shows the number of existing staff who are female, black and skilled or semi-skilled, who may be available for training to take middle management positions.

For more information on leveraging your data analysis to comply with the new BEE codes contact Alan Low on

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