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How MAC Consulting is Leveraging Data Analysis

How MAC Consulting is Leveraging Data Analysis

Data Analysis is currently one of the ‘buzz’ words in the business world. The problem, however, is because this is perceived to be a technical area of expertise, many companies choose not to pursue the subject matter further or to understand the underlying value. Data is, therefore, a major strategic asset that companies are still struggling to utilise to its full potential.

Entities end up sitting with huge data sets that they are unable to leverage because they either do not understand how to unlock value from the data or they do not have the necessary in-house skills.

Data regulation and the emergence of new technologies has forced companies to re-evaluate what data they have, where this data resides and to identify who the custodians and users of the data are. Companies are starting to realise that data (if trusted and aggregated correctly) can be used to create and add additional value to their current service offerings.


At MAC Consulting, we have the skills to help clients analyse, identify and refine their data needs and requirements through stakeholder engagements and facilitation of strategic workshops. There are two specific skillsets required in solutioning for such an environment:

  • Technical expertise – MAC has access to industry specialists to partner with the right people for the job. We are solution-agnostic and objectively focus on delivering the correct tailored solution to fit the client’s needs.
  • Non-technical/business expertise – MAC has highly experienced and skilled consultants who specialise in running and managing projects as well as data analysts who are skilled in the following areas:

Data governance and standards

  • Data discovery and identification: identifying what data the entity has and where it resides.
  • Data lineage: understanding the relationships between various data entities, attributes and metrics (understanding how data moves through the organisation, from source to report).
  • Data ownership: identifying accountable and responsible data owners and custodians through developing and implementing data ownership models aligned to the company’s operating model.
  • Data quality: trusting the accuracy, timeliness, completeness and uniqueness of the data through data validation, business process re-engineering and driving behavioural change.
  • Data classification: understanding what data is used for which business function and the related importance to the business.

Data compliance regulation

  • Data regulation and compliance: Protection of Personal Information regulation (POPI), General Data Protection Regulation (GDPR), ISO standards and performing gap analysis.
  • Defining, scoping and resourcing projects

Extracting / unlocking value from the data

  • Commercialisation of data: defining strategic value drivers, as well as designing and implementing business models and operating models to support commercialising the data.

Aggregation and modelling of the data

Partnering with industry and subject matter experts.

  • Technical skills, technology gap analysis and analysing the current technology landscape (quantitatively analysing, aggregating and reporting on the data).

By leveraging data analysis, we assist our clients in improving their understanding of their data. Our clients can therefore use their data to gain competitive advantage by defining business cases to assist in cost reduction and revenue generation.