Our Consulting Approach

Presidion’s approach to consulting: building a true partnership between the business and the analysts for sustainable business performance.


A real partnership between  the “business” and the “analysts” is key to drive our customers’ performance.

This partnership can be distilled to 3 fundamental principles:

  1. Consulting_Approach-Clarity of vision for analytics – “Setting yourself up to succeed in Advanced Analytics!”
    At the outset, there has to be complete clarity regarding the business case for advanced analytics covering how analytics will improve business performance as a whole and how an analytics capability will be developed over time.
  2. Controlled design and build – “Advanced analytics is no magic”
    The development of analytical models and insights need to be done hand-in-hand with the business in a controlled manner to address a tangible problem/challenge by “actioning” insights.
  3. Embedded change – “Insights driven changes to business processes”
    Change enabled by the created insights need to be truly embedded within the business in a continuous improvement culture.

Consulting Approach

1. Building the Vision for Advanced Analytics

  • Discovery for Advanced Analytics
  • Assessment for Advanced Analytics
  • Advanced analytics capability building roadmap
  • Business case for advanced analytics

2. Design and build insights

  • CRISP-DM Best Practice Modelling Approach for business impact
  • Performance Management

3. Implement insights and embed change

  • Training and coaching
  • Deployment and Change Management
  • Return on Invesment Calculation

1. Building the Vision for Advanced Analytics

  • Advanced Analytics Discovery Workshopvision-comp
    Advanced Analytics deliver significant business benefits in Marketing, Fraud, Finance, HR, Operations etc… – discover the “World of the Possible” linked to the succesful deployment of this capability as an Enterprise Solution
  • Assessment for Advanced Analytics
    Understand your starting point across People, Process and Technology with Presidion’s Advanced Analytics Capability Assessment Framework
  • Advanced analytics capability building roadmap
    Establish the development roadmap across People/Process/Technology that will lead to the delivery of your vision for Analytics as an Enterprise capability
  • Business case for advanced analytics
    Establish the business case for advanced analytics by clearly articulating the business beneftits that will be realised and the necessary investment to build the underlying Advanced Analytics Capability

2. Design and Build Insights

Presidion are one of the founders of the CRISP-DM* Industry best practice approach to Data Mining.

  • Collaborative- between the Business, the Analysts and the Data Owners
  • Iterative– structured around key phases of work that take into can be revisited depending on progress and findings of the project
  • Deployment in mind – focused on delivering actionable insights that can be deployed in the business
  • 7 Phases:CRISP-DM
    1. Business understanding
      This initial phase focuses on understanding the project objectives and requirements from a business perspective, then converting this knowledge into a data mining problem definition and a preliminary plan designed to achieve the objectives.
    2. Data understanding
      The data understanding phase starts with initial data collection and proceeds with activities that enable you to become familiar with the data, identify data quality problems, discover first insights into the data, and/or detect interesting subsets to form hypotheses regarding hidden information.
    3. Data preparation
      The data preparation phase covers all activities needed to construct the final dataset [data that will be fed into the modelling tool(s)] from the initial raw data. Data preparation tasks are likely to be performed multiple times and not in any prescribed order. Tasks include table, record, and attribute selection, as well as transformation and cleaning of data for modelling tools.
    4. Modelling
      In this phase, various modelling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data input. Therefore, going back to the data preparation phase is often necessary.
    5. Evaluation
      At this stage in the project, you have built a model (or models) that appears to have high quality from a data analysis perspective. Before proceeding to final deployment of the model, it is important to thoroughly evaluate it and review the steps executed to create it, to be certain the model properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. At the end of this phase, a decision on the use of the data mining results should be reached.
    6. Deployment
      Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organised and presented in a way that the customer can use it.
    7. Monitoring
      Models need to be monitored on a regular basis to ensure they perform to optimal levels. The monitoring process should include regular measurement of the outputs of the models and refreshing and re-tuning models which fall below agreed accuracy levels.

*: Cross Industry Standard Process for Data Mining

We follow Project Management best practice approaches to ensure complete control of project delivery from a time, quality and budget perspectives.

3. Implement insights and embed change

The creation of a model is not the end of a project, it is only the beginning of change.

  • knowledge&skills-transferKnowledge and Skills Transfer – Making our clients self-sufficient in the development of Advanced Analytics
  • Change Management – Designing changes to our client Business Processes to best deploy the insights delivered through advanced analytics
  • Performance Management – Ensuring that KPIs and Benefits are tracked to establish a Return on Invement and/or take corrective actions linked to the deployment of a particular capability

Request a Consultation!


Would you like to see how Predictive Analytics can help you achieve your goals?
Request a Consultation with a member of our insight team!