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Build Your Data Governance Career

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  1. About this course

    6 Easy Steps to Win at Data Governance e-Book
  2. Who this course is designed to help
  3. The basics of data in your organisation
    What is data and why care for it?
    7 Topics
  4. Why data matters
    What “data-driven” means and how to use it to excite stakeholders
    6 Topics
    1 Quiz
  5. Why your organisation struggles to become data-driven
  6. What does bad data look like, and how do you find it?
    How to identify bad data and identify its risks and costs
    3 Topics
    2 Quizzes
  7. How to fix bad data with good governance
    Defining what good data means
  8. The importance of business process data touch points
  9. What is governance anyway?
  10. Where governance and data collide
  11. Kicking off your data governance initiative
    Data Governance explained – 6 easy steps to win at data governance
    6 Topics
    1 Quiz
  12. What are the basic features of a Data Governance Framework?
    8 Topics
    1 Quiz
  13. Five critical data governance deliverables
  14. How to Implement Data Governance in your firm
    Let’s start Governing Data – the Cognopia Methodology
    14 Topics
  15. Data Governance case studies – winning with data governance
    9 ways data leaders are winning with Data Governance
    1 Quiz
Quiz 1 of 6

How much can good customer data be worth?

In this test, we want to apply what we have learned about Customer experience, bad data and business value. The aim is to get you to apply what you’ve learned so you can use the same approach in your own organisation.

We will use StarHub as an example. Let’s start by looking at their reviews on the website TrustPilot:

A quick glance at this rating tells us it is not going to be good news. But can you calculate StarHub’s NPS score from this information?

In order to help:

  1. We can assume a rating of 1, 2 or 3 stars (out of 5) corresponds with an NPS score between 1 and 6 (Detractor)
  2. A TrustPilot rating of 4 corresponds with an NPS score of 7-8 (Neutral)
  3. And a rating of 5 would be a Promoter – corresponding with an NPS sccore of 9 or 10
  4. The calculation for NPS scores is shown below

Net Promoter Score (NPS) = % of Promoters – % of Detractors

Bain and Company

Question 2

Take a look at one of the 1-star reviews and think whether this has anything to do with bad data or bad data management practices:

Question 3

Having calculated the Net Promoter Score, we need to put a dollar figure on the value of improving NPS. Without digging deep into the number of data problems they have, we can’t truly guess how much improving those data problems could unlock for Starhub.

As a result, we will assume that 5% of 1* customer complaints are coming from customers that would not complain if we had a better handle on their data. Perhaps we give them a more custom service, or just give them consistent answers when they call different staff, teams and departments.

To work the impact out we have to know their annual revenue. In 2020, StarHub delivered $2,028.8M total revenue. To make your sums easier, let’s assume the revenue is $2 Billion dollars exactly. Here’s the source of this info.

StarHub annual revenue announcement 2020.

From our course, we know that for every 10% reduction in the number of detractors by 10%, then your revenue will go up by 4.14%

How much could StarHub expect to increase their $2Bn revenues by if they could reduce detractors by 5%?

Question 4

StarHub could, of course, decide they want to delight some of their customers with data. Perhaps they want to make their customer experience the best in the business, by creating a concierge-level service. Providing great data to Customer Service Reps could help increase their Promoters by 5%.

We know from the course that if you can increase the number of promoters by 10%, then you can drive up your corporate revenue by 1.42%.

How much more revenue could StarHub expect to make if they could use data to add another 5% of Promoters to their customer base?

Question 5 – more bad reviews

Is this review related to data?

Overpromise at your peril!

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