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Thriving in the Age of Digital Adoption: Embracing the Workforce Ecosystem (part 2)

In the first part of this series, we looked at how the fears of technological innovation are resulting in an unproductive resistance toward modernisation, even as it gains extraordinary pace in 2021. We also delved into the importance of a growth mindset in allowing...

Thriving in the Age of Digital Adoption: Overcoming the Fear of AI (part 1)

“What if artificial intelligence takes over my job? What if I become redundant?” Every one of us has experienced technology encroaching on our lives, more and more so with each year that passes. It appears that technological innovation is a certainty that is only...

Starting & Thriving in E-Commerce in South Africa: The Payment

In our previous article, Starting & Thriving in E-commerce in South Africa: The Customer, we looked at a few variables that affect the customer’s experience with a business; these include how you can build valuable information about and around your...

Starting & Thriving in E-commerce in South Africa: The Customer

In our previous article, Starting & Thriving in E-commerce in South Africa: The Foundations, we explored the various options and requirements for taking your business online as well as the importance of meeting your audience in the places they naturally...

Starting & Thriving in E-commerce in South Africa: The Foundations

The COVID-19 pandemic has transformed our lives in a number of drastic ways. While some big corporations struggled to remain relevant in the shape-shifting business climate around them, many start-ups have found their time to shine and are performing remarkably well....

Leveraging Data to develop a competitive advantage

Not a lot of people have heard of the term ‘dark data’ before; Gartner defines it as the information assets organisations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business...

Leveraging data-driven agility in an agile world

At MAC we have recently been having discussions about the concept of Data Driven Agility; how can organisations start to leverage data more intelligently in an agile-delivery process, select data which is fit-for-purpose and, importantly, help solve business problems?...

What is a data journalist and why do you need one?

One of the biggest pain points in many organisations – although they might not even be aware of it – is the disconnect that exists between the data analytics department and the other business units. Despite the concept of a data journalist not being well recognised in...

How to get people onboard your data journey to drive usage and adoption

In the preceding articles we have covered topics on building the correct foundations so as to implement data analytics in an organisation with a centralised data function. Using the Sit. Crawl. Walk. Run. Paradigm, we explored how best to embark on a data journey and...

How to get people onboard your data journey to drive usage and adoption

In the preceding articles we have covered topics on building the correct foundations so as to implement data analytics in an organisation with a centralised data function. Using the Sit. Crawl. Walk. Run. Paradigm, we explored how best to embark on a data journey and...



Mac Consulting

The Trust Dilemma – how to build trust in Data

In Computer Science, garbage in, garbage out (GIGO), is the concept that flawed inputs will yield flawed outputs, or ‘garbage’. This principle applies to all analysis and logic, in that arguments are unsound if their premises are flawed. In data analytics, it is a mammoth challenge.

A famous example of garbage data from history is the Mars Planet Orbiter, launched by NASA in December 1998. The mission was to learn more about Mars, its climate, atmosphere, and surface conditions – but one piece of bad data caused the probe to fire its thrusters incorrectly. The problem was that one piece of software supplied by the manufacturer calculated the force the thrusters needed to exert in pounds of force, but a second piece of software, supplied by NASA, took in the data assuming it was in the metric unit, newtons. This resulted in the craft dipping 170km closer to the planet than expected – causing a $327.6 million mission to burn up in space.

Building trust in your data is interwoven with how you source your data (for more information, please see our article Why a centralised analytics function, rather than distributed, makes sense). Garbage data often arises when businesses either don’t have the capacity or have not set up specific processes to acquire and clean data before it is analysed. Naturally, one would like to remove the human element from data acquisition and cleaning, enabling efficiencies through automation.

Yet how could this be achieved? Firstly, a sound Master Data Management (MDM) strategy is needed. An MDM is the core process used to manage, centralise, organise, categorise, localise, synchronise, and enrich data. It is also a key enabler for providing a single, trustworthy view of critical business information. Trusted data sources help reduce the costs of application integration, improve customer experience, and yield actionable analytic insight.

Secondly, human beings have a tendency to make mistakes and take short-cuts which could dirty your data. With automation and an MDM strategy in place, clean, trustworthy data will be one step closer.

Much like the previous article in this series that explores the ‘Sit. Crawl. Walk. Run.’ Principle which drives data and analytical maturity, ensuring that clean data is produced as an incremental process. Building organisational trust in data, however, is not a quick task; money, time and effort is needed to ensure that insights received are clean.

Step one is having a data strategy. One of Stephen Covey’s habits in his book, “Seven Habits of Highly Effective People,” is beginning with the end in mind. When you are creating a data strategy, know why you want those analytics. As important is having a data champion within the organisation who knows where all the data silos live. In a perfect world, this data champion, or the head of the data team should be sitting at Exco or tactical level within an organisation, building a rapport with business unit stakeholders and presenting how data can have a high impact on the business, fulfilling the role of an organisational enabler.

In a data world, however, perfect data does not exist. Data is not created equal; it is incomplete and inconsistent at best, with many little intricacies, thus it is always important that the analysts are very clear as to potential caveats within their findings. People can be lazy too, especially when it comes to data capturing. Could there be a possibility within your oganisation to automate data acquisition, cleaning and transfer?

If you fuel your business engines with bad data, then, much like the Mars Planet Orbiter, it will not be long until you crash and burn. Be an enabler for data analytics in your organisation. Develop a strategy and execute it; don’t use humans to clean and capture; and Introduce automation to capture data efficiently. It is imperative an organisation collectively realises that data can be used as a strategic asset. And the starting point is clean data. Without that, it is garbage in, garbage out…

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