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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...

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...

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...



Mac Consulting

Leveraging data-driven agility in an agile world

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?

For anyone who isn’t familiar with agile, here’s a summary: Agile teams are best suited to innovation – that is, the profitable application of creativity to improve products and services, processes, or business models. Bosch, a global supplier of technology and services with over 400 000 employees and operations in more than 60 countries, took this approach. As leaders began to see that traditional top-down management was no longer effective in a fast-moving, globalised world, the company became an early adopter of agile methods. Bosch’s first attempt to implement what it called a “dual organisation” – one in which new businesses were run with agile teams while traditional functions were left out of the action – compromised the goal of a holistic transformation. In 2015 members of the board decided to build a more unified approach to agile teams. Today Bosch operates with a mix of agile teams and traditionally structured units, but it reports that nearly all areas have adopted agile values, are collaborating more effectively, and are adapting more quickly to increasingly dynamic marketplaces.

When you are building data capabilities in an organisation, naturally it is going to be reliant on hiring a set of data analysts, scientists and a data journalist (have a look at the previous article in the series if this is a new concept for you). These people need to become your data champions inside the business before they start to empower other people in the organisation. Thus, a large part of the whole maturity process is educating your organisation and taking them on a journey with you to explore what data is available and what it can be used for.

In an earlier article in this series, we highlighted the ‘Sit. Crawl. Walk. Run.’ Principle with regards to data maturity, and if this is actioned correctly, you will get to a point where your data is relatively mature and there will be an abundance of data at your fingertips. Data agility, however, is about selecting the right data to leverage to actually get you an answer, not simply to dive through everything you have collected every time you are searching for a solution.

Organisational lessons have to be learnt to be successful. Rely on your data champions in the organisation, and do not be scared to ask for more data. It is often trial and error as there is no perfect science for every business as each business is unique, but over time you will come across pockets of data which can be useful. For instance, if people are asking you for the same sales reports week in and week out, but suddenly start asking you for something different, you should be able to know how and what to source should a bespoke request come up.

It is not just about understanding what data is where and how to use it, but also, how you can leverage the data to help solve business problems in an agile way, whilst simultaneously applying agile methodologies inside the organisation. There are likely already specific problems you need to solve, be it a daily task, monthly task or a strategic objective, and you are most probably already doing it in an agile manner, but can you now leverage data to help you drive that agility forward? As much as the data team can deliver solutions following an agile delivery method, success often depends on helping driving data adoption inside the organisation. Hence, non-data agile teams should be educated on the possibilities with regards to data, and how asking the right question can enable the organisation to become more agile through strategically leveraging data as an asset.

An analogy would be walking into a car dealership and asking someone sitting behind the sales desk to give you all the technical specifications of the cylinder heads of a particular model – they may not know what you require, but the mechanic would, therefore data analysts should be leveraged as domain experts within an organisation to help drive the company forward.

In general, data teams may have to be more outspoken and pragmatic on what they have achieved, and what can be done with their outputs. In such a way, it should be a top-down and bottom-up approach at the same time as some people/business units are naturally inclined to ask for help in certain areas, while with others you need to be a bit more forceful about leveraging data to help drive the analytical prerogative.

For an agile organisation, data can also be leveraged to be agile – and agile means different things to different people. Ultimately leverage data in an intelligent way which does not deviate from your data maturity process; position yourself so that data can be leveraged day-to-day in the operational side of the organisation as well as from a strategic perspective.

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