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How to make change management happen in the most effective way

Many things can put your business at risk, including changes to your organisation, people or technology. The current pandemic has put many businesses under unprecedented pressure, and some even teetering on the brink of survival. Crises like this quickly turn change...

Future-ready part 2: Diversity – an essential ingredient for a future-ready organisation

In his seminal 1937 essay, “The nature of the firm,” the economist and eventual Nobel laureate Ronald Coase argued that corporations exist to avoid the transaction costs of the free market. Yet with transaction costs plummeting (spurred by rising connectivity)...

Future-ready part 1: What does a future-ready business look like in the new normal?

The pressure to change had been building for years. Well before the COVID-19 pandemic, senior executives routinely worried that their organisations were too slow, too siloed, too bogged down in complicated matrix structures, too bureaucratic. What many leaders feared,...

Organisation Design: Restructuring or Reshuffling to enable Strategy

Customer expectations are not just changing; they are exceeding the ability of a business to deliver on time. They are looking for alternatives, with more emphasis on experience and convenience. To keep up, companies are evolving their offering to meet the...

How to fill the gap between Strategy and Execution

Organisations are great at setting their strategy and identifying their goals, but they fall short when it comes to their operating model review and redesign, the key component that enables the strategy and drives the achievement of goals. Operating models consist of...

Culture PART 2: The role of leaders in a culture shift

In the previous article in this series we examined the effect of COVID-19 on an organisation’s culture. Now, we turn the focus onto the role of leadership and technology in leveraging culture. When an organisation decides to change its culture – be it planned or...

Culture PART 1: Did COVID-19 signal the end for hierarchical organisations?

According to the Organisation for Economic Co-operation and Development, human capital is defined as: “the knowledge, skills, competencies and other attributes embodied in individuals or groups of individuals acquired during their life and used to produce goods,...

Meet MAC’s Executives: Karina Jardim, Senior Executive

“Exceptionally talented consultant” “An absolute pleasure to work with” “Driven by a desire to see people grow” If you know Karina Jardim, you know that these phrases used to describe Karina ring undoubtedly true. She is a name that is valued to every MACer, whether...

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...



Mac Consulting

Why a centralised analytics function (rather than distributed) makes sense

Why a centralised analytics function (rather than distributed) makes sense

Data analytics is the process of analysing raw data to extract valuable insights. Data analytics techniques can reveal trends and ‘dark data’ that would otherwise be lost in the mass of information. This insight can then be used to optimise processes, reduce costs, or leveraged to unlock additional revenue streams.

Research by international advisory and research company, Gartner, points to the fact that as many as 60% of big data projects started this year will fail to go beyond piloting and experimentation. The reasons for such failures are many, and vary from organisation to organisation. The software, the talent hired or the lack of a well-defined business problem all could be responsible to some degree.

One common reason for data projects failing, however, is as a result of how the analytics team is structured, with the wrong choice putting the project in a position where it cannot produce tangible benefits. The two most common team structure approaches are distributed and centralised.

When analytical teams are centralised, the data and talent to provide analytical expertise and support reside in one group. This group serves a variety of functions and business units and works on diverse projects. The centralised team is also responsible for setting the analytical direction of the entire organisation, whilst also being in a position to develop a Centre of Excellence (CoE) where best-of-breed solutions and ideas are shared amongst peers. A distributed analytics team, on the other hand, pushes the analytics experts out into the different business functions. Effectively, each unit manages and creates its own data ecosystems independent from the main hub, with analysts tasked to interpret the data from within that team.

Naturally, there are pros and cons to both approaches. However, in my experience, a centralised data and analytics function works more effectively, if governed by the appropriate processes and principles. A centralised function can leverage and share knowledge more effectively, work on a team standard of analytical ‘toolboxes’, communicate domain and business knowledge in an agile way, and collectively endeavour not to work in isolation redoing fundamental work each time. Inversely, with distributed analytics, the analysts focus only on a single product of function, often in isolation, without considering the rest of the business. Often, distributed functions are operationally bogged down, focusing on delivering on the immediate business task, whilst not taking a tactical approach towards sourcing outside insights. Although analysts are doing the same type of job, with the distributed model there is often limited data and knowledge sharing, a duplication of effort and the value of the analytical effort is diluted.

With a centralised analytics model, how methods and processes are executed can have stronger oversight from the core data analytics team outsourcing their efforts to the entire organisation. They can optimise on each skillset their team holds and utilise them in a way that is most cost-effective and efficient.

Although it can be more resource and time intensive to set up centralised data analytics in an organisation, the value of the data, and the fact that it is much easier to set up a CoE within this model, are both strong positives. A centralised approach enables efficiencies that are not easily achievable otherwise, with the added benefit of being able to reincorporate tools, insights and lessons learnt back into the team to improve future delivery.

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