South Africans saw peaceful democracy at play when the ruling party lost its majority for the first time since 1994.

Dozens of other countries had elections with varying degrees of peaceful acceptance of the results. The “year of democracy’s” climax is no doubt the US general election.

While politics around the world can be described as messy, the goal of democracy, the end – as it were – is equal rights for all.

It is the democratisation of choice, voice, opportunity and more. That, despite challenges, is a beautiful ideal that many dedicate their lives towards either achieving or protecting.

This ideal has the power to reshape how we approach data in the business world. Why? Because data is fueling our digital age.

However, many organisations today whilst familiar with the term “data democratisation,” are still stuck in the opposite of a democracy, a data autocracy, where control and access is limited to a select few. While many may talk about data democracy and enablement, in practice they only enable a part of it.

To be liberal and agile with data requires the confidence to enable knowledge workers at all levels of an organisation. Whilst tools and processes play an important role here, cultural change is one of the biggest drivers of true data democratisation.

A major barrier to the cultural change required is fear. This is not just a top-down fear of relinquishing control, it’s also a fear driven from the bottom up with concerns around job security.

In other words, like in the world of politics, much of the resistance comes from a fear about the prospect of transformative change.

Yet, the truth is that true data democratisation is not about taking power away from anyone, rather, it is about empowering everyone in an organisation. It’s about fostering a culture where data is seen as a shared resource, a tool for collective problem-solving and innovation. It is for the benefit of the organisation as a whole.

As the world of politics has taught us, embracing democracy when emerging from an autocracy, or even a dictatorship, requires a change in culture. And this isn’t easy.

There are many ways of achieving cultural shifts in organisations, but in 2024 and beyond, in a workforce dominated by millennials and an increasing number of Gen Zs, old-fashioned project management presentations and change management techniques are just not going to work. It is out of touch with the zeitgeist.

How, then, can a business achieve the cultural buy-in it needs to truly embark on a journey of data democratisation? One avenue we are exploring is through the power of gamification.

The rise of gamification is all around us. By tapping into the innate human desire for community, competition and achievement, it is possible to create data-driven experiences that are not only informative and effective at driving a data culture, but also genuinely enjoyable.

Leveraging AI: Augmented intelligence

At the heart of data democratisation lies the concept of “augmented intelligence”, a new AI. This AI refers to how human creativity and critical problem solving is amplified through technology such as artificial intelligence.

Reimagining AI as augmented intelligence can transform how we approach problem-solving and decision-making within our organisations.

Rather than artificial intelligence being a threat to human jobs, its ability to excel at tasks such as data analysis and pattern recognition makes it useful because it frees up humans to bring real value to an organisation.

Turning the lens inwards into our own business, a leader in our organisation was facing a challenge with the manual processing of invoices. The admin team, despite their best efforts, was taking an inordinately long time to capture data which was having an impact on payment cycles. The leader recognised that the delay was a business problem, affecting efficiency and productivity.

In the space of a few days, and thanks to the talent of the internal development team, he implemented an AI-powered tool that leveraged optical character recognition (OCR) to automatically extract key information from the invoices, such as the VAT number, customer details, and product codes. This simple, automated step eliminated the need for hours and hours of manual data entry.

By automating this repetitive task, the business was able to free up valuable resources and improve the overall financial management of the organisation.

By committing to the democratisation of data, businesses can unlock value at every stage of the data value chain, from data acquisition to decision making – not just in the realm of advanced analytics.

This is the very definition of augmented intelligence because humans, up and down the data value chain and in various levels in the organisation, are empowered to think critically.

The future belongs to those organisations that see data not as a weapon of control, or a resource to hold closely within a few hands, but rather as a catalyst for democratisation and collective progress. Organisations need to focus on a cultural evolution rather than a technological revolution.

The post Data Democracy, Much like Political Democracy, Seeks to Empower Everyone for Transformative Change appeared first on Tech | Business | Economy.

The African continent is poised for economic growth, rapid urbanisation and increasing incomes with its population expected to reach about 1.7-billion people by 2030, making it crucial for businesses to fast-track data strategies if they are to remain competitive and benefit from these developments.

A major challenge in achieving these goals is that data and its associated processes have historically been seen as an IT function; while the IT department might have the knowledge and technical expertise to execute data strategies, many fail to deliver a return on investment as the initial process of defining the ‘what’ of the data strategy is not business-led.

Often, data strategies that are led by IT departments focus on the ‘how’ – the technologies required for big data, data cataloguing, data governance, data integration, data lakes and metadata management – without an initial and ongoing investment into understanding ‘what’ data is needed for the strategy to be a success.

Ultimately, there is a mismatch between the execution of data management and what the business actually wants, resulting in various departments disregarding the organisation’s technical data strategy in order to satisfy their data needs. Counterintuitively, this leads to the creation of data silos, a lack of data integrity, multiple versions of the truth and eventually a data strategy that fails.

Instead, what is needed is for the data strategy to align with business strategy, and for it to be able to evolve in line with changing internal and external environmental factors. This is usually only possible when an organisation’s data and information strategies are led by senior members who understand the business and where it wants to be in the future, and are ardent proponents of using data to continually inform business decisions.

By definition, a data strategy has to have impact across all levels of the organisation; yet, we see instances where data is available, but is not used to its full potential because the business people don’t know how to use self-service business intelligence (BI) tools to guide decision making. This is a common pitfall of having only the IT department lead data strategies, but it also points to the requirement for data literacy across all levels of the business.

Does this mean that all senior business executives in an organisation should be data scientists? No. On the contrary, all that is needed is for them to be able to use the self-service BI tools and formulate their questions about the business in a way that allows the actual data scientists to manipulate data and get the answers.

One way to get this right is by having a data team that brings together business analysts (who are responsible for various business functions), supported by data scientists and developers who can assist with complex transformations and creating predictive algorithms.

Not only does this ensure consistency in an organisation’s data strategy, but it also means that the business’s unique data requirements are met while IT best-practice is also adhered to.

The continent is blessed with a natural abundance of talent and potential, and having data strategies that are driven by a combination of business and IT – with the weighting of efforts allocated differently at various stages of the process – will be key to unleashing this potential. Meanwhile, this combination will also enable organisations to perform data projects that are directly linked to business goals and empower business people to make data-driven decisions on a daily basis – helping drive the competitiveness of businesses in Africa.

It should be noted, however, that there is no one-size-fits-all approach that can be applied in different countries around Africa, especially given that some challenges are unique to the continent.

There are also issues such as data sovereignty that need to be carefully considered; it is best that organisations work closely with a partner that has on-the-ground experience as well as a deep understanding of the conditions in different regions across the continent.

The post The Synergy of Business and IT Will Be Key to Harnessing Africa’s Full Potential appeared first on Tech | Business | Economy.

This requires that there be true empathy and understanding between developers and end users from the outset, as well as continuous enhancement of data projects in order to meet the constantly evolving needs of the business.

Most developers focus purely on the technical elements of the data projects (such as governance, modelling, security and structure), while most end-users are focused on the business benefits that the data can bring.

A change in approach is required to converge the thinking of the business users and the developers. 

Design thinking is an approach that aims to do exactly that, by looking beyond conventional processes, procedures and practices of problem solving, and toward addressing challenges from the perspective of those who will be ultimately using the product or service.

Designers who adopt this approach are more interested in the why, rather than with the how.

Whereas traditional methods stress the importance of data and logic, design thinking requires that designers focus on the desired outcomes and improving the overall experience for end users – all while taking their own biases into account. It encourages designers to scrutinise all the options available, identify potential problems, and choose the best way forward.

Design thinking can be distilled down to five phases: empathising with users in order to understand them and their needs, defining the problem that needs to be solved, ideating multiple ideas to meet the requirements, quickly prototyping in order to learn and refine, to finally testing in order to confirm ideas and plot a course of action.

Crucially, these steps are not linear, as prototyping and resulting observation can lead to many more ideas on how to tackle the challenge.

Design thinking in data

Those developers or business consultants who adopt data projects with a design thinking approach can be better aligned to the overall business strategy as they are now focusing on the ‘why’ from a business perspective, rather than the ‘how’ from a technical perspective.

On the other hand, developing a data project that focuses purely on technical delivery is like constructing a building without understanding who will be using it. 

Rather than building something that is tailored for the users, it will simply check all the boxes from a list of requirements.

While there may be an extensive list of boxes to tick, the architect will need to use intuition and experience to guide the occupants on choosing the best possible options, ensuring that the building best meets their requirements.

Doing the same with data projects will ensure that developers don’t simply build something that works, but something that works for the right people at the right time.

Don Norman, the father of UI design, states that designers should take the given problem as a suggestion rather than a final statement, and should resist the temptation to immediately jump to a solution to the stated problem and rather look more broadly in order to fully understand the actual problem, which may have only been partially articulated. 

A hybrid approach

While this is a departure from traditional project management, there is room in these data projects for a hybrid approach that incorporates the principles of design thinking in each step of the project.

This requires that the designers shift away from delivering in a big bang approach and towards keeping users involved in each step of the project.

Traditional approaches to developing data projects have included business analysis, technical analysis, data extraction, data modelling, data integrity testing, data application development, quality assurance, end-user workshops, enhancements, user sign-off and go-live.

While nothing changes in this new approach, there is a need to apply the principles of design thinking with each phase.

Naturally, some phases, such as business analysis, might have a larger component allocated to the design thinking phases, the challenge for developers is to keep the end-user experience in mind across all steps.

Unlike the conventional approaches, this requires revisiting earlier steps. For example, in the ideation phase of developing a front-end data application, developers might identify a new way of looking at the data in order to bring more value; this in turn might require going back to the data modelling phase to change the model.

Fortunately, doing this is made possible by the multitude of business intelligence tools that lend themselves to an incremental, design-thinking approach, allowing organisations to rapidly release prototypes and experiment with various ways of answering crucial business questions.

Ultimately, applying design thinking principles to data projects helps developers and consultants to connect with end-users, empathise and understand their real challenges, and use this context to create solutions that add true value, and foster key partnerships that are long-lasting and symbolic.

By Upuli De Abrew is a Director at Insight Consulting

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