Data Storytelling for Disruptors

Disruptive innovations have become common in these 'postnormal' times. Organisations that seek to be disruptive are fusing big data analytics with storytelling to nurture better business cultures. Narratives and data stories encourage participation in innovation. We share our recent experience and insights into data storytelling and disruptive innovation.

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The School of Lifelogging

Could lifelogging address the educational "achievement gap" while preparing children from all backgrounds to work with data? Securing personal data from unwelcome eyes will likely remain a challenge. Lifelogging is not just about quantitative analysis. Highly engaging, qualitative storytelling can also emerge from data gathered by learners. Stories of self-determination would involve the learner's own understanding of personal ambitions and dreams.

Lifelogging is part of a growing movement known as "the quantified self". Wearable sensors and cameras capture data about an individual's everyday experience to improve self-understanding. Ordinarily focused on health and wellbeing, lifelogging could disrupt education as we know it. Smartphones were only the beginning. Performance data from wearable devices could personalise learning in ever more intimate ways.

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Data-driven entrepreneurs

Leading a business intelligence project can be like bootstrapping a startup. Doing it well demands the mindset of a data-driven entrepreneur, along with BI tools that leverage open data and enable an agile approach.

Data-driven entrepreneurs are individuals responsible for successfully applying their resources to transform data into actionable insights and profitable opportunities. They may be analysts, managers, or executives who work in an established organisation or are independently in charge of a startup. Their goal remains the same either way: to persuasively propagate the power of data into the lives of others, so that decisive actions can be taken quickly to grow income, reduce risk and save money.

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5 Biases of Business un-Intelligence

To effectively engage in data-driven decision making we must confront the five biases of business un-intelligence . Until the day when future BI technologies can help mitigate cognitive bias, big decisions call for a collaborative effort that harnesses data and intuition.

The top reason that the highest performing organisations had for implementing BI, according to the 2014 annual Business Intelligence Gleansight Benchmark report, isexecutive level demand for data-driven decisions (92%). Yet unreasonable biases can impede both analysis and intuition, especially when we're making sense of big data, or tapping into the power of business intelligence.

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Data denial and the downfall

Mid-sized organisations are looking to business intelligence in order to build resilience amidst the uncertain and complex business climate. It’s about having the capacity to change before the case for change becomes desperately obvious, but building resilience rarely happens in isolation. 

Having to reform an entrenched business model is a daunting task, particularly for the giant corporations among us. To build resilience is to develop the capacity to respond intelligently to changing circumstances. When we, as executives, notice a reason to change strategy, but choose to avoid what the data reveals, we're in data denial, and that could make the difference between a mid-sized organisation taking market share and the downfall of a behemoth.

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Manufacturing intelligent services

We highlight a trend toward value-added services as a means for medium-sized manufacturers to drive innovation.

When you're a manufacturer on a slim budget, it's not easy to muster up the courage to innovate. When product demand begins to decline, however, innovation becomes a necessity. In an effort to survive organisations are seeking ways to commercialise successful innovations quickly. In some cases, the challenge has led medium-sized manufacturers to pursue a strategy called 'servitisation'. It is no quick journey to success, but research insight lends credence to the idea.

To 'servitise' means to combine value-added services with products that deliver additional capabilities to customers on an ongoing, 'pay as you go' basis. If, like other manufacturing companies, you're eager to pump all your innovative forces into generating income and saving money, then you might want to take note of the term, 'servitisation'. Even if you're not interested in the jargon, it's worth considering its essence: the shift from a focus on product to service.

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Merger Analytics solution for M&As

Why successful mergers and acquisitions harness analytics to drive engagement around a shared vision, while reducing the headaches of system integration.

The ideal merger of two organisations should be a 'match made in heaven', but how rare they are. It has been said that 'successful mergers are usually about people and culture'. But by the time the executives discover that one culture is largely incompatible with the other, the merger has already disintegrated into a 'hellish wedlock'. What a headache.

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Maslow's Market Intelligence

We share an idea for how charities might become smarter in a networked world by using business intelligence concepts and a transparency index based on Maslow's Hierarchy of Needs.

There is no shortage of challenges facing charities. As humanity grapples with complex issues, it is through the power of networks that the not-for-profit sector can best address human needs. To make a greater impact, donors and charities should take a closer look at their organisation as one option in a marketplace of alternatives.

Entrenched poverty, food security and failing education systems are large-scale challenges that also present massive opportunities. Through open data, these opportunities can become more apparent as a form of market intelligence. In this blog post we hone in on how open data may be structured for the benefit of all.

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