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.

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.

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, is executive 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.

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.

Winning BIG with Self-Service BI

Experts indicate that midsized organisations can win BIG by outsourcing business intelligence to a self-service provider in the Cloud.

Business Intelligence has long been considered a challenge for midsized organisations. With costly systems overburdening IT departments, there is a failure to focus on the changing needs of business users. New business intelligence services in the Cloud are inviting IT departments to outsource their biggest data challenges to data specialists who can deliver a single version of the truth to multiple business users, on demand.

Decisions in the Age of Abundant Data

A summary of bite-sized insights and views about decision making from fresh articles around the web.

Over the last several months at Cause Analytics we have been scouting the web to bring you some helpful articles from experts in business intelligence. Today, our team will summarise several insights we have collected in our Google+ community in order to save you time and help your organisation with decision making.

You probably know of the debate between speedy decisions based on intuition versus slower decisions that draw upon evidence and rational deliberation. It's a complex debate because both sides have a role to play in various contexts, but in the 'Age of Abundant Data', business intelligence can aid you on your quest for faster results. As analytics and algorithms increasingly offer practical predictions, executives and managers will be able to act upon these predictions to communicate decisions reflexively, and perhaps, intuitively.

Mobile Shoppers with Smart Assistants

In a future when shoppers have the assistance of artificial intelligence, will you have the Big Data to guide them to your store?

How often do you see someone shopping at a store with a smartphone in hand? Mobile devices will continue to augment customer experiences in ways that retail executives ought to know about. Consider a scenario: A future when the tastes of mobile-empowered customers are satisfied with the help of intelligent personal assistants. As smartphones grow smarter with apps like Siri and Google Now, users will shop more intelligently with greater satisfaction.

Internet of Things: Getting a better sense of the opportunity

The Data Strategist of Cause Analytics, David Pidsley, shared a cool article in the Anticipate Change community about how the adoption of wireless and battery-free sensor platforms in industrial settings are resulting in the “real meat and potatoes” use cases behind big data. The article from Practical Analytics focuses on the 1% efficiency gains of the "Industrial Internet" advocated by GE. Have a look at the infographic.

When organisations seize their signals

How can organisations best utilise data to navigate these rapidly changing times? Answering that question does not require us to entertain the successes, failures and potential futures of big data. It simply requires tools and capabilities that enable everybody to notice what busy executives sometimes fail to see.