See things clearly
A point we make regularly here at OBASHI is that in today’s digital world, it is important to understand how data flows through and between businesses.
We occasionally blog about “data flow disasters” - events that occur when a flow of data is interrupted or compromised, so that there is a negative business impact on those who use or interact with that flow of data.
A recent example of such a ‘disaster’ is a pair of associated outages at the telecom company O2, which stopped data flowing to, and from, millions of customers for lengthy periods.
Faults in a database supplied by Ericsson caused the problems, and in financial terms alone it will cost at least £10 million to rectify the issue. But it is likely there will be associated business impacts in terms of, for example, customer loss and reputational loss.
Today, as the increasing number of such data related outages, ‘glitches’ and security incidents demonstrate, minimising the frequency and impact of such problems is a key business issue, and it will continue to be so for many years.
But this focus on critical business flows is not a new phenomenon.
Businesses and governments are well-used to understanding, exploiting, regulating and managing more traditional flows. Things like flows of components on a production line, or flows of water through a power station, or flows of aeroplanes around the planet.
And yet, despite many decades, even centuries, of engineering, architecture, standards, and best practice, problems can still arise with flows.
Sometimes such problems are technical in nature when an asset supporting a flow inevitably wears or fails, and sometimes they are caused by the actions of us pesky humans. But when flow doesn’t flow as it should, business problems usually follow.
Here are some recent news stories where the impact of changes in flow is clear.
As individuals and as organisations we rely on flows for much of what we do on a daily basis - washing our hands and cooking our food, lighting our offices and supplying our customers.
But as the above examples demonstrate, when a flow doesn’t flow as expected...we often have a serious problem.
And that applies to flows of data too.
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