Empowering Modular Automation to make data-driven decisions
At Idiro, we empower growth-oriented organisations with the capabilities and tools needed to make effective and quick management decisions that are driven by data and analytics to increase efficiency and productivity, thus helping companies integrate a data driven approach within their ecosystem.
Modular Automation, founded in 1986, is a leading global automation partner providing end to end, bespoke and build to print solutions for the worlds most advanced medical and technology manufacturers. They have experienced rapid growth over the last few years.
To ensure the senior leadership and management team has the ability to make strategic decisions based on the latest internal data in a timely manner in order to maintain momentum, we designed a clear and intuitive suite of data visualisation dashboards which provide reports that reflect the current situation based on real-time data and are accessible on PCs and smartphones.
“In Modular we’re always looking for ways of doing things better and smarter. We had an abundance of historical and live data on our projects that we wanted to fully utilise to help us understand our business better and inform our decisions. Partnering with Idiro means we are now leveraging the power of data to produce important insights, this has transformed how we make decisions about the future of our business”
Digicel wanted to improve overall customer experience, reduce churn, and increase revenues. To do this, they wanted to implement targeted marketing campaigns to reach their customers on the right channel at the right time. Idiro provided the intelligence behind this targeting, analysing customer behaviour at various lifecycle stages.
A good friend said to me recently that Big Data and analytics is a bit like teenagers and sex; everybody is talking about it but very few are actually doing it. I think he may need to update his knowledge of teenage behaviour but I got his point nonetheless.
The rush to Big Data has the usual hallmarks of other past industry hot trends i.e. lots of hot air and hype. Additionally, there are a lot of definitions of what Big Data actually is and what differentiates it from, say, your bog standard Oracle BI/data warehouse.
So what’s my definition of Big Data? If pushed I’d say something similar to the following: “Big Data is the discipline of analysing vast volumes of structured and/or unstructured data with a view to generating insights and predictions that improve business performance” (OK, I know that’s not very inclusive of non-business activity but you get the general idea).
My gripe about some soi-disant Big Data companies is that all they have done is moved their dashboard reporting tool to Hadoop (if even that). I can understand the temptation to rebrand an existing BI tool as a Big Data platform but it would be unfortunate if anyone actually fell for that.
Here in Idiro we like to differentiate between BI and predictive analytics – there are many companies offering BI tools of varying levels of sophistication. However, there are far fewer suppliers of predictive analytics platforms (and even fewer still who provide predictive social network analysis like ourselves). In essence, BI tells you what did happen (i.e. after the horse has bolted) and predictive analytics tell you what will happen (while the horse is still happy in the barn). A smart company will use both.
But back to the definition of Big Data…some would argue that Big Data is all about analysing unstructured data such as blog postings, tweets and other such rubbish. Sorry, yes, I know there is useful information in there but there’s a lot of junk too. We prefer not to discriminate against data and believe that any data can form the input for a Big Data platform.
A word of caution lest anyone think that by installing some Big Data platform that all their problems will be solved. The analytics generated by any such platform need to be used to change business behaviour – this is probably the biggest challenge to the successful deployment of analytics within a company. Often there is political resistance within a company to the use of analytics that makes the Israeli-Palestinian problem seem like a walk in the park. Simply put, people and processes need to change if a company is going to capitalise on its investment in analytics.
As for the aforementioned teenagers, I think that when it comes to the adoption of behaviours that they find “beneficial” they exhibit a lot more openness to change than some large companies who desperately need to reinvent themselves. Big Data may or may not be a panacea for all a company’s problems but, once we step away from the buzz and the hype, what we see is that companies small and large, who intelligently leverage analytics for business really do get the edge over their competitors. Call it Big Data, call it analytics, the important thing is to call it right.
Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model is used to motivate a community-matching strategy for efficiently identifying and tracking dynamic communities. Evaluations on synthetic graphs containing embedded events demonstrate that this strategy can successfully track communities over time in volatile networks. In addition, we describe experiments exploring the dynamic communities detected in a real mobile operator network containing millions of users.