Tom Fountain is Chief Technology Officer for Pneuron.
The conventional paradigm for value creation is being abandoned, and IT organizations are struggling in the face of three major challenges. We need to look at how to extract value from an ever-growing mass of data spread across disparate sources. We must find strategies to cope with the impact and opportunity that the Internet of Things (IoT) brings. And, we need to adapt to evolving work habits and a mobile workforce.
The Promise of Big Data Analytics
This is the third generation of transformative change in IT in recent years. There’s been a shift from bespoke applications serving specific business purposes to enterprise resource planning (ERP) ushering in an era of more integrated software that helped us to better manage the execution of our businesses.
Now, with big data analytics, we’re looking for insights in all the wonderful transactional data we’ve been gathering for years. Failures in data governance and data model definition are making it difficult for analytics. In many cases, the data is simply too diverse and disparate. The full potential benefits will only be realized when we connect it together.
We need to go beyond the identification of insights and connect our analysis to systems that can execute. But it’s vital to seamlessly blend in our planning and take the time to understand our true capabilities.
As predictive capabilities improve, we have greater lead time to prepare ourselves to take full advantage of any new insight and craft an optimal response. Innovation springs from our ability to effectively mix and match our analytics, transactions, and planning.
IoT represents a wealth of non-traditional data sources that can enrich our knowledge and help us orchestrate our businesses in a more optimal fashion. However, we need to work out how to collect it, secure it, transmit it, leverage it, and discount it, if it proves unreliable. An organization’s ability to manage big data analytics is critically important to its success or failure with IoT, according to Dresner research. The survey also reveals that IoT advocates are 50 percent more likely to be users of advanced and predictive analytics.
Enlarging the space within which we capture data, both for predictive modeling purposes and for optimal response planning, significantly boosts our chances of success. Experimentation is vital. We can’t include every piece of data from every source. We need to identify which ones will have the most positive impact on our planning and execution and that means testing cheaply with fast cycle times. Flexibility is key, so we can always take advantage of the next new data source, processing platform, or customer relationship. Only through rapid trial and error can we uncover the best combinations.
The Changing Digital Workplace
Mobility and movements like DevOps that break down traditional siloes have led to an unprecedented level of collaboration. Employees today can work with anybody, at any time, in any part of the company on whatever device is at hand.
“In a successful digital workplace, engaged employees are more willing to change roles and responsibilities and embrace new technology,” according to Matt Cain, vice president and distinguished analyst at Gartner.
The challenge is to foster this collaborative environment without losing control and oversight, or sacrificing governance and security. Innovation plays a major part in establishing that working balance between the needs of the emerging workforce and the desire for appropriate controls, on the part of the organization.
A digital business is built upon a combination of internal and external technology. There’s an inward focus to improve business operations, support the workforce, govern, and manage assets. Then there’s the external face, striving to improve customer experience, and manage a complex network of partners supplying and receiving services. The path to innovation, agility and value comes from fluid integration, enabling customers to participate directly and influence how service is delivered.
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