We are probably living in the most defining period of human history, characterised by the move in computing from large mainframes to PCs to cloud. What makes this period exciting for someone like me is not the move as such, but the democratisation of the tools and techniques resulting from it. Welcome to the age of the Information of Everything (IoE).
One of the ironies of corporate life is that the lower you are on the ladder, the more specific and detailed information you have to inform your decisions. Your decisions, however, influence a smaller community and typically have little impact on group results. At the top levels, it is the other way around.
Fortunately we live in the age of big data that can remove much of the risk of executive decision-making.
Big data is a result of the Internet of Things (IoT) – billions of devices that are connected and communicate with each other. Everything in the digital mesh produces, uses and transmits information that goes beyond the textual, audio and video varieties to include sensory and contextual information.
The IoT as an entity is too large to be of value in its own right, and much of the data it produces has limited enterprise use. But it would be foolish to ignore this wealth of valuable information. The value of big data does not lie in the fact of its collection; it lies in the analysis, decisions, actions and automation of the analytics once the data has been collected from the various sources. Businesses should therefore adopt the mentality of learning how specific groupings and interpretations of data can help them, rather than focus on all the “things” that provide feedback.
This mentality is the essence of the Information of Everything (IoE): the strategies and technologies used to link data from disparate sources, and make sense of it.
Understanding IoEIoE consists of two aspects:1. Process digitisation, which is best explained by using companies like Apple and Amazon as examples. All their customer interactions are digitised and fully automated – there is no human intervention when you buy music from iTunes or any of the thousands of products on Amazon. The platform, aka the machine, makes all the decisions that enable the interaction.
Process digitisation drives 3 things:i. Cost reduction – companies reduce costs by between 30% and 40% when they service customers in an automated and digital fashion.ii. Customer satisfaction – people like helping themselves, which explains the 20% rise in satisfaction measures.iii. Time to onboard – it is much quicker and easier to create an iTunes account than it is to get a credit card from a bank. On average, the time to get a product into a customer’s hand is slashed by 63% through process digitisation.
Sadly, most banks and telcos still have processes designed for human interaction; data is processed afterwards and manually. As a result, they cannot benefit from the processed information that comes from automated customer interactions and use it for intelligent decision-making in real time.
2. Advanced analytics is the second aspect of IoE. The IoT means that data about customer satisfaction and interactions, market share, buying patterns, pricing and much more is readily available in abundance. However, it needs to be processed to be useful. Advanced analytics customises individual experiences for the masses. Amazon and Apple again serve as examples.
Both platforms use information about your past behaviour on their platforms and others to shape and improve your interaction experience by, for instance, recommending products, and then making it easy to execute your decision.
The next wave in analytics is the application of machine learning, or artificial intelligence (AI), to big data. AI machines learn from every customer interaction and continuously expand and automatically refine the algorithms to create an increasingly personalised and satisfying customer experience. Each of these interaction algorithms learn in real time from every individual interacting with the platform to shape individualised digital customer experiences. iTunes already gives us a taste of what is to come. Even if 100 million customers are logged into the system, each individual has an individualised experience. It is based on analytics of big data collected from that customer in the past (such as the music or movies in their personal library, and which ones they listened to or watched frequently), and from data being learned during that specific interaction (what the customer is currently browsing).
This is where the value in IT will lie in future. Companies will no longer invest capital in datacentres and networks; the investment will be in analytics intelligence and machine learning that constantly improve the algorithms that interact with each individual customer.
IoE is a business strategyProcess digitisation and the use of advanced analytics to inform business decisions or improve customer service do not amount to a technology strategy. These are not enhancement or tweaks. They are disrupters. In fact, Gartner regards the intelligence that drives decision-making based on big data as one of the most disruptive business trends currently unfolding, and states that IoE has to be part of the business strategy of enterprises such as telcos, banks and insurance companies.
The process of incorporating IoE into the business strategy starts with leadership understanding the consequences of a fully transparent organisation and making a conscious decision to embrace it. There can be no barriers to data if the aim is to unlock maximum value. The organisational culture and openness of leadership will determine if and how digitisation can take place.
Once the strategic decision has been taken, the process of advanced analytics enablement starts:• Determine your source of value; in other words, what you want to achieve. For example, what do you want which customers to start doing or do more often; or what questions about which customers do you want answered.• Determine what data will deliver this value, and which points of interaction and experience will be used to gather different types of data. These questions are answered in a data collection strategy that covers internal and external sources, as well as structured and unstructured formats.• Build the algorithms that can extract the information from the data and model the resultant insights.• Change business processes based on the customer interaction insights. The ultimate end state is selling digital-only products on intelligent, self-learning digital-only platforms. Google searches and AdWords or ad placements, for instance, are completely digital. All the company’s software engineers do is to make the algorithms more effective.
The most difficult aspect to build is the intelligence that will be applied to the data to give accurate, appropriate and useful information. The algorithms have to get better the more they are used to leverage the dynamic nature and the volume of data gathered in the IoT arena. Your analytics machine has to teach itself and must be able to build an individual relationship with each customer in order to customise each interaction experience.
How to build the algorithms is the biggest question for businesses, and the biggest opportunity for technology companies. Big data hardware is fast becoming enablers only; the real thing corporate customers are looking for is the intelligence that will make their businesses better. They need experts who can help them understand the information they have access to, and how they can use it to enable and improve their own digital processes.
Many companies talk about the 360-degree view of their customer, which refers to the vast quantities of customer-related data they have available when they interact with customers.Access to data, however, is only the first step. The company still has to apply process digitisation or intelligence to it to shape the next interaction.
IoE demands focusDigitising a business has to be somebody’s day job, and that somebody is not typically the traditional CIO. The best approach is to establish an analytics department – that is not part of IT – led by a chief analytics officer (CAO) who has a deep knowledge of business processes.
The analytics team should consist of analytic consultants, data strategists and data scientists whose tasks include data governance, key strategic analytical issues, supporting the business units, and managing the company’s databases and strategic analytics.
Each business unit (BU) in the company should have its own small analytics team that support its decision makers. These BU teams collaborate with the analytics department, and mine and report on data, but are not involved in actually developing the algorithms.
Besides developing the algorithms, the main analytics team has to establish the metrics and parameters with which to measure the success of customer interactions, and understand how to refine the algorithms based on the metrics. Once this learning is established, it can be programmed so that machines can learn to improve interactions themselves without human intervention.
In this digitisation model, IT’s role is to provide the tools and infrastructure that enable both the BU teams and the analytics department. In some organisations, “translators” are useful. They act as bridges between the BUs and the data specialists who build the algorithms. The translators will ensure, on the one hand, that the data specialists understand the problem to be solved and, on the other, that the patterns and insights they generate are taken back to the business units and acted on.
IoE, process digitisation and advanced analytics may appear to be the obvious choice for companies. However, two major obstacles can derail adoption:• Organisational resistance. People may not be comfortable with the intellectual confrontation resulting from data-driven insights. When information is transparent and visible, it is no longer possible to massage it before it goes up the reporting line. This resistance can exist at any and every level. Process digitisation successes are more likely to come from start-ups like Uber and Airbnb – organisations that were designed to be transparent – but some very large and visionary companies, such as Apple, Amazon and Google, also built their successes on digitisation and analytics.• Inadequate technologies. When the hardware and software cannot deliver everything that the IoE promises, disappointment and disillusionment can delay or completely halt adoption.
Adoption challenges aside, the operations platforms on which local telcos, banks and network operators are standing, are burning. Customers are already moving away due to bad experiences with unresponsive traditional engagement channels. Companies have to go the digitisation and analytics route, and be quick about it.
Organisations able to take advantage of the new generation of business analytics solutions can leverage the digital transformation to adapt to disruptive changes and create competitive differentiation in their markets.
There is little question that big data and analytics can have a considerable impact on just about every industry. In the near future, forward-thinking organisations will turn to this technology for better and faster data-driven decisions.Sources:• The End of Bad Decisions – a compendium of articles from McKinsey & Company and Russell Reynolds Associates• http://blog.redstone.com/iot-and-theinformation-of-everything• http://www.strategyblocks.com/blog/gartner-the-information-of-everything... be-poured-into-enterprise-strategy• http://www.interquestgroup.com/corporate/news/you-cant-have-iotwithout-i... BY NUMBERS• According to Gartner, the application opportunities for IoE will see a 1 663% increase in installed IoT sensors over the next five years.• Gartner also predicts a five-fold increase in IoT endpoints (or connected sensors) between 2015 and 2020, from an estimated 4.9 billion to 25 billion units.• The global IoE market will grow at a compound annual growth rate of 15.3% over the same period.• Research from Cisco indicates that the data within the IoT will create a staggering US$14.4 trillion in value for enterprises between now and 2022.PONDER THISProcess digitisation and intelligent, continuously learning machines have the potential to disrupt the socio-economic order on a scale equal to, but probably greater than, the industrial revolution.Machines are more productive than humans can ever be. Add to that the unlimited capacity to learn and improve, and it is clear that most, if not all, of the jobs that currently allow wealth creation and distribution will in future be done by machines. For example, the digitisation of music and movie distribution through iTunes and other platforms has already decimated jobs in music retail stores and in the logistics of physically distributing and transporting music. The same will happen as a result of the digitisation and virtualisation of computer capacity delivered in the cloud.While increased productivity escalates the ability to create wealth exponentially, the danger is that societies will become even more unequal as wealth will accumulate in the hands of the individuals who control the machines.It can be argued that humanity’s greatest task in the face of process digitisation is determining how to structure a world in which wealth is no longer distributed through jobs. Failure to do so would have dystopian consequences for the fabric of society.
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