Category: Informative

AWS Blog Informative Use Cases

Amazon AI Services

At Intellify we’ve been proud to partner with AWS since we were founded in 2018.

Our data scientists have always enjoyed using AWS’ ML service – Amazon Sagemaker. It reduces the time they have to spend on admin and engineering by taking away many of the tasks required to build, train and deploy the custom machine learning models when working with our customers like Alinta, Henry Schein and Vodafone.

While our data scientists always enjoy building an end to end machine learning project in this way, there is still a reasonable amount of effort still required to get a useful model into production, so it normally requires a project running for a month or two. While some customers have unique problems which are a good fit for this kind of customized, flexible approach, we have seen some use cases arise regularly, particularly forecasting, personalisation and image processing.

We’ve been really excited to see AWS expand its AI services portfolio with Amazon Personalize and Amazon Forecast becoming generally available this year. It means that for the most common ML use cases we can often dramatically reduce the work to do in areas like:

Solution architecture
Exploratory data analysis
Model selection
Model training and deployment
ML pipeline engineering
Production system monitoring

This means we can deliver projects at a fraction of the cost (both professional services and AWS platform) and time which was previously required. If the data is already on AWS, it’s often possible now to test out ML in hours to validate whether it’s a good use case.

At the moment we’re using Amazon’s AI Services including Amazon Forecast, Amazon Personalize, Amazon Rekognition and Amazon Comprehend Medical in use cases like

Inventory planning and control
Product recommendations
Marketing optimisation
Business metric forecasting
Removal of PII from images and reports
Pricing optimisation

It’s amazing how many features these products have and how quickly we can get the insights we need to impact key business processes.

We’re enjoying using these services and have some packaged projects defined to quickly deliver outcomes in the use cases above based on our experience working with AWS and their AI Services. If you’re interested in proving out ML use cases rapidly and at low cost, we’d love to talk.


Blog Informative

Preparing for Your Machine Learning and Artificial Intelligence Transformation

Machine Learning (ML) and Artificial Intelligence (AI) has use cases across multiple industries and business functions, but how do organisations ensure that they get the most from the implementation of ML and AI? Companies who reap the rewards of these tools usually undergo a common process to prepare for the changes that come with the application of new technologies and practices.  


This post aims to briefly cover the important steps that organisations undergo before realising the many benefits of Machine Learning, in other words, these are the critical aspects in a strategy for organisations starting their AI transformation journey.  


First: Where Are You? 

How will you know where to go if you don’t even know from where you are starting? 



Organisations wanting to implement Machine Learning and AI solutions need to clarify what their AI goals are – realistically. By assessing current skills of employees (both in IT and other departments), facilities or infrastructure, available budget, and free time to complete end-to-end ML solution delivery, organisations can develop a strategy. This strategy will include aspects such as: 


State of Data 

Organisations will need to assess current processes related to data – how is it being collected, cleaned, organised, accessed, and processed? In order to for ML models to produce accurate and useful outcomes, data needs to be in good shape and properly supported. 


State of Infrastructure 

85% of organisations are ill-equipped to begin their AI transformation. Artificial Intelligence and Machine Learning models process large amounts of data in real-time, therefore the infrastructure supporting it needs to have fast and modern processing abilities. 


Company Culture 

With the implementation of a revolutionary technology, employee mindsets need to be on-par. Leaders within the organisation need to be able to shift their decision-making to embrace data-driven thinking. Teams need to be open to adopting this new technology as well as educated about its usefulness to them (not just to the company at large). 


Internal Expertise 

Once experts have delivered the sought Machine Learning capabilities, there may need to be internal team members to continue maintaining the quality of data and observing data patterns for ongoing success. Most organisations do not have the internal capabilities to ensure ongoing success of the technology. This means that organisations need to consider support from external experts (or Managed Services). 


Second: Goals 

Now you know where you are, where to next?  

In order to determine what needs to be implemented, organisations need to decide what they want to achieve from the technology. A successful AI and ML implementation journey includes setting goals – what needs to be optimised, automated, or improved? Once these goals have been determined, the most important aspect about bringing them alive is: ensuring the process behind achieving these goals is optimised, rather than the goal (or job) itself. This may sound confusing at first but ultimately it means automating the process (for example, segmenting customers into target groups for marketing) rather than automating the job (the marketing co-ordinator’s job of deciding on targeted campaigns).  


Third: Enterprise Support 


Just as implementing AI and ML in an organisation cannot be done by one person, the AI strategy mapping cannot either. Keeping the core goals of the company in focus whilst determining AI and ML goals requires interdepartmental planning and collaboration. This usually means leaders from different teams and departments get together as do data engineers and data scientists (internal and external) in order to establish the state of all processes across the company for better goal setting.  


Final Notes 

An AI and ML transformation journey can only begin with a deep and guided company strategy – including the current state of things and the goal state of things – and this needs to be all-encompassing and all-inclusive in order to prepare an organisation properly for the start a new technological achievement. Skipping this step will likely result in time, resources, and money wasted – leaving all those involved feeling confused and frustrated.  


Being such a vital step of reaching AI and ML goals, the company’s AI and ML strategy needs to be done well. This is best achieved by ensuring those in charge of the strategy are experienced and experts. Intellify is Australia’s leading AI and ML consultancy – delivering end-to-end machine learning solutions from strategy, to enablement, to deployment, and ongoing support. Contact us to begin your AI transformation and stay ahead of the curve.   

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Machine Learning and the Finance Industry



Did you know that the term “artificial intelligence” was first coined in 1956? Even since then the world has rapidly evolved technologically. From the early days of radio, to television, to the internet, and now we have Artificial Intelligence and Machine Learning. Artificial Intelligence (AI) involves many aspects – including processing robotics and the process of automation by robotics. Due to large amounts of data and the increase in demand for understanding data patterns, AI and machine learning (ML) have become very popular (especially among large companies). When it comes to the financial services industry, thousands of organisations are using ML systems to more efficiently identify data patterns to gain target audience insights and much more. 


The financial services industry is full of data records. Hence the relevance of using ML to succeed in this domain. There are many use-cases of ML in the financial industry but risk assessment, fraud detection, financial advice, trading, and managing finance are the most popular (or obvious) business functions within the industry for machine learning systems. 



Risk Assessment 

To determine which customers are eligible for a credit card, banks use credit scores. However, this method of grouping customers is not necessarily efficient for business.  

Machine learning is now being used to scan thousands of personal financial records for loan repayment routines, the number of active loans, and the number of existing credit cards an individual has – to determine customised interest rates. Lenders can now determine those who are credit-worthy options if they do not possess an extensive credit history. For example, some machine learning algorithms use alternative data, such as mobile phone data, to evaluate loan suitability and customised loan rates. In addition to this, ML-powered models are objective – this removes any biases that a human credit officer may make. A vehicle lending company has experienced a 23% annual decrease in losses by implementing an AI system of loan evaluation. 


Fraud Detection and Management 

All organisations aim to reduce risk conditions. The financial industry (and banks in particular) take fraud very seriously – seeing as given loans are basically somebody else’s money. ML security and fraud identification systems are used to alert organisations of unusual behaviour by analysing past spending patterns across many transaction tools. A card being used in a faraway area from where it was previously used or a withdrawal of an amount of money that is unusual for the particular account, for example, are all detectable by ML systems. Understandably, these events may not be fraudulent. One of the benefits of ML-based fraud systems is that they can learn. If a regular transaction is marked as irregular by the system, it can be corrected and learn from its mistake. This allows for better informed decisions about what is flagged as fraudulent behaviour and what is not. 


Financial Advisory Services 

Pressure on financial institutions to reduce their commission on individual investments has increased. Luckily machines can give financial advice for a single down payment. Another use case for ML in financial advice is automated advisory – combining ML calculations with human insight to provide more efficient investment options to customers. This collaboration is key. An AI system is neither an all-knowing solution nor a simple marketing accessory; it is just as important to consider the human perspective along with the AI decisions when it comes to financial decision-making. 



It is not such a recent experience that investment companies have been relying on technology and data scientists to predict future patterns in the market. In the trading field, investments are dependent on one’s ability to accurately predict the future. ML systems are highly useful in this regard: they can produce calculations from large data in a short time. Machine learning allows such systems to predict patterns based on past data (even taking into account – and planning for – anomalies such as the 2008 financial crisis). Depending on one’s appetite for risk, ML systems can suggest portfolio combinations: a person with an interest in high-risk shares depend on ML-calculated decisions on when to buy, hold, and sell. Those with a tendency towards low-risk portfolios can make decisions based on alerts from ML systems about when the market is expected to fall. 



Finance Management 

In an increasingly materialistic and connected world, managing finances can be a challenge to many people. Personal Financial Management (PFM) is one of the more recent ML-based developments. By analysing where consumers are spending their money, ML models build algorithms to help consumers make more informed decisions about their money. The model creates a spending graph which is personalised based on individual data from one’s web footprint. This may upset advocates of privacy breaching, but this is becoming a more popular way to manage personal finances as it removes the need to make length spreadsheets or hand-written budgets. From these small PFM suggestions to bigger investment portfolio suggestions, ML systems can be beneficial time- and money-saving tools to both customers and employees in the financial services industry. 


Machine learning is the future of many business functions within the finance industry. Soon enough it will be able to handle more financially sensitive and tedious tasks and provide more efficient solutions. The long-term cost saving benefit of ML systems are encouraging multiple financial service organisations to implement this technology. Although, currently, the implementation of ML systems in the finance industry is still in its infancy, the speed at which it is helping the industry progress predicts losses will be fewer, trading will be smarter, and customer experience will be better.  

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Machine Learning & Customer Service 


Machine Learning (ML) in customer services is viewed by directors, decision-makers, and by employees as a blessing to both the customer and employee experience. While technology is not yet able to perform all the tasks a human customer service representative could, machine learning in customer service teams is expected to increase by 143% over the next 16 months as more teams turn to chatbots, text, voice analytics, and other use cases. Currently, ML models in customer service are being used to both assist and replace human agents. This is primarily to improve customer service experiences and reduce the service costs of having employees handle simple requests and tasks.

Let’s explore how ML can be used to assist customer service agents; rather than completely replace them. 


Messaging Assistants 

We already know (and have probably experienced) chat bots are being used to communicate with customers online. However, many customers and companies feel that bots cannot handle all tasks that human agents can. Whilst still using ML technology – but avoiding frustration – “assistant” bots are being deployed to increase employee efficiency. This system allows the bots to deal with the simple questions up until the conversation becomes too complicated: then the chat is handed over to a human. Once the complex task is finished, the bot can continue with the simple details. The results of this system increase efficiency up to a point where human agents have been able to handle up to 6 co-existing chats at once. 



Organising E-mails 

Reading emails to decipher and respond to customer needs is a time-consuming task. Luckily, ML systems can speed up the process. ML technology is able to scan and tag emails to direct them to the right person along with suggested responses to the respective emails. Companies needing to respond to a large amount of emails in a short period of time have been able to halve the time customers wait to receive a response. 


Enhanced Customer Phone Calls 

When it comes to getting answers about financial questions, customers prefer to do so via phone call than online by a rate of 3 to 1. However, from a technical perspective, it is significantly more challenging to deploy ML voice-based communication systems. Background noise, unusual speech patterns, accents, and poor pronunciation make it hard for an ML model to translate voices into text. Yet even with these difficulties, companies are using machine learning to assist phone-call customer service agents. Conversations can be analysed in real-time with deep learning. These ML models listen to changes in volume, pitch, and can detect mimicking for classifying how customers are feelings and how calls are going. Customer service agents are given suggestions simultaneously to improve their calls based on their performance. A trial of this technology showed a 30% improvement in customer survey scores, a 6% improvement in issue resolution, and a significant decrease in requests to speak to a manager. 




The Future of Machine Learning and Customer Support 

New interesting uses for ML in customer service have only recently been deployed. This is why it is hard to tell how much ML will impact customer service and employment. Call centre employment is only one of many areas where this ML technology is being used (there are many retail salespeople, cashiers, and service hosts that will become freed up because up to 30% of what customer service agents do has automation potential). 

Thanks to ML advances, companies are now able to make quicker and cheaper responses to low-level customer interactions and improve more complex customer engagement experiences with human agents. This may lead to a significant increase in demand and standard of service. Millennials now have a 68% higher expectation of customer service than they had one year ago. Almost 80% of young customers expect the contacted customer service agent to already know their contact and product information. 

In the nearby future, ML in customer service will not cause an overall decrease in customer service jobs; companies may increase the number of ML assisted employees to deal with the new demand due to ML system-related efficiency and customer satisfaction.  


Concluding Thoughts 

Machine Learning in customer service is being used to improve the quality of service and reduce the cost of employees across industries such as finance, food, retail, and insurance. Companies now trust chatbots and ML speech-analysis systems to handle simple tasks – leading to a current significant investment in these systems. The benefits of ML in customer service is being felt directly by regular customers in a very clear and immediate sense. Customer service is a space where exciting ML applications are being deployed in across industries at a significant rate. While ML systems are being used to fully take on monotonous tasks – making it easier for human agents to deal with the more difficult issues – as they learn, these systems should soon be able to deal with more requests without human involvement. This means the current level of customer service being provided will be provided in the future at a much lower cost. This is not to say that companies will be able to spend less on this part of their business. As customers expect better service as a result of ML technology, more employees may be necessary. 

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Quick Read: Product Development & Artificial Intelligence


How Can Artificial Intelligence be Involved in Product Development?  

Let’s focus on two main ways that AI can help with Product Development. Firstly, AI can do the repetitive and boring tasks or jobs in this industry that us humans don’t usually enjoy. Secondly, AI can gather the best information relevant to the product development so that better quality products can be produced.  

Testing Product Features 
The shortage of quality assurance engineers and analysts in the workplace means that the quality standard of products is not always up to scratch. Testing the features of products can make up 50% of the work being done on a product before it is even approved for production. This is seen as a huge time-consumer – especially when aspects such as customer and competitor research are being overlooked. 

An AI automated approach can help in this regard. By assimilating, machines can meticulously mimic human behaviour. This means that a team of testers can move beyond the traditional route of manually testing products and progressively move forward toward an automated and precision-based continuous testing process. AI models can test the features of the product to find faults (with a higher chance of finding faults that a human product manager may have missed). Not only are product faults lessened by using AI models, but human error is minimised too. Even the most meticulous tester is bound to make mistakes while carrying out monotonous manual testing. By performing the same steps accurately every time, AI models never miss out on recording detailed results. Testers are freed from repetitive manual tests and have more time to create new automated software tests and deal with sophisticated features. 

Designing for Ultimate User Experience 
Successful products are generally those with uses that resonate with the users. The product needs to be user-friendly and, in some cases, even fun to use. Product design calls for creativity across the board. The design team must consider the product uses (regular and unusual) and support those uses that embrace the business objectives as well as those that give the user value.  

 Artificial Intelligence can use behavioural data to determine how a product is used and how it can be built to best fulfil its use. The most useful part of this determination is that AI can alert the design team before the manufacturing phase as to which designs will and what won’t be successful. By analysing the intended steps to use a product, AI can determine whether a user will be successful in getting the desired action from the product or not. This means that it is no longer necessary to build different prototypes of a product for testing. Simply run all the different product designs through a simulation and let AI models determine which is best. This is ground-breaking for companies that are heavily reliant on manufacturing: saving them time and money previously spent on product development and research. 



As Artificial Intelligence has become relevant in the product development cycle, organizations are faced with the decision whether they should adopt it wholly within their practices. Initially, the set-up costs of an AI system may seem unjustifiable in the product development industry, but in time organisations will produce greater testing rewards for less money.  These new savings can be redirected towards quality assurance efforts, testing uncovered areas, or more exciting and creative parts of product testing. In the future, AI in product development won’t only be relevant in product testing but will be applicable to all roles across product development involved in delivering top-quality products to the market. 

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Artificial Intelligence in the Retail Industry – 5 Common Uses

How Can Artificial Intelligence be Implemented in the Retail Industry? 


If you ask any business leader or expert in the retail industry what the future of shopping looks like, you’re bound to hear something about “bots” shaking up the way things work. Artificial Intelligence (AI) in retail is being used across the entire service and product life cycle – from manufacturing to post-sale interactions with customers. This means that retailers need to change their strategy and thinking in order to include this technology and remain relevant. 


In this blog post we will look at 5 ways in which AI is being used in retail:   


1) Product Recommendation through Personalisation 


Products are now being recommended to customers based on their personal buying habits. Our online buying habits create data categorised into, for example, products bought, preferred brands, money spent and frequency of buying. Machine Learning algorithms use this data to predict which items a customer is likely to buy. Once this technology anticipates the intent of the customer, it provides a recommendation in real-time with incredible accuracy. These recommendations come in many forms – for example, links to other items (“customers who bought that also liked this….”) or marketing emails that contain the chosen items that are suited to your buying patterns. Whatever the form of the recommendation, customers are more likely to buy products or services that require little extra thinking and align with their already formed self-concept. Machine Learning algorithms and personalised product recommendation allows for just this.   


2) Customer Service and Complaints Resolution 


Artificial Intelligence software can help build a strong sense of brand trust through communication – by answering questions and resolving issues. Brand trust is earned when customers are satisfied by answers to their questions and by the outcome of their complaints. Artificial Intelligence software manage multiple communications at one time – meaning no customers are kept “waiting in the queue”. In addition to less time wasted, Machine Learning “operators” can answer customer queries more accurately by avoiding human mistakes (that a “mere human” operator would make in fetching answers). Artificial Intelligence software is also able to make decisions to solve customer queries or raise customer complaints with higher management immediately – giving customers a solution without delay. 


3) Inventory Management – Demand & Forecasting 


One item out of stock can bring a business to its knees. Machine Learning time series prediction models can estimate future demand levels for all items in an inventory. Although this is useful for retailers to know, Machine Learning models are even able to make decisions based on the demand information that they produced. This is a more advanced approach and is done by rewarding and punishing the model for acting incorrectly. For example, a model is punished for letting a particular inventory item run out of stock or for stocking a higher value for too long. A model is rewarded when in-demand inventory items are ordered within a safe window before it’s too late. These reinforcement systems produce outstanding results – more than 30% reduction in inventory operations costs in many cases! 


4) Pricing Strategy & Product Bundling 


Most of time, sales made depend on the prices of the items or services being sold. Customers will wait for prices to drop for the items they want or for items to be bundled at a better price. This leads to times when sales generate high revenue for short bursts and potentially overall unprofitable sales because of special price deals. Artificial Intelligence models can be used to drive sales by targeting a particular set of customers at their “optimal price” – leading to immediate sales. Different customer sets can be targeted by AI systems by determining which product combinations and at which price will lead to immediate or more likely sales. When it comes to non-urgent items, these methods are proven to make sales more consistent. 


5) Strategy Testing 


Artificial Intelligence allows retailers to test different strategies against each other without having to implement any of them – saving them potentially lost resources. By using AI and Machine Learning models to monitor customer behaviour (through computer vision and activity maps), individual shopping experiences can be created and resources can be better allocated to areas most needed. Retailers can find answers to their common questions, for example: 


Which rack is most explored by customers? 
Is it popular because of location or products? 
Can the most profitable products be placed on the most popular rack? 
Will people buy if prices are increased? 
Should high priced products be shown prominently? 



These 5 ways in which AI an ML are being used in retail situations are only a few of the possibilities that are available to retailers at the moment; and best believe there will be more to come! Despite these new technologies available in the retail industry, it will continue to remain heavily competitive. Retailers need to be aware of this technological shift and consumer trends that could drastically impact their business and the industry as a whole. As always, the key to success in retail is that the customer needs, wants, and expectations are understood and accounted for. As long as this is kept at the forefront of the retailer’s mind, then a changing shopping landscape is no cause for concern.


Informative Use Cases

Natural Language Processing in Business – How Is It Useful?  

What is Natural Language Processing? 

 Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. 

While natural language processing isn’t a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms.  



While we, as humans, may be able to understand and speak more than one language, most people do not have fluency in “machine code” or “machine language” – millions of ones and zeros that produce logical actions. In the past, early programmers would use punch cards to communicate with computers but now “Siri, what is the temperature today” triggers a real-time response in a human voice. This interaction is made up of multiple sections: device activation from your voice, an understanding of your command, and a response in a fluent human language. This common “conversation” is made possible by NLP thanks to machine learning and deep learning elements. 


Common Uses for Natural Language Processing in Business 


Chatbots are one of the more recent NLP examples. Most business bots are used in Human Resources. Some NLP tools are used to deal with common employee questions, for example, “how many days leave do I have” or even deal with workplace satisfaction requests such as “we need more milk in the breakroom”. Other chatbots are used to improve employee retention and morale – employee chatrooms are monitored by bots and when employees use certain complementary words like “kudos” or “cheers” then staff get rewarded. 


Conversational Search 

Other non-bot tools are voice-activated. For example, tools are being used to listen in on company meetings for trigger phrases like “what are” and “I wonder.” When it hears them, a search function activates and retrieves an answer. It would work something like this: “What was the ROI on that last year?” Silently, the tool would scan company financials and display the results on a screen in the room. This can save the 30% of the day that employees spend searching for information and up to US $14,209 per person per year.  


Hiring tools 

On the topic of HR, NLP software has long helped hiring managers sort through resumes.  

By using the same techniques as Google search, automated candidate sourcing tools scan applicant CVs to pinpoint people with the required background for a job. This is difficult however, as humans we try to stand out from the crowd and may use creative terms to describe our skills and this can cause CVs to be ignored by NLP scans. More recently, NLP technology has branched to include synonym searching on keywords. In addition to this, as women and minorities use language differently, this synonym-searching keeps qualified candidates from slipping between the cracks.  


Call Centres 

Natural Language Processing (NLP) technologies are increasingly active in the labour-intensive world of call centres. Both inbound call centres (usually administer customer and product support or information enquiries) and outbound call centres (used for telemarketing, debt collection, soliciting charitable etc.) are implementing NLP tools. 

The technology used in call centres roughly falls into four categories: real-time speech recognition; intent analysis, which classifies conversations based on context to predict customer intent in real time; conversation management, which ensures simultaneous processing of multi-pass conversations; and conversational analysis, which broadly analyses users’ dialogue. These previously extensively human-intense operations (demanding training, time, and money) are now more efficient and effective – thanks to NLP. 


These are only a few examples of ways in which Natural Language Processing can be implemented in business situations every day. While such Artificial Intelligence technology may seem to pose a threat to employee’s jobs; this is not the case. At closer inspection, these tools are simply allowing employees to work more efficiently by having the tedious and time-consuming aspects of their roles sorted out by machines and bots. Thus, ensuring that time spent is well-spent, that employees are satisfied, customers are being heard, and ultimately, businesses save time and money! 

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Data as an Asset

Why See Data as an Asset?
Even at a simple level, information helps a company make decisions. A retail store would have no idea what stock to order without a basic inventory list. With a bit more knowledge of the brand’s sales history, the store can devise what items sell best and at what price point, as well as what promotions are most successful and even the best locations to open new stores.

Information is a road to economic success if harnessed and used correctly, and infonomics, defined as “the theory, study and discipline of asserting economic significance to information” (Pettey, 2017), helps a company to value and use information as an asset.

Companies are learning the importance of data as a valuable business asset – A history of making informed and well-supported decisions is well-favoured by investors and financial analysts for extra funding or valuation, and also leads to actions with more accurate results. Strategy is key in making effective business decisions, and basing decisions on historical data increases accuracy and offers a way to measure their effectiveness by creating metrics to compare human or machine decisions with key business objectives.

Businesses always need to balance risk assessment with the opportunity for success and competitive advantage. Information allows businesses to analyse the risk and potential hazards an innovation or strategic decision may pose. Similarly, data can predict the benefits an innovation can offer, including growth prospects, and financial success.

A popular way companies are generating and using data as an asset is via the Internet of Things (IoT) – this is a network of objects with the potential to invoke change within themselves or the environment though sense or communication (Pettey, 2017). IoT opens up a whole new realm of opportunity for both data collection and application in every industry. Farmers are now able to use ‘smart pills’ to study and analyse the health of their cows to ensure they are in prime health in order to maximise sale prices and ensure healthy calves. Mining companies, on the other hand, are adding sensors to their machinery to minimise downtime and increase performance, safety, and operating conditions (Heppelmann, 2014).

Companies can also further increase efficiency by communicating and responding to data in real time. Wind turbines can be designed to respond to changes in the wind strength and direction to maximise power generation, boosting production by up to 20% (Bughin, Hazan, Ramaswamy, Chui, Allas, Dahlstr.m, Henke, Trench, 2017). This information is fed back to power companies, who can follow in real time how much energy is being produced and accurately predict how much power will need to be sourced from fossil fuels to avoid creating excess energy and pollution from non-renewable sources. In the coming years, mastering IoT objects will be a way to guarantee high performance and business outcomes.

The first step to using information as a value-adding asset is to properly value it for its’ full potential while beginning to measure and track every source possible. Gartner has created the following infographic focusing on six different areas that can draw value from data, which is helpful in identifying how the data should be used (Levy, 2017):

Large organisations are now instating a Chief Data Officer (CDO) to lead the process of transforming data assets into actual enterprise assets – this is done by monetising data through optimising the information available to a company and finding new data sources to incorporate. Gartner predicts 10% of businesses will have a designated unit for commercialising information assets by 2020 (Pettey, 2017).

While valuing data as an asset sounds like a complex process for large multi-national organisations, let’s go back to the example of the retail store deciding what stock to order. Information insights are valuable for even the smallest businesses, and backing decisions with data is imperative to growth and success.

Bughin, J, Hazan, E, Ramaswamy, S, Chui, M, Allas, T, Dahlstr.m, P, Henke, N, Trench, M 2017 Artificial Intelligence: The Next Digital Frontier? McKinsey Global Institute

Levy, H 2015 Why and How to Value Your Information as an Asset, Gartner <>

Heppelmann, J December 2014 How the Internet of Things Could Transform the Value Chain McKinsey & Company <> [interview]

Pettey, C 2017 Treating Information as an Asset, Gartner <>