Mark Vargo, Chief Technology Officer, CSI, suggests the right steps to ensure businesses are harnessing their business data correctly.
Cognitive computing is leading society down a path that is genuinely set to revolutionise how we live, work and behave. Previously, new ways of communicating online were big business, now Artificial Intelligence (AI) and Machine Learning (ML) take centre stage. These technologies are driving unprecedented developments in medicine, education and finance, as well as relieving us from a wide range of everyday tasks.
AI is heralding the Fourth Industrial Revolution and will quickly become indispensable. The opportunity is there for businesses to embrace, but with this comes a warning: if you aren’t taking advantage of AI now, your competitors are. It’s a case of being a disruptor, not disrupted. So, how can you realise the benefits of intelligent technologies today?
1. Know what questions to ask
The nature of business is to look forward; at new technology, new products to launch and new customers to win. But to take that step forward with confidence, an organisation must agree on the business insights that will bring the biggest competitive edge. Even with AI, asking the wrong question will always give the wrong answer, while asking the right questions of your data will deliver meaningful results.
Think about the business issue that is keeping you up at night, or what you’re most worried your boss will ask you. Imagine how solving that problem will make a difference. Perhaps it will help your company grow or maybe it will save time and money. Harnessing AI may accelerate innovation, or it may help to protect your company and your customers. Cognitive computing consultants can help you identify these critical issues so that you can take the next step in solving them.
2. Undertake an audit/inventory of your data
Once you know which questions you want to ask, the next step is to audit your corporate data to find the information that might hold the answers. Most IT teams are used to dealing with ‘structured data’. It’s organised in a format that’s easy for machines to process, such as a database or a spreadsheet. Historically, limited compute power meant that only structured data could be analysed effectively. This left organisations with vast pools of valuable but untapped ‘unstructured data’ such as images, documents, emails, video, audio, images and social media posts. The lack of a pre-defined structure made extracting answers from this data extremely time consuming, resulting in it being left on the cutting room floor.
Advances in high-performance technology mean that neural networks can now work with unstructured data, harvesting information that wasn’t accessible before and drawing out valuable insights previously hidden from the organisation. As a result, Gartner predicts that the volume of data will grow by over 800% throughout the next four years and the vast majority of it (80%) will be unstructured. AI means that you no longer need perfectly structured data to get answers. The technology can work with all types of data; it’s mainly about locating it, labelling it and making it available. Depending on the complexity of the business challenge, many data sources may need to be lined up, but there are ways around not having your own specialist data scientists. Working with an enterprise performance partner who has access to a pool of data scientists is one of them.
3. Train your AI model
Once you’ve figured out the problem and located the information to solve it, the next step is to work on developing your AI model. The first stage is to provide training data that contains the correct answers. For example, creating a model that recognises different vehicles requires images of hatchbacks that are labelled ‘hatchback’, images of tractors that are labelled ‘tractor’ and so on. Given accurate training data, Machine Learning algorithms will find the patterns that can predict the correct answers. Applying those patterns on raw un-labelled data is the where the AI model shows its value.
Proving the conceptual model can be done with a relatively small set of data, however to support robust decision making, comprehensive data sources will need to be used. The larger the data set, the fewer anomalies you’ll get. Use too little data and you’ll have an answer based on what that set of data is telling you, but not reflective of a true business pattern.
4. Choose affordable compute power to develop your AI capabilities
The volume of data you need to trawl through when training your AI model requires a powerful amount of compute, but once the model is trained to do its job, running it against real world data is not nearly as processor intensive. If you’ve invested lots of money in the kind of high-performance infrastructure needed to train your Machine Learning models, the chances are you’ll be left with a large chunk of it unused when that first phase is over.
An alternative approach is to use a cloud-based AI platform to do all the heavy lifting from the outset in gathering and analysing data, avoiding a considerable capex investment. Designed specifically for this type of data-intensive workload, these specialised cloud services offer ‘pay as you use’ access to some of the most powerful servers commercially available.
It’s important to work with a partner that can find you the right cloud environment for your AI model. Cost, performance and cybersecurity concerns need to be finely balanced. For example, once the model’s up and running, it can be hosted either on-premise or on a private or public cloud or a multi-cloud combination depending on what’s best for the application.
Real life scenarios of AI and cognitive computing
AI isn’t limited to any particular field or function and can boost the capabilities of any sector and sphere in the world. The public spotlight often shines on high profile examples such as analysing digital images for early cancer diagnosis, natural language processing in Siri and Alexa or the predictive capabilities of a self-drive Tesla, but Machine Learning can transform almost any market and often in seemingly mundane ways.
In retail, an AI-based personal sales assistant can prevent shopping trolleys being abandoned before a purchase is made by delivering real-time product targeting. Modelling millions of users every day can predict a shopper’s intent. It’s then possible to match a brand’s product offering to an individual’s preference and increase conversions.
Image processing can reduce waste in ready-meal manufacturing by sorting potatoes by size, so chips are made from the longest ones, hash browns from the medium sized and mash from what’s left. University students’ progress can be tracked and improved by analysing assignments and recommending personal study where knowledge gaps are identified.
Booking flights, hotels and rental cars is simplified when a chatbot can answer traveller queries. The process of sifting through vast quantities of data and spotting correlations and inconsistencies can be used to make predictions and solve complex problems for any business. Often the inspiration for successful AI project comes from exploring examples in other companies.
The route to lean innovation
There is now so much that the wider enterprise world can do with AI that just wasn’t possible five years ago. Plenty of pre-bundled software now exists from the main ‘cognitive in the cloud’ players such as IBM, Microsoft and AWS. For example, an application to recommend podcasts can be stitched together with modules that transcribe audio to text, search text for keywords, index podcasts based on keyword hits and display the results in a colour-coded dashboard.
Proof of concept facilities are also being made available by the main players that allow you to undertake a ‘trial’ with a much smaller dataset. This means that you can provide a business case to the board and demonstrate the business functionality of your idea without investing large sums of money upfront.
These services are now becoming more widely available demonstrating the many possibilities that AI can bring to an organisation. Imagine being able to sift quickly through your business data to recognise patterns and discern inconsistencies so that you can then make predictions about your business. Well, that time is here. Now is the time to uncover the insights in your data that will give your company its perpetual edge over competitors.
