Story image

Six steps to extracting real business value from artificial intelligence

23 Apr 2018

Article by Peter O’Connor, vice president of sales for Asia Pacific, Snowflake Computing

Artificial intelligence (AI) tools are rapidly growing in capability, and many enterprises are keen to put them to work.

However, very few enterprises are actually making progress or enjoying the value these tools can potentially deliver.

For the enterprises that do succeed, the rewards can be significant. They can obtain insights into market trends and customer requirements that would not have been possible previously.

AI tools can also be used to automate many processing tasks, thus freeing staff to focus on more value-adding activities.

For the many enterprises that have yet to reach this stage, there are a number of specific steps they can take to improve their chance of success. By reviewing these steps and putting them into action, an enterprise can quickly begin to enjoy the benefits that AI technology has to offer.

Stop throwing away data

Traditionally, companies have tended to classify data in one of two ways.

The data is either high-fidelity, high-value data (such as customer records and transaction data), or raw, unstructured data that is either archived or deleted. For decades this approach made sense, but times have now changed. With the rise of cloud platforms, the cost of storing and processing data has fallen dramatically, which makes deleting it just to save money nonsensical. So, if your company is still practicing data austerity, the first step in your AI journey is to stop.

Data is your most valuable asset, so keep it all. Better yet, start collecting and storing even more because modern approaches to AI are based on training algorithms with large amounts of data.

Generally speaking, the more data you have, the better your AI tools will become.

Check your intuition

When data was expensive to store and process, making data-driven decisions was challenging.

It was often too costly to get the right data in front of the right people at the right time. That’s why many businesses tended to rely on the intuition of senior managers when making important decisions.

Today, technology limitations are quickly fading and it’s now possible to collect fine-grained information about how people are interacting with your business, product or service. It’s also possible to make data available in near real time.

However, solving the technical challenges of data storage and processing alone does not guarantee success. To turn the corner on AI, a company must also get comfortable relying less on the intuition of managers.

Making better decisions with data, not intuition, is the entire point of adopting AI.

Experiment often

With more data informing the decision-making process, it’s time to start setting up experiments.

To achieve the best results, it’s important to create a culture where a broad group of individuals and teams (rather than a small set of senior leaders) has access to the data and is not afraid to try out different ideas and see what works. Most machine learning and AI approaches involve trying out lots of different algorithms with different metrics and parameters and observing the impact they have on specific problems you're trying to solve. If a business already has a culture of experimentation, adopting machine learning and AI-based practices is relatively straightforward. Chances are you already have processes in place to collect, analyse and use data effectively.

From there, it’s often easy to find opportunities where AI can augment these human-based processes.

Close the loop

When machine learning and AI algorithms are trained with data, it’s best to start with a large, rich, and structured data set that humans can query to answer questions on their own.

Next, train an algorithm with the same data to achieve the same goal.

This might be answering a particular question or identifying certain patterns. Then, evaluate the algorithm against the known, human-answered dataset to determine how well it performs. As the algorithm gets better, you can start asking it more advanced questions, with the goal of answering questions human trainers are less capable of answering themselves. The key to ensuring optimal results as algorithms become more sophisticated is “closing the loop.” Ensure input data is always good and constantly evaluate outcomes against the right metrics.

Choose metrics wisely

The more decisions are based on data, the more important it becomes to identify exactly what it is that you are optimising for, and defining the metrics being tracked.

Generally speaking, choosing metrics that are a proxy for customer satisfaction is a tried-and-true approach.

When measured properly, customer satisfaction can provide insights across every aspect of a business. For example, measuring renewal rates can not only tell you if customers liked your product but also whether they’re happy with the customer support experience.

Choose more than one metric

For the most accurate reflection of customer satisfaction, optimise across a range of metrics.

While it’s tempting to search and optimise for the one “perfect” metric, tread cautiously because over-optimising for a single metric can easily lead to distortions in product behaviour and create new challenges down the road.

By following these steps, a business will be better placed to extract significant value from its AI tools and initiatives.

Getting things right early will also position the business to gain further benefits as the capability of the tools is extended into the future.

Dropbox invests in hosting data inside Australia
Global collaboration platform Dropbox has announced it will now host Australian customer files onshore to support its growing base in the country.
Opinion: Meeting the edge computing challenge
Scale Computing's Alan Conboy discusses the importance of edge computing and the imminent challenges that lie ahead.
Alibaba Cloud discusses past and unveils ‘strategic upgrade’
Alibaba Group's Jeff Zhang spoke about the company’s aim to develop into a more technologically inclusive platform.
Protecting data centres from fire – your options
Chubb's Pierre Thorne discusses the countless potential implications of a data centre outage, and how to avoid them.
Opinion: How SD-WAN changes the game for 5G networks
5G/SD-WAN mobile edge computing and network slicing will enable and drive innovative NFV services, according to Kelly Ahuja, CEO, Versa Networks
TYAN unveils new inference-optimised GPU platforms with NVIDIA T4 accelerators
“TYAN servers with NVIDIA T4 GPUs are designed to excel at all accelerated workloads, including machine learning, deep learning, and virtual desktops.”
AMD delivers data center grunt for Google's new game streaming platform
'By combining our gaming DNA and data center technology leadership with a long-standing commitment to open platforms, AMD provides unique technologies and expertise to enable world-class cloud gaming experiences."
Inspur announces AI edge computing server with NVIDIA GPUs
“The dynamic nature and rapid expansion of AI workloads require an adaptive and optimised set of hardware, software and services for developers to utilise as they build their own solutions."