With the amount of data businesses collect and have access to, it’s safe to say that we are living in an age of data and analytics. To some, the idea of looking at complex data sets might sound overwhelming, but it’s important to remember that data is a huge business asset.

It comes from a massive range of sources, including structured and unstructured sources, organisational line of business applications along with sources like online, phones, payment systems, cameras, phone conversations and more. Data capacity has also increased and with that, the costs of acquiring it has dropped.

It’s changing multiple industries and the way humans interact with the world; from the way we advertise and the way we experience a product, to the way we perform tasks and do business, data can potentially touch everything, regardless of whether it’s an online or offline experience, increasingly we see that the lines are becoming blurred.

The power of data is exciting, but the reality is that many established organisations have not harnessed its full capability. We see that as databases and data points grow, many businesses struggle to keep up – even more so with the pressure of industry disruptors and digital natives.

In order for established businesses to avoid sinking, they need to approach business with a new mindset, applying data and analytical insights to streamline processes, understand their users and constantly improve.

That’s why artificial intelligence (AI) and deep learning are so important – as time goes on, machines and their algorithms become more and more sophisticated, giving businesses the opportunity to create plenty of value and room to set themselves apart from the pack. On the other hand, businesses who don’t adopt AI and deep learning may find themselves at a severe disadvantage.

 

AI can do plenty of amazing things, just some of these include:

  • AI can help to match ‘supply’ and ‘demand’ in real time

Any platform that acts as a marketplace or provides a link between two parties, for example: sellers and buyers, doctors and patients and teachers and students could benefit from AI. This is especially true in an environment where matching supply and demand has been grossly inefficient.

A good example is ride-sharing service Uber, which relies on real time data in order for it to work efficiently. Uber and other ridesharing apps use geospatial mapping technology to collect data about passengers and drivers to connect them in real time. In addition, Uber drills into the data at an aggregate level to provide different pricing for different trips.

  • Breaking down of organisational silos

The best way to get value from data is to have access to all information relevant to a singular problem, which would involve breaking down walls within an organisation to link existing data points together. By combining strings of data, you slowly form a tapestry which can produce plenty of interesting insights.

For many organisations today, internal barriers and the quality of the data itself makes this seem unfathomable. However, the process is important for painting a complete picture of a person or project, paving the way for cross-pollination, personalised communications, better risk assessment and highly effective marketing.

  • Radical personalisation

One of the most powerful uses of data today lies in user segmentation, which helps drive deep personalisation, giving the ability for businesses to improve a user’s experience with a service or product. As AI gets smarter, bigger insights and transformations are on the horizon.

Hyper-personalisation could have a huge impact on the way health care is delivered, if the industry could find a way to access and connect all the different data points connected to individual patients.

  • AI and deep learning support human decision making

When it comes to making decisions, the process can be difficult, especially when dealing with lots of moving parts and plenty of information. It’s all too open to uncertainty and human error. AI can change this by analysing data from new sources, finding links and inaccuracies in a matter of seconds.

In healthcare for example, an algorithm could flag allergies and drug interactions for health professionals instantaneously, which ensures their decisions are reliable and informed by fact.

 

 

In summary

Machine learning has the power to make our jobs easier and more accurate – and algorithms can be built to ‘learn’ and get even smarter with time. It means saying goodbye to traditional software which is coded by humans and embracing a system which is trained to identify patterns, insights and links with ease.

With more and more data being captured, we will see deep learning (a capability within machine learning) become more and more advanced. While deep learning is in its early stages, experts predict that it could have a huge impact on administrative duties, including customer service roles, supervisory positions and cashiers. Deep learning systems can even write news stories, and with advances in natural language processing, the impact may be felt even further.

How efficiently is your company using data? Significant opportunities for improvement could be left sitting on the tables.

 

To keep pace with change and improve your business processes, contact us today.