• Finding Gold in Your Data

Finding Gold in Your Data

The phrase “there’s gold in them thar hills” isn’t heard often nowadays. It was heavily used in the goldrush era – that is, until a mountain was mined-out and there was no longer gold in the hills. In those old mines, miners were of the belief that once they had extracted all a mountains worth, it was onto the next.

Mining techniques have seen quite a change since the goldrush era. In today’s technologically advanced world, mining techniques are sophisticated and more effective. This change in technology is seeing areas, once considered “mined-out” being reworked. They are now finding those mines had more wealth than they could have ever imagined and are now able to extract that worth.

In our world today, data is considered our gold. Most organisations have data, some even an abundance of it, but understanding the worth can be challenging. Organisations with old data may find it’s unstructured and therefore hard to analyse. Machine learning and artificial intelligence can assist in finding the value of this data. First, it’s important to recognise the differences in data:

  • Structured data – this is an analysts favourite type of data. It’s organised, stored in relational databases, with relational keys and generally doesn’t require cleansing.  Structured data is usually strictly defined, for example as a number, date, currency, or alpha field.
  • Semi-structured data – this data isn’t stored in a database and isn’t in a structured and organised table format, however, it exhibits similar properties to structured data. It may also be data from a document that has metadata tags for search functionality.
  • Unstructured data – this is where most data falls. Unstructured data includes word processing documents, email messages, videos, audio, and photos. This data does not fit into an organised database, and prior to machine learning would have required many man-hours to analyse.

Organisations will have a combination of all of the above, whilst some will have historical data that can provide value as well. Analysing semi-structured and unstructured data can be challenging and time consuming. However, artificial intelligence (ie machine learning) is aiding this process. For example, marketing companies are utilising unstructured data to develop target audiences. Machine learning takes keywords from social interactions and applies the context required in order to understand the intent of social interactions. This develops a “profile” of individuals spending habits.

From our perspective, SMG is utilising machine learning in the healthcare industry by learning from the past. Imagine a world where you understood your potential health journey. Now imagine this world where you are given preventative measures to assist with your health journey. This is now! SMG’s artificial intelligence and predictive analytics engine, Xela, is doing just this. Developed specifically for the healthcare industry, Xela will provide insights into our healthcare journeys.

Machine learning capabilities mean large volumes of data (structured and unstructured) can be analysed to provide answers from the past to improve our future. Whilst historical data should not be dismissed, the use of unstructured data should also not be taken out of the equation. What would have once taken hundreds (maybe thousands) of man-hours to cleanse and analyse semi-structured and unstructured data, today artificial intelligence can do almost instantly. Just as in the goldrush era, today’s technologies are helping find gold in the mountains of data.

Kylee Randall

Head of Consultancy & Special Projects

Kylee Randall has spent the last 17 years in roles focussed on developing high productivity, resilient cultures in corporations. She has successfully delivered complex projects that integrate technology and people. Kylee has witnessed the value wellbeing provides to organisations, striving to promote this mentality in all projects. Kylee’s qualifications include a business degree, Master of Business Administration (Sustainable Business) and she is currently studying a Post Graduate Diploma in IT.

AI is not here to replace us, rather work alongside us to streamline processes in the workplace. #watchthisspace #AI #Healthtech #HR

AI Plus Human Intelligence Is The Future Of Work via @forbes https://t.co/BiWxrnFW7g

A great article on the top 8 trends by @jam1eleach following the @CES Tech Conference last week #CES2018 #AI #WearableTech #HealthTech #Financialtech https://t.co/4mQAQ8dCGR

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