The huge amounts of data being gathered by businesses and organisations of many types and sizes is of no value unless useful insights can be gained from it. Amongst these is predicting future trends in terms of ‘looking into the future’ to help prepare for what lies ahead. Machine learning and predictive analytics are two components of AI (Artificial Intelligence) that enable future trends insights to be pulled from the masses of data gathered.
Organising your data collection and analytics
In order for you to benefit from this ‘looking into the future’ capability, you firstly need plenty of data for technologies such as machine learning and predictive analytics to work with: too little won’t provide them with enough information to determine certain patterns and thus provide meaningful trends-based intelligence. So it’s imperative you’re not only collecting plenty of data, but gathering and storing it in the right way to allow your AI capability to get to work. Also, you may not have the expertise and specialist tools in house to make the most of this tech, so experienced providers of predictive analytics and machine learning expertise can help you with this area of data analytics.
What’s the difference between predictive analytics and machine learning?
Predictive analytics uses both historical and present data to predict future outcomes while machine learning uses just current data to help it learn and create algorithms. It literally ‘learns’ from your data and becomes more useful the more it’s used.
What can predictive analytics and machine learning offer?
There is a raft of ways these technologies can help you with business activities and these will depend on your business type, but certain examples are:
- Cybersecurity – by spotting abnormal behaviour and usage patterns against the norm using algorithm-based intelligence, fraudulent activities can be spotted and often headed off automatically or alerts flagged up.
- Marketing – it can analyse marketing campaign effectiveness by monitoring sales or customer response activity.
- Marketing – past performance along with present can be used for analysing and monitoring marketing campaigns: past data can be used to help predict and forecast a campaign’s likely effectiveness based on metrics such as revenue, customer retention and conversion rates amongst others.
- Budgeting – financial forecasting based on past spending and income can be achieved to allow budgeting and adjustments in expenditure as appropriate.
- Customer behaviour – hugely valuable intelligence to help determine how customers may behave in the future in terms of their tastes, demands and buying habits can be gained. Predictive analytics helps you plan future product or service development and how best to market them.
Establishing what you want from your data
Understanding what information you require from your data will inform much of how your predictive analytics and machine learning is initially set up and run, and it’s another key area where professional data analytics partners can help you maximise the benefits of this powerful technology.