An Intro to Predictive Analytics: Can I predict the future?

An Intro to Predictive Analytics: Can I predict the future?

An Intro to Predictive Analytics: Can I predict the future?

Can I predict the future ?
Predictive analytics is an umbrella term used to describe the process of applying various computational techniques with the objective of making some predictions about the future based on past data. This encompasses a variety of techniques including data mining, modelling, pattern recognition, and even graph analytics.

Does this mean we can predict future lottery numbers based on past lottery numbers? Sadly no, but, if anyone wants to prove us wrong, we will require at least 3 successful live demonstrations before we are convinced.

We're not going to get into too many details in this article as the field is quite large and we are far from an expert. We are just going to touch on the general process used when trying to make predictions using historical data. Then we are going to poke our head into some cool tech within this field.

Step 1: Get the Data
The first step in the process is usually all about data mining and filtering. Many data sources are often quite large and unstructured. So this step is all about extracting structured data from sources. On the topic of sources, be sure to select relevant and trusted sources. If we were trying to predict election results we would probably avoid using  The Onion— although given political outcomes this year we may be wrong.

Read Also:
For CFOs, A Reminder of Why Business Intelligence Is Not for Everyone

Step 2: Analyse the Data
Here we need to start focusing on the contents of the data. This alone can prove to be quite a challenge. For example, if you are trying to make predictions about your own health, what information should you take into account? Do you smoke? What is your favourite colour? Where do you work? Often determining what is relevant and what is not is its own challenge. Proper pre-processing and filtering techniques are a must when cleaning up your data.

You should also ensure your data is of good quality. A reliable source alone does not ensure quality. What if you scraped your data from  wikipedia  on the day someone thought it would be fun to vandalise the articles you were mining? Running your data through existing analysis pipelines could be quite informative and a simple method of spotting questionable data. More formally you can use confirmatory factor analysis to ensure your extracted data will at least fit your model. It is also recommend that you apply other statistical techniques to ensure your data can account for variance, false positives, and other issues which often crop up from real world data.

Read Also:
Cloud computing delivers on outsourcing's promise of cost savings

Step 3: Model the Data
This step is fundamental as it allows you to structure your data in such a way that you can start recognising patterns that potentially allow you to extract future trends. Models also allow you to formally describe your data. This is helpful in understanding the results you get from your data analysis but is also a good starting point when it comes time to visualise your results.

Similarly to data extraction, your models should undergo the same scrutiny. You should ensure that your models are valid representations of the issue you are trying to predict. Consulting with domain experts is often a good idea.



Big Data Innovation Summit London

30
Mar
2017
Big Data Innovation Summit London

$200 off with code DATA200

Read Also:
Free data visualization with Microsoft Power BI: Your step-by-step guide

Data Innovation Summit 2017

30
Mar
2017
Data Innovation Summit 2017

30% off with code 7wData

Read Also:
4 tactics that put data ahead of drama when making IT procurement decisions
Read Also:
Analytics Will Treat the Healthcare Market to $33.5 Billion

Enterprise Data World 2017

2
Apr
2017
Enterprise Data World 2017

$200 off with code 7WDATA

Read Also:
Lawyers could be the next profession to be replaced by computers

Data Visualisation Summit San Francisco

19
Apr
2017
Data Visualisation Summit San Francisco

$200 off with code DATA200

Read Also:
Free data visualization with Microsoft Power BI: Your step-by-step guide

Chief Analytics Officer Europe

25
Apr
2017
Chief Analytics Officer Europe

15% off with code 7WDCAO17

Read Also:
How CIOs can master key tech trends to drive change

Leave a Reply

Your email address will not be published. Required fields are marked *