Artificial intelligence (AI) has been an influential concept not only in the scientific community but also in popular culture. Depending on the respective attitude towards AI, one might associate characters such as inhibited but likeable android Data from Star Trek or neurotic and vicious HAL 9000 from the Space Odysee movies with it. The form of AI those two represent is called General AI, i.e. the intelligence of a machine that enables it to perform intellectual tasks as good as or better than a human. General AI is – at least for the moment – still a topic for science fiction. What is interesting for various industries is the notion of Narrow AI (also termed Weak AI). These are technologies that enable humans to fulfil specific tasks in an automated manner just like or even better than they could.

In the context of data, machine learning can be seen as an approach to achieve artifical intelligence. Machine learning is about analyzing data, learning from it and using the insights gained for decision making or predicitions about something. 

The learning in machine learning can be attempted in two generic forms:

• The first is so called supervised learning. Supervised learning algorithms aim to make predictions based upon a given set of data examples for which properties are known. A machine learning algorithm can for instance look for patterns in historic stock prices and take any possibly relevant information into account that is available with it, be it days of week, indicators that describe company performance, the state of the economy or even the weather. As soon as an algorithm has found the relationship between the stock price (dependent variable) and independet variables (the ones that allegedly influence stock prices), it can be used to predict future outcomes by plugging in values for the variables. This might ring a bell for many who have come across regression models in their education. In fact linear regression is said to be the most popular machine learning model.
• In unsupervised learning data comes without pre-defined labels, i.e. any kind of classifications or categorizations are not included. The goal of unsupervised learning algorithms is to discover hidden structures. A practical example of this is the functionality of Apple’s iOS that tries to sort photos using the actual faces on them. Unsupervised learning seems much harder as we ask the algorithm to do something for us without telling it (or knowing) how to do it.

Going through all possibly relevant algorithms would go beyond the scope of this post that is intended to be an introductory one (more specific ones are planned, though). As I have started diving deeper into machine learning recently, I can recommend this cheat sheet by Mithun Sridharan before you start your desk research and do the googeling. You might also find Laura Hamilton's overview of the pros and cons of the most popular algorithms helpful for a start. If you can´t wait to start trying things, you might find this blog post on KD Nuggest worth taking a look at as it offers heaps of links to machine learning cheat sheets for different platforms. As a fan of R, I can particularly recommend this overview by Yanchang Zhao.

What can fundraisers do? I have to say that I did not come across many practice sharing posts or articles when I conducted research for this post. I doubt that either the availability of data or the competence portfolio of analysts and data scientists limit the possibilities of fundraising organizations in the context of machine learning techniques. However, there might be a certain level of insecurity regarding where and how to start. I found an inspiring blog post by Stephen W. Lambert in which he explains that all you basically need is a computer, a database with relevant data and your brain to start diving into machine learning techniques. I think Lambert’s text invites fundraising organizations to do their theoretical and conceptual homework, process and prepare their data accordingly and start experimenting with maching learning techniques. So - go ahead and try.

Big data has become a big and – as far as I perceive – somewhat controversial term. Google delivers  325.000.000 search results when you enter „big data“ with some 300.000 search requests per months according to Keywords Everywhere . This is way more than what one gets when searching for Internet of things (a term with high buzzword potential - 222.000.000 results) or Data science (75.600.000 hits). 

Some years ago Google contributed to the big data hype by launching Google Flu Trends (GFT). The aim of GFT was simple: By targeting specific terms within the millions of Google search requests, aggregating them and plugging them into a linear model it should be possible to monitor and predict the spread of the flu in various countries. The underlying idea of GFT was that modern means of gathering and storing data make staticial sampling, i.e. obtaining a representative sample to work with, obsolete because it is possible to observe the population as a whole. Two articles I can recommend in this regard, one from the Financial Times and another from Wired, discuss the fallacies that eventually stopped Google form continuing the project. The gist of the methodical critism was that, apart from weaknesses in the model Google had applied, the approach was essentially „theory-free“, i.e. neglecting any assumptions on causality and simply focussing on recognizing patterns in incredible amounts of data that is available. A large number of „false posivities“ might be a consequence of that which is particularly interesting from a marketing and fundraising context. I am quite sure you have received (digital) advertising that made you think: Ok, not so far-fetched to send me that but why actually me (wedding products, babywear, health problems from sleeping disorders to incontinence)?

I think that particularly smaller and medium-sized companies in industries where amounts of data flowing through systems are not as large as for retailers or digital businesses should definitely deal with big data – but in a pragmatic and down-to-earth manner. Their focus should be on how to use the data that is already available or obtainable at low cost and with minimal side-effects. I find Andrew Joss article on how to gradually build a 360-degree view on the customer highly interesting in this regard. One of the main conclusions I drew from it is that it might be worthwhile to have a close look at which (unstructured) data is acutally already available and to develop ways to analyze it.

A handful of terms starting with the letter V is often referred to in the big data debate. They are far from being a „receipe“ on how to succeed with big data but might be helpful for you to reflect and discuss big data issued. 

​Volume
The main characteristic that makes data big is its sheer volume. The amount of data created daily is incredibly high and continuously growing. There are estimates that 2.5 quintillion bytes are created daily, this is enough to fill 10 million Blu-ray discs. These volumes have been growing and continue to do so also in smaller and medium-sized companies across industries.
 
Variety
Data comes with different types. A basic way to classify types of data is to differentiate between strcutured data, i.e. highly organized data (names and addresses, payment transactions or quantitative survey results like Net Promotor Scores) and its opposite which is unstructured data. Unstructured data is mostly qualitative such as interactional data from social media (likes, shares, comments, tweets), stored customer contacts or surevy feedback as free text. Both structured and unstructured data are fundamentally different when it comes to data processing and analyses.

Veracity
Veracity as our third V-word reflects the overall „trustworthiness“ of data. Aspects to be considered in the light of the respective data source are authenticity, completeness, reproducibility and last not least reliabilty of underlying models and assumptions – particularly when it comes to things like scores or indirectly measured socio-demographics like income.

Velocity
The penultimate V represents what it takes to process big data with IT systems. It is where processing and response times as well as overall system performance come into play. Aiming to work with larger amounts and/or more complex data definitely sets a minimum baseline for the underlying IT infrastrucure.
​
Value
This V, last but not least, asks the „So-What“-question. The search for patterns and correlations is an essential and legitimate technique – but should not be practied for its own sake in order not to end up with spurious correlations. It is questionably whether the mere abundance of data makes up for theory ignorance or flawed overall reasoning.

Where does this all take us?
Technology will lead the way. Continuous progress in the are of data storage and processing capacities paired with the ubiquity of devices that collect data at points of sale, homes and even bodies will turn big data in an even bigger topic. However, it might be the first small steps like asking yourself „What does big data mean for me and us?“ that big data success stories begin with.