Presentation delivered at Better Boards 2014 in Adelaide, Australia by Chris O’Neill. chris@epix.co.nz +64 275 34 35 39 www.thefutureofcharity.com

Imagine a future where donors can search for non-profits with a proven record, provide in-depth reporting, and details on how funds are spent and the effectiveness of programmes. Without data backed story, your donors + funders will skip you for those who do. On top of your data - donors will be able to source community, govt, and online sources of giving and effectiveness. For example - a foundation reviewing annual funding of early childhood education will be looking at visualisations of past funding and reviewing effectiveness of that funding. Organisations spread across world will be able to collaborate, sharing what works, what doesn’t work, and reviewing the assumptions that underly their work. NFP working same region better able to coordinate. For example: A medical service, entrepreneurship group + educational program in a rural area, will be able to share transportation data systems, -> less fuel, fewer trucks -> serve more people @ same level of charitable gifts. Fundraising, as NFP we will get better + faster data to help convey value. Timing and content of the messages can be optimised for your most committed supporters. !

BIG DATA

DATA STRATEGY

EXAMPLES

Three core elements to presentation. Big data - what is it and how does it relate to us as NFP. Is it something you need to worry about? Key elements to consider if its going to be part of your data strategy (and its a buzzword) Creating data strategy Examples of what’s possible

IT IS MORE DIFFICULT TO GIVE MONEY AWAY

INTELLIGENTLY THAN TO EARN IT IN THE FIRST

PLACE !

ANDREW CARNEGIE.

All of these changes are driven by data and the increasing ease of access and analysis. Charities who take advantage of these opportunities will have a great advantage over those that don’t. Funder’s aren’t stupid. They know that not all organisations are equally effective. In the future they will be able to assess which one’s are most effective at meeting their mission and focus their funds on those charities.

Big DataMany charities already use data and measurement to quantify their work. They can tell you how many children they’ve sponsored or how many elderly they house, but increasingly the world of big data is going to become important to NP’s. To give you a sense of scale, the phone in you pocket probably has about 16 Gb of space on it. Your computer probably has a couple of hundred Gb space on it. 1 Petabyte is 1M gigabytes, or about 65k iPhones. When we are talking about big data we can be dealing with datasets that are in the petabytes in size, but they don’t have to be. It will all depend on the scale of your operation and the size and type of data you gather. This can seem a little daunting at first, but the mindset is the same regardless of scale.

When we talk about big data there are 4 defining characteristics. The velocity, or speed at which the data is being generated. The veracity, or accuracy. The variety of data, which takes into consideration the number of sources and different formats that the data might be in. And finally the volume of data, which we’re about to explore in more detail.

When we are talking about big data often we are discussing the shear volume of data available for processing. The amount of data means it is hard to process or analyse using traditional methods. You aren’t going to load petabytes of data in Excel and just click the ‘average button’. Let’s look at a couple of examples of just how much data we are talking about.

The Sloan Digital Sky Survey, for example, began amassing data in 2000 and collected more in its first few weeks than all the data collected before that in the history of astronomy. It’s now up to 140 terabytes and counting, and when its successor comes online in 2016 it will collect that amount of data every five days. !Facebook generates 3 Billion likes per day. Each of those likes has several data points associated with it, who made the post, who liked the post, what time did they like the post, from what type of device did they click like. Just with likes alone Facebook is likely generating 12-15 billion pieces of data, that’s not counting advertising interactions, IM, photos uploaded, etc.

NOT EVERYTHING THAT CAN BE COUNTED

COUNTS, AND NOT EVERYTHING THAT

COUNTS CAN BE COUNTED. !

ALBERT EINSTEIN

From the cosmology of the Sloan sky survey, to the reality of the being on the the ground in a NP. I want to recognise that not everything we do can be measured, boxed, and analaysed, but the problem we face is that our funders and donors don’t understand this distinction. Your funders will expect you to be able to provide nice reports with hard data in it that you are being effective. If you can’t they will simply move on to the next organisation that can. And don’t think that this won’t happen, because it already is and we’ll look at some examples later on.

It’s important we clarify the difference between data vs big data. While I’ve thrown about some pretty big numbers but there is a conceptual point that is worth exploring. Big data isn’t always about massive datasets. It can also refer to the coverage of your data. The goal with big data is to get as close to 100% coverage as possible. In the past we have relied upon surveying a portion of our clients, or summarising data a high level. Big data presents us with the opportunity to see the whole picture. Consider a movie frame of a horse, which by itself tells us little about the direction or speed of the horse. We can make some assumptions based on context such as a racetrack, but we can’t really know. Compare to a video of a horse. Once we can see the whole picture we can understand what’s really going on. It’s the same for data, once we start recording and analysing it all we see details that we couldn’t see when we only had part of the story.

Not only do you have to contend with large volumes of data, but also the potential speed at which is being generated. Streaming data comes from sources such as GPS, websites, donations Easiest example: Amazon recommends… 29% of their sales comes from their recommendation engine which is built on streaming data that computes what you are most likely to buy.

Old way of doing things - samples, retrospective, only slices Big data not just about volumes, but how comprehensive the data set is.

Full Picture

100% of the picture makes it easier to see what’s happening. More data trumps better data - seeing the full picture, even if there’s some fuzziness to the data is better than seeing on part of the story

It’s a simple point to make, but even if this picture is blurry you can still make out what it is. Data is the same. A blurry, but comprehensive version of the truth is better than slices of a hunch.

Next we are going to all about veracity. Mentioned before - it means accuracy or truth. All data has uncertainty due to inconsistencies, incompleteness, ambiguity, but big data tends to be more at risk. Due to the volume and velocity of the data it is more likely that random errors or systemic mistakes can creep in before they are spotted. In order to consider this aspect of data you are going to need to account for the inconsistencies on the data.

Correlation vs

CausationThere is currently a big debate going on over correlation and causation in big data. Causation is where one thing directly causes another, correlation is when two things happen in tandem but they might not be causing each other. My belief is that correlation combined with an educated understanding of your data and the world you operate in make it a valuable strategy to consider correlation. However, pure data scientists would say that you are misleading yourself. Correlations are important, they may not tell you why something is happening, but they highlight that it is occurring and that you need to consider it. However, if you aren’t engaging your brain then you may fall into traps. For example, when the sales of ice cream go up, so do murders. We’re smart enough to know that it’s unlikely that murder and ice cream are related, and that it’s more likely to do with the weather that spurs both. However, it’s not always so easy to filter this way.

TylerVigen.com

Look at this data for example. A foolish person would immediately ban margarine, for the sake of married couples everywhere. Silly examples aside, correlations can provide a strong indicator that a relationship exists, but it is up to us to assess what is really going on behind the scenes.

Many forms, structured, unstructured (e.g. in a database in neat columns or rows, or all jumbled up) text, multimedia. Generally this isn’t something you will have to deal with at a strategic level, it’s mostly a challenge for those working directly with the data.

Messy data vs clean data- Google vs IBM translate as an example, Google - massive amounts of data, very messy whole of internet in order to create translation engine. Performs better than IBM’s very tidy smaller data set which was created using Canadian Govt reports. 3 million documents vs billions. 1 source vs the whole internet Causation/correlation example - ZestFinance - US credit score company, uses far more data compared to traditional credit agencies. 10% of people are listed as dead, but that doesn’t affect their ability to pay back a loan.

Data Sources

Now we’ve covered off the conceptual understanding of data and big data let’s look at some of the things you need to know to implement a data driven strategy.

Measurement

Active Measurement - recording everything you want to know Can be time consuming, but may be the only way when dealing with human processes

https://www.youtube.com/watch?v=Q1XNU7qwPHU

Embedded Device Measurement GPS, cell phones, machine measurement, website, social media These kinds of sources will provide far more detail and breadth of information, but may not be appropriate for everyone. Now that we’ve covered off big data I want to get into what you need to consider when crafting a data strategy.

Collaboration

Taking on data driven initiatives can seem daunting so one way to achieve it is through collaboration with others. Health Care Cost Institute - Non-Profit org created by America’s biggest insurers. Evaluated 5B claims from 33M people They were able to see combined trends - e.g. costs - emergency room up 11% in one year, nursing facilities cost declined. The project was only possible because of Non-Profit intermediary, each org didn’t have the resources or amount of data to achieve it by themselves. So I would strongly encourage you to consider who you could collaborate with in the NFP or FP areas in order to get access to enough data.

Here is a real example of funders moving towards an evidence based system of evaluation. Currently run as a trail, but indicates a growing trend. Project Oracle - set up by London City Council. Projects related to youth development can be part of the evidence hub, goal is to promote strategies that have a positive measurable impact.

These are their standards of evidence. The purpose of the framework is to improve outcomes for young people, and help to identify the most promising approaches. Key detail - They help commissioners to identify which organisations and projects have the greatest potential to make a difference which will lead to future

funding decisions

https://www.youtube.com/watch?v=RDX8NmBm2TI

Short video from different partners in Project Oracle that discusses the benefits of an evidence or data driven approach. This approach of funders demanding better evidence driven by data will become more common and their expectations will increase over time.

INFORMATION IS THE OIL OF THE 21ST CENTURY, AND ANALYTICS IS THE COMBUSTION ENGINE. !

PETER SONDERGAARD, SENIOR VICE PRESIDENT, GARTNER RESEARCH.

There are many programme benefits to a data driven approach, but there are also potential finance benefits for your org. As I’ve noted funders are increasingly going to demand clear measurement of impact, but data also presents an opportunity for how you generate other forms of revenue.

Virtual ValueCan you make money of the data you hold? Anonymous or otherwise. Inrix - traffic analysis firm, Track GPS data from delivery vans, phones, etc. Their data was able to predict the US recession before the economists were, and is used by financial firms to assess the amount of business a retail outlet does, and therefore their future profits.

PersonalisationOne of the potential keys to charity success is personalisation through big data. If NFPs can target individuals with personalised, relevant messages which appeal to their interests and lifestyles, they're more likely to evoke responses and engagement than with generic messages. Say, for example, you are a children's charity. Big data will allow you to identify and communicate with your most likely responsive targets. A key attraction of big data is its monetizable ability to reach the right people at the right time with the right product, message or attraction, and it's no different for NFPs.

THE GOAL IS TO TURN DATA INTO INFORMATION, AND INFORMATION INTO INSIGHT. !

CARLY FIORINA, FORMER CHIEF EXECUTIVE OF HP

To create understanding from your data you are going to need some resources. In order to do this you need tools and skills to analyse that data

ToolsThe session is about strategy rather than implementation, so I’m not going to go into detail on complicated analytical tools. If you’re going to use those kinds of tools you’re going to have to contract or employ someone to use it as they are often quite specialised. However, it’s good for you to have a broad understanding. We’ll start with simple tools and them mention some of the more complicated ones.

Data on food prices is

fragmented and unstructured

import·io normalizes that data and extracts it from

the web

By analyzing this, and other data,

Oxfam can predict food shortages

before they happen

importIO pulls unstructured data from normal websites and converts it into usable, organised data. Monitoring the rise and fall of local food prices can help NGOs like Oxfam to provide better, faster and more targeted aid where it is needed most. Unfortunately, collecting data on local market prices is difficult because the data is fragmented and unstructured. Using import·io, Oxfam was able to create a database of food prices which they can now use to help monitor the situation in Africa and start to analyze and predict the causes of food shortages.

UshahidiUshahidi - map based tracking system, used for disaster response, or any location based info. One charity is using this system to track new water well sites.  If the well should break, anyone in the community can call a number and report the well’s code.  The service asks them to enter the number, confirm that the well is broken, and then leave a short voice message about the problem. This service speaks with the Watertracker Ushahidi, which stores and maps detailed information about thousands of water points throughout the country. When community calls are completed, the site immediately displays malfunctioning well reports instantaneously on a country map, including the voice message, and alerts the watertracker team. A Watertracker monitor listens to these voice messages and begins the process of responding to the community via mobile in order to provide technical assistance to help them resolve the technical issues that they face.

Open DataTalked a bit about your own data sources. There are a number of govt Open data sources data.gov.au Comprehensive data sets on all sorts of things ranging from crime to parks and gardens.

Databases / Data warehousing

DremelHadoop

Data Analysis & Platforms

Operational

Big Data searchBusiness Intelligence Data Mining

Social

Corona

Graph databasesDocument Store Raven DBKeyValue

Multimodel

Object databases

Picolisp XML Databses Grid Solutions

Multidimensional

Multivalue database

Data aggregation

Created by: www.bigdata-startups.com

In-Memory Computing

Programming

AnalysisSelection of different parts of the data puzzle. Complicated landscape, often complicated interfaces and configuration. Best left to the experts.

IF HISTORY WERE TAUGHT IN THE FORM OF STORIES, IT WOULD NEVER BE FORGOTTEN. !

RUDYARD KIPLING

Once you have your data it’s critical that you turn it into interesting stories.

Telling StoriesTelling a story online or in print is about a strong combination of words and imagery.

Urban domestic water usage. No fancy computer rendering, just cut up some sponges.

HP and Conservation International working together. "early warning system" for the health of the ecosystems and species it monitors Sensors based in 16 locations around the world. The data ranges from temperature, humidity and solar radiation, as well as thousands of photos taken by cameras that are triggered if an animal walks past. one real-life example of how Earth Insights helped was that data gathered from Costa Rica showed that certain species were in decline. The charity was able to attribute this to illegal hunting, so patrols in the area were stepped up and this helped prevent further losses.

ObstaclesObstacles Other orgs hold a lot of the data - suppliers, service providers Different NFP sectors use vastly different models. The way a theatre thinks and handles data is quite different to the way a rest home does. Proprietary data formats - different vendors, elements in the chain. Lack of collaboration platforms for sharing data, and a concern over privacy, competition, poaching.

ActionOnce you’ve put it all together you are ready to share your data driven stories with the world. I’m going to finish by sharing some examples of how others have used data, starting with a cautionary tale.

Target Pregnancy Women buy lotion, then supplements around 3 months. Target assigns a pregnancy prediction score, estimate pregnancy stage.

HIDING WITHIN THOSE MOUNDS OF DATA IS KNOWLEDGE THAT COULD CHANGE THE LIFE OF A PATIENT, OR CHANGE THE WORLD. !

ATUL BUTTE, PROFESSOR, STANFORD UNIVERSITY

In NFP It’s up to us to make the most of the data we have available. We have a moral responsibility to spend donors money in the most effective way possible. `

NPC Justice Lab project in UK. The Justice Data Lab pilot enables organisations working with offenders to access central re-offending data. The service provides vital information to organisations supporting the rehabilitation of offenders and help them assess the impact of their work on reducing re-offending. Participants get back anonymous data on the offenders they work with so they can compare how effective their programmes are compared to a control group. Over time programmes like this evolve where it will a requirement to participate in order to receive funding.

Researchers at the Karolinska Institute and Columbia University demonstrated that mobile data patterns could be used to understand the movement of refugees and the consequent health risks posed by these movements. !Trackied the movement of nearly two million SIM cards in the country. They were able to accurately analyse the destination of over 600,000 people displaced from Port-au-Prince, Later that year, a cholera outbreak struck the country and the same team used mobile data to track the movement of people from affected zones. Aid organisations used this data to prepare for new outbreaks.

Dollars to projects 1. You fundraise or donate 2. 100% of money to the field 3. When the project is completed they prove it

Example of a Charity:Water project report.

https://www.youtube.com/watch?v=BCHhwxvQqxg

Dollars to Projects

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