Adding value to data by Anna Faherty

by Heather Dandridge  yesterday at 12:27

Adding value to data Olympic style by Anna Faherty

Accounting and finance professionals are no strangers to sharing numerical data. Providing information to colleagues and clients is, of course, a key component of many roles in this sector. But that doesn't mean it's an easy task, especially if the people you are communicating with don't have the skills to interpret the figures you share or don't even want to trust them. The ability to make the numbers come alive for these people is therefore a crucial skill and something I hope to be able to boost through my new accountingcpd course, Presenting Numerical Data.

One way to bring numbers alive is to give them meaning by ordering them in some way or by supplementing them by additional information. Both these actions add value to raw data, by helping people understand what the numbers tell us about a situation. A simple example of this can be seen in the website medalspercapita.com.

Medals per Capita is a site that shares data about the medals won at every Olympic Games since 1896, including this summer's event in Rio. One way of looking at this data would be to simply list the number of medals won by each country, as shown below for a sample of countries in 2016.


COUNTRY
NUMBER OF MEDALS
Algeria 2
Argentina 4
Armenia 4
Australia 29
Austria 1
Azerbaijan 18
Bahamas 2
Bahrain 2
Belarus 9
Belgium 6
Brazil 19
Bulgaria 3
Burundi 1


Organising the countries and their medal hauls alphabetically can add value to this data if we want to make it easy for people to look up the number of medals won by a specific nation, but it doesn't deliver much meaning. If we order this data by number of medals instead, we generate a more useful 'medals table', which ranks the countries by the number of medals each won and starts to tell us something about their relative success.


RANK
COUNTRY
NUMBER OF MEDALS
1 United States 121
2 China 70
3 Great Britain 67
4 Russian Federation 56
5 Germany 42
6 France 42
7 Japan 41
8 Australia 29
9 Italy 28
10 Canada 22


This way of organising the data delighted many British sporting fans this summer, as it made it clear that Team GB finished ahead of every country apart from America and China. However, this is still a relatively one-dimensional representation there's not much to say apart from what you see: America won the most medals, China the second-most and so on. It tells us who walked away with the most medals but doesn't give us any hints as to why or, indeed, why poorly performing countries didn't win more.

If you introduce some additional information you can interpret this data in other, more nuanced, ways. As a minimum it would make sense to supplement the standard 2016 medals table with the piece of knowledge that athletes from the Russian Federation were prohibited from competing in many sports. You might also like to compare this year's table with medals tables from previous games, in which case you'd discover that the Russian Federation has ranked higher at the past five Olympics.

More radically, Medals per Capita takes into account the population of each country, to produce an alternative medals table ranked by 'population per medal'.


RANK
COUNTRY
NUMBER OF MEDALS
POPULATION PER MEDAL
1 Grenada 1 106,825
2 Bahamas 2 194,009
3 Jamaica 11 247,812
4 New Zealand 18 255,316
5 Denmark 15 378,400
6 Croatia 10 422,440
7 Slovenia 4 515,942
8 Georgia 7 525,571
9 Azerbaijan 18 536,186
10 Hungary 15 656,312


In this table, Grenada (which only won one actual medal) comes top, with a figure of 1 medal for every 106,000 or so people. Team GB comes 19th, winning 1 medal for every 972 000 people, while America and China appear much lower down (at 42nd and 76th place respectively).

This alternative medals table has transformed the raw data (the straightforward figures) into knowledge (something that helps us look at the raw figures from a new perspective). Of course British sports fans will still be happy, since Team GB actually beats America and China in this new arrangement. However, the people responsible for investing in British sport might start to ask questions about why we don't appear higher in this ranking.

Effectively, this alternative table helps answer the question we posed earlier about why some nations don't perform well it's a matter of how many people live in the country. China and America are two of the most populous countries in the world; they also consistently appear in the highest medal-winning positions at the Olympics, at least in recent years.

If you wanted to add even more value to the medals figures, you could do some additional research to source information about each country's economic situation or the amount of money it invests in sport. Without that, you can't draw any sensible conclusions about why Grenada comes top, or how Team GB could increase its position. If you want to explore this further, Medals per Capita also ranks medal wins by Gross Domestic Product. In this table Team GB drops to 36th. The Guardian has also published some interesting data about British investment in different sports and its impact on medals.

Aside from weighing up how much we should celebrate Team GB's achievements in Rio, what does all this tell us about sharing data in professional situations? I think there are three key points we can take away from how Medals per Capita adds value to raw data:

1. Think about how people will use your data: do people need to be able to look information up, or do they want to see the highest or lowest measures of something? Do they want to be able to make quick comparisons or do they need to see how something changes over time? The answers to these questions will help you decide whether to share data in a table or a chart and how best to arrange that data in these formats.
2. Think about 'behind-the-scenes' information: is there information (like the banning of Russian athletes from Rio 2016) that will help contextualise the data? This might be information about events that have taken place within or outside your organisation. It could also relate to how the data itself has been collected or to assumptions you have made while handling the data.
3. Think about information that can enhance understanding: is there other data or expert knowledge that you can use to help people understand the underlying situation? Would it help to compare your data with another dataset (such as figures from previous years, other regions or other organisations)? Would it help to bring in a different perspective (for instance, ordering product sales by variables such as price, launch date or production cost)? These new perspectives can often reveal issues and trends that remain hidden in the raw data, but tell us powerful things about what is really going on.

If you keep these three tips in mind when you're sharing data, you'll be staying at the top of your own game, but you'll also help clients and colleagues perform well too. Just like the Team GB medal-winners, doing well in business is a team sport where having information that provides deep understanding of situations is the best way to generate success.

Anna's course on Presenting Numerical Data is now available.
 
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