Accounting and big data
In my view, even though some accountants may not agree, big data will effect how accounting is done. This is particularly true for management accounting.
I was going to write an outline of my thoughts on big data and management accounting, but I found this great post on diginomica. It gives some really good practical insights. It notes how the following, for example, gets accountants interested in big data- it is being used to:
- Improve the quality of budgets, plans and forecasts
- Enhance top line revenue
- Reduce operational costs
- Detect fraud
- Assess the viability of a company as an on-going concern
So what is big data?
Big data has been the feature of many articles in professional accounting journals such as CIMA’s Financial Management. But what exactly is big data? Originally it referred to more data than information systems could process. But today we have systems capable of processing and analysing millions of transactions in seconds . So what does it mean now? Well, I think the answer to this question will depend a lot on who you ask. To me big data is still data analytics, with maybe some external or social data sources thrown in., with a defined purpose of adding value or saving resources (such as cash or time). This is of course a very broad understanding of what big data is, as value will not mean the same thing to all organisations.
I read an article on Forbes recently which has a similar approach to big data as that I suggest above. The key point the author notes is not to care too much about defining things like big data, but to remember “who cares”. To quote directly from the article “the goal should be to solve a business problem by using new analytics, not to worry about defining a term. That’s because definitions are a distraction from the simple question of “Does this data contain information that is valuable for my business?”
A management accounting system – in a police car!
More and more, technology is used to help many of us do our job. I read an article a few months in the Wall Street Journal about how increasing integration of technology in NYPD police cars is helping officers fight crime. Of course, in-car systems are not confined to NYPD, and many European police forces use technology in patrol cars.
The article mentions a smart car, which is being trialed in one NYPD precinct. The car is equipped with number plate recognition, video cameras and even radiation detectors. All data collected is transmitted back to a central location, where it can be analysed at a high level if needed. The technology also allows officers make decisions while on patrol – for example, ignore a car with an outstanding parking ticket, but stop if if stolen.
So, where is the management accounting system is this police car? Ok, this is a trial, but it is likely to reflect what an actual patrol car will do in the near future. I define management accounting as the provision of information to make decisions. Using this simple definition, there are two ways we could describe the smart patrol car as part of a management accounting system 1) it provides officers with information to make decisions on the spot and 2) the information gathered may also be used to inform higher-level policy and strategic decisions.
Data analytics – the human input
English: Apple director Steve Jobs shows iPhone (Photo credit: Wikipedia)
Big data is a big thing in the management accounting practitioner world, and in the professional journals too. I have previously written some posts on what big data is (see for example, here and here) and I have noted that humans are still needed to interpret data. Here’s a great example, below. Before I start, just keep in mind what I always say to my students about technology – technology within computing devices is essentially dumb, it is nothing more that a series of 0 an 1 which do exactly what we program it to do.
This post from CSO outlines how good analytic is essential. It cites an example of an analysis of social media to predict trends in the US unemployment rate. The analysis used twitter feeds and other social media. It attempted to identify key words such as “jobs” and “unemployment”. A huge spike in the number of tweets appeared. Why? Steve Jobs had just died, so the word “jobs” was all over social media. As a human, we can easily distinguish the meanings of words, but an automated analysis or word collecting tool cannot. I believe management accountants have a key role to play in such sense-making of business big-data – after all we know the business quite well.
Big data and business decisions – humans still needed
Here is a good article from CGMA magazine which highlights the importance of human interpretation of data. It is a reminder that although we have technology to analyse data which we could not do ourselves, we still need the human eye to make sense of data trends etc. and relate it to organisational context.
Related articles
- Infographic: The Physical Size of Big Data (domo.com)
The problems with big data?
Big Data (Photo credit: Kevin Krejci)
The previous two posts have hopefully given you a very brief insight into what big data is and how it can help even small organisations. Now let’s briefly consider larger organisations. Nowadays, even if a company like amazon can process a few million orders a day, the amount of accounting data associated with this (i.e. a few million invoice and a few million payments) seems insignificant if we start to think about other data that might be collected at the same time. For example – and these are just a guess on my part – the age, sex, location of the purchaser, the type of device they searched and bought on, what the looked at before buying etc. The amount of data starts to get really, really big.
A report by Deloitte includes two quotes that capture the perceptions of big data really well:
“Big data is the new oil. The companies, governments, and organizations that are able to mine this resource will have an enormous advantage over those that don’t.”
“Big data will generate misinformation and will be manipulated by people or institutions to display the findings they want.”
(Source: The insight economy Big data matters— except when it doesn’t, Deloitte, 2012, available at link above)
As the report says, both the above perceptions are right. The key things about big data is getting information out of it and transforming that information into business knowledge. In other words, like many other things organisations encounter on a regular basis, big data needs to support the organisation’s strategy. This may mean being more competitive, gaining some market knowledge before others or opening up new business channels. Whatever big data might mean for larger (and smaller) organisations, I believe management accountants in particular have a key role in making in useful information/knowledge – after all, we are good at analysing information and filtering out what is relevant.
Related articles
- The future of big data (infographic) (siliconrepublic.com)
Big data and (small) accounting software
Last week I wrote about big data in general. Now I will try to give an example of how accounting software used in small business can be a source of big data, which can ultimately help those same businesses.
Quickbooks is a common accounting software product used in many smaller and medium-sized businesses. Traditionally, Quickbooks was installed on a computer in the organisation, but nowadays it is also available as an online product. In other words, there is a cloud version. According to an article in Forbes in April 2012, as much as 35 million of Intuit (the owners of Quickbooks) customers use online software for accounting and tax returns. With anonymous data on 35 million small businesses, Intuit can obtain quite a lot of information for their own purposes in terms of capturing user needs and developing their products. But they are also using this information to assist their customers. One great example cited in the Forbes article is a Trends feature. With this feature, a business owner can compare their business to average performance trends in the same sector, and even with similarly sized businesses. A comparison of sales, operating margins and payroll cost is possible. This kind of information would be really useful for any small business and typically such a business would have neither the time or resources to obtain such data.
What is big data?
Big Data: water wordscape (Photo credit: Marius B)
This post gives you a brief introduction to “big data”, a term used in many circles and in many businesses. The following posts will then give some examples from real business to help you understand the effects this might have on accounting and accountants.
Although the term big data has become mainstream in recent years, it has been used for a decade or more by scientists to simply describe very large amounts of data. Diebold (2003) defines big data as follows:
Big data refers to the explosion of quantity (and sometimes, quality) of available and potentially relevant data, largely the result of recent and unprecedented advances in data recording and storage technology.
It is hard to believe that this definition although only a decade or so old, bears little resemblance to what can be achieved today in terms of data collection. Devices such as smartphones and tablets in a cloud-computing environment allow users to use cloud-based services (such as software or social networks) and, in turn, data can be collected through these devices and stored elsewhere in the cloud. The result is vast potentially vast amount of data, which can be analysed for many purposes, including business decisions. Facebook has about a billion users, there are about 500 million tweets per day sent on Twitter and Google handles about 3 billion search queries per day. These vast uses of each of the mentioned websites/network generates hitherto unknown amounts of data, some of which may be useful, some of which may not. In an article for Forbes, Feinleib notes three issues with big data, which give a good insight into what it is, and the problems facing business:
1) big-data is ill-defined.. We are not sure what exactly big data is, but a Jevons Paradox seems to exist in the world of big data. As technology evolved to allow the storage and analysis of large volumes of data, more data is being stored and analysed by organisations.
2) big data is intimidating. He asks “how do we make big data approachable” from perspectives such as having tools to analyse data, to getting the right insights and information from the data.
3) big data is immediate. Huge volumes of data are generated, but the analytical value of this data can decay rapidly. For example, in the near future companies like Google and Groupon may display adverts on mobile devices for businesses in the immediate proximity of a consumer – the time to analyse and act on this data could be a matter of minutes, or even seconds.
References:
Diebold, F. 2003, “ ‘Big Data’ Dynamic Factor Models for Macroeconomic Measurement and Forecasting” (Discussion of Reichlin and Watson papers), in M. Dewatripont, L.P. Hansen and S.Turnovsky (eds.), Advances in Economics and Econometrics, Eighth World Congress of the Econometric Society. Cambridge: Cambridge University Press, 115-122.
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