Crying out for recent and relevant data
“FIRST we must admit we have a problem. And work together for better, long-term and Fiji-specific solutions.” – Fantasha Lockington, CEO, Fiji Hotel and Tourism Association.
In her Thursday article on Tourism Talanoa, Ms Lockington made the assertion that the information tourism and other industries have to formulate policies, address labour shortages, and steer the growth of the economy is inadequate and outof-date. Having met with and spoken with several private and public sector executives since January, this column can only support her views. At the same time, I appreciate the opportunity given to have had those conversations since the new government came into office.
Data latency a major issue
But the points made in Ms Lockington’s article should be of concern and taken very seriously as she points out a relatively easy-to-fix issue of “data latency”, the out-of-date, irrelevant nature of data and derived information that we often work with. And that leads to simply ignoring whatever data there may be available.
Referring to the loss of skills and unfilled vacancies, she spoke of “notable vacancies in our job market particularly tourism and construction, while we’ll wager the rapidly emerging BPO sector will hit a brick wall in its expansion efforts, if it has not already”.
“While the tourism industry has been hit hard, we are wondering where Fiji’s unemployed citizens are. There are gaps in the market with the departure of workers overseas… unless of course our data on unemployment is outdated.”
Making decisions with 2019 data
“Stakeholders across industries have emphasised the need for up-to-date information to formulate policies, address labour shortages and steer the growth of the nation’s economy. The most recent (data) available from the Fiji Bureau of Statistics dates back to 2019,” Ms Lockington wrote.
“The dataset, derived from the Annual Employment Survey 2019, provides insights into the employment landscapes as of June 2019 shedding light on the distribution of wage earners and salary earners. The lack of current data presents a significant challenge in understanding and addressing the dynamics of Fiji’s labour market.
Reports not aligned with reality
“Why does the reported data not align with the actual experiences on the ground reality? With the gap between what’s reported and reality, we question the effectiveness of government surveys and data collection methods in accurately capturing the employment statistics rather than the standard response to employers “we’re looking into it”.
In saying that, Ms Lockington has been quite generous.
Data-based insight is the immediate, accurate, deep and relevant understanding without having to subject yourself to expensive and fruitless trial-and-error behaviour.
To do meaningful analysis to support and address her and probably many executives’ frustrations, we need access to good data. I’ve found, through direct feedback from conducting data literacy short-courses that we should first look at what data really is and how data can be used to inform for better decisions and strategising the medium term. Too many have the wrong interpretation of data use for analysis.
What is data in the context of analytics?
Data is a collection of facts. Collected at the source where it is generated. For instance, supermarket point-of-sale (POS) data is generated when customers present their purchases to the cashier for scanning and subsequent payment by cash, a debit or credit card, or any other form of payment. The POS may record date and time of the transaction, generate a transaction number, record the cashier’s identity, all the items purchased, price of item at that particular time, whether the item was on sale at a promotional price, VAT paid or not paid on each item, how the customer paid by payment type, which store the purchase was made at and other such detail.
Similarly, detailed data is generated in the telco industry recording every call you made, from which phone and sim card to which destination phone, how your call was connected via which towers, your location, and which towers and location you travelled through and past and details too much to go into here. The same for banking, very single transaction. Airlines, every single booking, cancellation, airfare paid and so on. Government agencies and departments, EFL, FRCS, FNPF, licensing, Judiciary, business and personal registrations, agriculture and pastoral, dairy, education records from primary through university, employment records from the very first job to date, to unemployment, housing, postal detail, money transfers in and out, remittance data, and of course transfers in and out of electronic money wallets.
We may not have easy access to all of that but be assured the data is available. So, I find it difficult to subscribe to the refrain that “we don’t have the data.”
Qualitative vs quantitative data
Qualitative data is generally used to classify and categorise things. Such as small, medium, large, shirts and other clothing. Qualitative data can classify people by personal characteristics such as different hair colours. But for today we’ll not be covering this in detail, until we’re ready to discuss AI and its applications. Today we’re talking about quantitative data.
Quantitative data is generally of two types. Discrete data and continuous data. Discrete data is numerical with a count and uses whole numbers such as when describing the number of students in a class, or number of children you have. You can’t have 1.5 children or 1.2 dogs at home.
Continuous data can take on any value in a certain range such as a person’s height being 173cm or environmental or body temperature.
Basics of data
This may be a trip back to distant memory to your days in primary or early high school years. It may be a refresher back to your high school or early university days of basic mathematics and statistics 101, but given the issues described in Ms Lockington’s article, it is worth a reminder that conceptually this is not beyond understanding.
Data is used to tell a story based on actual measures and can answer questions relative to the area of focus based on its accessibility. Data and its analysis is the evidence you need to back your story. Data makes it easier to find patterns and trends to support your position on issues, challenges and opportunities. It can get a little tricky sometimes such as when talking about books. You can describe and categorise a book which is a qualitative aspect, or you can speak quantitatively about the number of pages, and the wordcount in the same book, and you can break that word-count down by category where each chapter is considered a category.
Aside: One thing I hasten to add is that data is plural. The singular is “datum” meaning one piece of information or one numerical result. But common usage is “data” treating the word in both the singular and plural context.
Statistics is not equal to data
Unlike the word “data”, we must insist on differentiating between statistics and data. One of the biggest misconceptions in analytics and analysis is that data and statistics are the same thing. Statistics are the result of data analysis and the interpretation of data. The two should never be used interchangeably.
In the retail, telco, banking, airline and other detail data scenarios described above, the data is often referred to as “raw” data – data that has not yet been analysed or subjected to analytics.
When doing statistical analysis, there are certain rules and methodologies for collecting the data, exploring and presenting large amounts of detail data to help analyse underlying patterns and trends. There are two basic types of statistics. Descriptive and Inferential. Descriptive summarises the data using indexing methods such as “mean” standard deviation, regression, hypothesis testing, and sample size determination. Inferential draws conclusions from data using statistical testing.
And there is “business intelligence” (BI) a favourite of vendors who sell BI tools for visualising dashboards and data.
But the critical thing is the quality and detailed nature of the raw data used for statistical analysis and business intelligence. The idiom “Garbage In Garbage Out” is no less relevant today than it was several decades ago. Probably even more so with the rate at which data is coming at us.
Reporting is not analytics
To be clear, reporting is the process, a way of gathering, structuring, and presenting data in formats such as graphs and tables, usually aligned to predefined KPIs and measures to help understand past performances. That is not a bad place to start as long as the data is recent and relevant. Data presented from 2019, over four years ago with the pandemic in the middle of it all, I daresay does not meet even the basic reporting criteria.
And what about analytics? It analyses data that is recent, relevant to identify patterns and gain insights. To be able to take action on the insights. You’d be hard pressed to imagine a scenario where data that is more than four years old can be considered insightful, let alone actionable, or even useful for planning.
Fiji Bureau of Statistics
The Fiji Bureau of Stats (FBOS) doesn’t seem to have data, at least not made available on their website for usable insight, in many cases, action. But this is also the case on the publicly accessible web pages of the World Bank, ADB and other institutions with the most recent reports on Fiji going back to 2021. We can only hope there are services available to accommodate specific requests for information based on recent and relevant raw data. And requesters are not subjected to the standard “we’re working on it” response. Are there services and data accessible by authorised users who can gain access to anonymised (de-identified data) data sourced from the various agencies?
If you’re interpreting this as an attempt to put the blame squarely on FBOS, you’d be wrong. It’s not the fault of the FBOS. Almost for two decades now, it seems FBOS hasn’t had the freedom to collect, curate, and culminate raw data to best practice. Let alone freedom, the appropriate level of funding likely has not been there.
NDP – Why FBOS should be in the Prime Minister’s Office
This entity, FBOS, should ideally come under the Prime Minister’s Office as it should be collecting and analysing data across whole-of government and its agencies to provide stats on the population, economy, environment, and social to advise the whole-of-government, the PM and Cabinet.
A recommendation to the National Development Plan (NDP) is that FBOS is relaunched and established as the centre of a single trusted and integrating authority for whole-of-nation data. They should be responsible for the collection and integration of data, they would provide access to authorised users of the data, they would productise and monetise data products for accessibility by the private sector including investors and researchers with the necessary anonymisation, protection and privacy to ensure data security.
Some of the agencies and private sector entities to be prioritised could include the Ministries of Tourism, Immigration, Employment, Education, Social Services, Health, FRCS, and Public Service.
■ NALEEN NAGESHWAR is a data and digital strategy consultant. A Fijian citizen based in Sydney, he runs his own consulting practice Data4Digital and is managing partner Australia, NZ, and Pacific for AlphaZetta Data Science and Analytics Consulting. For feedback, email: naleen@data4digital. com. The views are his and not of this newspaper.