Big Data and what to do with it was a topic of mixed interest at the Emerging Technologies session at APA Congress 2019
. Only 2% of the 200 attendees highlighted Big Data as being a specific topic and opportunity in their payroll operations. Yet, subsequent offline conversations reflected a different story. Notable quotations included:
“We have a phenomenal growth in our data, but data protection and management is our current challenge. We need to address that first.”
“Big Data and how to leverage it for our business is a key strategic objective. We have payroll operations across 39 countries. Our first step is how to bring the data together. It’s on diverse legacy systems, in different formats and no one organisation unit or person knows where it all is.”
“We have been looking at the opportunity in Big Data Analytics for a few years now. We have mapped how to bring key datasets together. Our objective is to use the Big Data Analytics to help inform our strategic investment decisions.”
What is clear from these conversations with Global Payroll leaders at the APA Congress is that there is strong awareness among the Global Payroll community that the ever growing payroll data consists of key data that can be useful to the Global Payroll Leaders and the broader leadership team. However as payroll data is usually generated in-country and spans HR & Finance functions and related eco-system technologies, there is a large programme of work to be done to collate the data before the analytics work can start. With this insight into how Global Payroll leaders are thinking and how different organisations are at different stages of the Big Data Analytics journey, we are including Big Data in our Technology Blog Series. What is Big Data and why it is important? “Big data” is a relatively new term which describes the large volume of data that inundates a business on a day-to-day basis. However, it is not the amount of data that is important, but how organisations interpret it. Big data can be analysed for insights that lead to better decisions and strategic business moves. Big data is typically characterised by the four “V’s”: Volume: Organisations collect data from a variety of sources and new technologies make it easier to store it. Variety: Data comes in all types of formats and sources (both machines and people). Velocity: Data is being generated extremely fast even while we sleep. Veracity: As big data is sourced from many different places, you need to test the quality of the data.
Big data has changed our daily lives significantly and affects organisations across practically every industry. It’s something that plays a very important role in your business. By using big data you can effectively present and analyse all issues and achieve your organisation’s goals. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
- Optimise business decisions through data driven insights.
- Better understand your customers’ needs and further your business growth.
- Determining the causes of failures and issues.
- Detecting fraudulent behavior before it affects your organisation.
- Make money by selling insights from Big Data.
What is Big Data Analytics and why it matters? By definition, analytics is the discovery and communication of meaningful patterns in data. For businesses, analytics helps them to optimise key processes, functions and roles. Big data analytics examines large amounts of data to uncover hidden patterns, correlations and insights. With the evolution of technology, it is now possible to analyse your data and get answers from it almost immediately. Speed and efficiency are the main benefits which big data analytics brings. Organisations can use analytics to improve the efficiency and effectiveness of their decisions and actions. Big data analytics also helps organisations harness latent opportunity in their data and use it to identify new opportunities. IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data and summarised the information in his report “Big Data in Big Companies”. He found that companies got value in the following ways:
- Big data technologies can bring cost reduction when storing large amounts of data.
- The ability of analysing information immediately results in better decision making.
- Big data analytics allows companies to better understand their customers’ needs and give them what they want by creating new products.
Real life examples that we are familiar with as consumers would be the way in which Netflix
can suggest movies and documentaries that you would like to watch. Netflix
stores Big Data about its subscribers choices and builds this data into its algorithms. Netflix
uses the analysis of such data to profile its customers and make personalised suggestions. We see how Netflix offers specific cartoons for different aged children, movies in line with the tastes already “communicated” through the subscriber’s clicks and choices. The Netflix algorithm is designed to respond to a user’s most watched film genres and actors.
Another example is Spotify
, an on-demand music providing platform. Spotify
uses Big Data Analytics based on data collected from all its users around the globe, and then uses the analysed data to give informed music recommendations and suggestions to every individual user.
offers, videos, music, and Kindle books in a one-stop shop is also big on using big data.
More and more organisations, both big and small, are leveraging the benefits provided by big data applications. The healthcare industry is another massive industry that uses big data effectively, an example is wearable devices and sensors that can provide real-time feed to the electronic health record of a patient. One such technology is from Apple
has come up with Apple HealthKit
, and ResearchKit
. The main goal is to empower the iPhone users to store and access their real-time health records on their phones.
Pros and Cons of Big Data The more data collated; the more data needs to be protected. For ease of use, we won’t address data protection in this blog post … more on that another time … Enterprises report multiple advantages of big data like better decision-making, reducing costs and increased productivity. Another common use for big data analytics is fraud detection. Big data analytics systems are excellent at detecting patterns and anomalies and this can be particularly useful in the financial services sector, financial processes, including payroll and payments When organisations use big data to improve their decision-making and improve their customer service, increased revenue is often the natural result. Other benefits for companies are the greater innovation and the ability to increase business/ IT agility. Companies are also using big data to achieve faster time-to-market. This advantage of big data is also likely to result in additional benefits, such as faster growth and higher revenue. While there is no doubt that the big data revolution has created substantial benefits for businesses, there are also risks that go along with using big data. While implementing big data analytics initiatives, many companies have reported significant challenges like compliance, data quality and rapid change of data. Hiring or training data scientists and big data experts can also increase costs considerably, and the process of acquiring big data skills can take considerable time. On another hand, storing sensitive data, can make companies a more attractive target for cyberattackers. Protecting all the data you’re collecting sometimes can be hard to achieve. Despite the risks that big data can bring, it is here to stay so companies should be aware of the potential risks in order to embrace it.
The role of Big Data Analytics in Payroll Payroll is a significant cost for multinational organisations and even small persistent errors in global payroll can end as big problems. Big data provides the ability to easily see where and how often errors are occurring, and to track trends over time. This enables organisations identify areas for improvement and generate significant savings. Analysing payroll performance over time can provide accurate yearly forecasts, which can help manage budgets and cash flow in times of change or growth. The right analysis of payroll data can inform future strategies and improve hiring practices. Payroll data often shows the correlation between compensation and performance which gives you an insight on how to attract and retain the employees you want.
Getting consistent, high-quality data is key and it is still a challenge to many multinational organisations. A good global payroll solution will offer a real-time data collection, so you can get a visibility over the entire global payroll organisation by country. Payslip gives you accessibility to payroll information, increased efficiencies and reduced risk within the global payroll operations. Contact us today to learn more about how Payslip can help your company maximize the benefits of big data analytics in payroll.
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