We live in a data-driven world. The ever more networked nature of humanity has created a data deluge: a wave of information accumulation that has changed a great many fields. Applications are increasingly reliant upon the collection and analysis of data sets in the provision of services and the development of strategies by the companies behind said apps. Here are some application types that use data as a core resource.
Data Collection Software
The most ‘bare bones’ use of data in software involves the simple collection of the stuff. Data is inherently useful and businesses, governments and academic researchers have always been interested in collecting more qualitative and quantitative information to feed the accuracy of their projects and plans. Before the late 1980s, this involved the use of physical files or manual data entry. If, for instance, a government organization wanted to conduct a small-scale census, then it would have to employ workers to collect paper answers or conduct telephone interviews. These methods are far from perfect. There is a high risk of transcription errors and other informational inaccuracies when data has to pass through a human conduit.
The advent of the World Wide Web heralded a paradigm shift when it came to data collection. Software applications and web-based forms could be developed and linked so that data could be inputted into an easily accessible and analyzable database. The first data collection application software to see widespread adoption was Microsoft Access. Today, data collection is usually integrated into applications from the ground up, so the need for a dedicated data collection application has decreased in most sectors. The shadow of these applications still looms large, however.
Human resources is an increasingly technology-driven field. HR professionals are using data collection and analysis to augment their ultimately human-centric tasks. A fusion of technology-aided human-centred HR strategy is the ultimate goal of many HR departments. As businesses struggle to employ and retain the best talent in a marketplace that no longer exists in a ‘lifelong job’ cultural paradigm, they are turning to applications that make use of data.
Enter Human Capital Management software – or HCM for short. Human Capital Management software differs from traditional Human Resource Management Software in several key ways. One of the ways it differs is in its use of data. HCM software collects and uses data provided by employees to increase productivity, efficiency and engagement. According to Gallup, 85 percent of the workforce do not currently feel engaged in the workplace. This is a huge issue – and one the HCM software attempts to use data to solve. By offering plenty of methods for employers to engage with employees, HCM software creates a more constant sense of understanding between the two parties. Simultaneously, this engagement produces large quantities of useful data – employee responses to engagement. This reciprocal cycle of engagement and data collection should, in theory, mean that HCM software becomes exponentially more useful the longer it is used. Check out this list of the top ten Human Capital Management applications for more information, including an infographic clearly illustrating the difference between traditional Human Resources Management software and HCM applications. The world of work is changing rapidly and data is driving the change.
Big Data Analytics
Unless you have been living under a rock for the last few years, the chances are high that you have come across the term ‘big data’. Big data is an umbrella term used to describe any dataset that is so large and so varied that it can only by analyzed using computers. We live in an ever more networked world thanks to our increasingly online lives and constantly produce data for whoever can make use of it.
The use of big data is significant in just about every field of business and governance. Big data analytics software is a necessity for organizations that want to make use of the absolutely vast quantities of data that are available. Applications like Lumify, which is free and open-source, use complex algorithms to analyze and visualize data that would otherwise take a huge amount of effort and time to sift through and codify. These tools help organizations codify and understand patterns, anomalies and significant data events.
Social media applications like TikTok and Instagram are data-hungry and data-driven. TikTok is an especially data reliant application. The popular short-form video service collects a bewilderingly broad range of data from users – including biometric data. This data is used to provide an intelligently tailored service, but also to provide datasets that the company can use to secure market dominance and lure in advertisers. There is some controversy surrounding TikTok’s data collection model. The company has close ties to the government of the People’s Republic of China, which is known to use data to aggressively monitor and control elements of its population. The use of data to control populations is nothing new. British colonial occupiers, for instance, frequently used censuses to control the movements of people in occupied territories. The use of an app to potentially further population understanding and control is, however, a new development.
Facebook’s use of data has also come under fire. The leaking of personal data from the social media behemoth has been widely condemned, and the leadership of the company has been called to give evidence about data security to governmental investigations on multiple occasions. The pervasive collection and sometimes irresponsible use of data by social media companies is, unfortunately, here to stay.
The success and functionality of modern entertainment applications is heavily reliant upon user-generated data. Many consumers now take personalized services from applications such as Netflix and Spotify for granted, but these services are only made possible by the complex and constant collection and algorithmic analysis of data.
Take Spotify, for example. The music streaming giant – which has around 365 million users -has completely changed the way audio consumption works. This is at least partially due to the software’s clever use of consumer data. Machine learning algorithms collect data about what music users search for, where they listen to it, when they listen to it and how long they listen to it for. This is then used to provide personalized content. The more a user engages with the Spotify application, the more the application will engage with them by providing more honed personalized content. The ‘discover weekly’ playlist that is displayed to each user is the most ‘out in the open example of this reactive data driven personalization. User data also drives up Spotify’s revenue by enabling it to deliver very accurate personalized advertisements. The demonstrable accuracy of this personalization means that advertisers are willing to pay more to get their messages across. Spotify has a dedicated workflow manager called Luigi that manages data collection and use.
Netflix uses data in much the same way, but for visual content. Personalized entertainment content helps many consumers find what they want, but it does have a few downsides from a customer perspective. Ultimately, personalization creates a prescriptive entertainment environment, where branching out and discovering truly new things that have little to do with preexisting taste or habit can become rather hard.