Big Data (How MIS correaltes with HRM)
I wanted to show a correlation between Human Resource Management and MIS. This is my essay for my Human Resource Management class.
David Ahn
Dr. Avery
Intro to HRM
HRM in the news
Big Data
The current event I will be discussing today comes from a magazine that I acquired from the SHRM meetings. The magazine itself is called HR Magazine and it’s the October issue from 2013. I feel as though many other students just looked up information corresponding to this course from the internet, so I wanted to utilize the information from the benefits of joining SHRM. The topic I will be elaborating today will be about big data and the benefits of this relating to Human Resources.
How can HR use big data? Some examples that illustrates it’s potential includes Defense Acquisition University, which trains thousands of military and civilian professionals in defense acquisition, logistics and technology, analyzes internal and external data to determine the least expensive places to conduct classroom training, based on total cost. Variables include room cost, instructor salary and travel, expected attendance and travel cost for students. Another example is Silicon Valley based Juniper Networks, which develops network infrastructure products, uses LinkedIn to track and analyze the skills, knowledge, experiences and career paths of employees, former employees and potential employees. Big data represents a leap forward, however most HR organizations are still in the early stages of applying metrics. That’s why my advisor recommended me to take a metrics course.
If anything the term “big data” understates the tsunami of data available from transactional systems, relational databases and business applications that are available from a growing number of outside sources, including emails, social media and more. The amount of data businesses have increased because most information is now digitized and comes from new sources. Besides sheer volume, the variety of the information and the velocity with which it becomes available, also fuel big data. The variety includes data sources internal to HR and the enterprise as well as external sources, such as salary studies, industry benchmarks and work force demographics. Variety also refers to structured and unstructured data, each of which places has different demands on technology and users. Structured data is the standard of relational databases, including HR information systems (HRIS), accounting systems and enterprise resource planning systems. Unstructured data covers an array of free form information, including narrative answers on engagement surveys, social media posts, blogs, wikis, emails, and images including videos; all of which require new technologies for organization and analysis. Velocity refers to the increase in streaming data arriving in real time and to the speed at which data must be evaluated and made actionable for business value.
Of those three dimensions of big data, variety presents the biggest challenge in data integration. HR has two internal sources, which is data it owns and data it collects from other enterprise systems. HR data comes from systems such as payroll and HRIS. While HRIS and payroll systems have seen an increase in data volume, this by itself is not big data. Sometimes an HRIS is a module in an enterprise application suite, which can make accessing data from other modules relatively easy. Often an HRIS stands alone, not linked to other systems in or outside of HR. Point solutions have proliferated for talent management, recruiting, performance management, learning management and other functions, adding to the challenge of accessing and managing this enormous data. HR often needs a data mart to tie its data together before subjecting it to analytics. A data mart is a software layer on top of independent databases that gives users access to some or all of the data in each layer.
When peeling back the layers, analysis that combines HR data and financial data can provide human capital insights that lead to decisions and programs with business impact. Sales database and customer relationships management systems are fertile repositories for information; that can be used in advanced workforce analysis. For years, HR implemented unstructured narrative answers from engagement surveys and performance reviews for human capital insights. In the era of big data, the internal and external sources of unstructured data are multiplying to include social media, blogs, wikis, emails and more. These sources are likely to offer many actionable insights about employee engagement. For example Starbucks gained insight about employee motivation from a survey with many open ended questions the company used, in an employee segmentation study. It hired graduate students to assist with a manual content analysis of the massive amount of information. Tools to automate this type of process are still evolving.
A few HR organizations already mine social networking sites for data. For example Juniper uses LinkedIn. In addition to housing resumes, the site puts a lot of research and development into analytics. LinkedIn has become a superior solution because most professionals use it to as the go to place, to post and update their career profiles. Juniper is also among a group of corporate early adopters and power users that collaborate with LinkedIn, to identify emerging talent practices and innovative ways service might evolve. Social networking sites such as Facebook also hold promise.
There is a power of predictability relating to HR. According to this article, the crown jewel of HR’s big data is data based models that predict the probability of an outcome. For example, job candidates who are likely to succeed. Predictive models can be fine-tuned over time to become more accurate. Most HR organizations are not currently using predictive modeling. They’re still writing the earlier chapters of their own data driven stories. The stories I love to hear are from professor’s personal experiences. I would actually love to see your LinkedIn profile Dr. Avery, because you have copious amounts of knowledge and expertise. Also all of the universities and colleges you were involved in; carries so much weight, so I can completely understand why you’re tenured. As we progress into the future, data will exponentially grow and we’re in the process of managing big data more efficiently.