Establishing an Analytics Function

Introduction

Analytics, in the words of Pease, Byerly and Fitz-enz (2013), could be defined as the science of analysis (9). This paper will focus on the creation of a HR analytics function for Apple Inc. On this front, a systematic process will be followed so as to ensure that the said function is not only efficient, but also effective. The systematic process has been embraced owing to the fact that as Waters, Streets, McFarlane, and Johnson-Murray (2018) point out, although there exists no holy grail in as far as the establishment of a HR analytics function is concerned, there is need to embrace various best practices formulated with an aim of ensuring that resources and efforts in this particular case are focused. It would also be prudent to note that the present undertaking will be informed by the fact that the analytics function will likely be impacted by the various goals and attributes of the company.

Step 1: Assessment of the Climate

I am convinced that there is need for the organization to have in place a clear methodology that informs the development of human resource insights that are data driven. One way of achieving this is through the creation of a HR analytics function which will come in handy in efforts to develop Apple Inc.s HR analytics capabilities. This is especially important if we are to ensure that going forward, better decisions are made in relation to the promotion of positive employee experience, enhancement of various work processes, etc.

The creation of a HR function is a process that would ideally require the input and/or insight of various leaders across the organization (Pease, Byerly and Fitz-enz , 2013). Those to be actively involved in the process are top-level managers and the executive team. They comprise of the Chief executive Officer (CEO), Chief Finance Officer (CFO), Chief Operating Officer (COO), VP Human Resources, and VP Product Development. Senior departmental managers in finance, human resource, IT and product development will also be roped in. The top-level managers highlighted in this case will be instrumental in resource allocation efforts and ensuring that the present efforts are aligned with the long-term strategic goals of the company. They will also offer crucial insight in as far as the management of some key stakeholders (i.e. BOD) is concerned. The insight of various senior departmental managers will also be required in the assessment of diverse perspectives at the division level. This is more so the case given that they have intricate details of the day-to-day operations or workings of the organization and could, thus, have valuable insight about the effectiveness of diverse processes and systems of relevance to the successful development of the organizations HR analytics capabilities.

Ultimately, stakeholders of the present undertaking will be inclusive of the senior-level executives who will benefit from enhanced ability to make better data-driven decisions and analyze concerns of relevance to the workforce in more superior formats. The Board of Directors also has an interest in the success of the function as well as its deliverables because improved workforce processes and better HR decisions are likely to have a positive impact on the bottom-line. This is more so the case given that the board seeks to represent and advance the interests of shareholders.

1. My understanding of the vision for the companys analytics function and its purpose is completely clear

I have a pretty good understanding of the companys deficiencies in as far as the collection and application of data to guide various HR functions and aspects is concerned. I am, thus, aware of the need to further enhance the companys HR analytics capabilities.

2. My understanding of whom I should turn to for direction and input while developing and maintaining this function is completely clear

I have already recognized organizational leaders who will be instrumental in efforts to guide this function and ensure the availability of the relevant resources. I have also identified...…and Johnson-Murray (2018) indicate, there is need to ensure that all stakeholders are familiar with the HR analytics function and its relevance. For this reason, there is need to actively sensitize those who matter on HR analytics and continue reinforcing this particular message going forward. Towards this end, formal reports will be distributed to the relevant stakeholders on a weekly basis. The goal of the said report is twofold: to educate and to inform.

To begin with, with regard to educating, there will be need to ensure that stakeholders are aware of the various HR analytics data sources and metrics. Of relevance on this front could be recruiting data, employee demographic data, skill shortage data, employee engagement data, turnover data, revenue per employee, etc. There will also be need to see to it that stakeholders are sensitized on the relevance of developing the organizations HR analytics capabilities. For instance, as Waters, Streets, McFarlane, and Johnson-Murray (2018) observe, patterns in data can be better understood using HR analytics. Further, there may also be need to highlight the tasks and responsibilities of stakeholders if any. On the other hand, when it comes to informing, there will be need to ensure that stakeholders are aware of the progress made so far.

To see to it that end-users benefit from the function, there will be need to ensure that they gain access to any needed or required training. Constant updates on progress would also come in handy in this regard. Further, the relevance of collaborative efforts cannot be overstated on this front.

Step 7: Creation of the Organizations Roadmap

Step 1: Stakeholder requirement assessment

Roadblock 1: Lack of sufficient information to paint an accurate picture of stakeholder needs

Step 2: Clarification of the overall HR analytics as well as research

Roadblock 2: Erroneous classification of research and analytics needs, i.e. as tactical instead of strategic

Step 3: Data source identification

Roadblock 3: Unstructured data sources

Step 4: Data gathering

Roadblock 4: Inefficient/inappropriate data collection methods

Step 5: Data transformation

Roadblock 5: Lack of…