Article: How to align your HR Analytics strategy to business

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How to align your HR Analytics strategy to business

While analytics based interventions are set to take over across functions, it is important to be aware of the challenges and failures. Here are a few steps to ensure that it is aligned to your business.
How to align your HR Analytics strategy to business

HR Analytics has emerged as one of the hottest trends of 2016 in both business and technology. The current year is going to be even more eventful- realizing the vital role of HR data and its unexplored applications.  Companies are beginning to incorporate robust metrics systems by using digital technologies. These platforms measure and report valuable analytics on aspects such as time-to-hire, cost-to-hire, attrition rates, performance by department, leadership pipeline and training impact. However, research is also suggesting that many analytics based interventions are having challenges, and are failing. For instance, an article in Forbes predicted that half of all big data projects will fail to deliver against their expectations mostly because they lack the clear understanding of the key business objectives. 

Let’s look at some of the ways to align HR analytics with business to derive the maximum value:

Understanding of the business imperatives: It is critical to identify and understand the key ‘context’ in which the current business is operating. Attain a thorough understanding of organization’s core objectives and ascertain how HR analytics will help in achieving those objectives. Knowing what your business needs and where it is heading will give you insights and will help align analytics better with the business.  Always involve the leadership in this initiative, take their buy-in, consult them and keep them in the loop till the initiative concludes.

Understanding of the data: Once the key business imperatives are identified and understood, it is important to understand the nature and scope of data to be analyzed.  Having raw or unstructured data will yield nothing, one needs to understand the data and the pattern in it. Data literacy is about looking at data and being able to "ask the right questions and seek the right assistance to accomplish the goal." One should be able to answer these and many other questions around the data, like – what is the type of data, when was it updated, where it can be found, how to access it, is data private or can be used publically, is data complex, qualitative or easily quantifiable etc? 

Data literacy can yield many benefits, for instance, a Boston Consulting Group survey of 3,507 participants revealed that top-performing HR offices which focus on data literacy were better performers, were holding more strategic positions and were making better strategic and investment decisions. 

Interpreting Analytics: Once the data structure is identified, it is important to know which type of analysis should be carried out to derive maximum from the data set. Broadly the analytics can be categorized into three broad categories

  1. Descriptive: Simplest class of analytics that allows one to compress big data into smaller, more useful nuggets of information. The main purpose of descriptive analytics is to use data aggregation and data mining to provide insight into the past and summarize what has already happened. Descriptive analytics are useful when one wants to know what is going on in the organization and when one wants to learn from past behaviors to be able to influence future outcomes. Common examples include general reports, scorecard, sales, financials etc.
  2. Predictive: This category uses various modeling techniques to predict what might happen in future. It uses both historic or present data to make predictions. Simulations and dashboards are commonly used to determine which selections to make from a range of choices. For example: predicting person’s performance in a new role basis her past records and performance in simulation.
  3. Prescriptive: Prescriptive analytics allows one to “prescribe” a number of different possible actions and providing advice to reach a solution. This approach predicts both ‘what’ and ‘why’ it will happen and also provide recommendations.

One needs to select the most appropriate analytics considering the demand of the organization and nature of the data at hand. Furthermore, concerted efforts should be made to upskill organizational resources to become more analytics savvy.

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Design evidence-based interventions: The next step is to design interventions basis predictions or recommendations made by analytics. For instance, if an analytics hints at surging attrition in the coming months, organizations can focus more on designing and roll out  ‘employee engagement schemes’. Such evidence-based interventions apart from directly targeting the business imperative will also be better accepted by the employees. A recent study by Deloitte suggests that organizations which are using data-driven decisions are deriving better results as compared to other organizations not using analytics.

HR data can get tricky, mainly because of its vastness and unstructured form, however, its utility cannot be denied in the ambit of business. HR analytics brings enormous hopes to accurately address some of the critical people-related challenges, which have a direct bearing on the business. The only need is to align the data-analytics well with that of business so that more relevant and evidence-based decisions can be made. 

Topics: #TalentScience, HR Analytics

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