Cutthroat competition in an effort to get ahead in the game of business and capitalism requires effective collection and application of data in its vast multitudinous varieties. In addition, the data collected requires to be coherent and relevant. This shall give comprehensive details and diagnosis of problematic shortcomings in the workings of the company. It also needs to provide prognostic and predictive inferences from the data analytics to be able to successfully afford the company the opportunity of data-driven decisions and talent analytics. This will ensure skill-appropriate hiring, job allocation, management, and retention while simultaneously maintaining an upkeep of the employee morale and satisfaction – subsequently increasing the efficiency of business processes. Such an effort can only be possible if the company is aware of where they stand on the data maturity curve, and then take it from there, growing and nurturing their capabilities to mature from mere identification of problems to a prescription of practical and effective solutions and actionable insights – cue data analytics.
To begin an integrated system of data analytics, the first step is the data procurement journey. Data, often stored and found scattered in myriad disputed and diverse sources, need to be collated to form a compendious and insightful bunch stored in one single system. With the determiners of data requirements and its grouping in mind – whether it be age, demographics, income, or gender – the collected data can then be processed to filter out the unnecessary, inaccurate and incomplete. Depending on how far up a given company is on the data maturity scale, the process of data analytics will vary for each – even being as basic as upgrading to automation from pen and pencil record-making system.
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Further, in this bandwagon of data analytics is the effort to decentralize the data, widen the demographic of people beyond HR with access to it. Technological literacy and access to it should no more be a benchmark, but rather a norm in companies and workplaces on the regular. Widened access range of data not only serves to democratize it, eliminating the disconnect that ensues with rigidity in its access but also reduces unnecessary dependency on HR for employee data.
On the flip side though, excessive flexibility in data access too needs to be curbed to prevent setbacks especially in concern with data privacy and possible leakages – making it critical to put in place regulated permission control such that people only have access to data they are concerned with as opposed to an all invited, unrestricted or unregulated access.
The collated data after being concentrated into a single storage system has to then be put through a strainer to procure its relevance with and amongst the multitudinous variety of data that has been sourced and see how that insight can be applied to increase the efficiency of the company’s process. It is important to visualize and process the heterogeneous data, with the help of data analytics tools, to make insightful deductions out of them often presented in the forms of meaningful charts or graphs.
Next up in data analytics, taking a company further up the data maturity scale, is moving from descriptive and diagnostic analysis – wherein lies only problem identification and hypothesis – to the more salient – predictive and prescriptive analysis – that will, based on the collated data, predict events in the future of the company and prescribe courses of action remedial or otherwise. For example, predictions on employees likely to retire, resign or even most likely to succeed and be promoted – alerting the company and spurring them into planning ahead and setting in motion actions working towards retention and division of responsibility much in advance.
Importance of Data Analytics in decision making is recognized for its worth far and wide in the Indian economy as well, with the rate of data analytics adoption having increased especially remarkably in the past 6 years. Several Indian companies have been using data analytics to level the playing field in their respective industries for some time now.
Insurance companies too leverage analytics to estimate risk. The auto insurance sector of India, owing to the special allowance by the IRDA, the Insurance Regulatory and Development Authority of India, now have the flexibility to tweak their rates and, using data analytics-driven insights, propose differential pricing based on risk of the automobile as well giving them the opportunity to identify and target their most profitable segments.
Credit card companies and banks in India too use data analytics to gauge profitability and advantageous segments as well as identify and predict risky customer profiles, enabling them to offer interest rates accordingly. Also owing to the CIBIL (Credit Information Bureau [India] Limited) formed by the government that collates, aggregates and makes accessible consumer credit information from all major financial institutions in the country ensuring access to financial service companies to more comprehensive data and in turn more scope for analytics. With booming retail chains all over the country, retailers are looking to data analytics to stay ahead in the market, analyzing point of sales data to optimize promotion, store planning and increase their effectiveness in marketing.
The profusion of data at the disposal of the service providers in the telecom Industries, almost every major Indian Telecom company has dedicated a department in their organization solely to data analytics focused on minimizing employee attrition, increasing customers and customer profitability and lowering costs of acquisition.
References:
- Navigating through the analytics journey. People Matters
- Indian Companies Using Analytics. Gaurav Vohra
- Data Analytics. Jake Frankenfield
