Meaningful information has become critical for companies that are jostling for relevancy and competitiveness in the Digital Age. The ability to synthesize huge influx of data to facilitate astute insights and sagacious decision-making has led to a mushrooming of new functions that have evolved from largely-marginalized roles in earlier times, e.g., Statisticians have shed their backroom persona of a solemn data-cruncher to become effervescent Data Scientists as the front-end face of innovation-driven progressiveness. Terms like ‘Big Data’, ‘Machine Learning’, ‘Deep Learning’, ‘Neural Networks’, etc., have permeated the corporate landscape to proactively overcome any foreseen/unforeseen challenges that an organization is liable to face in the race to excellence.
However, the rediscovered infatuation with data has a foreboding cost in terms of the ‘human factor’ that is being systematically buffered by technology. One of the exacerbating factors in promoting divergent employee relations within hypercompetitive organizations has been the pervasive use of ‘efficiency-focused’ metrics in analyzing the fulfillment and efficacy of desired performance parameters. Consequently, executive decisions are increasingly being subservient to the dazzling displays of an extensive range of ‘panacea’ software, e.g., through HR Dashboards peppered with KRAs/KPIs, without empathetically heeding the simmering ‘Humanistic Concerns’ that result in parched expectations as a consequence of deteriorating psychological contracts. Prominent Ecommerce titans are also coming under close scrutiny in the respective context, e.g., Amazon for its work practices (https://lnkd.in/f9kb2cp).
Additionally, the undulating economic environment with the increasing encroachment of entities empowered with Artificial Intelligence (AI) has resulted in a large pool of qualified candidates for limited positions being routinely subjected to the efficient categorization and filtration of the Applicant Tracking System (ATS) software. However, these tech marvels are substantially impervious to the long-term potential of the candidates since they are programmed to screen within the narrow boundaries of tightly scripted job specifications to satisfy current needs. The ‘Performance Fit’ often supersedes ‘Cultural Fit’ in such cases with organizations having to suffer embarrassing episodes of scandals with significant damage to the Employer Brand, e.g., Google for a series of sexual harassment cases against ‘prized’ executives (https://lnkd.in/ffpZspk). Furthermore, the inherent biasness and lack of inclusion has been routinely observed when technology is leveraged for hiring, e.g., Amazon had to scrap its AI-driven recruitment tool when it was found to routinely discriminate against women (https://lnkd.in/ffpbr_d).
The term ‘Glass Ceiling’ also seems to have taken a new meaning in the Digital Age due to the curtailment of data-driven processes/procedures/policies when the upper echelons of leadership hierarchy are the direct affectees, e.g., designing of performance-driven compensation packages, inculcating astute succession management initiatives, ingraining lucid accountabilities and ensuring effective corrective/preventive actions. A steady stream of shocking corporate blunders, collapses and scandals are a sobering testament in the respective context, e.g., the spectacular fall of WeWork as the next ‘big thing’ of the startup world (https://lnkd.in/fY7umPS).
Consequently, there is a critical need for prudent and effective Data Analytics that is judicious in aggregation, assessment and application of kosher data. Following questions can provide the relevant guidance with respect to HR:
1. How is HR evolving into ‘People Science’ in your organization?
2. Is the space for HR as a ‘People Art’ being squeezed as a consequence or can they co-exist in harmony?
3. Are employees aware of what kind of data is being collected on them and how is their feedback being incorporated in the respective context?
4. How are you assuring the quality of data in facilitating the ‘true’ metrics for enabling astute decision-making?
5. How has such data helped you in making better business decisions? Do you benchmark with other organizations/competitors?
6. Do you feel constricted by the proliferation of qualitative data and the lack of quantitative data? If so, what measures are you taking for resolving it effectively?
7. How are you overcoming the challenges of relying too much on data?
8. Are you hampered by the quality of data analyzers/scientists? If so, what are your measures to overcome such shortcomings?
9. Does the ‘Humanistic’ connection suffer as a result of emphasis on ‘People Science’? If so, what fruitful remedial measures are you taking?
10 Are you in compliance with relevant legislation on such initiatives? Are privacy concerns being addressed appropriately? Would you consent to an independent audit?
11. Can such data become like a ‘credit score’ for employees and follow them around in their career progression with other employers? Would this become more important than a resume?
12. How are you protecting such data from hackers/unauthorized parties?
13. What measures/safeguards are being used in your organization to proactively assure that ‘accumulated’ data is not used for malicious profiling/discrimination in terms of hiring future talent?
14. How is ‘People Science’ helping in galvanizing Diversity & Inclusion initiatives within your organization? If it is not then what are your remedial measures?
15. Have you established long-term collaborations with academic institutions for producing the future crop of talent with the desired skills for Data Analytics? Are such initiatives bearing fruit?
It is important to remember that information derived from Data Analytics has a ‘useful shelf-life’ and needs to be managed and preserved accordingly to meet the needs/expectations of the stakeholders while bolstering the ‘knowledge bank’ of the organization.
The true value of Data Analytics in an organization lies in its ability to leverage the intrinsic drivers of ‘customized ownership (how it appeals to me)’ and ‘personal investment (what do I gain from it)’ to inculcate profound engagement and ensure enriching productivity in the workforce for achieving desired goals. This is especially true in case of the Middle Management who are the backbone of the corporate entities. They have a finger on the pulse of the organization and are critical to the smooth operationalization of strategic imperatives. Any disturbance within their ranks, e.g., due to a fractured psychological contract, can send echoes of discontent throughout the organization and create major impediments for any progressive initiatives. This is further complicated by the fact most of the resentment is borne internally and is reflected through passive resistance which may not be even discernible to an apathetic leadership.
Consequently, the remedy for avoiding brinkmanship with corporate disaster requires an honest self-appraisal and venturing into the ‘dark alleys’ of the organization to realize the sources of ‘real discontent’ and taking transparent, accommodative and prudent steps to alleviate organizational discord before embarking upon any improvement/transformative measures based upon exhaustive Data Analytics. This calls for a well-balanced organization to sustain the drive for organizational excellence as depicted in the following figure:
The ‘art’ of leadership has to be balanced with the ‘science’ that works behind most of the technological solutions. The decision-maker and the decision-affectee are both humans, therefore, while technology boosts efficiency aspects (the inorganic side); care has to be taken in terms of alleviating humanistic concerns (the organic side) as the ‘soul’ of the organization should not perish in its attempts to become more nimble in overcoming business challenges. How are your Data Analytics initiatives faring?