Article: How big data will revolutionise business

Performance Management

How big data will revolutionise business

The predictive power of Big Data, when applied to human behaviour, is set to revolutionise how business operates
How big data will revolutionise business

In the modern world of business, one adage seems to be common in the leadership circles, “Every entity that calls itself part of the business has to justify its investment. Why should HR be any different?” While there is meat to the argument, one cannot deny the fact that the part of business that deals with people faces a peculiar challenge. While the business expects a return on investment immediately, most of HR’s investments are usually with an outlook spread across several years. Perhaps it will not be unfair to say that most of HR’s effort in propagating the value of its investments to the business has been through blood and sweat, inventive manipulation, and ingenious alteration of perspectives to view business problems. The advent of big data, however, has changed the way an organisation looks at people and people management. With predictions on the likelihood of success of an investment, big data promises to alter every traditional paradigm of human resource management.

Big data analytics can be one of the major differentiators of progressive HR organisations from the ones that are not. When we talk about analytics, the journey that HR really needs to traverse is from big data to big insights. A majority of the business leadership of today would have started their careers with processing data amounting in kilobytes. With the advent of the data and information age in business, modern day professionals encounter terms such as “Petabyte” (capable of storing 13 years of high-definition video recording) and “Exabyte” (thousands of years of high definition video storage) among other dizzying levels of storage-byte pinnacles. While the quantum of data and storage sizes goes on increasing beyond conceivable indexes, what that essentially means for the business is that the role of data has steadily grown across the years to have become one of the key differentiators for business success.

An enterprise or a business entity of today is flooded with data in several forms starting from global customer data, to cross-geography employee engagement data, to external talent market data. While data continues to proliferate through the enterprise, the key challenge that most organisations have started to realise is that vast resources of information is meaningless without intelligent interpretation. As a result, every business entity is on the lookout for gaining insightful lessons from past events or clever predictions about future business situations.

Simply having more data does not aid business decisions

In a recent CEB global survey, 85 per cent of business leaders revealed that more data was not aiding their business decisions. The conversations, therefore, has moved on from big data to big insights. Effective analytics is the bridge between big data and big insights and four key factors indicate the effectiveness of analytics in an enterprise.

RELEVANCE: While intended to address business issues, many business leaders question whether analytics really add value to business decisions. Organisations looking to invest in big-data analytics first need to have a clear definition of the intended purpose of an analytics platform that they are trying to build.

IMPACT: Impact refers to how an organisation, through the effective use of analytics, can increase the odds of positive outcomes. For example, it has been proven through research that through effective analytics, a company can scientifically increase the odds of success against every failure.

PERSPECTIVE: Perspective implies the breadth of the organisation’s view toward data and business capital measures, such as talent. It is important for an organisation to have a broader view of talent to address the questions that the organisation needs to address. Of course, for the organisation, it is also important to ask if the questions are coming from the right perspective.

ACTION-ORIENTATION: The ultimate objective of big data and analytics is to have a list of outcomes at the end of the exercise. Any big data and analytics exercise has to be aimed at achieving a set of outcomes intended to raise the odds of business success.

Stella Hou, HR Director at C&A China, in a conversation with People Matters, indicates that though data should be used as an aid, some of the best business decisions are a result of intelligent interpretation and instinct gained from business experience. While data does provide some very useful pointers, a good business decision is an outcome of a thought process that is open to intuitive inference. The business environment is comprised of too many unknown and dynamic variables. Big data is useful in predicting the trajectory of business performance of an organisation in the future by analysing outcomes of the past and the present. Precedence has shown many examples of business corporations gaining phenomenal and unpredicted growth in ways that were never imagined, for example a disruptive leadership style by a maverick leader. While big data may offer the possibility of a predicted future, it may also mean inducting only a set of people in the organisation who are only prepared for the predicted future. In an uncertain business environment, it will be unwise to assume that big data predictions will hold strong for all future business uncertainties. On occasions when business conditions change beyond predicted certainties, the organisation may be faced with a crisis of people and processes being underprepared. This makes a strong case for a business corporation to balance data with human intuition.

Can big data really show where to invest in talent?

In growing economies, such as India, retention is a big challenge that all organisations grapple with. Organisations spend an inordinate amount of time grooming talent only to realise later that the talent does not stay, oftentimes leaving the organisation red-faced with a succession crisis. Eugene Burke, Chief Science & Analytics Officer – SHL says, “The talent exodus in growing economies of India and China are larger than the total population of many small European nations. Big data insights can predict the behaviour of talent with the level of accuracy that enables a company identify a typical profile of people that it should invest its efforts around.”

Many companies have high potential development programs that run with the aim to develop a succession pipeline for the organisation. High potential programs are expensive investments and the risks of failure are very high. There are two primary risk areas associated with every high potential program― the risk of whether the people identified for the program will actually be successful leaders and the risk of whether the people will be around long enough to take up future leadership positions. On many occasions, HR has to face tough questions from the business leadership about not getting any return on investment on the money and effort invested in talent which the organisation could not retain. In the absence of accurate indicators, it is hard for HR to identify which talent to invest more on.

Big data insights play a central role in helping the organisation identify the talent which is not only likely to succeed in a future leadership position, but also likely to stay on in the organisation until that happens. In several recent global research studies conducted by analyst firms such as McKinsey & Company, AT Kearney, and SHL it has been statistically proven that an organisation can improve the odds of its success or influence positive outcomes by targeting their efforts around those individuals whose predicted career trajectories align with the company’s business needs.

An interesting real-world experiment conducted by SHL revealing the striking difference in impact that big data insight can cause is worth mentioning. Through use of big data analysis, four sets of talent were identified in an organisation and were segmented into four quartiles. Big data insights showed how the odds change progressively from the bottom quartile to the top quartile. In the lowermost quartile, the odds of success came out to be 1 in 10 (indicating 1 success against 10 failures). Such a price is too heavy to pay for any organisation. The same analysis revealed that the odds of success literally grew two-fold in the next quartile, with a 1 in a 5 chance of success and even further in the next quartile, with a 1 in a 3 chance of success. Most interestingly, the analysis revealed that there was a 1 against 1 odd of success in the top quartile of the talent group. By focusing their energy and investments on the talent group identified as the top quartile, the costs savings and impact that the organisation can realise can, at the least, be termed as unprecedented. Compared to the potential of big data, most of the organisation’s investment in talent would have otherwise relied on metrics and measures that would appear primitive and un-scientific.

Detractors of the big data trend, however, caution that human observation and judgement will continue to drive some business decisions, such as recruitment. While it is handy for a recruiter to have big data insights such as a personality profile while assessing or interviewing a candidate, hiring the right candidate will continue to be a factor of sound judgement and observation.

It pays to increase the odds of success

A central question for any organisation would be around how to ensure that by employing big data, the organisation has actually increased its chances of success. Recommending an excellent starting point for any organisation looking to implement big data analysis for positive business outcomes, Burke said the organisation has to find the answers to the following three questions in that order:

  1. How the people get what it takes to be successful in senior positions or the qualities that people need to be successful in executive positions?
  2. When these people get there what does it take to be sure that they are effective?
  3. When they get there, would they be with you?

Perhaps the most fitting cases on the potential that big data insight holds for an organisation can be illustrated with the following example. In a mining company based in Australia, one of the central areas of sustained business concern was around mine accidents. While the company had taken all necessary precautions to avoid any form of disaster, these odd incidents contributed in a great way to mar the brand’s reputation in both the customer as well as the talent market. Upon analysing the data of mine accidents that happened in the company since inception, it was evident that most of these accidents were human errors and happened even when the company had taken all necessary precautions to avoid them. Here is how big data helped the organisation. Through big data intelligence, a host of parameters across the spectrum of a miner’s portfolio were analysed. Factors such as skills, past experience, and behavioural attributes of all miners were analysed and the outcome of the exercise revealed an ideal profile of a miner with the least odds of meeting with an accident was created. Equipped with this information, the company made changes to its work allotment protocols, created personality profiles during hiring, and changed workplace practices to suit or drive behaviours that matched the personality profile least prone to accidents. The results that the organisation realised were no less than striking; the organisation was able to reduce the incidence of accidents reduced to zero.

Of course, a few thinkers fear that we are progressively moving to a world where an over-reliance on data may lead to clouding of sound judgement and intuition. K. Ramkumar, Executive Director of ICICI Bank said in his blog ‘The Other View’, “Data does not help us think. It provides us a launch pad for thinking and not as an inconvertible means to make decisions.” With an obsession for correctness and concreteness, over-reliance on data thwarts exploration or any abstraction of thought, many leaders predict. Daniel Kahneman, in his book, “Thinking: Fast and Slow” refers to this phenomenon as the “availability bias” where proof or validation through data limits thinking and root us to where data takes us and no further.

By allowing an organisation to identify the most probable indicators of success, big data analytics is likely to disrupt all conventional paradigms of business measurements. Talent conversations will cease to rely on soft indicators such as engagement, productivity, and performance and move to hard-numbers grounded solidly on personality profiles, predictive outcomes, and needs and preferences. While the terrain of big data management might appear nebulous, business leaders cannot deny that the impending wave of big data management will define the difference between successful and unsuccessful business corporations sooner than we think. It would not be too farfetched to predict that business leaders will soon be heard saying, “In God and big data we trust, for everything else there’s the gut.”

5 ways by which Big Data analytics can become a game changer

  1. Frequency of use: Big Data analytics enables a company to make information more transparent and usable at a much higher frequency compared to traditional business intelligence.
  2. Spread of analytical possibilities: Big Data analytics can virtually predict behaviours of any and all business processes. Progressive companies employ Big Data to conduct controlled experiments, ranging from product inventories to sick days.
  3. Product and service positioning: Through Big Data analysis, businesses can create micro-segmentation of customers, thereby allowing precision-perfect tailoring of products and services.
  4. Board decision making: Through Big Data analytics, business indicators such as revenue and business-line performance can be predicted more accurately across short-term and long-term horizons.
  5. Development of products and services: Through Big Data analytics, an organisation can predict customer responses to changes and innovations to existing products and services. As an example, manufacturing companies use data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance.
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Topics: Performance Management, Technology

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