Introduction to Negotiation Strategy
At Oak Spring University, we provide corporate level professional Negotiation Strategy and other business case study solution. Why Big Data Isn't Enough case study is a Harvard Business School (HBR) case study written by Sen Chai, Willy Shih. The Why Big Data Isn't Enough (referred as “Data Scientific” from here on) case study provides evaluation & decision scenario in field of Leadership & Managing People. It also touches upon business topics such as - negotiation strategy , negotiation framework, .
Negotiation strategy solution for case study Why Big Data Isn't Enough ” provides a comprehensive framework to analyse all issues at hand and reach a unambiguous negotiated agreement. At Oak Spring University, we provide comprehensive negotiation strategies that have proven their worth both in the academic sphere and corporate world.
What’s my BATNA (Best Alternative To a Negotiated Agreement) – my walkaway option if the deal fails?
What are my most important interests, in ranked order?
What is the other side’s BATNA, and what are his interests?
This is an MIT Sloan Management Review Article. As "big data" becomes increasingly integrated into many aspects of our lives, we are hearing more calls for revolutionary changes in how researchers work. To save time in understanding the behavior of complex systems or in predicting outcomes, some analysts say it should now be possible to let the data "tell the story" rather than having to develop a hypothesis and go through painstaking steps to prove it. The success of companies such as Google Inc. and Facebook Inc., which have transformed the advertising and social media worlds by applying data mining and mathematics, has led many to believe that traditional methodologies based on models and theories may no longer be necessary. Among young professionals (and many MBA students), there is almost a blind faith that sophisticated algorithms can be used to explore huge databases and find interesting relationships independent of any theories or prior beliefs. The assumption is: The bigger the data, the more powerful the findings. As appealing as this viewpoint may be, authors Sen Chai and Willy Shih think it's misguided - and potentially risky for businesses that involve scientific research or technological innovation. For example, the data might appear to support a new drug design or a new scientific approach when there isn't actually a causal relationship. Although the authors acknowledge that data mining has enabled tremendous advances in business intelligence and in the understanding of consumer behavior - think of how Amazon.com Inc. figures out what you might want to buy, or how content recommendation engines such as those used by Netflix Inc. work - applying this approach to technical disciplines, they argue, is different. The authors studied several fields where massive amounts of data are available and collected: drug discovery and pharmaceutical research; genomics and species improvement; weather forecasting; the design of complex products like gas turbines; and speech recognition. In each setting, they asked a series of questions, including the following: How do data-driven research approaches fit with traditional research methods? In what ways could data-driven research extend the current understanding of scientific and engineering problems? And what cautions did managers need to exercise about the limitations and the proper use of statistical inference?
By interests, we do not mean the preconceived demands or positions that you or the other party may have, but rather the underlying needs, aims, fears, and concerns that shape what you want. Negotiation is more than getting what you want. It is not winning at all cost. Number of times Win-Win is better option that outright winning or getting what you want.
Options are the solutions you generate that could meet your and your counterpart’s interests . Often people come to negotiations with very fixed ideas and things they want to achieve. This strategy leaves unexplored options which might be even better than the one that one party wanted to achieve. So always try to provide as many options as possible during the negotiation process . The best outcome should be out of many options rather than few options.
When soft bargainers meet hard bargainers there is always the danger of soft bargainers ceding more than what is necessary. To avoid this scenario you should always focus on legitimate standards or expectations, clearly understanding the arbitrage . Standards are often external and objective measures to assess the fairness such as rules and regulations, financial values & resources , market prices etc. If the negotiated agreement is going beyond the industry norms or established standards of fairness then it is prudent to get out of the negotiation.
Every negotiators going into the negotiations should always work out the “what if” scenario. The negotiating parties in the “Why Big Data Isn't Enough” has three to four plausible scenarios. The negotiating protagonist needs to have clear idea of – what will happen if the negotiations fail. To put it in the negotiating literature – BATNA - Best Alternative to a Negotiated Agreement. If the negotiated agreement is not better than BATNA (Negotiations options), then there is no point in accepting the negotiated solution.
One of the biggest problems in implementing the negotiated agreements in corporate world is – the ambiguity in the negotiated agreement. Sometimes the negotiated agreements are not realistic or various parties interpret the outcomes based on their understanding of the situation. It is critical to do negotiations as water tight as possible so that there is less scope for ambiguity.
Many negotiators make the mistake of focusing only on the substance of the negotiation (interests, options, standards, and so on). How you communicate about that substance, however, can make all the difference. The language you use and the way that you build understanding, jointly solve problems, and together determine the process of the negotiation with your counterpart make your negotiation more efficient, yield clear agreements that each party understands, and help you build better relationships.
Another critical factor in the success of your negotiation is how you manage your relationship with your counterpart and other people doing the mediation. According to “Sen Chai, Willy Shih”, the protagonist may want to establish a new connection or repair a damaged one; in any case, you want to build a strong working relationship built on mutual respect, well-established trust, and a side-by-side problem- solving approach.
According to
Harvard Business Review
, there are three types of negotiators – Hard Bargainers, Soft Bargainers, and Principled Bargainers.
Hard Bargainers – These people see negotiations as an activity that they need to win. They are less focused less on the real objectives of the negotiations but more on winning. In the “Why Big Data Isn't Enough ”, do you think a hard bargaining strategy will deliver desired results? Hard bargainers are easy to negotiate with as they often have a very
predictable strategy
Soft Bargainers – These people are focused on relationship rather than hard outcomes of the negotiations. It doesn’t mean they are pushovers. These negotiators often scribe to long term relationship rather than immediate bargain.
Principled Bargainers – As explained in the seven elemental tools of negotiations above, these negotiators are more concern about the standards and norms of fairness. They often have inclusive approach to negotiations and like to work on numerous solutions that can improve the BATNA of both parties.
Open lines of communication between parties in the case study “Why Big Data Isn't Enough” can make for an effective negotiation strategy and will make it easier to negotiate with this party the next time as well.
Sen Chai, Willy Shih (2018), "Why Big Data Isn't Enough Harvard Business Review Case Study. Published by HBR Publications.
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