Application of Big Data in Decision Making

Data driven decision making is one of the most trending optics of interest & focus in business organizations; and multi-national corporates are investing big-time in devising their information infrastructure so as to store & process transaction data; and massage them in a way so as to extract valuable business insights from the underlying data structure.
Before coming to the application of Big Data in decision making; it is imperative to understand the concept of Big Data, and how are organization leveraging this data to derive insightful inferences on business & market prospects.



What is Big Data? How relevant is it for businesses?
The term ‘Big Data’ refers to exceedingly large datasets which can be computationally analyzed to derive relevant trends, patterns & interactions – which may in turn enable valuable descriptive analytics (i.e. a picture of the past & present) as well as predictive analytics (i.e. a picture of the foreseeable future).
The relevance of Big Data for businesses is to enable smart & real-time tracking of business performance (including effective of campaigns, operational effectiveness, financial reporting etc.) as well as predict sales forecasts, market sentiments, customer preferences etc. All of these enables business to take a proactive measure towards business growth & risk management.

Key applications of Big Data in Decision Making
While Big Data technologies and systems are leveraged by business organizations in a multitude of ways in their respective capacities; there are three key areas wherein businesses have largely employed Big Data & Analytics to derive actionable insights for the management. These key areas are mentioned below for reference – 

  1. Customer Relationship Management (CRM, customer retention & engagement) – A common factor and focus area for all business organization is ‘customer satisfaction’. Therefore, customer engagement, retention, acquisition as well as customer-focused campaigns extensively use Big Data for better customization. The best example in this field is Kroger – the company has information systems & infrastructure to capture and process data for more than 700 million consumers. This strategy pays rich dividends in the way that 95% of their overall sales is fueled from this
  2. Enhancement of operational effectiveness – Automation is the order of the day in today’s operational floor; and there’s no company which do not invest in the same. A perfect example of a business using operational data for effective decision making is Tesla – the company embeds sensors in their vehicles so that live-data can be tracked and transmitted to servers for processing.
  3. Capacity optimization – Although this is an offshoot of operational efficiency; capacity optimization is also an important use-case of businesses leveraging Big Data in decision making.









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