Big Data: Question Defines The Answer
After getting into the data, the analysis may remain constrained to the available data. Hence, defining the right question prior to the analysis is necessary so that one considers the external data too. Big Data provides capability to combine business data with data located outside four walls of the business.
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"You can't manage what you don't measure", said Dr W. Edwards Deming, the father of quality evolution and a management consultant. To measure a business, data from all possible sources in the business from product design to customer feedback is essential. Traditional businessmen may believe they can succeed with improvements in their operations, service, and other functions without using data capabilities or analytics. But it is hard to know which measure is working and which is not without measurement. How do we measure the vast customer data, feedback, transactions history, and operational data? Big data comes in handy as it helps in analysing varieties of large volumes of data with high velocity.
In this internet and mobile era of instrumented services, data is generated at a quick pace and in large volumes. It is estimated that data in the range of 2 Exabyte (10^18) is generated on internet daily with an upward trend. Now with the advent of Big Data, we have become capable of collecting and storing this data from sources like social media, enterprise systems, documents, and e-mail. Are we effectively utilising this enormous data?
The data in its original format is not useful but the insights drawn generate value and the insight will lead to right action. Defining the right problem and asking the right question is crucial to strike upon the insight. For example, in a subscriber business, the most critical problem is the subscribers leaving the platform. While tackling this business problem, the question 'how many customers left us?' will provide a number or percentage of subscribers left. In addition, asking 'why are the customers leaving us?' will enable you to identify the factors leading to the subscriber churn. The factors can be geographic locations, demographics, services, or others. Further analysis of the factors leading to churn will provide the probable reasons for dissatisfaction of subscribers.
After getting into the data, the analysis may remain constrained to the available data. Hence, defining the right question prior to the analysis is necessary so that one considers the external data too. Big Data provides capability to combine business data with data located outside four walls of the business. Our analysts at Hansa Cequity were helping a leading beverages company in identifying untapped market potential across Indian cities for demand creation. We set out to profile customer neighbourhoods to attract customers in a competitive environment and effectively grow the brand's network across India. We added Geographic Information System (GIS) data to the existing data and developed CEQUITY GEO ONETM, an intelligent decision support platform. It enabled them to visualize sales data at outlet level as well as demographic data at ward level on a map. The platform includes figures like number of households, schools, colleges, restaurants, malls, and movie theatres along with household potential index which are key indicators of demand for beverages. This enabled the executives to find the potential markets and address the potential demand.
We should look at the data in context of the business to solve problems. By contextualising the data, we can reveal its true potential to improve customer insights. Netflix used subscriber data with appropriate context to tailor plotline and characters of 'House of Cards', a popular political drama series. They have 60 million subscribers globally. They looked at scenes with events like pause, rewind, and fast-forward and their analysts put them in context. The observations from the analysis were used later to improve the viewing experience. They knew what people like to watch and their interests because of putting the past viewership data in right context. The analysis gave them confidence that they could find an audience for the series.
Big Data is a tool to enhance decision making which will help you immensely when you have a tradition or willingness to make fact-based decisions and ability to translate available analysis into a business action plan. In consumer products industry, firms have been traditionally using data to make better decisions even before the advent of Big Data. The firms use modelling and simulation to analyse data from various sources and make fact-based decisions. They use comments on social media, sales data, seller data and other digitized processes to react more quickly to changing market conditions.
Using Big Data is like putting on running shoes, it might seem like a luxurious accessory to run a business but it will help you run faster and prevent the harms done by stepping on thorns of wrong decisions while running on the rough terrain of business. You don't need to be a technology based or internet based company to implement big data and reap its benefits. Businesses in industries as diverse as restaurants, agriculture, manufacturing, non-profit, healthcare and insurance have successfully implemented and used big data across functions like sales, marketing, operations, research, and other revenue generating activities. So, to utilise big data, set the right context with business acumen and ask right questions to the data.
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