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Contents

• Big Data in Business Organizations
• Introduction
• Project Objective
• Project Scope
• Literature Review
  • Big Data in Healthcare
  • Big Data in Retail
  • Research Gap
• Conclusion
• References

Big Data in Business Organizations

Introduction

The role of data in decision making is not unknown to most of the business organization; specifically with the rapid increase in budget that businesses are allocating to information technology & systems. The research was furthered by a study from McKinsey & Company which claimed that the velocity and volume of data that is being generated on a day to day basis would bring in a paradigm change in the way leaders look at business.

Another study from Accenture threw light on the concept of a customer halo which refers to a cluster of data and information pertaining to the purchasing behavior and mindset of the customer – effectively used by marketers to target the correct set of customers; and position their products in a way that they have the maximum propensity to purchase. (Peter Groves, Basel Kayyali, David Knott, Steve Van Kuiken (2013))

Different firms use their underlying data structure in their own way; based on the nature of business. While B2C businesses use it in a more leveraged manner; B2B also does employ their data into good use. A few of the best practices which can be considered by organizations are mentioned below –

1. There have been researches performed by McKinsey which suggests that majority of the browsing time is spent by users in either scanning for data or transferring data from one device to other storage. Therefore, this is an exceptional scope for process improvement via proper data maintenance
2. Data driven analysis can be used in effective and unbiased performance appraisal or evaluation as well
3. Adding customizations in commercial platforms to ensure personalized views for every customer is a common use case in analytics.
4. Forecasting, predicting the future trends and prescriptive analytics as well are used by advanced users.

Now, a common question arise – what is the basis of this analysis? The answer to this is ‘Big Data’. Now, the way business organizations collect data, store data in a logical format, and then use data to derive business insights goes a long way in providing them a competitive advantage over their competitors.

Project Objective

With a brief context on Big Data and its implications in the backdrop; the next step is to derive and categorically mention the key objectives of the project related to the big data. In order to substantiate the point, multiple research journals from authentic sources are referred; and deliberate on the way business organizations leverage and use their data infrastructure in service sectors.

Now, the case reference or the industry reference that has been used for the implementation of the project is that of the retail & healthcare sector. The primary reason behind this is that ‘retail’ is one of the predominant users of analytics specifically in terms of customer insights; and on the other hand, the healthcare sector is one of the newest adopters of analytics wherein they aim to use analytics to serve and cure patients in a way that they not only target their immediate disease but make them immune to future probable diseases as well.

Project Scope

As already mentioned in the section below; this analysis is in the form of a research proposal wherein various published literatures and hypothesis would be studied, probable research gaps identified and more importantly, leading to a new research idea which could cater to the mentioned research gaps. In order to substantiate the research and provide a practical perspective of the same; two industry verticals are referred – retail and healthcare. This would give a particular direction to the research.

The project analyses how Big Data analytics can be used in ensuring better decision making in business cases pertaining to these two business industries or domains – retail and healthcare; and how can technology take these two business areas forward.

Literature Review

Before getting to the literary journals and academic journals, it is worthy to understand the key drives of analytics and understand the reason behind the creation of so much of data now a days as opposed to yesteryears. The primary reason of the same is the influx of technology in business operations; which has enabled storage and logging of transactional data (or POS data). Depending on the scale of business; the quantum and velocity of data is determined. With this backdrop; let’s look into a few research journals talking of analytics in the two key industry sectors – retail & healthcare.

Big Data in Healthcare

Starting with the healthcare sector; there’s a research paper titled ‘Big Data revolution in Healthcare’ which was published by the business technology team of the ‘Center of US Health System Reform’. The backend research which made the base to this journal was done by McKinsey & Company. The paper talks about the inception of data driven decision making; and claims the fact that the ideation of this is rooted in the healthcare sector wherein medical researchers were able to find a correlation between people (or residents) in a place in London suffering from Cholera to a particular water pump which was contaminated by a baby’s diaper having that particular virus. In addition to this, the paper also deliberated on multiple issues & concerns which may come to the forefront; as well as the immense scope of improvement that Big Data Analytics can bring in to the operations of the healthcare sector. Valid data sources such as scan reports, medicine dosages, diagnosis, prescription made by the doctor etc. can be employed to collect a rich set of data; worthy of analysis. (Peter Groves, Basel Kayyali, David Knott, Steve Van Kuiken (2013))

Apart from this, IDC published another astounding research journal on ‘Bigger Data for Better Healthcare’ advocating the usage of data analytics in the healthcare sector. The paper deliberated on certain key use-cases wherein medical practitioners and analysts can not only cure the current disease faced by patients but also they can predict the possibility of any other future disease that the particular may fall prey to, or predict any epidemic that may fall on the particular region etc. Therefore, this journal talks of a pro-active measure to medical strategies taking the predictive aspect into the forefront. (Silvia Piai, Massimiliano Claps (2013))

Other than these service areas, ‘customized treatment for patients’ can be another field of interest wherein historical data on patients can enable doctors to tailor a customized diagnosis plan for the patient ensuring that better services can be provided to him.

Big Data in Retail

A famous research journal in the field of analytics in retail was published at the IBM Institute of Business Value. The research paper titled ‘Analytics: The real world use of big data in retail’ takes both a generalist and a specialist view on the leverage of analytics in the retail sector. The importance of analytics in retail (specifically online retail) is extremely high primarily due to the fact that there’s no real face of customers for the business. The business doesn’t have a view of the customers and everything that the business could know of their customer is via analytics.

The research paper then emphasis on the KPIs which businesses should abide by or focus on for implementing data analytics in the retail sector. Customer business intelligence is one of the key use cases wherein big data is used to categorize the perception of each of the customer; so that they can classify them into multiple clusters (based on the homogeneity of their characteristics) and then create campaigns targeted at specific clusters of customers.

There are research journals which extends the analysis to other operational modes as well (in addition to customer business intelligence) such as supply chain management, inventory, resource optimization an so on; wherein big data analytics can be used to link the end to end value chain (i.e. from the suppliers’ supplier to the customers’ customer) leading to minimal bullwhip effect; which in turn would better the P&L performance of the business. (Keith Mercier, Bruce Richards, Rebecca Shockley (2013))

Now, the key question which arise is the ‘how’ factor i.e. how does business analysts enable seamless data collection or which logical points does the analysts tap on to collect data. There have been multiple researches in that field – and most of the papers or journals talks of the marketing funnel concept as a valid method of data collection and customer analysis.

The figure above shows the marketing funnel which has different levels – each levels can be defined by the market based on their business model; now, all the required data starting from a prospect user entering into the system till the time the customer is churned out from the system can be tracked by embedded analytics in their e-commerce sites. Now, depending upon the stage from where the customer leaves the system, the analyst would comment or predict on the customer mindset as to whether the user is not satisfied with the website design, or the user is not able to find the relevant product of usage and so on.

Now, considering the perspective of web analytics; the figure below elaborates the different parameters that customers focuses on; while visiting any web portal or e-commerce portal of a retail chain.

The figure (on a scale of 5) shows that customers or online visitors provide the maximum priority to ‘design’ and ‘content’ of an ecommerce site; post which comes the technicality of the website, followed by usability and then customization. Therefore, this research piece suggest or recommend website designers & analysts to ‘perform the basic tasks’ first as visitors generally lay more importance to them rather than high end customization algorithms.

Apart from that, there have been researches performed on the customer perceptions as well i.e. the factors which customer prefer the most in an online purchase – below mentioned are the key findings of the same.

The above analysis clearly shows that ‘quality’ is by far the highest priority factors for customers. Therefore, this is a lesson for e-commerce players to ensure that they have sufficient checks and certifications & quality audits for the suppliers before bringing them on-board to the portal. Amidst the other important factor are availability, variety and customer service; followed by price & brand of the products.

Combining the learnings from the previous two papers; the key KPIs that can be considered for online retail are –

1. Each and every campaign or source of advertisement can be provided a unique code or an identifier; which can be used to track the return on investment for each promotional activity
2. Number of views, number of unique users visiting the websites, levels at which majority of the customers go in the value chain, time spent in each of the web pages of the portal; and so on.

Research Gap

A key gap in the researches that have been referred is that they intend to provide a high level picture on how big data analytics can be leveraged to bring in a lot of value in business organizations. However, it misses to talk of the ground root realities i.e. –

1. Implementation mechanism – How would business organization with minimal information technology/system acumen scale up their analytics base? Should they set up an in-house team or department, or go for out-sourcing it?
2. Does the sales leaders and account executives receptive to the usage of analytics in business; as this takes away to a certain extent, their gut feeling on customer basis their interactions?

In addition to this, the research paper on retail focuses primarily on online retail (or e-tail). It does not deliberate much on offline retail in terms of linking the supply chain network, warehouse management and so on.

Conclusion

It is commonly stated that the database or the data hub referred to is similar to an iceberg wherein businesses tend to use less than 10% of their underlying data in a proper manner. More so, majority of the data collected may not be usable as well. Hence, the first learning is to have data collection pointers in such a way that it makes logical sense; and there’s not much resources expended in data which is not usable or not strategically connected to business.

In addition, the role of analytics is more critical for online business as it is the only mechanism to understand customer perceptions; therefore, Big Data analytics has the potential to be a differentiating factor in the industry; and firms who are early adopters of the same would have a competitive edge.

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