I have been reading, listening and absorbing a lot about big data since the last 3-4 years. And, I came to realize that there are many marketers/business managers/analysts who still find the concept of ‘Big Data’ a little chaotic. In this write up I have tried to simplify this concept by a few examples which would help one understand the logic and the utility of big data.
So, what exactly is big data?
A lot of us work around with data and try to find solution/conclusion/insight/inferences. Most of us would have a defined way of analysing data – query and reporting, data mining, data visualization, predictive modelling, optimization and more. Some of us would be using analytical intelligence along with tools such as SAS.
Well, that is a thing of past now. The way in which we get or we are feeded by data has changed dramatically over years. Data today is collected from so many touch points – online, offline, POS, consumer usage points and many more; in so many forms – structured, unstructured, text, audio, visual etc; in wide variety and rapidly changing velocity – data today is very dynamic – it changes every second. Just imagine a simple click on a web page is enriched and indexed using so many data points – url, location, preferred date, time, social interactions, cookie data, keywords used, meta tags, title, income, age etc. All this has resulted in huge data – both structured and unstructured – with great variety/complexity, volume and velocity. In order to keep pace with this rapid change we need to be smart and adept enough to use these large streams of data. This large data (petabytes or exabytes or even larger) along with the advanced analytical tools together form the concept of big data which is meant to revolutionize businesses and critical decision making for the human race.
Suppose the refrigerator that we use in our homes was enabled to send us signals/data on when to increase/decrease the temperature of various sections in the refrigerator, when to give it a service, when it is overloaded, what power it is consuming etc., it would increase the output by such a great extent. Now, also imagine if the same data from across all users of this brand’s refrigerator goes to the manufacturer. The manufacturer will come to know about various needs of families of various sizes, usage patterns and more – all this would help the manufacturer to improve the product and this would in the end improve the end users life.
Another example could be of the car that we use. A car produces vast amounts of data while being driven and when it is parked. While in motion, the driver is constantly updated with information about the vehicle’s acceleration, braking, battery charge and location. This is useful for the driver, but the data can also be streamed back to car engineers who learn about customers’ driving habits including how, when and in what pattern they use their cars. And while the vehicle is at rest, it continues to stream data about the car’s tire pressure and battery system to the nearest smart phone. Imagine how this feedback can help improve the next version of the car.
Obviously this large data would be structured and unstructured and would require analytic intelligence and tools to get some really good and useful business insights.
The below image actually simplifies what big data can do – Provide actionable knowledge and insight.
Big data value chain – The 4 Es of Big Data
- Educate and generate data – Focus on knowledge gathering and market observations
- Explore and aggregate data – Develop strategy and road map based on business needs and challenges
- Engage and Analyse data – Pilot and validate big data initiatives
- Execute and derive value – Deploy the initiatives and continue to use analytics to reap benefits
Four basic benefits that I could think of instantly would be:
1. Consumer insights
Big data will help in answering some of the basic and tricky customer related questions that we all want answers for. Such as:
- Why are our customers leaving us?
- What is the value of a ‘tweet’ or a ‘like’?
- What products are our customers most likely to buy?
- What is the best way to communicate with our customers?
- Are our investments in customer service paying off?
- What is the optimal price for my product right now?
Big data examines a broad range of sources that include structured information such as purchase histories, customer relationship management (CRM) data and intelligence from industry partners, as well as unstructured information such as social media. In the case of the airline, those partners could include credit card companies, hotels and other travel industry sources.
Big data analytics also brings unstructured data into the fold, information gleaned from social media feeds, blogs, videos and other sources. Sorting through this information would have helped the airline answer a big-picture question that companies have struggled for decades to answer: How do we treat all of our customers like rock stars?
2. Business operational efficiencies
Similar to point 1, big data can help us answer out operational challenges that a business might face
- How can I save on cost?
- How can I increase profits?
- How can I increase TAT for a particular service?
- How can get the stock information on a real time basis?
- How can I get instant feeback on issues?
- How can I automate maintenance of products/machines?
- How can I have real time TAT for each player in my supply chain?
- How can my call centres offer real time script suggestion to my representative for each customer that he/she interacts with.
3. Developing new business process and products
If the challenges around customer intelligence and operational efficiency can be answered, this will lead to a humongous change in business process and would also lead to new product lines – as mentioned in the refrigerator and car example above. In this mobile age the ability to provide real time insights to employees, employers and customers would mean immediate actionable and will embark a huge change in the way business function.
4. Sea scale change in the use of data
In this era of minimal differentiation on product features within a category, customer service has become a factor of prime importance for businesses. With go to data and mobile analytics data and analytics offered as a service will help. The flood of inputs from social media and other unstructured information that is characteristic of big data doesn’t fit the traditional spreadsheet model any longer. The volume, variety and velocity of data have made it too complex to analyse using old-school tools, and not everyone wants to become a data scientist. Having big data as a real time service will gel with and enable the first three points.