With this article, Let's take a deep dive to understand how BigData Technology space is related to Market Research and it's different attributes.
So let’s take Big dive !!
1) What Big Data is Accounted for?
Big Data Analytics involves examining large amounts of Data in order to Uncover the hidden patterns , correlations and also to give insights to make proper decision. Importantly, Businesses wanted to be more objective and data-driven so as to embrace the power of data and technology.
It is a systematic field to analyze ways to systematically extract information from or otherwise deal with data sets that are too large using traditional data processing Hardware. Data with many many rows (cases) have more statistical power, where as data with higher complexity (more attributes or columns) may lead to higher false discovery rate. It also has several challenges including capturing data, data storage , data analysis, search, sharing, transfer, visualization and querying the data source.
Big Data allows companies to observe various customer related patterns and trends. In modern business era and with the use of technology, it is easy to collect all customer data company needs. It might be very easy to understand modern day client. Businesses has all the capability to derive critical behavioural insights that it needs to understand customer base.
2) How companies are leveraging Big Data Technology?
Big Data Analytics is done using advanced software systems. The modern big data analytics systems allows for efficient analytics process. Companies often invests in Big Data analytics. To understand real-world examples of how big brands are using big data analytics we can take an example pf coca-cola that drives customer retention program. In the year 2015, Coca-Cola managed to strengthen its data strategy by building a digital-led loyalty program.
There are many examples of corporation and mid size companies that takes quick and agile decision to remain competitive using Big Data Technology.
3) Is Big data really Big?
As per todays trend, the big data ecosystem is just often too large, complex and redundant. It’s a complex market for companies who have bought into the idea of big data, but then stumble when they are faced with too many decisions, at too many layers in the technology stack. The big data ecosystem has too many standards, too many engines and many vendors. An ecosystem that exists right now, alienates customers, inhibits funding of customer projects within organizations.
According to Lenovo.com, Many small businesses that uses tools won’t involve big data that has thousands of points of information, but meaningful analysis of your own customers and transactions. It requires technology to track that customer data. E.g A small retailer might use an advanced point-of-sale (POS) system that enables data analysis of every transaction that takes place at the business. It gives hard data on whether or not a [sales] special was successful, or what hours it was sold at different types of goods. It is big data with a microscopic focus, and it enables small business owners to more effectively leverage the information that’s coming into their businesses. For most small businesses, that ability to hone in their customers is going to be more important than analyzing broader trends in the marketplace.
So, despite the buzz and hype surrounding “big data,” there are plenty of opportunities for small businesses to study and respond to information that they already have in their businesses, but may not be using.
4) What is Technological Eco-system of Big Data Technology?
The Big Data environment allows businesses to store, process, analyse and visualise data. It starts with the infrastructure, and selecting the right tools for storing, processing and often analysing. There are then specialised analytics tools to find the insights within the data. Further on from this, there are also applications which run off the processed, analysed data. All of these are valuable components of the Big Data Ecosystem.
According to dataconomy.com, Big Data Ecosystem components can be broadly divided into the following aspects.
Let's checkout the about the aspects of above components.
It is complete tool designed for the storing, processing and analysing of data. The core Hadoop technologies work on the principle of breaking up and distributing data into parts and analysing those parts concurrently, rather than tackling one monolithic block of data all in one go.
- Massively Parallel Processing (MPP) Databases:-
- Analytics Platform:-
It integrates and analyse data to uncover new insights, and help companies make better-informed decisions. There is a particular focus on this field on latency, and delivering insights to end users in the most timely manner possible.
- Visualization Platforms:-
- Business Intelligence Platforms:-
- Machine Learning Platforms:-
- Many Others:-
Infrastructural technologies are the core of the Big Data ecosystem. They process, store and often also analyse data. For decades, enterprises relied on relational databases– typical collections of rows and tables- for processing structured data. However, the volume, velocity and variety of data mean that relational databases often cannot deliver the performance and latency required to handle large, complex data.
It means Not Only SQL. It is also involved in processing large volumes of multi-structured data. Most NoSQL databases are most adept at handling discrete data stored among multi-structured data. Some NoSQL databases, like HBase, can work concurrently with Hadoop.
MPP databases work by segmenting data across multiple nodes, and processing these segments of data in parallel, and uses SQL. Whereas Hadoop is usually run on cheaper clusters of commodity servers, most MPP databases run on expensive specialised hardware.
Although infrastructural technologies incorporate data analysis, there are specific technologies which are designed specifically with analytical capabilities in mind. Sub-categories of analytics on the big data map include:
It is specifically designed as the name might suggest for visualizing data; taking the raw data and presenting it in complex, multi-dimensional visual formats to illuminate the information.
It is used for integrating and analysing data specifically for businesses. BI Platforms analyse data from multiple sources to deliver services such as business intelligence reports, dashboards and visualizations.
It is one the cool technology in todays time. The analytics platforms input processed data and output analytics/dashboards/visualisations for end users, the input in machine learning is data the algorithm learns from, and the output depends on the use case.
Applications are big data businesses and startups which revolve around taking the analysed big data and using it to offer end-users optimised insights. Fields in which applications are used include:
Some companies run a compendium of 3D brain scans and neurological information which can be accessed by neurosurgeons from all over the world and help in the diagnosis, prognosis, and treatment of patients with brain diseases.
Some retailers run mobile shopping apps that offer insights for food production companies into variables that affect food purchase, such as brand loyalty and price flexibility.
Energy companies uses data from smart meters, building management systems, voltage regulators and thermostats to help consumers track and curb power use, reduce waste, balance the grid, improve system operations and even predict future consumption.
It is just a brief insight into the multi-faceted and ever-expanding cartography of Big Data. In the coming weeks in the ‘Understanding Big Data’ series, I will be examining different areas of the Big Landscape- infrastructure, analytics, open source, data sources and cross-infrastructure/analytics- in more detail, discussing further what they do, how they work and the differences between competing technologies.
There are many different types of technologies out there, which can offer infinite opportunities to their users. The key is identifying the right components to meet your specific needs.
6) How Big Data can be useful for Market Research?
Since 25 Years, Market Research Industry focuses on specific market problem, e.g How to Construct an Optimal Product using cojoint analysis or how to segment market or shopper at specific target. Or understanding which idea resonates most with users or segment of users.
One of the biggest reason that Big Data will not replace market research and can only co-work with it is that it has a lack of specificity when addressing narrow project goals. One can easiy understand general sense about brands using sentiment analysis of twitter or facebook data or by extracting data from an immense customer database or produce an impressive reports using Data mining techniques. The more focused market research solutions can only be addressed through focused methods like surveys. This sort of research is not going to disappear.
Despite having several limitations, Big Data technology can enhance market research. Predictive analytics by those companies that hold large customer information, can append information to survey data to add segmentation value. Also, survey researchers can use data mining from such warehouses to create custom reports or segmentations. Conventional data mining tools, such as Regression Analysis, CART Trees, or Neural Networks, are employed by both Predictive Analytics and marketing research. Within the social networking space, Sentiment Analysis, Text Analytics, and Social Network Visualization are valuable tools to add texture to a full-service marketing research report. In some ways it is a mixture of the qualitative/quantitative relationship found in marketing research. In many ways this is already being accomplished with online chats performed after a quantitative survey. Tweets, Flickr groups, and Facebook can add valuable insights to a well constructed branding project—but cannot fully replace it.
Big Data and marketing research represent a formidable toolkit in the hands of those who know how and when to exploit both to their full potential. Because as per our understanding Big Data along with analytic techniques augments marketing research.
So More is Yet to explore !!