Challenges of Big Data in Business Marketing

Running head: CHALLENGES OF BIG DATA IN BUSINESS MARKETING
CHALLENGES OF BIG DATA IN BUSINESS MARKETING
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1CHALLENGES OF BIG DATA IN BUSINESS MARKETING
The term big data refers to the huge amounts of data (both structured as well as
unstructured) which is used in business marketing and public relations on a daily basis. With
the use of predictive and user behaviour analytics, big data allows businesses to extract and
analyse data effectively for effective marketing. It is used to predict business strategies as
well as better decision making in marketing (Katal et al. 2013). Big data analytics helps
businesses to enable time reductions, cost reductions and optimized product development
allowing the user to accomplish tasks related to marketing.
For the past 30 years, businesses primarily used the traditional data systems to
analyse data such as data warehouses and relational databases. This systems were designed to
handle structured data which were organized in records using Structured Query Languages.
The systems were capable of reading only 8k and 16k block sizes of data. As the programs
were small, processing large volumes of data was inefficient. With the dawn of advanced
analytics where statistical data was used for machine learning algorithms, the world of
business marketing was introduced to predictive analytics. It is a section of advanced
analytics which utilizes statistical techniques to make predictions about events which have
not happened yet. Targeting advertisements, analysing behaviour of customers and flagging
fraudulent transactions are some of the applications of predictive analytics (Hilbert and
Martin 2015). Predictive analytics have gained momentum with big data using text analytics,
classification models and deep neural networking to analyse data.
The concept of big data gained momentum when Doug Laney, the industry analyst
articulated the concept of big data in the three Vs (volume, velocity and variety). Although
most companies have the infrastructure to archive data, not many of them have the capacity
to process the data due to the usage of conventional data storage systems (such as NAND
storage systems). Every year, the volume of data increases substantially with new customers

2CHALLENGES OF BIG DATA IN BUSINESS MARKETING
generating pictures and videos on a daily basis. As the sources of data increase, the volumes
of data needs to be stored and analysed because these petabytes of data did not exist a decade
ago. The need for distributed approach to querying and scalable storage is a challenge for IT
structures. Although most companies have the infrastructure to archive data, not many of
them have the capacity to process the data. For parallel processing architectures, certain
databases are used such as Apache Hadoop and Greenplum for storing and analysing this
massive amount of data.
Hadoop was developed by Yahoo as an open source platform and utilizes the
MapReduce approach that was pioneered by Google for compiling the search indexes. It
distributes the data set among multiple servers (the map stage) and recombines the partial
data (the reduce stage). The HDFS (distributed file system of Hadoop) is used to store data
using multiple computer nodes (Katal et al. 2013). It involves loading data in the HDFS,
using MapReduce operations and retrieving the data. Facebook uses the Hadoop
infrastructure by storing its data in MySQL database. This data is then analysed to create
recommendations based on the interests of the friends.
Companies have used conventional batch processes to analyse data which is often slow and
not based on real time analysis ("IBM Big Data And Analytics - Marketing And Sales:
Industry Use Cases - Kenya" 2018). This works when the incoming data is slower than the
processing rate and despite the delay, the data stays useful. With mobile and social
applications, this process breaks down as the data streaming services occur in real time and
the data stays useful only if the delay stays minimal. The data velocity is crucial as some
level of analysis is required while the data is streaming. This is where the concept of big data
analytics comes into consideration.

3CHALLENGES OF BIG DATA IN BUSINESS MARKETING
Data variety is another aspect of big data. Over the past decade, data structure have
evolved to add thousands of formats such as photo, audio, sensor data, documents, GPS data,
PDFs and flash. The structure cannot be imposed like conventional analysis systems to keep
control over the analysis. With the help of big data, companies such as Google uses smart
phone sensor data to determine traffic conditions which was not possible a decade ago (Jin et
al. 2015)
Together the three Vs determine the analysis conditions and determine the data set
which defines the main concept of big data.
With the vast number of technologies in the market, enterprises harness their data with the
help of a number of analytics tools that make up the big data ecosystem. The core of the
ecosystem is handled by the infrastructural technologies. As the databases are getting more
and more complex day by day with respect to its volume, velocity and variety, enterprises
cannot relate on rational databases which captured data in mere tables and rows. Hadoop,
MPP or Massively Parallel Processing Databases and NoSQL are some examples of
infrastructural technologies. Unlike infrastructural technologies, analysis technologies are
specifically geared towards analysing data such as Analytics Platform, Visualization
platforms, Business Intelligence Platforms and Machine learning ("How Big Data Can
Improve Manufacturing" 2018). Visualization platforms takes the raw data and presents it in
a multidimensional visual format. Analytics and business intelligence platforms analyses data
and presents it through visualizations in a timely manner. The applications platform of big
data takes the analysed data and presents it to end users in an optimised format. In the health
sector, for example, neurosurgeons can check neurological information with the help of
Mintlabs and offer diagnosis and treatment. Avansera is used in the retail sector for providing
companies with the food purchasing variables (such as price flexibility).