The Use of Big Data in Digital Analytics World

Internet Marketing

THE USE OF BIG DATA IN DIGITAL ANALYTICS WORLD

The blog title sounds very technical but the information has been broken down in the simplest form so that the beginners can easily understand. Let’s get familiar with some terms which will be used in this blog.

Big Data is a theoretical concept for defining problems that arise do the large size of data where traditional data-handling tools are not capable enough.

Data Science is a science or study of data.

Data Analytics is a bunch of tools and techniques to perform analysis of data (big and small).

Data Scientist: As a Data Scientist, one would use Data Analytics to solve any Big Data problems.

Digital Analytics are tools and techniques to perform analysis on data, big or small, extracted from Websites, Social Media, Customer Relationship Management, etc. How Digital Analytics is changing the marketing, read this blog:

https://www.datatechnologyllc.com/digital-analytics-the-new-powerhouse-of-marketing/

Data is created constantly, and at an increasing rate. It is merely difficult to keep up with this huge influx of data but substantially more challenging to store this vast amount of data somewhere for some purpose.

Big Data is sometimes described as having 3V’s: Volume, Variety, and Velocity. Following are three attributes that stand out as defining Big Data characteristics.

  • Huge Volume of Data– Big Data can be billions of rows and millions of columns instead of thousands or hundreds.
  • The complexity of Data– Big Data reflects the variety of new data sources, formats, and structures, including digital traces being left on the web and other repositories for analysis.
  • Speed of Data: Big Data can describe high-velocity data and near real-time analysis.

Bigdata-madesimple has explained top tools used to store and analyze Big Data:

  • Apache Hadoop: A java-based free software framework that can effectively store large amounts of data in a cluster.
  • Microsoft HDInsight: A big data solution from Microsoft powered by Apache Hadoop which is available as a service in the cloud.
  • NoSQL(Not only SQL): While traditional SQL can be used to handle a large amount of structured data, we need NoSQL to handle unstructured data. There are many open-source NoSQL Databases available to analyze Big Data.
  • Hive: A distributed data management for Hadoop, primarily used for data mining purposes.
  • Sqoop: A tool that connects Hadoop with various relational databases to transfer data.

Digital-Analytics

Data Analysis and Value Creation in Digital Marketing:

As per Mckinsey.com “ In 2012 alone, Internet users generated four exabytes (4 x 1018 bytes) of data, fed by more than one billion computers and one billion smartphones. On Facebook alone, users share 30 billion pieces of content every month. What’s more, in 2010, there were five billion mobile device users and 30 million networked sensor nodes — numbers that have grown by 20 and 30 percent a year respectively since then.

Value creation should be the ultimate objective of every big data strategy. Value can be considered from two perspectives:

Value to CustomerValue to Firm
MarketProduct AwarenessMarket Volume/Size
Product AttractivenessMarket Growth
Product UniquenessNumbers of Competitors
BrandBrand Advertising/ConcernsBrand Penetration
Brand AssociationsBrand Penetration
Brand ConsiderationsBrand Sales
CustomerCustomer SatisfactionCustomer Engagement Value
Customer Effort ScorePath to Purchase
Reviews, Volume & ValenceCustomer Lifetime Value

 

As organizations begin to take advantage of the power of Big Data and how it can be used for marketing strategies. The algorithms and platforms that interpret Big Data helps in personalized  experience of users. It helps to interpret the needs and intent of the customers.

With growing Artificial Intelligence, the companies that can analyze the data and convert into useful information will benefit. Technology is evolving everyday and customer expectation are also maturing. A good Digital Marketing strategy has to take in account these changing trends. Analysts will need to work more on the creative side of the industry, while Artificial Intelligence takes care of the automation.

One can also read the following on Artificial Intelligence on marketing and SEO,
https://www.datatechnologyllc.com/artificial-intelligence-impact-on-industry-marketing-and-seo-part-1/
https://www.datatechnologyllc.com/artificial-intelligence-impact-on-industry-marketing-and-seo-part-2/
https://www.datatechnologyllc.com/artificial-intelligence-impact-on-industry-marketing-seo-part-3/

Posted in: Business Intelligence, Digital Analytics, Digital Marketing Strategy

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