Claranet | What is Big Data?

March 30, 2021 Claranet Limited

What is Big Data. Why businesses need to focus and invest in their data. 

Prabhat Handoo
Senior Cloud Sales Specialist
CLARANET


Big data  can be defined as large amounts of data that is generated from different sources and in different formats. The world of big data technology has changed and evolved over the years. With the past worries of where and how to store, businesses today are not just storing their data efficiently, they are making more sense of it too.  

With the tool sets and the cloud services available now, it is simple to ingest, process, and present varied amounts of data. T Google, Microsoft, and Amazon have invested in these technologies and are focussed on making their services better than the others. It’s an absolute win for their customers and partners delivering and building these services.  

Big data is more than a collection of data sets with different formats. It is an important function of the technology that can be used to deliver several benefits. Data analytics technologies and techniques provide the means to analyse different sets of data and digest new information—which can help organisations make informed business decisions. Business intelligence (BI) queries help answer basic questions about business operations and performance, which is popular within several C-level executives to identify how the business is doing and what can be done better. It acts as a single source of truth for organisations, which is visual and easy to interpret.  

Businesses like to collect data every day in various ways. There is data from each of the transactions (traditional and ecommerce), from phone logs, chat sessions, browsing histories, buying patterns, surveys, applications, and marketing campaigns. In short, businesses do have access to a massive amount of data, which they might not even be using.  

Big Data can help change that! It can help pick the right data set and then make logical and business sense out of it. Which then enables the decision makers to change the way they currently operate – it could mean providing the right recommendations to your users or helping a C-level exec identify the health of cost centres and measures to fix them.  Its often said Big Data is the new oil, you store it the right way, refine and treat it well and only then you can use it the way you want!  

What is the Importance of Data Analytics? 

Big Data Analytics, if done via specialised systems and software, available on the three Cloud Service Providers – GCP Big Query, AWS Redshift and Azure Datawarehouse, can help businesses make positive outcomes and prepare for the future. Some of these outcomes are: 

  • New revenue opportunities 
  • Increase in efficiency of marketing campaigns 
  • Increasing customers’ satisfaction 
  • Increased and relevant user interaction   
  • Competitive advantage  
  • Reducing risks 
  • Improving products and making decisions  

More often than not, the customers’ desire and ambition to streamline their data by saving it correctly leads to analytics. I have been involved in several customer projects where they didn’t see a need of analytics, till they spotted their storage and data problem. Once they look at solving that, analytics seemed the right and natural next step.  

Big data analytics applications help data analysts, predictive modelers, statisticians, data scientists and other analytics professionals to analyse large volumes of structured transaction data, including different forms of data that can be left undiscovered by conventional analytics and intelligence tools. This often is a combination of semi-structured and unstructured data.  

For example, internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the internet of things (IoT). All these are sets of data that can help enrich customer experience. Of course, keeping in mind the privacy of the users.  

How does analytics work in reality? 

The first stage is always to create a staging area and landing zones for different forms of data – for example, Hadoop clusters and NoSQL systems. This is from where the data is loaded into a data warehouse or data lake for analysis. In some architectures, data can be analysed directly in a Hadoop cluster or run through a processing engine like Spark.  

However, data stored in Hadoop systems must be organised, configured and portioned correctly to achieve the desired performance of the ETL (Extract, Transform and Load) process and analytical queries.  

Now that the data is ready, it can be analysed using some of the services and tools available on the cloud platform. These include tools for the following: 

  • Data Mining, which is a technique of examining a large data structure to find patterns, trends, hidden insights which otherwise would not have been possible using simpler query-based techniques. 
  • Predictive Analytics, which helps build models to forecast user behaviour and customer patterns to predict the future purchases 
  • Machine Learning, which builds algorithms and models that help in analysis of large data sets  
  • Deep Learning, which is an advanced version of machine learning but expected to behave like a human mind. Similar to Natural Language Processing (NLP) 

Cloud services focussed on data and analytics services are often a competitive space for each cloud vendor. They are all great at what they do – just look at starting somewhere and a partner like Claranet can help make that choice.  

Where to begin? 

It’s a challenge and we get it! Most customers often ask this question and rightly so. Start off small, look at solving a current complex problem and how analytics can help make it simple. My go-to solution to such questions is always to pick two to three data sources, get them together in a modern data platform and create one or two dashboards for visualisation. Claranet’s offering benefits customers who are looking to aggregate multiple data sources into a single point so they can start to query their structured or unstructured data. As well as looking at the ingestion of data, the engagements can help analyse business outcomes on the selected data sources and allow the customer to build dashboards so the value can be seen in a dynamic and cohesive way.  

No matter what stage of your data journey you are at, whether it’s Migrating on-premise Hadoop systems to the cloud, using SaaS applications like Cloudera or Snowflake, building a datalake on any of the three cloud providers, Claranet can help.  

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