The Ultimate Guide to Big data for businesses

The term “big data” refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around for a long time. Big data essentially is a large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It is what the organizations do with the data that matters

Importance Of Big Data For Businesses

The Big Data concept was born out of the need to understand trends, preferences, and patterns in the huge database generated when people interact with different systems and each other. With Big Data, business organizations can use analytics, and figure out the most valuable customers. It can also help businesses create new experiences, services, and products.

Using Big Data has been crucial for many leading companies to outperform the competition. In many industries, new entrants and established competitors use data-driven strategies to compete, capture and innovate. You can find examples of Big Data usage in almost every sector, from IT to healthcare.

Types Of Big Data

Big Data is widely classified into three main types

  • Structured: This data has some pre-defined organizational property that makes it easy to search and analyze. The data is backed by a model that dictates the size of each field: its type, length, and restrictions on what values it can take. An example of structured data is “unit’s produced per day”, as each entry has a defined ‘product type’ and ‘number produced’ fields.
  • Unstructured: This is the opposite of structured data. It doesn’t have any pre-defined organizational property or conceptual definition. Unstructured data makes up the majority of big data. Some examples of unstructured data are social media posts, phone call transcripts, or videos.
  • Semi-structured: The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appears to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily

Categories Of Big Data: The Many V’s

Big data commonly is characterized by a set of V’s, using words that begin with v to explain its attributes. Doug Laney, a former Gartner analyst who now works at consulting firm West Monroe, first defined three V’s — volume, variety and velocity — in 2001. Many people now use an expanded list of five V’s to describe big data:

  • Volume: There’s no minimum size level that constitutes big data, but it typically involves a large amount of data — terabytes or more.
  • Variety: Big data includes various data types that may be processed and stored in the same system.
  • Velocity: Sets of big data often include real-time data and other information that’s generated and updated at a fast pace.
  • Veracity: This refers to how accurate and trustworthy different data sets are, something that needs to be assessed upfront.
  • Value: Organizations also must understand the business value that sets of big data can provide to use it effectively.

Another V that’s often applied to big data is variability, which refers to the multiple meanings or formats that the same data can have in different source systems. Lists with as many as 10 V’s have also been created.

Examples And Use Cases Of Big Data

Big data applications are helpful across the business world, not just in tech. Here are some use cases of Big Data:

  • Product Decision Making: Big data is used by companies to develop products based on upcoming product trends. They can use combined data from past product performance to anticipate what products consumers will want before they want it. They can also use pricing data to determine the optimal price to sell the most to their target customers.
  • Testing: Big data can analyze millions of bug reports, hardware specifications, sensor readings, and past changes to recognize fail-points in a system before they occur. This helps maintenance teams prevent the problem and costly system downtime.
  • Marketing: Marketers compile big data from previous marketing campaigns to optimize future advertising campaigns. Combining data from retailers and online advertising, big data can help fine-tune strategies by finding subtle preferences to ads with certain image types, colours, or word choice.
  • Healthcare: Medical professionals use big data to find drug side effects and catch early indications of illnesses. For example, imagine there is a new condition that affects people quickly and without warning. However, many of the patients reported a headache on their last annual check-up. This would be flagged a clear correlation using big data analysis but may be missed by the human eye due to differences in time and location.
  • Customer Experience: Big data is used by product teams after a launch to assess the customer experience and product reception. Big data systems can analyze large data sets from social media mentions, online reviews, and feedback on product videos to get a better indication of what problems customers are having and how well the product is received.
  • Machine learning: Big data has become an important part of machine learning and artificial intelligence technologies, as it offers a huge reservoir of data to draw from. ML engineers use big data sets as varied training data to build more accurate and resilient predictive systems.

Business Advantages Of Big Data

  • One of the biggest advantages of Big Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks.
  • By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. Big Data provides insights into the customer pain points and allows companies to improve upon their products and services.
  • Being accurate, Big Data combines relevant data from multiple sources to produce highly actionable insights. Almost 43% of companies lack the necessary tools to filter out irrelevant data, which eventually costs them millions of dollars to hash out useful data from the bulk. Big Data tools can help reduce this, saving you both time and money.
  • Big Data analytics could help companies generate more sales leads which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their products/services are doing in the market and how the customers are responding to them. Thus, they can understand better where to invest their time and money.
  • With Big Data insights, you can always stay a step ahead of your competitors. You can screen the market to know what kind of promotions and offers your rivals are providing, and then you can come up with better offers for your customers. Also, Big Data insights allow you to learn customer behaviour to understand the customer trends and provide a highly ‘personalized’ experience to them.

Big Data Technologies And Tools

The top technologies common in big data environments include the following categories:

  • Processing engines: Spark, Hadoop MapReduce and stream processing platforms like Flink, Kafka, Samza, Storm and Spark’s Structured Streaming module.
  • Storage repositories: The Hadoop Distributed File System and cloud object storage services like Amazon Simple Storage Service and Google Cloud Storage.
  • NoSQL databases: Cassandra, Couchbase, CouchDB, HBase, MarkLogic Data Hub, MongoDB, Redis and Neo4j.
  • SQL query engines: Drill, Hive, Presto and Trino.
  • Data lake and data warehouse platforms: Amazon Redshift, Delta Lake, Google BigQuery, Kylin and Snowflake. Commercial platforms and managed services. Examples include Amazon EMR, Azure HDInsight, Cloudera Data Platform and Google Cloud Dataproc.