Understanding Big Data: Concepts and Applications

• Big data refers to extremely large and diverse collections of structured, unstructured, and semi-structured data which grows exponentially.
• Datasets are too huge and complex for traditional data management systems to store, process, and analyze.
• Digital technology advancements like connectivity, IoT, and AI spur the rapid growth of data.
• New big data tools are emerging to help companies collect, process, and analyze data faster to gain the most value from it.
• Big data is used in machine learning, predictive modeling, and advanced analytics to solve business problems and make informed decisions.
• Revealing insights using big data helps businesses understand various aspects that affect them such as market conditions, customer purchasing behaviors, and business processes.
• Definitions of big data may vary, but the characteristics are always described in terms of volume, velocity, and variety.
• These characteristics are referred to as the "3 Vs of big data," first defined by Gartner in 2001.

Volume

• The most common characteristic associated with big data is its high volume.
• Describes the enormous amount of data that is available for collection and produced from a variety of sources and devices on a continuous basis.

Velocity

• Big data velocity refers to the speed at which data is generated.
• Data is often produced in real-time or near real-time, and therefore, it must be processed, accessed, and analyzed at the same rate to have any meaningful impact.

Variety

• Data is heterogeneous, meaning it can come from many different sources and can be structured, unstructured, or semi-structured.
• More traditional structured data (such as data in spreadsheets or relational databases) is supplemented by unstructured text, images, audio, video files, or semi-structured formats like sensor data that can't be organized in a fixed data schema.

Additional "Vs"

• In addition to the original three Vs, three others often mentioned are veracity, variability, and value.

Central Concept

• The more visibility you have into anything, the more effectively you can gain insights.
• This leads to making better decisions, uncovering growth opportunities, and improving your business model.
• Key element to becoming a data-driven organization.
• Discover patterns and unlock insights that improve operational and strategic decisions if you can manage and analyze your big data.

Increased Agility and Innovation

• Big data allows you to collect and process real-time data points and analyze them to adapt quickly and gain a competitive advantage.
• These insights can guide and accelerate the planning, production, and launch of new products, features, and updates.

Better Customer Experiences

• Combining and analyzing structured and unstructured data sources results in more useful insights for consumer understanding, personalization, and ways to optimize experience to better meet consumer needs and expectations.

Continuous Intelligence

• Big data allows you to integrate automated, real-time data streaming with advanced data analytics to continuously collect data, find new insights, and discover new opportunities for growth and value.

Cost Reduction

• Process data faster and generate insights that can help you determine areas where you can reduce costs, save time, and increase your overall efficiency when using big data analytics tools and capabilities.

Risk Evaluation

• Analyzing vast amounts of data helps companies evaluate risk better.
• Easier to identify and monitor all potential threats and report insights that lead to more robust control and mitigation strategies.

Challenges

• Requires time, effort, and commitment to leverage it successfully.
• Businesses struggle to rework established processes and facilitate the cultural change needed to put data at the heart of every decision.
• Becoming a data-driven business is worth the work.

Creating a data strategy

• Starts with understanding what you want to achieve.
• Identify specific use cases and the data you currently have available to use.
• Evaluate what additional data might be needed to meet your business goals and the new systems or tools you will need to support those.
• Big data technologies and tools are made to help you deal with large and complex datasets to extract value from them, unlike traditional data management solutions.
• Tools can help with the volume, speed, and complexity of data.
• Data lakes ingest, process, and store structured, unstructured, and semi-structured data at any scale in its native format.
• Data lakes act as a foundation to run different types of smart analytics, including visualizations, real-time analytics, and machine learning.

Winning approach concepts

• There is no one-size-fits-all strategy.
• Build what you want using the tools and solutions you want.
• Big data is one that contains multiple interfaces, open-source technology stacks, and clouds.
• Big data environments will need to be architected to be both open and adaptable to allow for companies to build the solutions and get the data it needs to win.
• Leverage smart analytics and AI and ML technologies to save time and effort delivering insights that improve business decisions and managing your overall big data infrastructure.
• You should consider automating processes or enabling self-service analytics so that people can work with data on their own, with minimal support from other teams.
• Big data analytics need to support innovation, not hinder it.
• Build a data foundation that will offer on-demand access to compute and storage resources and unify data so that it can be easily discovered and accessed.
• Choose technologies and solutions that can be easily combined and used in tandem to create the perfect data toolsets that fit the workload and use case.
• Imperative to build trust into your data—trust that it's accurate, relevant, and protected.
• No matter where data comes from, it should be secure by default and your strategy will also need to consider what security capabilities will be necessary to ensure compliance, redundancy, and reliability.