BIG DATA: Explained

Big Data

Big data is the term used to describe extraordinarily vast and varied sets of unstructured, semi-structured, and organized data that keep expanding exponentially over time.

This data can be mined by organizations and businesses for actionable information that can be utilized in a variety of areas, such as machine learning, predictive modelling, and a slew of other sophisticated analytics.

Traditional data management systems are unable to store, process, and analyze these datasets because of their enormous size and complexity in terms of volume, diversity, and velocity.

Advancements in digital technology, such as connectivity, mobility, the Internet of Things (IoT), and artificial intelligence (AI), are driving a rapid growth in both the quantity and availability of data.

New big data technologies are emerging to assist businesses in gathering, processing, and analyzing data at the speed required to maximize its value as information continues to grow and proliferate.

Since data can be a company’s most valuable asset, big data can reveal insights that help business owners, managers, and shareholders understand the areas that affect the business, such as business processes, market conditions, and customer purchasing behaviour, among others.

Examples of Big Data

• Monitoring and analyzing payment patterns and comparing them against past consumer activity to detect fraud in real time.
• To help fleet operators optimize last-mile delivery, data and information at every stage of an order’s shipment journey are combined with local traffic insights.
• Tracking and analyzing consumer behaviour and shopping habits to deliver highly personalized product recommendations, particularly to individual customers
• Using unstructured medical data such as research reports, clinical notes, and lab results to gain insights that would help improve and enhance diagnosis, treatment, and patient care. A combination of data from electronic health records, social media sites, the web, and other sources can provide healthcare organizations and government agencies with up-to-date information regarding infectious disease outbreaks and infections.
• Collating image data from GPS satellites, cameras, and sensors to detect potholes, drainage issues, and other anomalies to improve road maintenance and service delivery by cities and municipalities.

The V’s of Big Data

Big data is often described in terms of V’s, i.e., volume, velocity, and variety

• Volume

Volume is the most commonly cited characteristic associated with big data, as the name suggests, and it describes the stupendous amount of data that is available for collection and continuously produced from a variety of sources and devices.

• Velocity

Velocity refers to the speed at which data is being generated, which today is often produced in real time, unlike in traditional data warehouses where data is generated in daily, weekly, or monthly updates. For data to be up-to-date and have any meaningful impact, it must be processed, accessed, and analyzed at the same rate as it’s being generated.

• Variety

Data is diverse and varied, which means it can come from a host of different sources and can be structured, semi-structured, or unorganized.

• Veracity

Because of the variety of sources, big data can be messy and error-prone, making it difficult to control the quality and accuracy of the data. Large datasets can be cumbersome and confusing, while smaller ones can present a partial or incomplete picture. Veracity is the measure of how accurate and trustworthy the data is.

• Variability

The meaning of collected data is changing constantly, which inevitably leads to inconsistency over time. These shifts include changes in context and interpretation as well as data collection methods based on the information that companies want to capture and analyze.

• Value

Big data ought to contain the right data and then be effectively analyzed in order to yield insights that can help drive decision-making.

How Big Data works

The principal concept of big data is that the more visibility you have into anything, the more effectively you can gain insights that would aid in making better decisions, improving the business, and uncovering growth opportunities.

To make big data work, three main actions are required, i.e.

• Integration

Big data often collects terabytes or even petabytes of raw data from a variety of sources. This data must be received, processed, and transformed into the format that business users and analysts need to start analyzing it.

• Management

Big data requires storage, whether local, cloud-based, or both. The data must also be stored in whatever form is required, as well as be able to be processed and made available in real time. Cloud solutions are increasingly becoming more popular for companies due to their unlimited compute and scalability.

• Analysis

Finally, for the investment to be worth it, the data must be analyzed and utilized. This can be done by sharing data and insights across the business in a way that is understandable. Data visualization tools can be used to create meaningful and actionable charts, graphs, and dashboards.

How Big Data is stored and processed

Big data is often stored in data lakes, which can support and contain various data types, unlike data warehouses, which are commonly built on relational databases and can only contain structured data.

Data lakes are typically based on Hadoop clusters, cloud object storage devices, NoSQL databases, and other big data platforms.

A big data environment combines multiple systems in a distributed architecture; for instance, a central data lake might be integrated with other platforms such as data warehouses and relational databases.

The data in a big data system can be left in its raw or native form and then filtered and organized as needed for specific analytic uses, or it can be pre-processed using data mining and preparation tools so it’s ready for applications and processes that are regularly run.

Organizations and businesses can deploy their own cloud service or utilize offerings from cloud service providers, such as managed BDaaS (big data as-a-service).

Why is Big Data important?

Enhanced customer experiences

When structured data sources are combined and analyzed together with unstructured ones, useful insights can be gleaned that can help better understand consumers, create personalized marketing campaigns, and optimize their experiences to better meet their needs and expectations. These actions can ultimately increase revenue and profits.

Operational efficiency

Big data analytics tools and capabilities can help organizations process data faster and generate valuable insights that can help determine areas where costs can be reduced and time saved, increasing the overall efficiency of the organization or business.

Increased agility and innovation

An organization can collect and process real-time data, quickly analyze the data, and use the insights to gain a competitive advantage. Such insights can guide and accelerate the planning, production, and launch of new products, features, and updates,, as well as help discover new opportunities for growth and value.

Improved decision-making

When big data can be analyzed and managed, patterns can be discovered and new insights unlocked that can improve and drive better operational and strategic decisions in an organization.

Risk management

Analysis of the vast amounts of collected data can help organizations better evaluate risk. It becomes easier to identify and monitor all potential threats and thus develop better risk control and mitigation strategies.

Challenges of implementing Big Data analytics

• Insufficient supply of data scientists, engineers, and analysts is one such bottleneck that prevents organizations from realizing value from big data environments.
• Big data contains valuable and sensitive business, individual, and customer information and is thus a valuable target for attackers and hackers.
• By nature, big data is rapidly growing and changing, and it can be unwieldy and difficult to manage without a solid infrastructure in place to handle storage, network, and security needs.
• Raw data is messy and can be difficult to curate. The quality of business decisions and strategies is directly tied to the quality of the data and insights gleaned from such data. Unless the data is accurate, relevant, and properly organized for analysis, it can lead to misleading results, worthless results, and even catastrophic consequences when key decisions are based on such data.
• Because big data is sourced from a host of avenues and sources, it sometimes contains sensitive data that might breach and violate privacy and regulatory requirements.

Sample Big Data platforms and services

• Google Cloud
• Amazon EMR
• Microsoft Azure HDInsight
• Cloudera Data platform
• HPE Ezmeral Data Fabric (Helwlett Packard Enterprise)

Boney Maundu

Tech Contractor & Writer

Slim Bz Techsystems

Nairobi, Kenya