Big Data and Data Analytics—What’s the Story?
According to a 2021 Forbes article, How Big Data Empowers Organizations To Work Smarter, Not Harder, “...experts believe that 463 exabytes of data will be created daily worldwide by 2025. Brands and organizations are incorporating this data into their business models — some more successfully than others. Precise big data fusion and analytics are dramatically altering the way organizations function….we must ‘work smarter, not harder’….With the right big data platforms in place, the more data, the better — as long as organizations have the capabilities to properly analyze it.”
Big Data‚ in brief.
Data is nothing new. Before computers, companies collected and stored transaction information, customer records, and so forth as paper files. Computers brought databases and spreadsheets—methods of organizing, storing, and easily accessing larger amounts of information.
We live in an increasingly digital world. This was already true prior to 2020, but the two-year pandemic that pushed us out of offices, schools, restaurants, theaters, and stores—and into remote work, Zoom classrooms, virtual gatherings, online shopping, and binge-watching shows on streaming services—accelerated this reality at a mind-blowing pace.
Today, data is generated at a far greater speed and abundance than ever before—and it just keeps growing.
Big Data refers to the massive amounts of information from diverse sources that are now created, and the resulting huge, complex data sets, which cannot be managed by traditional processing methods and software.
Big data has 3 main defining factors, often called the 3 V’s:
Volume - The sheer amount of information that is generated, which is increasing exponentially.
Variety - The vast number and types of sources from which data can be gathered (social media feeds, clicks on a webpage, mobile apps, product purchases, device sensors, etc.)
Velocity - The ever-greater speed at which the data is produced, received, and can be interpreted, including in real-time.
Big data can also be viewed through 2 additional “V” principles:
Veracity - The quality, accuracy, and integrity of the information.
Value - How much real insight the data provides and how useful it is for decision-making.
The bulk of Big Data comes from 3 general sources:
Social Data – Information publicly shared by social media users, including metadata such as their location, language, biographical details, post time and type, likes comments, shared links, and so forth, which can provide customer insights.
Machine Data – Digital information produced by activity on smartphones, computers, networked devices, applications, sensors, embedded systems, the vast Internet of Things (IoT), clickstreams, business process logs, call records, and so forth.
Transactional Data – Information captured during a transaction, including where and when, price point, payment method, sales or discounts, quantities, etc.
All of these sources together produce 3 primary types of data:
Structured – Data, mostly numeric, generated by both humans and machines, which fits predefined, relational models and can be organized, stored, and managed in traditional systems like databases and spreadsheets.
Unstructured – Diverse types of information that do not fall neatly into predetermined models, including photo/video/audio in social feeds, comments, texts, instant messages, emails, and so on. This type of data can provide valuable insights into customer needs and behavior, for example, but requires more resources (Machine Learning, AI, natural language processing, and more) to manage and analyze it. It is typically stored in native format until it can be extracted for analysis.
Semi-Structured – A combination of the above, this data does not conform to a traditional data models but does have some structure—definite and consistent characteristics (ex: XML files).
All this data is only as useful as it is interpretable and actionable. Harvesting big data is not enough on its own. The value of this enormous body of information and its fast-paced ongoing feed hinges on a company’s ability to manage and analyze it effectively and expediently—to connect the dots and understand the story the data tells.
Data Analytics provides crucial insights by systematically processing and examining huge amounts of data from a vast array of sources, using advanced techniques to find patterns and identify clear narratives. It can offer almost immediate—often real-time—feedback that allows companies to track historical data and leverage that information to make fact-based decisions for the future.
Big Data and Data Analytics are a power couple that drive an enterprise’s growth, competitiveness, and profitability. Together, they can help your company:
- Understand your customers’ needs and buying behavior and make them happier
- Predict key market trends, identify opportunities, and market new products
- Optimize your company’s day-to-day operations
- Improve inefficiencies in supply chain, workflow, etc.
- Identify potential security threats
- Make better decisions faster
- Allocate and invest valuable resources (capital, time, staff, etc.) for maximum efficiency, cost-effectiveness, and growth
As Forbes also points out, “It’s critical that leaders understand their goals before investing in this technology.” Many businesses today are inundated with data but lack the necessary tools to analyze it, implement insights rapidly, and keep it secure.
At Lukasa, we understand the opportunities—and challenges—that Big Data presents. Our business modernization and digital transformation experts partner directly with your team to gain a deep understanding of your company’s unique goals and needs, and provide custom data collection and analytics solutions—helping your enterprise harness the power of Big Data.
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Lukasa is a business and technology modernization firm focused on process analysis and improvement, system and data unification, cloud migration, tailor-made software and implementation—maximizing efficiency and growth.