In industries like commercial real estate (CRE), telecommunications, and government, big data analytics has evolved from a buzzword to a fundamental tool for driving business strategy. While terms like “data lakes” and “Hadoop” may make the eyes of most of us glaze over, understanding how big data is being applied and the key aspects of it can break down these concepts into practical insights.
CRE firms use big data to forecast property values, while telecommunications companies leverage analytics to enhance network performance. With data being produced at an extraordinary pace from millions of sources, such as social media platforms, it is no wonder businesses are tapping into this resource. Facebook alone generates over 500 terabytes of data each day, and Walmart processes more than 1 million customer transactions every hour, feeding into databases with an estimated capacity of over 2.5 petabytes.
Experts predict that the volume of global data will double approximately every 1.2 years, further emphasizing the importance of mastering big data analytics for a competitive advantage.
THE 3 V’s OF BIG DATA:
Volume: Vast amounts of data generated every second. Handling such large volumes of data requires scalable storage solutions and advanced processing capabilities.
Velocity: The speed at which new data is generated and processed. For many applications, especially in real-time analytics or streaming data, the ability to handle data at high speed is crucial.
Variety: The diverse types of data available, including structured data (like databases) and unstructured data (like text, images, and videos). Managing this variety requires adaptable data processing and analysis tools.
Big Data vs. Big Data Analytics
The term “big data” gained popularity in the early 2000s, as technological advancements made it possible for organizations to collect, store, and analyze unprecedented amounts of data. Data comes in various formats, such as structured, semi-structured, and unstructured. Structured data, like what you find in an Excel spreadsheet, follows a specific format. Semi-structured data includes things like emails, while unstructured data consists of images and videos. Together, these diverse types of information form what is known as big data.
Big data analytics refers to the process of examining vast and varied data sets, those that are often too large or complex to be dealt with by traditional data processing software. This is more than just handling massive amounts of data. It’s about uncovering hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information that can inform strategic decisions.
To make sense of this data, businesses utilize advanced tools and techniques like clustering, regression analysis, and machine learning from platforms like Snowflake, a cloud-based data warehousing solution that offers flexibility, scalability, and performance. These methods and platforms allow companies to sift through mountains of raw data to find actionable insights, whether it’s predicting consumer behavior, optimizing operations, or enhancing customer experiences.
Chief Technology Officer at LightBox, Eric Bollens explained: “In today’s fast-paced, data-driven world, companies and governments need to embrace big data technologies and analytics, or else they risk underserving their customers and constituents.”
Use Cases for Big Data Analytics
A company’s greatest asset lies in its data. Leveraging big data analytics uncovers key insights, offering a deeper understanding of the factors that impact an organization’s operations, including market trends, customer buying patterns, and internal workflows.
Here is how big data analytics is making an impact in some key sectors:
1. Commercial Real Estate
In CRE, big data analytics is transforming how properties are valued, marketed, and managed. By analyzing data on property transactions, market trends, and even foot traffic patterns, real estate professionals can gain a deeper understanding of market dynamics and make more informed investment decisions. For example, predictive analytics can forecast property values based on historical data and current market conditions, helping investors identify lucrative opportunities.
2. Telecommunications
The telecommunications industry generates massive amounts of data from customer interactions, network usage, and service operations. Big data analytics helps telecommunication companies optimize network performance, improve customer service, and create targeted marketing campaigns. For instance, analyzing call detail records can help identify network bottlenecks, allowing for proactive maintenance and improved service quality.
3. Government
In the government sector, big data analytics is being used to enhance decision-making, improve efficiency, and deliver better services. It’s applied in areas such as public health surveillance, fraud detection and prevention, smart city development, policy making and public administration, and supply chain management, to name a few. During the COVID-19 pandemic, big data analytics was used to track the virus’s spread, predict hotspots, and allocate resources such as ventilators and vaccines.
Three Steps to Harnessing Insights from Big Data
The core principle of big data is that greater visibility leads to more informed decision-making, allowing an organization to identify growth opportunities and refine business strategies. To harness the power of big data, companies need to focus on three essential steps:
Integration: Massive volumes of raw data, often reaching terabytes or even petabytes, are gathered from various sources. This data must be received, processed, and transformed into a usable format so that analysts and business users can begin extracting insights.
Management: Handling big data requires significant storage capacity, whether through cloud-based systems, on-premises infrastructure, or a combination of both. The data needs to be stored in the appropriate format and made accessible in real time. Many businesses are opting for cloud solutions due to their flexibility, scalability, and virtually limitless computing power.
Analysis: The final and most crucial step is analyzing the data and using the insights to drive action. Without this, the investment in big data offers little return. It is not just about exploring the data, but also about presenting the findings in a clear, accessible manner. Tools that create visual representations—such as charts, graphs, and dashboards—help ensure that insights are easily communicated across the organization.
Unlocking Business Insights for Powerful Data Integration
While companies like LightBox have built data platforms custom-tailored to the scale of their operation, platforms like Snowflake have emerged as a leader in big data management with its easy-to-use cloud data platform. As Bollens explained, “Capabilities of Snowflake unlocks the power of LightBox data and enables customers to join our rich property location and transaction data sets with their own proprietary data to derive new insights almost immediately.”
Combining LightBox data with Snowflake’s ability to handle complex queries and provide real-time analytics aligns with the needs of industries like CRE, where timely and accurate data is crucial for decision-making. With the right tools and platforms, businesses can harness the power of big data to drive growth and innovation.
As big data analytics continues to evolve, it is increasingly being integrated with emerging technologies like machine learning and artificial intelligence, and we can expect to see even more transformative applications of this technology in the years to come.
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