Recent history shows that companies are looking to data to inform their business decisions, identify challenges, and capitalize on opportunities. In fact, 92% of businesses are using analytics to gain greater marketing insights, while 72% rely on social media information to improve customer relationships. Data can also help organizations:
Track potential customers
Map company performance
Improve customer retention
Predict sales trends
Today, many industries are looking to harness big data to create a competitive advantage. With the integration of the internet of things as well as an increase in connected devices, companies are turning to digital transformation services to help collect insights gained from big data.
Big data is the collection of large pools of data that are brought together to be analyzed in order to make better, more informed decisions. These pools of data are so large and complex that traditional data processing software can’t manage them. Many different enterprises use big data to make more accurate decisions about their business quickly. This process, also called “situational awareness,” allows businesses to identify anomalies in patterns and behaviors that can affect the outcome in a business or process. In short, big data provides companies the insight to pivot business decisions to create more successful outcomes.
Big data can be more clearly defined by the five V’s:
This characteristic plays a big role in defining big data, as the size of data helps determine its value. Big data consists of large amounts of data that is typically unstructured. One example is demonstrated by Netflix; the streaming giant deals with a data warehouse that is more than 60 petabytes in size.
This characteristic defines how fast big data is collected. With the advancement in technology, the speed can increase year over year and allow businesses to capture a large number of data points at the same time. An example of an organization that is experiencing this is Google; the search engine receives over 38,000 searches per second every day.
This characteristic shows the worth of the big data that is collected. Businesses should review all data that is collected to determine exactly how it is valuable to them. Examples of data that is valuable include customer lifetime data, average order data, and cancellation rate data.
This characteristic defines the different types of data captured. Big data is generally a mixture of structured data (e.g., an email address or phone number captured in a form) and unstructured data (e.g., free-form comments).
This characteristic defines the trustworthiness of the data. Businesses must be certain that their data is not fraudulent or inconsistent; otherwise, the data cannot be trusted. Additionally, bulk data can create confusion so it’s important for businesses to have data processing in place.
Businesses across all industries have been storing and processing data for years. However, with the increased use and advancement of technology, the rate that data is being collected has risen exponentially. The way data is used and how it is processed have also changed – as have the techniques used to gain insights. Now, businesses are using big data to solve business problems that traditional data-processing software cannot handle. Examples of how big data is different include:
Flexibility: Big data uses dynamic databases that include structured and unstructured data. Traditional data is based on a fixed, static database that could only work with structured data.
Real-time analytics: Traditional data gets analyzed after an event. Big data allows companies to analyze data in real-time, so they are able to make decisions faster.
Scalability: Big data uses distributed database architecture while traditional data is based on a centralized architecture. This helps make big data more scalable and cost-efficient, and improves performance.
Increased data sources: Nowadays, there are many sources that capture data almost all the time. Formerly, data sources were limited or highly siloed.
Preliminary analysis: Traditional data necessitated questions at the start, which means that businesses needed to know what was wrong before they analyzed. Big data allows businesses to discover the questions that should be asked.
Essentially, big data allows companies to work faster now than they were using traditional data-processing software — providing a competitive advantage when correctly leveraged.
Disruptive technologies or innovations are events that replace or “disrupt” highly established processes and products. Disruptive technologies or innovations are typically more accessible and cheaper – and they use business models with structural cost advantages. These characteristics can give a business a competitive advantage if it is leveraged correctly.
Big data is considered a disruptive technology because it changes the way companies view and analyze data. In general, adopting this technology means:
Companies must acquire new tools to analyze data
Employees must gain knowledge, skills, and insight
Companies must compete with other businesses and industries that have already begun to use big data tools by creating new business models, services, and products
Companies can take advantage of big data in many ways including:
Searching and compiling data can take a lot of time and create inefficiency in the workplace. Big data can help make information more transparent by analyzing and storing data in real-time, allowing workers to gain more insight and become more efficient.
It utilizes a dynamic IT infrastructure, which can help reduce costs. The volume of big data requires accessible storage, making cloud-adoption a necessity. By using the cloud for storage, companies can save money on infrastructure hardware.
Big data offers sophisticated analytics that can help improve decision-making and minimize risks. This can help companies make fewer mistakes that would affect their production costs.
It can help reduce the time it takes for businesses to respond to cybersecurity threats. In addition, analysts are able to observe and detect irregularities within a network to help deter cybercrime.
Companies can use big data to enhance the customer experience. They can collect accurate and detailed information to improve business strategies, including email follow-ups, better marketing initiatives, and inclusive customer service.
Companies can use predictive analytics to create improved products that increase consumer value and minimize risks associated with the product’s launch.
Big data can offer a variety of benefits to companies as they move forward. By understanding how big data is different from traditional data and how to leverage it, businesses can make more informed decisions that can directly impact their revenue and their customers.
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
Application Modernization at Speed and Scale Enterprises are pursuing greater application scalability, cost efficiency, and standardization with containerization and virtualization platforms. So, what’s the difference? Containers are a type of virtualization technology that allows users to run multiple operating systems inside a single instance of an OS. They are lightweight and portable, making them ideal for running applications across different platforms.Explore
Container Orchestration or Compute Service? Amazon Web Services (AWS) offers a range of cloud computing services to meet enterprise needs. Included in its service offering is the elastic compute service (ECS) and elastic compute cloud (EC2). Choosing between these two services can be difficult, as one focuses on virtualization while the other manages containerization. In the following article, we will explore the differences between Amazon ECS and EC2 to help you better understand which service is right for your use case.Explore