Big Data Analytics: The Competitive Advantage Companies Need

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:

  • Monitor competitors

  • Track potential customers

  • Build apps

  • Generate leads

  • 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 Definition

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.

The Five V’s of Big Data

Big data can be more clearly defined by the five V’s:

Volume

Volume

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.

Velocity

Velocity

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.

Value

Value

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.

Variety

Variety

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).

Veracity

Veracity

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.

How Data Has Changed

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.

Big Data as a Disruptive Technology

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

How to Leverage Big Data

Companies can take advantage of big data in many ways including:

Workplace efficiency

Workplace efficiency

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.

Cost reduction

Cost reduction

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.

Risk management

Risk management

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.

Detecting cybersecurity threats

Detecting cybersecurity threats

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.

Customer engagement

Customer engagement

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.

Develop new products

Develop new products

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.

Experience the Trianz Difference

Trianz enables digital transformations through effective strategies and excellence in execution. Collaborating with business and technology leaders, we help formulate and execute operational strategies to achieve intended business outcomes by bringing the best of consulting, technology expertise, and execution models.

Powered by knowledge, research, and perspectives, we enable clients to transform their business ecosystems and achieve superior performance by leveraging infrastructure, cloud, analytics, digital, and security paradigms. Reach out to get in touch or learn more.

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