For most modern businesses, data can be used and manipulated at the touch of a button. However, the ability to use that data and interpret the information is more effortless said than done. New careers are being forged every day to solve that problem.
Data analytics stores organizes, and analyzes data for future business decisions or processes. Every truly modern business is in a race to incorporate data and analytics into their day-to-day business decisions to increase efficiency and decision effectiveness.
But there is a long way to go. A study by NewVantage in 2021 discovered that only 39% of executives believe their organization is correctly using their data, and only 24% see their company as being “data-driven.”
This is a primary reason why data analytics careers are growing in number and salary faster than nearly every other field. Businesses demand data analysts to help them bridge the gap between the present and the future.
What is Big Data Analytics in Modern Business?
A data-driven company can be described as a business that uses data to:
- Describe and analyze the company’s strengths and weaknesses
- Prioritize investments
- Allocate resources
- Build KPIs to manage progress
- Become more data-driven and competitive
Data analytics can also help businesses study the effectiveness of their current workflows, analyze different outcomes of processes, automate workflows, and refine them over time to keep them up-to-date.
Three types of business analytics can be used to gather this insight, and they are:
- Descriptive Analytics – what has already happened
- Predictive Analytics – what could happen
- Prescriptive Analytics – what should happen
Big data is defined as the use of large volumes of aggregated data points to predict outcomes, determine changes in trends, and target audiences.
What is Big Data, and How Is It Used in Business?
In its simplest terms, big data is defined as larger, more complex data sets, particularly from a new data source. This type of data set is so large that it cannot be handled by traditional data processing software.
Big data’s value lies in enabling predictive modeling, allowing businesses to discern patterns and trends in how people interact with systems and each other. This actionable insight empowers companies to understand customer preferences better and develop effective marketing strategies, customer service, and customer-centric initiatives driving business growth. Leveraging big data is crucial for maintaining a competitive edge – new entrants rely heavily on it to innovate and compete against incumbents, while established firms utilize it for faster, data-driven decision-making. Across industries, companies that effectively harness big data hold a significant advantage by making more informed decisions.
The Three V’s of Big Data
A big data system, like a data lake or a data warehouse, that stores and processes a large amount of data is commonplace in data management; big data is uniquely classified by “the three V’s:”
- Volume: Unlike other forms of data management, big data involves a colossal amount of data processing.
- Velocity: The speed at which the data is collected and processed. With big data, this happens at a rapid pace.
- Variety: Big data encompasses a wide range of data types, including structured data (organized in databases and spreadsheets, like financial transactions and customer records), unstructured data (lacking predefined formats, like text documents, multimedia, and social media), and semi structured data (containing some organization through tags and markers, such as XML, JSON, logs, and certain multimedia metadata).
Additional V’s of Big Data
While no definitive size threshold exists, big data deployments often involve processing exabytes of data collected over time. In recent years, two additional characteristics have emerged: value and veracity. Value reflects the potential worth of data to a company, while veracity refers to its accuracy and trustworthiness. Raw data collected from various sources can often contain quality issues that may be difficult to identify and address. These additional dimensions highlight the importance of ensuring data quality and integrity when leveraging big data for business insights and decision-making.
Sources of Big Data
Big data can come from several sources, including customer data, emails, medical records, and social media networks. It can also include machine-generated data, such as sensor data from manufacturing equipment and other industrial machines.
Big data often incorporates external data, including weather and traffic patterns and geographic information. Audio and video files are frequently included as forms of big data, as streaming data is often processed and collected to aid certain companies with big data applications.
How is Big Data Analytics Important to Modern-Day Programmers and Developers?
The role of programmers and developers in modern-day business is increasingly important and relies on data analytics.
Collaboration Between Developers and Data Analysts
Data analysts and back-end programmers work closely together to interpret data and act on identified patterns and trends.
Front-end developers leverage data to create and maintain websites/platforms aligned with current trends and competition.
Data Visualization for Effective Communication
Beyond using data to construct, data visualization is also essential in all aspects of business. Tools like Tableau and MySQL empower developers and software engineers to communicate more effectively with decision-makers.
Importance of Collaboration in Software Development
These days, software developers must know more than just how to program in their chosen languages. Developers now must be experts in collaboration, relying on communication between various teams, their managers, and often even other companies.
With the increasing use of big data in software development, this type of collaboration is becoming more and more streamlined daily.
While many different types of companies use it in myriad ways, big data’s application in software development covers almost every category. From customer service to product development, correctly analyzing big data provides developers with information about consumer habits and preferences, potential bugs or errors, key functionality points of an app or program, and much more.
Taking advantage of the availability of this information allows a team of developers to streamline much of the research and development and correctly identify and avoid potential user issues before they occur.
A software development team that properly utilizes big data will end up with a product that users are more likely to prefer, and they will be able to get it into the hands of the customer faster than their competition.
Outside of developing their applications or programs, developers are increasingly being called upon to program predictive analytics systems that can be used in other software applications and business processes.
Examples of Big Data Applications
Leading companies like Netflix and Amazon leverage big data technologies like artificial intelligence (AI) and machine learning for audience segmentation and data-driven decision-making.
Netflix utilizes advanced analytics on viewer data cross-referenced with demographics to predict content performance and strategically invest in future productions.
Amazon employs machine learning across customer experience, productivity, process optimization, and innovation, offering these ML tools via its AWS platform.
Big data is vital in predicting customer demand to guide product development strategies, enhancing customer service through personalization and proactive issue resolution, and identifying cyber threats for security teams to respond faster.
Additionally, the accumulation and analysis of industry data present new business opportunities as companies seek to capitalize on available insights.
Data Analytics As a Career
Thousands of schools now offer specialized data analytics training programs to meet the growing demand for big data expertise.
CIAT provides an Applied Bachelor’s Degree in Software Development – Data Analytics and an Associate of Applied Science in Business Data Analytics.
These courses equip students with the necessary skills to advance in data analytics roles and become competitive job seekers.
Growing Demand for Big Data Expertise
The big data market is projected to double by 2027, driving a surge in demand for developers proficient in managing and utilizing big data effectively. This increasing demand fuels innovation and collaboration in software development, reflecting the competitive importance of data analytics and generative AI.
A Data scientist is among the most sought-after professionals, commanding an average salary of $108,020 annually (as of 2023).
A data scientist is responsible for a wide range of operations, including:
- Querying data
- Data mining
- Data cleansing
- Examining and interpreting data trends
- Preparing reports
- Summarizing data sets
- Collaborating with pertinent stakeholders
The potential for continuous learning and skill development in this field is endless, making it a valuable asset across corporations of all sizes.
