Companies are now able to collect more data than ever before, increasingly enabling them to make data-driven decisions bringing greater efficiency to supply chains and consumer marketing. Businesses must be swift at anticipating future events and reacting to them, looking at ever-changing consumer spending habits and demand patterns to optimize inventory management, supply chain infrastructure, and how they deliver their goods and make their customers happy.
Data analytics explained
Data analysis is an element of data science that covers the tools, technologies, techniques, and processes employed by organizations that use data in order to improve productivity. It ranges from fundamental business intelligence, reporting, and analytical processing to advanced analytics.
Advanced analytics includes data mining, which involves processing and examining large data sets to identify patterns, anomalies, and correlations to calculate outcomes; predictive analytics, which is a way to enable organizations to draw on facts and figures, both current and historical to make predictions about future events such as customer behavior; and machine learning which is a part of artificial intelligence that studies the mathematical models and algorithms used by computer systems to improve the performance of a specific task.
By extracting and cataloging data that is gathered, companies are able to pinpoint and evaluate patterns, relationships, and trends to gather insights and draw conclusions based on that data in order to make informed decisions. This can include information on customers, business processes, industry competitors, and market conditions.
This kind of data analytics is important to help businesses boost their revenue, improve efficiencies, cut costs, respond quickly and effectively to market events and industry trends. These qualities are integral for young and developing businesses, and help to strengthen customer focus and customer service, the most important relationship which enables a business to excel.
What is big data?
Big data is a combination of unstructured, semi-structured, and structured data that can be mined for information and used in predictive modeling, machine learning projects, and other advanced analytics applications. It is often characterized by the three V’s. The first known as the large volume of data; the second named the wide variety of data; and the third, the velocity at which data is generated, collected and processed.
It does not necessarily equate to a specific volume of data, but big data deployments will often involve terabytes (a unit of digital data equal to approximately 1 trillion bytes), petabytes (a measure of memory of data storage that is equal to 2 to the 50th) and exabytes (equal to 1,000 petabytes or one billion gigabytes) worth of data collected over a particular period. A byte is generally a unit of data that is eight binary digits long.
Big data comes from a variety of sources such as transaction processing systems, documents, e-mails, customer databases, medical records, mobile apps, social networks, and internet clickstream logs. It can also include data from sensors on manufacturing machines, industrial equipment, and network and server log files. It encompasses a range of data types, too, including structured data such as financial records and transactions, unstructured data which includes text, documents, and multimedia files, and semi-structured data such as streaming data from sensors and web server logs.
Often, sets of big data are updated on a real or near-real-time basis rather than the daily, weekly, or monthly updates made in traditional data warehouses.
Big data and how it is changing business
Big data has allowed companies to obtain new insights into customer shopping behavior. Rather than relying on sales for all their data, big data allows companies to capture even the smallest customer actions, enabling them to create more effectively targeted marketing campaigns. Big data can provide specific information that is based on browsing and purchasing history, so organizations can create personalized offers to existing customers via e-mail, websites, streaming services, and online advertising.
Big data can also be used to analyze videos, images, text, and audio data on social media, review sites, and other websites in order to identify patterns and attitudes to help deliver the right kind of content.
Customer service can also be improved by the collection and analysis of big data as it allows businesses to know what their customers need before they say they need it. Big data allows for real-time analysis of product issues, allowing companies to provide automated assistance in the form of a chatbot, for example. This process is quicker and more efficient for both business and consumer than the traditional method of calling a company’s helpline in the face of an issue with a product.
Big data can also enable organizations to make customer-responsive products, utilizing the information collected via surveys and analyzing buying habits to predict what customers are looking for. It is now also allowing companies to improve operational efficiency by supplying information about every product and process. This means that engineers can look for ways to make processes more efficient, recognize constraints more easily and thus enabling effective solutions and improvements to be found.
Companies can use big data to find trends and predict future events within their industries, allowing for more accurate forecasts and planning; this can help with understanding how much of a product to produce and how much inventory to keep. Carrying inventory can be expensive, unnecessary inventory ties up useful capital, so enabling organizations to work out when sales will happen and therefore when production needs to occur will lessen the expense of spending on goods that are not needed.
Big data can also assist businesses in identifying possible problems within their supply chain, allowing them to proactively switch suppliers, reroute goods, or use different shippers where necessary. Organizations such as Amazon offer one- or two-day delivery options and many companies now use big data to manage their delivery fleet by co-ordinating delivery schedules and therefore saving spending on fuel.
Fraud detection and cybersecurity
Businesses operating within financial services and insurance use big data to detect fraudulent transactions by finding anomalies. For example, by allowing banks and credit card processors to identify problems before a cardholder even knows their card has been compromised. Analysis of big data can also reduce the incidence of false positives within fraud detection, reducing the need to freeze a merchant’s account, for example, for what may have turned out to be a false alarm.
Cybersecurity and IT professionals are also able to use big data to predict vulnerabilities and threats in order to prevent data breaches. Data is gathered from mobile devices and computers as well as from cloud systems, networks, sensors, and smart devices in order to identify possible problems.
Using big data effectively
It’s important for any organization to be clear about the purpose of its uses of big data right from the beginning, considering the cost of implementation, the anticipated impact, and the length of time it will take for results to be seen. Being transparent with clients and employees is key to building effective relationships, thus a strong business.
Data often has different implications for the various parts of any business so building a collaborative culture allowing shared access and analysis of information will help in creating effective new initiatives based on what is found. The potential huge volume of data involved means organizations may have to use a data center for storage, and as data is an asset, it’s vital to evaluate potential centers based on management practices, costs, backup, reliability, scalability, and security.
Another thing to consider is how much data will be used. Although it may seem a good idea to use all of the data a company has collected over the years, better results can be had by choosing only the most relevant types of data relative to each specific task.
It is important to consider security issues once data has been collected. Big data results are the intellectual property of a business and therefore need to be protected.
Working in big data
The opportunities to work in big data are growing, with companies seeking technical experts to help them understand and use data to improve their businesses. A wide range of sectors employ IT experts including health insurance carriers and online retailers.
According to the Bureau of Labor Statistics, the employment of data scientists is expected to grow by 36 percent from 2021 to 2031, which is much faster than the average for all occupations, with about 13,500 openings projected each year, on average, over that decade. Jobs in data science include:
- Big data tester: Duties include testing data plans to assist in the delivery of data-related products, writing and executing test scripts, analysis, and defining and tracking QA metrics such as defects and test results.
- Database manager: These are creative technical professionals who understand the technology of databases on a large scale They maintain the database environment and undertake project management tasks as well as leading the data team and dealing with budgets.
- Data analyst: Duties include problem-solving and analyzing data systems, as well as creating automated systems that retrieve information from a database.
- Big data developer: Similar to a software developer, big data developers complete the coding and programming of applications and develop and implement pipelines that extract, transform and load data into a product. They may also help to create high-performing and scalable web services that track data.
- Data scientist: Those employed in this field mine, analyze, and interpret data, then present their findings to business leaders. They also make recommendations based on their findings and trends in order to help companies make better decisions.
- Data architect: Combining creative skills and an understanding of database design, data architects create data workflows to assist organizations to achieve their overall goals.
- Big data engineer: Acting as a mediator between data scientists and business executives, big data engineers ensure all work aligns with and enables the company’s overall goals.
Getting the right qualifications
In the ever-growing field of data science, getting the best qualifications to ensure the right jobs are available to candidates is vital. The program offered by St. Bonaventure University, the online business analytics master, is designed to prepare graduates to be strategic problem solvers by giving them an advanced knowledge of business strategy, analytics, and communication. Students will be well prepared for working in the world of big data, learning how to manage and create data warehouses from large datasets in a corporate setting, with a program strategically designed to prepare graduates for high-demand roles.
Final thoughts
A wide knowledge and the confidence gained from a well-balanced course in data science is a sure-fire way to ensure jobseekers are able to keep on top of industry demands. With more advanced big data tools arriving regularly, increased investment in business intelligence software, and the implementation of data lakes, a well-grounded and highly regarded education will give each candidate a competitive edge in the job market.
