Big data provides a wealth of information for businesses that, when analyzed, helps inform decisions that can streamline resources, improve efficiency, optimize product development and other procedures, and drive growth and revenue opportunities. The insights one gathers from big data are indispensable.
Because big data management and analyses can be costly for businesses, many choose to take advantage of managed service providers’ big data services to assist with the collection, management, and analysis. Big data’s insight and value are too important for businesses to ignore.
Understanding Big Data and Analytics
Big data is data that is too large in volume or complex to be processed by traditional data-processing methods. It is classified by its volume, variety, and velocity, known as the “three V’s.” This refers to the collection’s size, the scope of nonstandard formats, and the required processing speed and efficiency.
The types of big data fall under three categories: structured data, unstructured data, and semi-structured data.
Structured data is often the most accessible data set because of its organization and clearly set parameters. Structured data is organized like a spreadsheet and includes quantitative data such as age, contact, address, and billing.
Unstructured data is unorganized data, most often generated by a computer, and requires teaching an application to understand the data. Because this data lacks organization, it is stored in data lakes, which maintain the raw data’s format.
Semi-structured data is, essentially, unstructured data with metadata attachments. Identifying patterns within the metadata can be integral to machine learning and AI training. This data type includes information such as location, time, email, and device ID stamps.
Regardless of the type of data, big data analytics is required to make sense of it. Big data analytics utilizes mining, cleaning, integrating and visualizing techniques to identify patterns and relationships among the data set. The four techniques most used are diagnostic analytics, descriptive analytics, predictive analytics, and prescriptive analytics.
Real-World Examples of Big Data Types
Understanding big data can be simpler when applied to real-world situations. Here are some examples of how the various types of big data fit in to specific industry verticals.
In the healthcare industry, structured data is commonly found in electronic health records (EHRs) that store patient information such as demographics, medical history, diagnoses, and treatments. This structured data enables healthcare providers to easily access and analyze patient information for accurate diagnosis and treatment planning.
Unstructured data in healthcare includes clinical notes, medical imaging, and pathology reports. These unstructured data types require specialized applications or natural language processing techniques to extract meaningful information from the data. For example, an application can be trained to analyze medical imaging data and detect abnormalities or assist in diagnosing diseases.
Semi-structured data in healthcare might include data from wearable devices, patient-generated health data, or data from medical devices. The metadata attached to this data, such as time stamps or device IDs, can be used to identify patterns for predictive analytics or personalized healthcare interventions.
Financial Services Industry
In the financial services industry, structured data consists of transaction records, customer account information, and financial statements. Banks and financial institutions utilize big data analytics to process large volumes of structured data for fraud detection, risk assessment, and customer profiling.
Unstructured data in the financial industry includes customer communications such as emails, social media interactions, and customer support chat logs. Analyzing this unstructured data can provide insights into customer sentiment, identify potential issues, and improve customer service.
Semi-structured data in finance might include trade data or stock market feeds that contain metadata such as time, location, and trade details. By analyzing this data, financial institutions can make informed investment decisions, detect market trends, and develop trading strategies.
In the retail industry, structured data includes sales transactions, inventory data, customer profiles, and purchase history. Retailers use big data analytics to analyze structured data and gain insights into consumer behavior, optimize pricing strategies, and improve supply chain management.
Unstructured data in retail includes customer reviews, social media posts, and product descriptions. By analyzing this unstructured data, retailers can understand customer preferences, sentiment analysis, and gather feedback for product improvement.
Semi-structured data in retail can include clickstream data from online shopping platforms, which contains metadata such as timestamps, user locations, and device information. Analyzing this data can provide valuable insights into customer browsing behavior, identify potential bottlenecks in the online shopping experience, and personalize product recommendations.
Types of Big Data Analytics
Diagnostic analytics refers to identifying the “why” of the data by recognizing relationships or patterns in the data set. This type of analysis helps businesses better understand the data and inform their questions.
Descriptive analysis is beneficial when working with finance, production, or sales. One of the most used analyses, descriptive analysis, performs simple mathematical operations on raw data to identify trends and insights through samples and measurements.
Predictive analytics utilizes machine learning and other predictive tools to assess the data set and generate forecasts of the future, helping businesses make informed decisions and point toward possible directions.
Prescriptive analytics utilizes information gathered from descriptive and predictive analyses and other techniques to identify ways businesses can optimize practices.
When properly utilized, big data provides businesses with valuable insights based on real-time data. Through analysis, businesses can draw conclusions that can help streamline management, optimize development, improve efficiency, and drive growth opportunities.
Real-World Applications of Big Data Analytics
Here are some real-world examples of how analytics might be carried out in several industries.
Finance Example: A financial institution wants to analyze its investment portfolio to understand the performance of various assets over a specific period. By performing descriptive analysis on historical financial data, they can calculate key metrics such as average return, standard deviation, and correlation between different assets. This analysis helps identify trends, assess risk, and make informed investment decisions.
Production Example: A manufacturing company wants to analyze production data to identify bottlenecks and optimize its manufacturing processes. Descriptive analysis allows them to calculate production cycle times, identify the most time-consuming steps, and pinpoint areas for improvement. By analyzing the data, they can optimize production schedules, allocate resources more efficiently, and improve overall productivity.
Sales Example: A retail company wants to understand its sales performance across different regions and product categories. By conducting descriptive analysis on sales data, they can calculate metrics such as total sales revenue, average sales per region, and top-selling products. This analysis helps identify successful sales strategies, optimize inventory management, and make data-driven decisions to drive sales growth.
Healthcare Example: A hospital wants to predict patient readmission rates to improve care and reduce costs. By applying predictive analytics to historical patient data, including demographic information, medical conditions, and treatment history, they can develop a predictive model. This model can forecast the likelihood of readmission for individual patients, allowing healthcare providers to implement proactive interventions and personalized care plans to reduce readmissions.
Marketing Example: An e-commerce company wants to predict customer churn to retain valuable customers. By analyzing customer data such as purchase history, browsing behavior, and demographic information, they can build a predictive model using machine learning algorithms. This model can identify customers at risk of churn and enable targeted retention strategies, such as personalized offers or loyalty programs, to reduce customer attrition.
Supply Chain Example: A logistics company wants to optimize its supply chain operations to minimize costs and improve delivery efficiency. By combining data from descriptive and predictive analyses with optimization algorithms, prescriptive analytics can suggest optimal routes, inventory levels, and transportation modes. This analysis helps the company make data-driven decisions to streamline its supply chain, reduce operational expenses, and enhance customer satisfaction.
Energy Management Example: A utility company wants to optimize its energy generation and distribution to meet demand while minimizing costs and environmental impact. By leveraging data from descriptive and predictive analyses, along with optimization techniques, prescriptive analytics can recommend the optimal mix of energy sources, grid management strategies, and demand response programs. This analysis helps the company maximize efficiency, reduce carbon emissions, and ensure reliable energy supply.
Managed Service Providers and Big Data
A Managed Service Provider, or MSP, provides businesses with IT services, including but not limited to IT infrastructure management, cybersecurity, technical staff support, user access account management, and hardware outsourcing.
MSPs help streamline IT for businesses and free up resources that would otherwise be spent on staffing and hardware for in-house solutions. Choosing an MSP for managing and analyzing big data is a natural solution for businesses that are unable to, or would prefer not to, handle it in-house.
When a business partners with an MSP, it gains access to equipment, resources, and expertise it otherwise might not have access to. By hiring an MSP specifically for big data resources, the business can reap the full benefits of big data analysis without the costly in-house investment.
A managed service provider can provide big data services in operation, consulting, and system integration, covering everything from data management and custom analysis to cybersecurity.
The benefits of working with an MSP include improved data collection and storage and enhanced data analysis. MSPs can customize their approach and techniques to suit businesses’ unique needs, cutting down on the time and cost necessary for in-house big data management and analytics.
Best Practices for Working with MSPs for Big Data Projects and Beyond
Choosing the right managed service provider for your big data project, or for other work, can be complicated. However, there are key factors businesses can focus on to help ease the process and set up a successful partnership.
Businesses should consider the following needs when choosing an MSP: services, security, expertise, customer service, customization, onboarding, and cost.
Businesses must determine their needs to ensure they partner with an MSP that provides the appropriate services. Not all MSPs may provide big data services, so it is essential to establish this early on. It is also vital that the business and MSP are on the same page regarding security.
Partnering with an MSP means combining expertise, so the business should feel confident the MSP is knowledgeable in the field the business is looking to outsource. This is also important when considering the MSP’s customer service procedures.
Because the MSP will handle essential aspects of the business, both parties should be able to foster a respectful and helpful relationship. One of the many benefits of an MSP partnership is customization, so it is vital to gauge the room for customization early to optimize procedures.
It is also vital that the business understands the MSP’s onboarding procedures and cost to ensure a smooth-running relationship from the start.
Communication is key both in the beginning and throughout the partnership. When a business can clearly identify its objectives and goals and communicate these ideas transparently on a regular business, the MSP can determine the steps necessary to achieve these goals.
Analyzing and Implementing Big Data
Big data can hold the key to efficiency and success within a business; however, the information can be lost without the expertise and resources to perform big data analytics. By taking advantage of big data, businesses can better understand the success of their current procedures and formulate a plan for the future.
Because big data analytics can be costly and arduous for businesses, partnering with a managed service provider can alleviate the stress while providing invaluable insight and expertise. With the help of an MSP, businesses can focus on implementing valuable big data insights without any distractions.
Need help with big data or with your IT systems or processes? TAG Solutions has a skilled team of managed service providers in the Albany, New York area. We are happy to help talk through your systems and offer suggestions. Contact us today!