What is data analytics? Why is it important? Why should companies be interested in it? This article will explain data and video analytics and explore the different types, the benefits, the role it plays in smart technology and digital transformation, and how businesses can use it to help add value to their organization’s operations and efficiency.
April 22, 2024
Data analytics uses computer modeling and algorithms to make sense of large volumes of data. It can help reveal hidden values, trends, and patterns that the human brain cannot compute. Organizations collect huge volumes of data and use data analytics to analyze the data, which can uncover valuable insights to help optimize operations, manage expenses, boost efficiency, improve customer satisfaction, and increase revenue.
Data analytics is pivotal to smart technologies. For example, the data analytics that are integrated into smart buildings technologies can help measure, assess and compare the performance of facilities, including water usage, energy consumption, or maintenance requirements.
In retail, stores can use data analytics to help create more optimal customer experiences by helping to predict customer spending based on historic purchases or by providing a more personalized customer experience. Retail companies can analyze their data to predict demand for potential new products and track inventory levels of current stock, helping businesses better plan and manage their inventory. Retail businesses can also use data analytics to help enhance marketing efforts, allowing brands to help connect sales metrics to the efficacy of their campaigns, while gaining insights from store activity that help inform future campaigns.
Data analytics plays a key role in enabling smart technologies across various sectors, including building management, retail and transportation. By harnessing the power of data, businesses can work to optimize operations, enhance customer experiences, and drive informed decision-making for sustained growth and competitiveness.
Descriptive analytics is often considered the simplest form of data analytics because it focuses primarily on summarizing and presenting historical data without using complex statistical models. It involves organizing raw data into understandable formats such as graphs, tables, dashboards, or reports to uncover patterns, trends, and anomalies. For example, descriptive analytics can provide insight into how different promotions performed or how different sales categories compared to one another during a given period. By using this information, organizations can make informed and data-backed decisions to identify areas of growth and optimization.
Predictive analytics is a more advanced form of data and video analytics. It uses predictive models, such as decision trees, regression, and neural networks, to make predictions about unknown or future outcomes or performance. Predictive analytics looks to answer: “What might happen next?”
Financial services companies might use predictive analytics to build new portfolios. Retail organizations may predict future sales based on a specific event, like a major holiday or seasonal trends. Buildings might predict potential future energy consumption patterns or occupancy rates. Predictive analytics is a powerful tool for business owners looking to gain insight into future patterns, challenges, and opportunities.
Where predictive analytics use historical data to make predictions about future outcomes, diagnostic analytics examines the data to understand why certain patterns or events occurred. Diagnostic analytics seeks to answer the question of “why” by exploring factors, anomalies, correlations, and the impact of actions or changes. For instance, it could reveal insights into marketing strategies, connecting shifts in sales to specific promotions or external factors.
Diagnostic analytics focuses on the correlation between what happened (descriptive analytics) and the anticipated potential outcomes (predictive analytics). These detailed insights, and more holistic view of the data, can help provide companies with a fuller understanding of influencing factors, and empower them to make more precise decisions.
Diagnostic analytics can be used in a variety of verticals, such as healthcare, financial services, retail, and more, helping businesses better understand what factors contributed to an outcome. It then enables these organizations to take corrective measures in future.
Prescriptive analytics focuses on recommending actions based on the insights from descriptive, diagnostic, and predictive analytics. Using advanced algorhithms and machine learning, prescriptive analytics seeks to answer the question: “What should we do next?” It aims to remove human bias and instinct from the decision-making process by using data to predict the likelihood of how different decisions will lead to a specific outcome. It can provide insight into the ‘worst-case scenario vs. successful outcome’ by forecasting the likelihood of a certain outcome occurring based on a specific decision.
For example, financial services organizations can use prescriptive analytics to optimize upselling and cross-selling or to recognize weaknesses such as improper financial planning. In retail, organizations can use prescriptive analytics to predict certain outcomes based on the launch of new products or services, while property owners might use it to improve resident retention based on different decisions, or by aiming to reduce expenses.
Retail data analytics provides retailers with tools to help manage expenses, drive revenue, reduce overhead costs, and avoid overstocking. By understanding how customers behave and act, data analytics can help personalize the buying experience by suggesting new products based on someone’s purchase history. For instance, digital signage technologies can display personalized recommendations, enhancing customer experience and increasing sales.
Data analytics can also help identify different buying patterns based on geography, driving sales and helping manage transportation costs. It can also help optimize energy usage in stores and warehouses, which can contribute to improved energy efficiency and lower costs.
Property owners and management are using data analytics to transform how they are running multifamily properties, to provide timely maintenance and repairs, and to help maintain resident satisfaction. Property managers can integrate data sources to help identify recurring problems, prioritize necessary repairs, and predict upcoming maintenance requirements. Smart Buildings technology, like energy management and water monitoring, can provide real-time alerts if an anomaly occurs, while Smart EV Charging technology can identify and often remotely resolve charger issues, helping to maximize charger uptime and minimize any disruptions to resident charging needs.
Safety is often a consideration for residents of multifamily properties; data analytics can help enhance security and property safety through its real-time data tracking, using derived insights to make decisions that take security into consideration.
Data analytics can transform hospitality establishments by providing a personalized guest experience through the collection and analysis of data on guest preferences and behaviors, whether it’s a customer’s favorite meals or when they’re more likely to visit. This intel is valuable in crafting a personalized experience that drives customer satisfaction and leads to increased loyalty.
Data analytics offers additional benefits for hotels, helping them optimize their operations, manage labor efficiently, and allowing them to continue to compete in a competitive marketplace. It can assist hotels by analyzing customer demand and behavioral patterns, which informs more effective customer base management. Anticipating guest needs becomes more streamlined, enabling hotels to provide tailored recommendations based on guests’ interests, from bespoke dining suggestions to spa treatments and local attraction recommendations. IoT devices and sensors can gather data on guest preferences to personalize room settings or suggest popular meals, driving guest satisfaction.
It can also help enable improved marketing efforts by helping to understand key demographics, more efficient management of inventory and staffing requirements, and reduction of waste and by analyzing food and beverage sales.
There are several necessary steps when performing a successful analysis of data analytics. First is to establish your goal and identify the questions you are trying to answer, which will determine which type of data analytics can provide the most value.
The next step is to collect and organize datasets, which can be quantitative (such as sales figures) or qualitative (such as customer experience and feedback). This data will need to be structured in a way that organizes the parameters being observed and measured. Once collected, review the data using advanced data analytics tools, removing duplicates and filling in any missing values.
Once you have a prepared dataset, you should run it through your chosen data analytics tool, which could include diagnostic, predictive, or others, depending on your needs. Results should be easily visualized and shared as dashboards, infographics, maps, scatter graphs, or any number of visual tools to help explain the information. Many data and video analytics tools automate the entire process, providing the operational and strategic insights you require.
To optimize data analytics for your organization, factor in your size and complexity. In larger setups, where analysis demands are high, having the resources to employ a dedicated data analyst can be beneficial. These dedicated analysts can drive the discovery and understanding of nuanced insights, making it a strategic investment for organizations to navigate data complexities more effectively.
Based on recent trends we believe that data analytics will be increasingly integrated into business practices and processes. As technologies such as AI and machine learning advance, data and video analytics tools will provide greater insights into even larger data sets. More and more, we will see real-time analytics come to the forefront, allowing organizations to spot trends, patterns, and anomalies, and make data-driven decisions almost immediately.
Data analytics is becoming a necessity, not a luxury. To help your business run effectively and provide optimal experiences for customers, the insights made available by data analytics will be a critical tool.
If you would like to find out more about how Comcast Smart Solutions can work with your company, get in touch today. We’ll work with you to deliver smart technology solutions that are tailored to your needs and give you the ability to operate at your best.
Get In Touch
