Advanced Data Analytics: Revamping the Transportation and Logistics Industry
Embedding Advanced Analytics into Business Decision Making
What is Advanced Analytics?
Advanced analytics describes data analysis that goes steps ahead than simple mathematical calculations such as sums and averages or sorting and filtering. Advanced analytics leverages statistical and mathematical formulas and algorithms to recognize patterns, generate new information, and to forecast outcomes and their respective probabilities. It can be applied to several cases, ranging from the prevention of contract termination, classic customer value and success forecasts, or sales, price, and demand forecasts to new tasks such as the machine failure prediction, monitoring of social media and evaluation, and predictive policing. Advanced analytics enables both innovation and optimization. It has the potential to improve existing processes.
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What are the Different Advanced Analytics Techniques?
In advanced analytics, “advanced” refers to quantitative methods such as algorithms, statistics, and stochastic processes. All the advanced analytics techniques are not predictive, but they are future-oriented as the core idea of the techniques is to support data-driven decisions in the future. Furthermore, the advanced analytics techniques can be categorized into three functions:
The descriptive analysis focuses on understanding an underlying process or phenomenon. The descriptive analysis has the potential to answer questions such as, “Which products do consumers usually purchase together?” or “What are the typical characteristics of customers who tend to churn?”
The predictive analysis aims at studying hidden relationships between factors and outcomes. The data derived is then used to forecast or estimate an unknown value. For example, a predictive model will allow us to estimate how much revenue will be lost if temperatures drop 10 degrees or forecast which customers are going to churn.
Simulation and optimization
Simulation copies the operation and characteristics of a process and condenses the outcome whereas, optimization prioritizes the options of decision-based on a key performance index.
Are you struggling to manage the high volumes of data generated by high-tech sensors? Leveraging advanced analytics solutions may help. It has the potential to generate predictive insights that facilitate proactive business decisions and pre-emptive planning. Request a free proposal here to check out our portfolio of advanced analytics solutions.
Why Advanced Analytics Is Becoming So Important for Businesses?
In this era of digitalization new technologies and analytic methods are improving business operations and are helping businesses to maintain a competitive advantage in the following ways:
- New big data technologies enable processing and analysis of large amounts of data and cost-effective storage
- Modern and intuitive user interfaces allow user groups to derive informed insights and make better decisions
- Advanced analytics software facilitates better analysis of collected data, relationships, and future events.
Today, modern technologies like predictive analytics accelerate and improve decisions and processes along the value chain. Therefore, you should carefully evaluate how advanced analytics can be used within your company in order to excel in the competitive landscape.
Key Benefits of Advanced Analytics
Detailed analysis helps in personalized marketing
Advanced analytics helps identify patterns from less aggregated datasets, such as at the store level or at an individual customer level. For example, advanced analytics can help businesses to capture the characteristics of every individual customer and offer him or her customized products by analyzing the pool of data. This eliminates the method of simply concluding the overall market demand of a customer segment and then offering the full audience’s favorite product. By leveraging big data analytics solutions businesses can significantly improve marketing effectiveness as well as customer satisfaction.
Better performing advanced analytics techniques improve accuracy
Most of the advanced data analytics techniques used today are more robust, faster, and accurate. For example, artificial neural networks greatly enhanced pattern recognition performance by generating a collection of functions that transform the input variables to approximate the required outcome. Predictive analytics and deep learning algorithms are applied to create more powerful hierarchies. Additionally, big data solutions allow machines to learn the complex business world in a much more flexible way. As a result, the prediction from analytical models is becoming more accurate and reliable.
Identify the level of risk in the implementation of the decision
By leveraging predictive analytics solutions, businesses can gain a confidence level for their prediction and select models based on performance measures. Furthermore, it helps companies by conveying the level of risk involved in the recommended action. Additionally, advanced analytics benefits businesses by conveying the risk level of implementing the recommended action. There are circumstances when there is a high cost for acting on false-positive or false-negative predictions. In such cases, companies can adjust the parameters of the predictive analytics model to choose not to act on low confidence predictions or avoid high-cost false prediction.
Advanced analytics can help organizations to deal with confusions and challenges by delivering future-oriented and fact-based insights. Furthermore, it strategically extends traditional analytics solutions from being descriptive to predictive, and ideally prescriptive.
We understand the challenges companies face in managing large volumes of data and set up analytical frameworks to derive real-time insights that facilitate more informed decisions. Our advanced analytics solutions provide best-in-class frameworks for multi-dimensional data aggregation and use visualization-based data discovery tools for insight generation. Request a free demo below for more insights.
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Art and science are often seen as two different entities, especially with art exploring what gives it meaning and science focused on explaining how the world works. But today, the trend in the media and entertainment industry to link the two and get better returns by using technologies like advanced analytics, the Internet of Things (IoT), artificial intelligence (AI), and machine learning is growing. In this competitive era, companies in the media and entertainment industry are also facing the heat of increasingly competitive and uncertain markets. The growing focus on data-driven marketing and digitization has induced a revolution in the media and entertainment industry on how it uses advanced analytics. The future of the media and entertainment industry is dependent on the fusion of both digital and analytical solutions. Now, enterprises are eager to transform media platforms in order to provide more personalized content to its audiences. In this article, we have offered a detailed insight into how advanced analytics can revolutionize the media and entertainment industry and how it can be a palpable game-changer in the current circumstances and dynamics of the quickly changing world of media.
Today, transportation and logistics (T&L) companies have embraced advanced data analytics in their operations since it allows them to run sophisticated networks globally. But unfortunately, the investments made in advanced data analytics is not satisfactory. Now, it is time for that to change if the companies in the T&L sector really want their sales performance to grow. The commercial analytics capabilities of this sector lag average performance, which is primarily because of the reason that sales forces in this sector rely heavily on outdated processes and lack proper insights into preferences of their customers and growth opportunities. Here rises the need for big data analytics for T&L companies. They already have enough data and can utilize big data analytics extensively to earn desired outcomes. With years of experience in offering data analytics solutions to businesses, we have noted that companies in this sector who embrace advanced data analytics can generate an additional 2-5% percent return on their investment. In this article, we have summed up three things that T&L companies need to do to realize the true potential of advanced data analytics:
At present, the oil and gas industry is going through a tough time, owing to the escalation in costs and need of refinement in operational processes. In a bid to overcome these challenges, oil and gas companies have turned towards advanced technologies. Improvements in software tools are creating huge scope for oil companies to boost their production and operational processes. A huge amount of data is transmitted and collected through logging tools and sensors but hardly 1% of this data is used for making informed decisions, which indicates a huge untapped opportunity. Advanced analytics has immense potential to enhance operational processes across the oil and gas value chain, ranging from exploration to production to processing to retail, distribution, and operations at back-office. The use of advanced analytics in oil and gas industry can help bring in a new wave of operational efficiency and help save on time, efforts, and costs. This article sheds light on four different ways in which advanced analytics ensure a competitive advantage for oil and gas companies.
Insurance Industry Trends
As technology innovation, disruptive newcomers, and higher customer expectations redefine the marketplace, insurance industry players remain focused on improving bottom-line profitability, top-line sales, addressing challenges, and contending in this dynamic industry. Additionally, insurance industry players have a lot on their plates. Regulatory and political commotions across the globe are changing some of the ground rules about how organizations may operate. Also, a fast-tracking evolution in the way business is conducted is being driven by higher customer expectations and innovation, while troublemaking newcomers are looking to grab the market share from incumbent insurance industry players.
Advanced Analytics Techniques
Advanced analytics represents a collection of techniques and methods used to model internal and external data to produce valuable insights that can drive business-refining actions. Advanced analytics also refers to quantitative methods such as algorithms, statistics, and stochastic processes. Advanced analytics techniques can be categorized into three major functions:
The predictive analysis evaluates the hidden associations between factors and conclusions and then forecasts or estimates an unfamiliar value.
The descriptive analysis aims to comprehend a fundamental process or phenomenon. The analysis will answer queries like, “What are the typical features of customers who tend to churn?”, and, “Which products do consumers usually buy together?”.
Optimization and Simulation
Optimization ranks the decision options based on key performance indexes. Simulation emulates the characteristics and operations of a process and summarizes the outcome.
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