Advanced analytics has become very important for all consumer-facing industries owing to its ability to deal with increasing complexity easily. But companies in the retail industry are still lagging in embracing analytics and prefer “gut-feel” over data-driven decision making. These companies are finding it difficult to invest in analytics capabilities due to obstacles such as poor data quality, highly competitive business landscape, logistics complexity, and limited analytics expertise. It is high time for companies in the retail and apparel industry to leverage fashion retail analytics that can help them in smarter decision making, improving relationships with customers and boosting team management. In this article, our analytics experts have curated a list of important steps that companies need to follow to start successfully with fashion retail analytics.
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How to leverage fashion retail analytics to reap maximum benefit?
Step #1: Prioritize the areas to win
To effectively start with fashion retail analytics, players in the retail and apparel sector need to first decide where analytics will help achieve the greatest business impact. Businesses need to develop a cross-functional road map to make tough decisions on where analytics matter most. Also, to gain the desired outcome, retail and apparel companies should properly analyze all areas of the business to identify the new fashion retail analytics opportunities. Using these insights, functional teams can build individual use cases that must be well-defined and specific.
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Step #2: Extract actionable insights
Discovering the limits of the data available is one of the major challenges that retailers and apparel industry players face. Available data can offer useful insights if used appropriately. But players in the retail and apparel industry face some unique challenges such as an ever-changing assortment, rapidly changing trends, and non-standardized SKU that result in collection of relatively poor data. Therefore, to achieve desired outcome from fashion retail analytics solutions, companies need to build an appropriate data ecosystem.
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Step #3: Use an agile operation model
Fashion retail analytics can completely transform the way apparel industry works. By using an agile operating model, companies in the retail and apparel sector can get a huge departure from the calendar-driven world in which they operate and plan all steps in advance. Although shifting to an agile operating model may be the toughest change to manage in this fashion retail analytics journey, but it will substantially accelerate the analytics road map.
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The use of big data is no longer limited to transforming customer-facing functions such as sales and marketing alone. In the pharmaceutical industry, big data analytics is helping companies to deal with declining success rates and stagnant pipelines. Big data analytics is creating immense opportunities for companies in the pharmaceutical industry to deal with complex business environments amidst the explosion of data sets. The effective utilization of these datasets can help pharmaceutical companies in drug development. Also, big data analytics has enabled companies to improve clinical trials, manage risks efficiently, and improve patient safety.
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How is big data analytics revolutionizing the pharmaceutical industry?
Improving sales and marketing
Until recently sales and marketing were grey areas in the pharmaceutical industry. But by leveraging big data analytics, pharmaceutical companies can easily focus on specific geographical areas to promote their medications. Consequently, pharmaceutical companies can create targeted marketing campaigns, thereby saving both on time and effort. Therefore, the role of big data analytics has become even more critical in devising marketing strategies and sales plans.
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Enhancing the efficiency of clinical trials
Clinical trials are an integral part of the pharma industry. The patients undergoing these trials must meet some prerequisites. Big data analytics solutions help companies merge the databases from multiple sources, to filter out patients who do not meet the basic requirements. Also, these solutions help researchers monitor the patients on a real-time basis and predict the side-effects of drugs.
Helping in early detection of diseases
Predictive analytics is helping companies in the pharmaceutical industry in the early detection of drug toxicity as well as improving the chances of patient survival. The algorithms used by predictive analytics helps in gaining detailed insights into patient data and deliver personalized care to the patient.
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Providing real-time feedback
Today leveraging big data analytics solutions has become imperative for the pharmaceutical industry to reach out to their end-users in a better manner. Digital apps can help companies build relations with the target audience. The data collected on these apps are linked to various verticals of the pharmaceutical and healthcare industry which provides primary data on patient compliance and instant feedback on the health of patients.
Allowing doctor-pharma collaboration
One way in which big data analytics is making improvements in the pharmaceutical industry is by predicting the best treatments for individual patients. It can help in looking through data faster than humans and find the interventions likely to cause the most significant advantages for ill patients and their caregivers. With help from big data analytics, pharmaceutical companies can reach out to physicians treating patients that fit appropriate criteria and advise on how a certain medication could and should fit within a person’s treatment plan. Also, some physicians collect real-time data about whether treatments have intended effects, especially when their patients use Internet of Things (IoT) enabled wearables.
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What is Manufacturing Analytics?
Manufacturing analytics is a technique that integrates big data, predictive analytics and industrial internet of things with the manufacturing processes to bring efficiency in the business operations. Manufacturing processes are successful when businesses can streamline their operations. This is where manufacturing analytics can help. Today manufacturers can leverage manufacturing data analytics techniques to examine, test and re-test every single process and implement innovative ideas to make improvements in the business operations. Manufacturing analytics can make manufacturing processes faster and more efficient by offering more focused and actionable insights that can help companies to fine-tune their production line.
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Benefits of Manufacturing Analytics
Manufacturers can maximize the operating time of critical assets by leveraging manufacturing analytics solution to anticipate their failure. By utilizing data analytics in manufacturing, businesses can gather historical data (structured and unstructured both) to gain in-depth insights into operations that can’t be assessed with conventional techniques. Using manufacturing data analytics, companies can identify the circumstances that tend to cause breakdown of a machine and monitor input parameters so they can intervene before breakage decreasing downtime. Furthermore, manufacturing analytics can help companies understand how downtime for a single machine can affect the manufacturing chain and how different configurations may improve overall efficiency.
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Gain better insights into machine utilization and effectiveness
One of the major challenges that manufacturers face is wastage of time. While manufacturing chains can be built with efficiency in mind, different factors may play a contributing role in reducing the overall efficiency of the line because of poor installation, misuse, or simply a lack of downtime coordination. By combining existing IoT systems with manufacturing analytics solutions, companies can gain real-time insights into how well their manufacturing lines are operating. Also, it can help in generating actionable insights to help companies realize real improvements in the overall process.
Improve demand forecasts
Any product manufactured today aims to cater the demands that will emerge in the near future. This is the reason why demand forecasts are important from a business perspective. Demand forecasts guide a production chain and help identify the reason between strong sales and an unused inventory. For most companies, forecasts are based on historic values and not on more actionable forward-looking data. By leveraging manufacturing analytics solution, manufacturers can combine existing data with predictive analytics to build a more precise projection of future purchasing trends.. These insights are derived not just from previous sales data but from how well current manufacturing processes are operating, leading to smarter risk management and less production waste.
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Better warehouse management
Storage is generally the most overlooked aspect of the manufacturing process. Every stage is important when products are ready to be shipped especially in a world that is increasingly embracing zero-inventory models to reduce overstock scenarios. This is where warehouse management is important. Establishing an efficient warehouse management system can further improve product flow management, and improve business operations, as well as the bottom line. Manufacturing analytics framework makes it easier to understand how to improve your inventory and manage your warehouses better. This can, further, help in improving manufacturing KPIs and processes. By incorporating a robust manufacturing analytics solution companies can build a more granular understanding of how their production line operates, and how they can streamline it to avoid unnecessary costs
Manufacturing leaders today are visionaries, pioneering increasingly efficient methods to produce and move physical goods while thinking beyond cost savings, productivity, and risk reduction. Future survival needs manufacturers to become nimble and analytics driven. This can help organizations minimize risk and seize opportunities through actionable operational insights and confident decision making. Manufacturing analytics solutions from Quantzig can help companies to fine-tune their production operations for minimal cost and risk while capitalizing on data as an asset that can further help them deliver innovative services and quality products. We, at Quantzig, deliver proven value and help leading companies unlock the immense potential of their digital transformation through our customized manufacturing analytics solutions.
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Tackling the Modern Last Mile Challenge with Advanced Analytics
What is Cloud Analytics?
Cloud analytics is an approach that involves the application of advanced algorithms to identify data patterns and extract valuable insights. Today companies are increasingly relying on data analysis to improve business operations and gain an edge over competitors. By leveraging cloud analytics approaches, companies can improve their decisions significantly and enhance business performance. Cloud analytics provides an indispensable platform that can help in scaling analytics capabilities and reduce the burden of management and implementation.
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Components of Cloud Analytics
Data sources are the original sources of data that comprise ERPs, social media data, CRM, and website usage data. Twitter sentiment data is an example of a cloud-based data source.
Cloud-based data models explain the relationship between various data points. They are created with structured data types.
Analytics-based models are mathematical models that are used to forecast business outcomes and drive change using data-driven and fact-based information.
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Types of Cloud Analytics
Public clouds are operated by third-party cloud service providers and within a public cloud, all hardware, software, and other supporting infrastructure is managed by the cloud service providers.
A private cloud is a cloud computing resource that is used exclusively by a single organization. A private cloud is usually located on the company’s on-site datacenter. Also, some companies pay third-party service providers for hosting their private cloud. A private cloud facilitates the maintenance of the services and infrastructure on a private network.
Hybrid clouds comprise both public and private clouds which are connected by technology that allows the sharing of data and applications between them. A hybrid cloud-based platform provides better flexibility and deployment options to the business by allowing data and applications to move between private and public clouds. Furthermore, this helps organizations to optimize existing security, compliance, and infrastructure.
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Benefits of Cloud Analytics
Cloud analytics helps businesses to improve performances through fast and efficient computing hardware. This offers several benefits over a single corporate data center, including reduced network latency for applications and greater economies of scale.
A better understanding of the bottom line
Cloud analytics can transform every segment of an organization by giving access to human resources, marketing, and operations to the important financial information they need to execute the visual analysis. This can provide a better understanding of the bottom-line impact of their decisions.
Breaking down departmental silos
For any organization financial data is very important. Therefore, if business users outside the finance department have access to this data, it can help in improving the business functions. Cloud analytics provides the ability to query and analyze data from a single source. Furthermore, cloud analytics solutions help in sharing strategy and discipline across the core business operations.
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