The Challenges of Data Privacy and Security in the Age of Big Data

The goal of supply chain planning is to improve forecast accuracy and optimize inventory costs throughout the supply distribution network. Without proper planning, there is a chance of overstocking leading to high inventory costs or understocking leading to stock out situations causing revenue loss.

When a company produces more than the demand, the stock sits unsold in the inventory. Therefore, this increases the inventory holding cost, later leading to waste and obsolescence costs. When a company produces less than the customer demand, there is a revenue loss and in today’s competitive business environment this might also lead to future revenue losses.

Getting demand forecasting accurate is the key to success in today’s supply chain planning. However, there are various reasons why this demand-supply mismatch occurs and forecasting accuracies drop. Customers’ needs and requirements constantly change, maybe due to:

• Introduction of new technology
• Fast fashion
• Promotional discounts
• Point-of-sale
• Weather
• Strikes
• Lockdowns

For example, when the first wave of the pandemic hit, people minimized their purchases like clothes, cosmetics, etc., thinking they won’t be using these items quite often. However, there was an exponential rise in the purchase of luxury goods as well as insurance (health and life). People also bought immunity boosters, comfort foods, groceries, digital services, and appliances. Additionally, there was a shift in how people perceived and bought commodities. This leads to uncertainties in aggregate demand. As companies try to fulfill the demand, there is a mismatch between supply and demand.

Traditional classical forecasting methods find it difficult to predict demand accurately in today’s dynamic business environment. However, Statistical forecast models rely solely on historical sales data and they fail to evaluate the impact of various other variables that impact sales demand. Product manufacturing and distribution must be aligned with supply-demand volume variabilities so that the companies can have accurate demand forecasts, close to the actual sales, preparing them to stock at the right place at the right time in the right quantities.

Using modern AI / ML technologies Demand Sensing has now made it possible to analyze the impact of these variables on sales demand and enable them to predict demand more accurately. Therefore, it is fast becoming an indispensable tool in supply chain planning for accurate demand forecasting. Moreover, it builds upon the classical traditional forecasting methods to develop baseline forecasts and then refines these forecasts for higher accuracy by taking into account other variables that impact the sales demand on a near real-time basis. Demand Sensing leads to better demand forecasting accuracy helping organizations to improve customer demand fulfillment, enhance revenues and optimize inventory throughout their distribution network and reduce costs.

Other than optimizing the inventory to meet demands, supply chains can also migrate to a just-in-time inventory management model to boost their responsiveness to consumer’s demands and lower their costs significantly.

Data Required for Demand Sensing

AL/ML-based Demand Sensing tools can make use of a variety of data available to predict demand more accurately. Such data includes (but not limited to):

• Current Forecast
• Actual Sales data
• Weather
• Demand disruption events like strikes, lockdown, curfew etc.
• Point of Sales
• Supply Factors
• Extreme weather events like floods, cyclones, storms etc.
• Promotions
• Price
  
  

The variable may change for different businesses & organizations and any given variable can be modelled in Demand Sensing to analyze the impact on sales demand for greater accuracy.

The list above includes current data, historical data, internal data, and external data. Hence, this is exactly why AI/ML-based demand sensing is more accurate than traditional demand sensing. As large volumes of data are analyzed and processed quickly, predictions are specific making it easy for supply chains to make informed business decisions. An important factor to conduct demand sensing accurately is the availability of certain capabilities by supply chains. Let’s learn more about these capabilities.

Capabilities Required by Supply Chains for Demand Sensing

• To template demand at an atomic level
• To model demand variability
• To calculate the impact of external variables
• To process high volumes of data
• To support a seamless environment
• To drive process automation
  
  

Benefits of Demand Sensing

The major benefits of Demand Sensing for an organization are:

• Greater Demand Forecasting accuracy
• Reduced inventory and higher inventory turnover ratios.
• Higher customer demand fulfillment leading to increased sales revenues
• Enables citizen demand planners and supply planners.
• Auto-modelling and Hyper parameter

Who Benefits the Most from Demand Sensing?

• Retail/ CPG/ E-commerce
• Distribution
• Manufacturing/Supply chain/ Industrial automotive
• Chemical/ Pharmaceutical
• Food Processing
• Transport/ Logistics
• Natural Resources
  
  

Demand Sensing – Need of the Hour

As already discussed, demand sensing is required mandatorily by supply chains to manage and grow their business. In this dynamic market where most supply chains are opting for digital transformation and an automated process system, traditional methods to sense demand do not work efficiently. To gain a competitive edge and to keep the business running in the current unpredictable times, AI/ML-based demand sensing is the need of the hour.

How aptplan Can Help You

Aptus Data Labs’s AI/ML-based tool “aptplan” helps businesses access accurate demand sensing and forecasting data to plan their supply accurately. aptplan uses internal and external data with traditional techniques and advanced technology to train AI/ML models are used to predict accurate sales demand sensing on a real-time basis. It uses NLP technologies to collect a wide variety of unstructured data to convert into a structured format for use. Aptplan delivers highly accurate demand plans for better business decision-making and lower inventory costs. To know more or to request a demo, click on https://www.aptplan.ai/

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In the age of Big Data, privacy and security are major concerns for businesses and consumers alike. With the increasing amount of data being collected and analyzed, it is becoming increasingly important to ensure that the privacy and security of this data are protected. In this blog post, we will discuss the challenges of data privacy and security in the age of Big Data.

How to overcome these challenges

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The amount of data being generated is increasing at an exponential rate. According to a report by IDC, the amount of data in the world will increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This data is being generated by various sources such as social media, online shopping, and IoT devices. Therefore, this data is valuable to businesses as it helps them make informed decisions and improve their products and services.

However, with the increased collection and analysis of data, there is a growing concern about data privacy and security. Additionally, a breach in data security can result in sensitive information being exposed, which can be harmful to individuals and businesses. In addition, the unauthorized access to data can result in financial losses, reputational damage, and legal repercussions.

The challenges of this are multi-faceted. Moreover, one of the main challenges is the lack of awareness and understanding of data privacy and security issues. According to a survey by KPMG, only 36% of businesses believe that, as they are adequately prepared to deal with a cyber-attack. Furthermore, this lack of preparedness can be attributed to a lack of understanding of data privacy and security issues.

Another challenge is the complexity of data privacy and security regulations. In addition, with the increasing amount of data being collected, there are various regulations that businesses need to comply with such as GDPR, CCPA, and HIPAA. These regulations can be complex and difficult to understand, especially for small and medium-sized businesses.

Furthermore, the growing amount of data being collected is also resulting in an increase in the number of cyber-attacks. According to a report by McAfee, there were 1.5 billion cyber-attacks in 2020, which is an increase of 20% from the previous year. This increase in cyber-attacks is a major challenge for businesses as they need to ensure that their data is protected from these attacks.

To overcome these challenges, businesses need to adopt a comprehensive approach to data privacy and security. This includes implementing data encryption, using secure networks, and implementing access controls. In addition, businesses need to ensure that their employees are trained on data privacy and security issues. They have a clear understanding of the regulations that they need to comply with.
In conclusion, data privacy and security are major concerns for businesses in the age of Big Data. The challenges of data privacy and security are multi-faceted and require a comprehensive approach. By adopting best practices for data privacy and security, businesses can ensure that their data is protected. Also, that they comply with the regulations that are in place.

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The world of data analytics is constantly evolving, and businesses are increasingly turning to cloud-based solutions to manage and analyze their data. In this blog, we will explore the advantages of cloud-based data analytics solutions.

Advantages of using cloud based data analytics solutions

First and foremost, cloud-based data analytics solutions offer businesses greater flexibility and scalability. With cloud-based solutions, businesses can easily scale their computing resources up or down depending on their needs. Hence, this means they can quickly respond to changes in demand and avoid over-provisioning or under-provisioning their resources. As a result, businesses can optimise their IT spend and reduce their operational costs.

Another advantage of cloud-based data analytics solutions is that they offer greater accessibility. Also, cloud-based solutions can be accessed from anywhere with an internet connection, which means that employees can access data and insights from their mobile devices or laptops while on-the-go. Therefore, this also enhances collaboration and enables employees to make data-driven decisions more quickly.

Cloud-based solutions also offer greater security. Furthermore, Data stored in the cloud is often more secure than data stored on-premises, as cloud providers typically have advanced security measures in place to protect against cyber threats. Moreover, Cloud providers also regularly update their security protocols to ensure that they stay ahead of new threats.

Cloud-based solutions also offer greater reliability and availability. Also, cloud providers typically have multiple data centers around the world, which means that data is replicated across multiple locations. Therefore, this ensures that data is always available, even if one data center experiences an outage. Additionally, cloud providers often have service level agreements (SLAs) in place that guarantee a certain level of uptime and reliability.

Finally, cloud-based solutions offer businesses greater agility. Moreover, with cloud-based solutions, businesses can quickly spin up new environments and test new hypotheses without having to make significant capital investments. Therefore, this also enables businesses to experiment with new analytics tools and technologies and iterate more quickly.

These are some of the reasons why cloud-based analytics has been gaining such traction in the recent past and there’s no signs of slowing down.

• According to a report by Grand View Research, the global cloud-based analytics market is expected to reach USD 77.4 billion by 2026, growing at a CAGR of 23.5% from 2019 to 2026.
• A survey by IDG found that 90% of organizations use cloud-based services in some capacity, with 73% of those organizations using cloud-based analytics.
• A study by Dell EMC found that organizations that use cloud-based analytics are able to complete data analysis tasks 3.3 times faster than organizations that do not use cloud-based analytics.
• According to a report by Cisco, 83% of all data center traffic will be based in the cloud by 2021.
• A study by Nucleus Research found that businesses that use cloud-based analytics solutions achieve an average of 2.7 times the return on investment (ROI) compared to on-premises solutions.
• According to a report by McAfee, 73% of organizations that use cloud-based solutions experienced improved security as a result.

These statistics demonstrate the growing popularity of cloud-based analytics solutions and the benefits that they can offer to businesses. Furthermore, from faster data analysis to improved ROI and enhanced security, the advantages of cloud-based solutions are clear. As businesses continue to invest in cloud-based analytics, we can expect to see even more innovation and growth in this exciting field.

In conclusion, there are many advantages to using cloud-based data analytics solutions. Also, from greater flexibility and scalability to enhanced accessibility, security, and reliability, cloud-based solutions offer businesses a range of benefits that can help them stay ahead of the competition. As the world of data analytics continues to evolve, businesses that embrace cloud-based solutions will be better positioned to succeed in the digital age.

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