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Big Data for Digital Marketing and Moment of Truth

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Big Data
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January 1, 2025
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“An approach to accelerate the client’s data & analytics solutions journey from data to decision making (D2D)”

Our proprietary Data to Decision (D2D) framework is the heart of our advanced analytics ecosystem. As our platforms or solutions are built on this. It encompasses meticulously placed Data Engineering, Data Science, Advanced Analytics, and Decision Science components at the crux. Refer to Figure 1. These components, which utilize industry-standard software tools, statistical models, behavioral science, design thinking, and decision tools. These facilitate businesses to take the most pragmatic approach for data management till decision-making.

Unlike other D2D frameworks that directly collect Big Data in heaps. Aptus Data Labs instigates by understanding the real business query and assimilates vertical and functional data according to the requirement. The vertical and functional data, which can be structured, unstructured, or semi-structured, is sourced from enterprises, businesses, syndicates, machines & sensors, geo-locations, web click streams, server logs, or social media. The assimilated data is processed through several integrated layers and modules, encompassing accelerators, reference architectures, and algorithms, to arrive at the required insights, as per the business query. Some of the key components of our D2D framework, which help businesses navigate through the data analytics path easily – right from Primary Data to Data Engineering and then Data Science to Decision Science, include:

Data Engineering

Our mission-focused data engineers utilize data engineering concepts to understand a business query requirement, using the raw data. Right from data acquisition, data storage, data processing, and data workflow management, our data engineering model leverage the power of algorithms, technology, and third-party data management tools to extract the underlying information from the big data – irrespective of its volume, velocity, and variety

Data Science, Analytics & AI

Once the information is extracted from Big Data, the functional data is further processed using Data Science and Advanced Analytics tool.  This is a niche area that extracts nontrivial knowledge from the surfeit of functional data to improve decision-making. Built on our customizable accelerators, algorithms, and reference architectures, our Data Science (advanced analytics) components. These help enterprises make strategic, operational business decisions with the right statistical and mathematical techniques. Hence this will maximize profits, efficiently allocate resources, reduce risk, and minimize costs.

Our computer scientists, operations researchers, mathematicians, statisticians, and above all Data Science researchers. They are the ones who have hands-on expertise in applied mathematical algorithms, econometrics, statistics, pattern recognition, operations research, machine learning, and decision science. Data science includes – extrapolate key business values using descriptive, predictive and prescriptive capabilities. The focus is to drive advanced analytics on NLP, AI, Cognitive, and multimedia domains with business KPIs.

Decision Science

Since descriptive, diagnostic, or predictive analytics are not sufficient to arrive at Big Business decisions, Aptus Data Labs utilizes its key decision-making systems and expert intervention to deliver streamlined decision models. This will help to reduce operational costs and optimize business operations.

Our domain architects, business domain experts, and business analysts utilize human-driven decision-making systems, decision support approaches, operational intelligence platforms, intelligent business process management (BPM), business rule processing, management science/operations research, and more to transform meaningful insights into Big Business Decisions.

Advanced Analytics through Technology

Covering the complete Advance Analytics value-chain, our D2D Framework is empowered with technological components of Advanced Analytics.

Since Advanced Analytics is a niche area of analyzing data using sophisticated quantitative methods to produce insights, our proprietary D2D framework helps enterprises to optimize the data supply chain with the right data, at the right time and at the right place, to arrive at Big Business Decisions.

We at Aptus Data Labs have gained good Advanced Analytics acumen by working on various data science problems, Big Data Analytics, Predictive Analytics, Real-time Text Analytics, NLP & Artificial Intelligence with industry-recommended tools & technologies. Time and again, clients have approached us for our hands-on expertise in machine learning, forecasting, optimization, simulation, computer vision, conversional AI, NLP & text mining, document mining, sensor/signal analytics, web click stream analytics, geospatial analytics, and more. While we continue to hone our skills, we are recognized for our services in the area of AI solutions for different industries.

Data & Artificial Intelligence Accelerators

We are inventing new Data, AI, and Cloud components, Data framework, a catalog of data sets, algorithms, analytical components, PoC & Pilot that stimulate ideation, and accelerate to resolve our customer’s challenges. These accelerators are embedded and augmented across technology and business functions that would have an immediate impact on your business, scaling AI across your enterprise to unleash your digital advantage and full potential.

CRISP-DM Process & Methodology

We drive the analytics engagement and delivery using agile and CRISP-DM (Cross Industry Standard Process for Data Mining) process to ensure the step by step approach as follows:

By applying our years of domain experience and the industry’s best practices for business process and technology integration, we deliver streamlined decision models that help optimize business operations and reduce operational costs.

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Demand Sensing Optimising Supply and Demand Mismatch

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

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

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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Analytics solutions journey with D2D framework

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

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.