Transition to Big Data Processing

The advent of smartphones and SaaS systems has led to the generation of information at an enormous rate that cannot be handled by traditional data processing tools and methods. Nearly 90% of all data generated has been in the past two years (Kommandotech). The use of digital tools to analyze huge sets of data and retrieve essential information and interpretations of the data, forms the foundation of Big Data Processing. Smartphones and other devices generate vast amounts of data containing highly sensitive information like bank details, transaction details, and personal details too that could be retrieved from data storage using big data analytics, bringing about the need to create fail-safes that will prevent abuse of these tools.

Data Security using Big Data Analytics

Big Data Analytics has a wide number of applications in Data Security as it helps facilitate information retrieval from various security sources like firewalls, security devices, web traffic etc. Its ability to integrate unstructured data from multiple sources under a single analytical network enables superior data analysis and interpretation for companies and enterprises. A few of the applications of data security using big data analytics are:

• Network Flow Monitoring to Track Botnets – Analytical tools like MapReduce can identify and track infected hosts participating in a botnet by evaluating enormous amounts of NetFlow data within a short span of time, largely simplifying data processing as compared to traditional processing systems. It is the process of discovering patterns in large data sets using methods from artificial intelligence, machine learning, statistics, and database systems. Data mining is used to extract information from a data set and convert it to an analytical structure.

• Enterprise Event Analytics – Multinational Companies and enterprises generate overwhelming amounts of data every day, creating a need for highly efficient analytical tools to generate valuable information by analyzing data. An effective enterprise analytics strategy can provide a comprehensive vision and end-to-end roadmap for data management and analysis. It can help with risk management, mapping out a company’s data management architecture, identifying and removing redundant data, establishing responsibility and accountability, and improving data quality, among other things.
   
• Advanced Persistent Threats Detection – Advanced Persistent Threats are one of the most serious threats faced by organizations today. It is the strategized attack of specific, high-value assets in the digital architecture that operates in different modes like “Low profile” and “Slow” to avoid detection and prolonged execution respectively. Detection and tracking of such threats are cumbersome as huge loads of data must be evaluated to identify them, making big data analytics the ideal solution for tracking them. It is suitable for compliance needs and forensic investigations while also offering insights on user behavior that help track future threats efficiently.
• Data Sharing and Provenance – The use of big data analytical systems allow companies and enterprises to research and review the results of cybersecurity experiments conducted across the world. The Worldwide Intelligence Network Environment (WINE) (Cloud Security Alliance) provides a platform for data sharing and analysis to research on the field data aggregated online by Symantec. These platforms allow companies to test out and validate novel ideas on real-world data and compare different algorithms and systems against reference data sets to evaluate efficiency. Data Provenance is information about the origin and process of data creation.  Such information helps in debugging data and transformations, auditing, evaluating data quality and trust, modelling authenticity, and implementing access control for derived data.

Conclusion

Big Data Analytics holds the potential to unlock high levels of efficiency and performance from companies and enterprises as it simplifies data analysis of massive amounts of data and provides access to actionable information easily. The element of versatility it holds in serving various applications in data analytics makes it a critical requirement for data processing companies. Big data analytics helps in making better-informed decisions, improving the supply chain, operations, and other strategic decision-making areas.

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When we started this company sixteen years ago, we knew the challenges of this cutthroat industry. We understood how important it is for us to remain determined to help clients harness the power of digital technologies. Our clients’ success matters more than anything else, but we also don’t shy away from celebrating our wins.

This is such an overwhelming achievement for us. Over the years, we’ve worked hard to gain the confidence of our clients through our strong digital focus. Starting with less than 20 teammates, our company has rapidly grown into what it is today, thanks to the opportunities introduced to us by our partners.

Having worked with some of the biggest names in their industries, our team has shown what it takes to deliver impactful results. Their efforts have helped us gain recognition from multiple industry professionals and award-giving institutions such as The Manifest.

The Manifest is a business resource platform designed to help users learn more about the IT, marketing, and business services industries. The website focuses on publishing insightful content, market surveys, how-to guides, and data-driven agency rankings.

Based on their latest research, Black Rock IT Solutions ranks among India’s top 15 Data and Analytics services providers. The Manifest highlighted our company for the solutions delivered, value creation for the customer, and results delivered from harnessing data science solutions.

Our D&A practice has specialists skilled in Data Management, Visual Analytics, and Artificial Intelligence with deep expertise in industry-leading technology platforms such as Microsoft, AWS, Tableau, Snowflake, Informatica, etc.

“We are extremely proud that we now have the skills and experience to work both with customers who are just starting their data journey and need help in defining a strategy and taking baby steps, as well as working with the more mature organizations who have their own data lakes and are looking for more ways to make the most of their data investments,” says Siraj Alimohamed, who heads the D&A practice.

We are grateful to our clients for their unwavering support for our company – this award reflects our commitment to them.

Do you need help with your Big Data efforts? Are you looking for a reliable digital transformation service provider who’s clued in to the latest emerging technologies? Then we’re the right team for you! Send us a message at sales@blackrockdxb.com to schedule a free consultation. Let’s talk about how we can work together.

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Like many other industries, supply chains, which are the backbone of national economies, also had to change their strategies to stay efficient and meet the new market requirements. As a result, they have embraced digital solutions to cope with our current times’ uncertainties, challenges, and restrictions.  

The supply chain ecosystem became all the more critical once the vaccines for COVID-19 were developed – it was the need of the hour to get the vaccines to the public quickly and in the right quantities.    

Challenges and roadblocks in the COVID-19 vaccine supply chain    

The biggest problem in any supply chain management is matching demand & supply efficiently.  

Supply chain management companies are currently facing issues due to the disparity in demand and supply for COVID-19 vaccines from region to region. In addition, the lack of raw material and human resources has caused delays in the supply of COVID-19 vaccines.  

Inequalities regarding vaccine distribution have become a global problem, as well. For example, reports show that financially stable countries are getting vaccinated 30 times more than those with lower incomes.   

Vaccine management issues such as storage capacity, handling management, effective distribution between supply chain levels, and shipment procedure are also of rising concern as two doses are required for most vaccines.    

Security issues such as theft and mishandling are other grave concerns, especially considering the demand for COVID-19 vaccines is higher than the supply. Time delay and lack of visibility into supply are also issues researchers have noted.   

Digital solutions reshaping Supply Chain Management  

Digital solutions have played a crucial role in helping retailers, suppliers, and distributors drive the transformational changes required to address the challenges posed. Therefore, the accelerated adoption of these solutions in supply chain models is essential not only for the present but also for the future.  

Here are some of the ways digital solutions have made an impact on supply chain ecosystems:   

Machine Learning: 

Shifting supply chain dynamics, changing ways of working, and increasingly volatile demand has been a concern for suppliers, distributors, manufacturers, and retailers globally when it came to an efficient distribution of Covid-19 vaccines.  

McKinsey predicts that machine learning’s most significant contributions will be providing supply chain operators with significant prescriptive insights into how supply chain performance can be improved by anticipating anomalies in logistics costs and performances before they occur. In addition, machine learning models and techniques can ensure streamlined production planning, inventory management, and anomaly detection and can offer an exceptional customer experience.  

Artificial Intelligence: 

AI-based tools can help understand which geographic regions to target for vaccine supply to flatten the curve of the pandemic sooner, provide insights in customizing the supply chain management system to ensure maximum vaccination in the least amount of time, and ensure the processes are being followed as designed.  

Artificial intelligence tools can also be leveraged for capacity planning, predicting the demand for raw materials, work-in-progress components, and post-vaccination surveillance. With AI, supply chain management companies can improve responsiveness to vaccine demand, minimize risk, and increase visibility & transparency across the supply chain.  

Data Analytics: 

The race to vaccinate the global population is a daunting task and needs data-driven strategies and action plans to optimize the supply chain. Data analytics tools capture inventory, demand, capacity, and other related data across the distribution chain, to create a strong distribution strategy to help supply chain management companies handle the fluctuating demand and supply.  

Data analytics tools like predictive analytics have also helped distribution companies predict vaccine demand in any geography and streamline production and distribution accordingly. In addition, using advanced data analytics technologies, governments can identify and create priority populations in different geographic locations and formulate a vaccination policy that maximizes vaccination rates and minimizes wasted dosages.  

IoT: 

IoT sensors are used to keep track of the temperature in storage facilities and vehicles during transportation. Armed with real-time alerts, IoT solutions let stakeholders be aware of any system failures & let them monitor and optimize the vehicle routes. IoT systems can also track vaccine stocks in financially stable countries to ensure a smoother redistribution of any surplus vaccines to developing countries across the globe.  

Blockchain: 

Blockchain-based solutions have been recognized as the backbone in developing a reliable and transparent supply chain management system to manage COVID-19 vaccine rollouts.  

With its unique capabilities, Blockchain technology can help supply chain companies to track the transportation and storage of vaccine batches in real-time, verify vaccines’ provenance and authenticity, quick detection and identification of faulty products, and identifying and blocking counterfeit vaccines from entering the supply chain. Blockchain-based solutions ensure accurate traceability, enhanced security, and greater transparency in vaccine distribution. 

In Conclusion   
COVID-19 has revealed the fragility of existing supply chain management systems and has proved the potential of digital solutions to cope with unprecedented disruption effectively and efficiently. Advancements in digital technologies such as machine learning, the internet of things (IoT), blockchain, artificial intelligence (AI), and data analytics will pave the way for agile, reliable, and efficient supply chain management systems capable of handling dynamic supply and demand.  

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It is common for us to look back into our past and then form corrective actions to adopt in the present. But that usually isn’t enough to satiate the human mind – concerns about the future usually leave us in a state of constant worry and pressure. And so, throughout history, people have relied on various methods for predicting the future, trying to be as prepared for the future as they can be.  Corporates are no different. Regardless of the business functions (Strategy, Marketing & Sales, Operations, or HR), predictability of the future is one of the most sought out capabilities. Such predictions of ‘probable outcomes/situations in future’ help businesses to be proactive and devise strategic plans to mitigate risks, prevent undesirable outcomes, realize greater sales, improved profits, and deliver greater customer delight. Of course, these predictions need to be highly objective and quantifiable, as they often have immediate implications in the way a business operates in the present.  

So, the obvious question now is ‘How do we make predictions that are objective, with a quantifiable trustworthiness attached to it?’. Predictive Analytics can help businesses with future-state predictions based on past, historic data related to their business use cases.

What is Predictive Analytics?

Predictive Analytics is the application of advanced analytics techniques to predict the outcomes of future events. Data scientists usually build these models by bringing together relevant data engineering practices, statistical models, artificial intelligence, and machine learning including deep learning techniques as required. This model is then run against all available historical data, which forms the foundation for the predictive model. The model then identifies hidden patterns and relationships between attributes in the data, learning to predict future outcomes based on the data. The variability of identified patterns in historic data would help the model to quantify the ‘prediction accuracy’ level. This accuracy level specified by the model could then be used as the benchmark for trustworthiness – but do remember that accuracy could also be an indication of data quality or data insufficiency. 

Predictive Analytics is finding an increased usage among businesses world-over, to utilize ‘big data’ irrespective of it being ‘structured’ or ‘unstructured’, numeric or text, and real-time or non-real-time.  The beauty of the approach is that predictive analytics models are also capable of performing ‘continuous learning’ and improving prediction accuracy by themselves, by making use of incoming, incremental data across time. 

Predictive Analytics – Opportunities to be Proactive

Predictive Analytics can be applied in various industries, across multiple functions of the business, and in numerous use cases. All these applications are opportunities for business teams to be proactive and act in advance to capitalize on opportunities & avoid any undesirable events. 

Here are some of the use cases for predictive analytics: –

1. Predictive Maintenance – One of the ways to improve profits, other than an increase in sales, is by controlling costs efficiently. For organizations that have a large number of machinery operated all-around the year, or are generally heavy on their usage of assets, it becomes highly important to track the efficiency of these assets and prevent equipment downtimes. Using Predictive analytics models, we can now predict the ‘need’ for maintenance, any deviant behavior, part replacement, etc. for every machine across the whole outlay. 

2. Customer Relationship Management – Customer Relationship Management is a critical function for any business, helping with customer retention and customer loyalty, thus making a direct impact on sales, revenue, and profits.  Predictive Analytics has always been finding an ever-increasing adoption in a variety of use cases in CRM, such as targeted marketing, churn prediction, lifetime value analysis, etc.

3. Quality Management  – Quality Management always has the risk of being viewed as a cost, but if you look closely it is a great enabler for better business outcomes. Effective quality management can decrease unnecessary expenses and also increase sales, revenue & profits. Both manufacturing & services sectors would benefit from getting insights on any potential quality issues in advance. Using Predictive Analytics, we can get insights into any threats to quality and thus be more efficient, prevent potential damage to brand value, etc.

4. Fraud Prevention & Risk Management – In Insurance & Banking, fraud in various forms is a concern that has been growing at an alarming pace. On top of this, they face a constant challenge of staying on top of risks related to customer behavior, competition, natural and man-made catastrophes, etc., which has only increased manifold especially in the wake of increased digital adoption, developing markets, and socio-environmental challenges. For all these various scenarios, Predictive Analytics helps to develop a better understanding of potentially hidden threats and opportunities ahead of time.

Of course, none of these are new approaches to data and analytics. Most big brands in the world have been doing this for many years – even for as long as a decade! What is important to realize is that with the advances in analytics and cloud computing, even the ‘kick-starters’ of the world can make use of the opportunities that predictive analytics presents.  

Analytics & Cloud Computing – A Perfect Partnership

Cloud Computing is already more than just a buzzword – most industries have already started reaping the benefits of cloud adoption and today, the technology world sees more and more products & services being built on the foundations of the cloud. Greater processing power and storage capability that can be scaled up and down on a need-basis in the cloud, in contrast with on-premise infrastructure is much more business-friendly. Cloud computing helps industries to work with larger volumes of data in real-time, thus setting the foundation for increased usage of data analytics, especially big data and predictive analytics. 

As per Gartner estimates in June 2020, by 2022 public cloud services will be essential for 90% of data and analytics innovation. Cloud computing can help to overcome constraints related to data volume, speed, etc., and hence can empower industries to reap huge benefits out of their predictive analytics & big data analytics models. Using these cloud computing platforms, data science teams not only develop & deploy custom analytics models in the cloud but also choose from a wide variety of “off-the-shelf” SaaS analytics offerings to suit their business needs.  

The Future

Digital adoption has surpassed the inflection point in a hockey stick growth curve and is growing at an ever-increasing pace. Well for one, if any businesses were hesitant to get their feet wet, COVID-19 has given them the extra boost to jump head-first! 

With all this digital transformation, remember – more data is being generated at every digital touchpoint in every second across the world. As per a Forbes report in 2018, more than 90% of the world’s data has been generated in the last two years. Information is considered to be the most valuable asset and resource by economists. Analytics helps businesses get ahead of their competition, contribute to the growth of the economy and society in general. In short, one can even say Predictive Analytics will empower the world to be better prepared & geared up for forthcoming days. 

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It is obvious that the better the customer feels about the product, the better the sales.  A delighted customer usually becomes an evangelist for the product and thus, an extended member of the sales team. To ensure a delightful customer experience, the product team should know the customer – his motivations, his pain points, his aspirations. 

Product Analytics can help them figure this out.

What is Product Analytics?

Data about customers are everywhere, and collecting this data is the first step in the process of understanding it. Once data is collected, the necessary steps to obtain meaningful insights from these data points need to be carried out – these become valuable pointers on the table during product strategy discussion. The whole process of ‘understanding the customer’, starting from data collection to generation & presentation of actionable insights for decision making is called Product Analytics.

Product Analytics is especially crucial for technology products, as customer footprints are placed digitally here and can be accurately tracked to better the performance of the product and make it better suited to the customer’s expectations.   It also enables automated classification/segmentation of users across their life cycle based on multiple parameters and factors. The data generated would also be beneficial to understand customer churn and probable reasons for it. Such analysis would also pave the way to predictive insights which can point out customers who are probable to churn in the future.  Analytical insights on customer journeys across various ‘funnels’ would also help to identify pitfalls and avenues for improvements in user interface and user experience offered by product websites/apps. 

Product Analytics must be adopted as a vision by the product team and should be driven as a holistic operation that encompasses continuous data collection from multiple sources (about customers), continuous integration of such data into a consistent single source of truth as a historical data warehouse, algorithms to discover meaningful insights from the data and presentation of the insights in self-intuitive visual formats via dashboards & reports, back to the product team. Strategies developed based on customer understanding can help to meet customer needs (even latent needs), improve customer experience, and thus create a delighted customer.

Why Product Analytics?

We live in an era that is becoming increasingly digital every day. The digital transformation wave has reduced switching costs for customers in almost all sectors & segments of the business.  Customers are empowered with capabilities to gain more product information, make faster buying decisions, and make their opinions on product websites/apps known to the public domain within no time. 

Thus, marketing strategy and advertisements for the product must be crisp and targeted segment-wise. It helps to increase the quality of incoming leads and conversion rates, and decrease marketing expenses as well. It is also crucial for organizations to be proactive & have systems in place to ensure customer retention and increase sales by upselling/cross-selling.

With the volume, variety, and velocity of incoming data increasing day by day, any organization would require an automated, tightly coupled, robust Product Analytics system in place that accepts data from multiple sources, processes it, and provides actionable insights, all in real-time. It enables the product teams to understand what the users do in real-time rather than assuming these actions from gut intuitions. According to a Mckinsey report,

“Companies that make extensive use of customer analytics are more likely to report outperforming their competitors on key performance metrics, whether profit, sales, sales growth, or return on investment. For example, companies that use customer analytics comprehensively report outstripping their competition in terms of profit almost twice as often as companies that do not.”

Who should adopt Product Analytics & when?

Product Analytics is a vision that needs to be adopted as a culture at any product company, irrespective of their size and volume of business.  Anytime is the right time to start with product analytics and it has to be considered a continuous exercise rather than a single-time activity. Digital transformation is bound to bring on more innovative changes to the business world, as it does today, across sectors like banking, retail, healthcare, education, travel & logistics, agriculture, etc. Data will always be available in plenty and competitive advantages will continue to be built around customer data.

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mHealth apps have been able to provide an even more granular level of data with the introduction and adoption of wearables, and both patients and doctors alike are benefiting from this democratized data. In a press release from Stanford University School of Medicine, Dean Lloyd Minor, MD, said: “We found that current and future physicians are not only open to new technologies but are actively seeking training in subjects such as data science to enhance care for their patients”.

This huge inflow of data from wearables has also changed the way data is collected, stored, managed, visualized, and utilized. It has even caused many notable changes in the way the healthcare sector operates by opening up new avenues for the accuracy of diagnosis, the prevention of disease, tracking the effectiveness of treatment, and providing safe medications. It has proven that a patient-centric individual approach to medicine is what elicits the most effective results, rather than a blanket cure for everyone.

What is healthcare analytics?

Healthcare Analytics is a broad term that means different things to different stakeholders in the healthcare sector. 

To a healthcare practitioner, healthcare analytics data can help in consulting remotely, accurately diagnosing ailments, bettering the effectiveness of the treatment they suggest, monitor their patient’s health status, and use predictive analysis to help them make decisions.

For governments, healthcare analytics services are essential to monitor and track their citizens, a feature that’s especially vital during the virus-ridden times we live in. They are able to analyze trends in the disease at regional and community levels and appropriately intervene, aiding them not only in curbing the spread of viruses but also in formulating the right kinds of strategies to mitigate large scale spread. They are also able to track fraudulent claims, mismanaged insurance companies, and illegal drug distribution. 

Pharmaceutical companies use healthcare analytics platforms for research and development, sales and marketing, drug discoveries, and customer engagement.

And finally, patients can use health data they generate to stay more in control of their own choices. Having their health-related data analytics readily available means they can take the necessary steps needed to better their health and also gives them the option to work with their healthcare providers, rather than working with the old notion of blind trust. 

How can data analytics change healthcare?  

The vast scope of data analytics in healthcare is difficult to condense into a few short pointers. But if the most pertinent impacts it will bring about had to be picked out, these would be the top contenders for how this revolutionary technology is changing the landscape of healthcare around the world. 

Comprehensive Utilization of Electronic Health Records

There is a lot of data collected in hospitals every single day. One big step forward for the health sector was for all this data to be converted into electronic records instead of physical copies – what is now called Electronic Health Records or EHR. Now imagine this being taken a few hundred steps further – what if all this data could be analyzed to leverage the best possible use of all this information – using healthcare analytics for quality and performance improvement? 

Reducing Preventable Harm

“Preventable harm” is defined as the presence of an identifiable, modifiable cause of harm – medication adverse effects, central line infections, and venous thromboembolism for instance.  The huge amounts of data that are generated and stored in hospitals can be analyzed to provide crucial answers to why these happen. This enables doctors to make better decisions, resulting in much better patient care. This hospital found that they reduced preventable harm by 40% after they decided to operate in a more data-centric fashion. 

Eliminating Diseases

Yes, you read that right. Data analytics can help the world completely eliminate life-threatening diseases or pandemics like COVID-19. For instance, the project Visualize No Malaria, initiated by a renowned NGO partnering with the government of Zambia, aims to completely eradicate malaria from the country. The project is focused on using data to meet its goals and has trained healthcare workers around the country to use data to track, report, and treat malaria before it spreads. They also use data collected to figure out where crucial supplies need to be provided first, avoiding wastage of these resources by distributing them where they are not needed. The same strategy can be implemented to control the spread of  COVID-19 – by tracking, monitoring, and isolating potential positive patients, and ensuring PPE are delivered where it is most crucially needed. 

Improve Computational Medical Treatment

Computational Medicine is an emerging discipline that leverages sophisticated software,  computer models, and analytics to figure out the root causes of disease and find the most accurate diagnoses. With sufficient data and analytical tools, you can build models that answer complex questions in the medical world like how certain diseases develop, how environments shape a patient’s wellbeing and how biological systems change from healthy to unhealthy states. To read more about how computational medicine utilizes data analytics to enhance the way doctors detect and treat diseases, you can read this paper from John Hopkins.

Empowering The Patient 

With data being democratized, patients will have the information they seek at their fingertips – they will be more likely to stick to a doctor-prescribed health plan when they are aware of the actual numbers in the game. Data analytics, if shared, can lead to collaborative physician-patient decisions which allow the patient to feel more in control, achieving far better results from the treatment planned. 

In conclusion

Considering the pace at which individuals, as well as enterprises across the globe, are adopting digitization, data analytics is set to revolutionize the healthcare industry in the immediate future. With a more patient-centric approach, medicine will finally be seen as an approachable and democratic domain, and more and more individuals will become eager to make healthy choices, once they see data-driven results making a different world over, that is also accessible to them at their fingertips.

At Black Rock IT Solutions, we have ample experience in developing world-class healthcare solutions that adhere to healthcare regulations around the world. If you have a remarkable idea for a data-driven solution in the healthcare sector and are looking for an IT partner, drop a mail to sales@blackrockdxb.com

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