Page 43 – DataToBiz

Parindsheel Dhillon
January 22, 2020
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Data Warehousing as a Service (DWaaS): Faster Business Decision Making

The role of data is changing significantly with advanced methodologies used for upgrading sales and integrating business intelligence in the organizational schema for better outcomes. But in the absence of proper tools and techniques to manage and organize such a large pool of data, results might appear too far-fetched. So, what is the way out to handle bundles of unstructured data and harness its true potential for catalyzing growth? Data warehousing is the solution that enterprises seek to support in decision making and grabbing potential markets using real-time insights provided by it. Data analytics helps in reporting, analyzing, and creating numerous use cases that revolve around solving your business problems. Today, several industries around the globe are partnering with data warehousing consultants or data warehousing companies to streamline their business processes. What is Data Warehousing & How It is Helping Enterprises Grow Rapidly Through DWaaS Data warehouse is an advanced system designed to help enterprises perform data analysis and reporting. The organization has multiple sources to collect data; therefore, it requires an advanced approach that structures all collected data and harnesses the value from it to speeds up the decision-making process and contribute to the faster growth of the organization. But modernizing the use of such a huge chunk of unstructured data requires an innovative approach. Therefore, enterprises trust DW-a-a-S for this purpose where data usage by integrating the organization’s operational system (ERP, Historian, PI System) can be simplified under one hood termed as data warehouse and refining process can work better to fit in the use cases that directly contributes to the growth and market capitalization. What is the Purpose of Data Warehousing? A data warehouse is a repository of digital data stored by the enterprise. The primary purpose of data warehousing is to allow companies to access the huge amounts of data stored in the centralized database. The data is cleaned, formatted, and analyzed to provide actionable insights. The right data warehouse is used as a data management system where business intelligence and data analytics can be performed to understand patterns in the data and derive actionable insights for decision-making purposes. The data warehouse is used to run queries to find the information the business wants and can seamlessly deal with large datasets, unlike database systems. Data warehousing is not a new concept in the industry. With data becoming increasingly available from several sources, enterprises need to find a way to store this data and use it for analytics. A data warehouse is a comprehensive solution as it can store huge volumes of data in its multi-tier structure. It can be a physical storage unit located on-site or a cloud storage platform accessed through the internet. Depending on the size of your business and the data collected, you can opt for either of the data warehouse architecture: A data warehouse is important to run business intelligence tools without spending too much money on querying. It helps define the data flow within the enterprise and makes it easy to access and share data among the departments and teams. Working of Data Warehousing, Data Lake & Benefit They Can Bring For the Enterprise Data warehouse works like a big data lake of information that is designed to store and preserve a large chunk of information. Business requires real-time insights that support trend analysis by setting up an efficient system that improvises the processing and sorting of details; thus preventing data duplication and creating a better usable data history that yields results. Such an approach or working of data warehouse helps in maintaining a complete data history even if it has been purged from different source transaction systems. When the data gets assembled from a data lake is one place working like dashboards, the resulting outcome is different business applications working efficiently to deliver quality as they have just one data source from where they can extract information and refine the data for different purposes in the organization. Why Enterprises/Corporations Prefer Data Warehousing as a service (DWaaS) for Business Intelligence? Bettering Business Intelligence Decisions are important in business and they can be perfect when fuelled by insights from real-time data collected. While framing the business strategy or setting up a specific operational module as a standard operating procedure (SOP), corporations have to rely on data-driven facts that can help them take concrete decisions. Real-time insights from unstructured or parsed data that establish a better data visualization can increase the efficiency in market segmentation, inventory management, financial management and lastly sales. In this way, the business can grow up to become much more competitive and market-ready to withstand any disruptions occurring at the technology, trends, or consumer behavior. Setting Up High Performance Data Analysis Data warehouse is not just a godown storing information rather a place that expedites the data retrieval process and organizes data engineering and analysis. When you need to make quicker decisions, the presence of a system that can query the stored data in a structured manner and provide real-time feedback simply makes the process swift. The high-performance data analysis helps in understanding trends and adapting as per the change so that better decisions can be made in due time that delivers results for the corporations. Faster Data Access Data warehouses create one single dashboard that stores KPI or Key Performance Indicators. Some of the data indeed are KPIs that influence decision making and when they are available in a flash, it saves a lot of time for the corporation. When one dynamic dashboard works in the organization, the higher management does not have to rely on the IT team for collecting data. When you have access to one single dashboard that performs multiple functions, decision-making becomes faster as you do not have to spend much time on gathering data, rather, you can focus more on the analysis that can influence the outcomes. Quality Enhancement Quality enhancement is necessary for creating a frugal data visualization that has the potential to influence decision-making with long-term objectives. In data warehousing,

Vaishali
December 13, 2019
4 Comments

Face Recognition: ONNX to TensorRT conversion for Arcface model problem?

Are you also fascinated to get an inference from a face recognition model on jetson nano? I fail to run TensorRT inference on Jetson Nano, due to PReLU activation function not supported for TensorRT 5.1. But, the PReLU channel-wise operator is available for TensorRT 6. In this blogpost, I will explain the steps required in the model conversion of ONNX to TensorRT and the reason why my steps failed to run TensorRT inference on Jetson Nano. Steps included to run TensorRT inference on Jetson Nano : The first step is to import the model, which includes loading it from a saved file on disk and converting it to a TensorRT network from its native framework or format. Our example loads the model in ONNX format i.e. arcface model of face recognition. Next, an optimized TensorRT engine is built based on the input model, target GPU platform, and other configuration parameters specified. The last step is to provide input data to the TensorRT engine to perform inference. The sample uses input data bundled with model from the ONNX model zoo to perform inference. Sample code: Now let’s convert the downloaded ONNX model into TensorRT arcface_trt.engine. TensorRT module is pre-installed on Jetson Nano. The current release of TensorRT version is 5.1 by NVIDIA JetPack SDK. Firstly, ensure that ONNX is installed on Jetson Nano by running the following command. import ONNX If this command gives an error, then ONNX is not installed on Jetson Nano. Follow the steps to install ONNX on Jetson Nano: sudo apt-get install cmake==3.2 sudo apt-get install protobuf-compiler sudo apt-get install libprotoc-dev pip install –no-binary ONNX ‘ONNX==1.5.0’ Now, ONNX is ready to run on Jetson Nano satisfying all the dependencies. Now, download the ONNX model using the following command: wget https://s3.amazonaws.com/ONNX-model-zoo/arcface/resnet100/resnet100.ONNX Simply run the following script as a next step: We are using Python API for the conversion. import os import tensorrt as trtbatch_size = 1 TRT_LOGGER = trt.Logger() def build_engine_ONNX(model_file): with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.ONNXParser(network, TRT_LOGGER) as parser: builder.max_workspace_size = 1 << 30 builder.max_batch_size = batch_size # Load the ONNX model and parse it in order to populate the TensorRT network. with open(model_file, ‘rb’) as model: parser.parse(model.read()) return builder.build_cuda_engine(network) # downloaded the arcface mdoel ONNX_file_path = ‘./resnet100.ONNX’ engine = build_engine_ONNX(ONNX_file_path) engine_file_path = ‘./arcface_trt.engine’ with open(engine_file_path, “wb”) as f: f.write(engine.serialize()) After running the script, we get some error “Segmentation fault core dumped”. After doing a lot of research we have found that there is no issue with the script. There are some other reasons why we are facing this problem. The reasons and explanations are discussed in the following paragraphs. What are the reasons for which model conversion failed? Jetson Nano is a ARM architecture-based device where TensorRT 5.1 is already pre-installed. The image which is written on SD card of NVIDIA Jetpack SDK does not includes TensorRT 6. It is possible to convert other models to TensorRT and run inference on top of it but it’s not possible with arcface. The arcface model cannot be converted because it contains a PRELU activation function which only supports TensorRT 6. Model cannot be converted because we are unable to upgrade the TensorRT version from 5.1 to 6. So, unless and until NVIDIA provides us a Jetpack SDK OS image with the latest version of TensorRT 6 specifically the arcface model cannot be converted. Why can’t we upgrade from TensorRT 5.1 to TensorRT 6? The installation file of TensorRT 6 is only supportable for AMD64 architecture which can’t be run on Jetson Nano because it is an ARM-architecture device. That’s why, the arcface ONNX model conversion is failed. Future Work and Conclusion As soon as, NVIDIA Jetpack SDK releases OS image with TensorRT 6 the arcface ONNX model will get converted to TensorRT and we can run inference on top of it. I am all ears to know your thoughts/ideas to make it happen if NVDIA is taking its time to update jetpack SDK. We at DataToBiz always strive for latest tools & technologies to get ahead from our competitors. Contact for further details About Author: Sushavan is a student of B.Tech in Computer Engg. at Lovely Professional University. He worked as an intern at DataToBiz for 6 months.

Ankush Sharma
November 21, 2019
1 Comment

Kickstart data analytics for e-commerce business with unbelievable $299 Budget

How we at DataToBiz helped ecommerce start-up in kick-starting data analytics journey with unbelievable $99 budget by using freemium and open source tools

Vaishali
October 03, 2019
26 Comments

Fixing WiFi connectivity on Nvidia Jetson Nano

Are you struggling with fixing wifi connectivity issue with Nvidia Jetson Nano? After going through Nano official forums, if you are planning to buy expensive wifi modules, just because of reported issues of connectivity loss, then you need to check this blog. This blog contains 5 minutes of verified & tested hack to solve buggy driver, so that, jetson nano never loses its connectivity. What is NVIDIA Jetson Nano? Nvidia is a multinational computer systems design company based in California, US. It’s no surprise that some of the best software applications and developer kits belong to this multinational company. Jetson Nano™ is one such developer kit module that can empower countless artificial intelligence-based systems. The Jetson Nano™ kit is a cost-effective solution to build AI systems in less time. It helps create a range of embedded IoT (Internet of Things) apps, AI robots, intelligent gateways, and several artificial intelligence software/ solutions. The kit allows developers to build low-power AI systems and entry-level NVRs (Network Video Recorders). From image processing to object recognition, the kit has all you need to build an AI application. Another advantage of Jetson Nano™ is that it has been created for beginners. If you are still learning or want to learn about artificial intelligence and robotics, grab the starter kit and get going. It has ready-to-try projects that help you understand the use and purpose of AI in the real world. The following are some applications of Jetson Nano™: Fixing WiFi connectivity on Nvidia Jetson Nano Firstly, check wifi module compatibility from jetson nano official forum. We are setting up Edimax EW-7811Un for testing purposes for which maximum issues are reported: sudo apt-get update sudo apt-get install git linux-headers-generic build-essential dkms git clone the following repository:https://github.com/pvaret/rtl8192cu-fixes? Checking if your device uses this driver from the list below,ASUSTek USB-N13 rev. B1 (0b05:17ab)Belkin N300 (050d:2103)D-Link DWA-121 802.11n Wireless N 150 Pico Adapter [RTL8188CUS]Edimax EW-7811Un (7392:7811)Kootek KT-RPWF (0bda:8176)OurLink 150M 802.11n (0bda:8176)Plugable USB 2.0 Wireless N 802.11n (0bda:8176)TP-Link TL-WN725N (0bda:8176)TP-Link TL-WN821Nv4 (0bda:8178)TP-Link TL-WN822N (0bda:8178)TP-Link TL-WN823N (only models that use the rtl8192cu chip)TRENDnet TEW-648UBM N150 sudo dkms add ./rtl8192cu-fixes sudo dkms install 8192cu/1.11 sudo depmod -a sudo cp ./rtl8192cu-fixes/blacklist-native-rtl8192.conf /etc/modprobe.d/ sudo echo options rtl8xxxu ht40_2g=1 dma_aggregation=1 | sudo tee /etc/modprobe.d/rtl8xxxu.conf sudo iw dev wlan0 set power_save off sudo reboot now DataToBiz’s Hack: Enable auto-login so that wifi never loses its connectivity on ssh. Here is how to make it happen: sudo nano /etc/gdm3/custom.conf In this file, uncomment the following:I. AutomaticLoginEnable = trueII. AutomaticLogin = user // put your user name here e.g. jetson After following the above 10 steps, Edimax module will never drop the connection. Enjoy playing around with Jetson Nano with no connectivity issues!!!! At DataToBiz, we have been experimenting with various edge devices to solve business problems. Contact us in case you are looking for computer vision solutions on Nvidia Jetson Nano. About Author: Aanchal is a deep learning engineer with DataToBiz having expertise in deep learning technologies and currently working on various IOT devices for computer vision product SensiblyAI & related client projects.

Parindsheel Dhillon
January 19, 2019
1 Comment

DataToBiz is a Firm That Delivers on Clutch

The optimisation of artificial intelligence in the coming era will bring unprecedented changes to all aspects of society. We at DataToBiz want to stay on top of this cutting edge technology, and we want to help your company do the same. Our talented team of IT experts and consultants has been trusted by thousands of companies and individuals. If you’re still not convinced, take it from Clutch, a B2B ratings and reviews platform in Washington, D.C. We joined their platform to see how we rank against the competition and already we’re reaping the benefits: we’ve achieved a position on their list of the top artificial intelligence companies in 2019! Clutch incorporated many factors in their evaluation of our company, including our industry expertise, social media presence, media recognition, and former client projects. Their analysts conducted telephone interviews with our former clients, gaining an insider’s perspective on our company’s project management skills, technical expertise, communication, and ability to deliver. These reviews were then transcribed and posted to our Clutch profile. Here’s a snippet of the praise left by our former clients so far: Our work has provided a pivotal source of revenue for this former client, and it could do the same for your company. Reach out to us today with your data science and AI needs and we will offer the same devotion and passion to a customised, data-driven solution for you. And if you’d like even further confirmation of our superior service, check out Clutch’s sister websites, The Manifest and Visual Objects. The Manifest is a resource that offers industry insights, how-to-guides, and recommendations of top service providers like us; we scored a spot on their list of AI developers in India. Visual Objects is a new platform that equips buyers with a digital portfolio of B2B companies’ previous projects to aid buying decisions; our portfolio is on their list of top custom software developers. We’d like to thank our friends at Clutch for including us in their 2019 research, and we thank all those who have helped our company along the way. These achievements encourage us to work even harder for our current and new clients in the upcoming years.

Nishant Thakur
January 12, 2019
1 Comment

What it’s like to be an intern at DataToBiz

When it all began… In this blog, I am going to talk about my experience of being an intern at DataToBiz. I still remember the day when I reached the DataToBiz office on day 1 at 9:30 am sharp and met Mr. Parindsheel and Mr. Ankush, both co-founders, about my previous experiences and my expectations from the internship in their start-up. Mr. Parindsheel was my internship mentor. I was assigned a project by Mr. Parindsheel after 10 minutes of my reporting. The project was related to a solution for the FinTech industry. I had never previously worked in this domain and it was a completely new challenge for me. So, I tried to explore the domain as a first step as suggested and advised. As quickly as post-lunch, sir asked for my insights towards the dataset I was handed to explore. As a beginner in the machine learning field, I found an insight that could be predicted from the given data. It amazed me when I was explained about the data from a business point of view and was guided to look at each dataset from similar angles. That is how I realized that I was at the right place. I began right that moment and because of the spot on pointers, successfully completed my project well in time. I also got the opportunity to work on another project related to computer vision in the remaining time. People at DataToBiz… People at DataToBiz are amazing and unique. The best thing about my internship was the people around me. Team DataToBiz is a beautiful balance of talented people who work as a team. They will be the main reason for my learning curve which I felt was exponential. The team was really supportive and ready to help at any time. The talks over tea… This was the best time of the day when the entire team assembled in the cafeteria for tea or coffee and shared their experiences and ideas. This was also the best time to interact with all the people at DataToBiz. We talked about future plans, college life, our hobbies, interests, and whatnot. This was the part of my internship that I will never forget. Life at Chandigarh… Chandigarh is one of my favourite cities since childhood. The public transport is really efficient and you don’t have to wait for more than 15 minutes for a bus and there are no traffic-jam problems. There are many amazing places to visit in Chandigarh like rock garden, Sukhna lake, Elante Mall, Mansa Devi temple, and quite a number of gardens. There is no problem in getting good food in Chandigarh and you can get all types of cuisines. The PGs are also affordable here. In my view… DataToBiz is an amazing company or start-up and I got more than I expected from my internship experience. I learned many new techniques and approaches in machine learning. DataToBiz helped me to look at any problem from a business point of view. I also developed an interest in computer vision during my internship. In my view, DataToBiz has infinite opportunities for data scientists as well as for developers. They have some really cool projects in every field like computer vision, fintech, natural language processing, healthcare, etc. A team worth aligning your career with. About Author: Nishant is a student of B.Tech + M.Tech (Dual Degree) in Biotech at IIT Kharagpur. He worked with DataToBiz for 1-month internship in Dec, 2018 & still aligned with the company on a part-time basis.

Kavika Roy
October 04, 2018
No Comments

5 Applications of Data Science in FinTech: The Tech Behind the Booming FinTech Industry

Data Science has played a significant role in transforming the finance and banking industry by completely changing the ways in which they previously operated. Life has been made easier for the banking officials as well as the customers. FinTech: a new term coined for the innovation and technology methods aiming to transform traditional methods of finance with data science forming one of its integral components. Whenever you use your credit card, Amazon Pay, PayPal, or PayTm to make an online payment, the commerce company/seller and your bank, both utilize FinTech to make a successful transaction. With time FinTech has changed almost and every aspect of financial services, which includes investments, insurance, payments, cryptocurrencies, and much more. Fintech companies are heavily dependent on the insights offered by machine learning, artificial intelligence, and predictive analytics to function properly. In this article, we will look at the contributions of data science in FinTech. What is Data Science in FinTech? Data science is a knowledge area that prepares data for analysis and delivers insights using advanced analytical tools. It cleans, structures, and manipulates large volumes of data to derive actionable insights. Data science helps organizations to use analytics at every level of the business. It is not a single subject or concept but a combination of different fields such as statistics, mathematics, data analysis, quantitative finance, algorithms, and visualization. It uses a combination of tools to understand big data easily and use the insights in real-time. The FinTech industry uses data science to get a deeper insight into customer behavior. This helps financial institutions create products and services that will align with the market trends and increase returns for the business. There are several roles of data science in the FinTech industry. The most important ones are as follows: Analyzing Customer Behavior Data mining, natural language processing (NLP), and text analysis are used to understand customer behavior. The FinTech industry also depends on customers to become successful, just like every other sector. Analyzing customer behavior gives FinTech companies numerous benefits such as- Data science allows organizations to develop customer behavior models and run predictive analytics in real-time. Predictive Analytics Predictive analytics is a part of advanced analytics where future trends are predicted using historical and real-time data. Statistical modeling and machine learning algorithms are used on data collected and processed through data mining. Data science is used in FinTech to: Data science also helps with algorithmic trading, where issues related to pricing, trading volume, and timing are managed to increase the efficiency of the trading platform. We can observe this trend in the crypto market. R&D Research and development are an integral part of every industry. Data science is used to improve product development strategies so that the establishment can make the most of the changing market conditions and customer requirements. Data science and artificial intelligence are used together to achieve goals. Understanding the weaknesses in the company’s existing products/ services will help the management make changes to overcome the weak points and strengthen its presence in the industry. Advantages of Data Science in FinTech Applications of Data Science in FinTech 1. Credit Risk Scoring With an aim to make “credit accessible to more number of people”, FinTech companies use robust machine learning algorithms to predict the creditworthiness of people. This lets them reach a wider customer base and reduce the rate of credit defaults. Traditionally, banks use very complex statistical methods to determine the credit score of an individual, but with the help of data science, the good and bad borrowers can be separated in a fraction of seconds. In order to accomplish this task, a large number of data points are utilized by the companies. Also, all the data that is collected is further used to train the machine and improve its performance. Therefore, data science provides a holistic view of one’s creditworthiness. Companies like Alibaba’s Aliloan are an automated online system that provides small loans to entrepreneurs who otherwise would have been rejected by the banks because they have no collateral against which the loans could be given. This automated system collects information such as online transactions, business performance, ratings from the customers, and much more to calculate the creditworthiness of the business owner. 2. Fraud Detection & Prevention Fraud detection and prevention have always been a top priority for FinTech companies. At present, it is estimated that financial institutions lose about $80 million every year due to fraudulent activities. With the evolution of data science, the ways to detect fraudulent activities have also changed. Machine learning-based algorithms are able to detect fraudulent activities better than the traditional systems that may sometimes even produce false positives and classify a normal transaction as a fraud as well. The advanced fraud detection systems work on supervised and unsupervised machine learning (ML) algorithms. Supervised ML-based systems are fed with historical data that has been labelled as fraudulent and non-fraudulent. This data set helps the system to classify any ongoing transaction as normal or anomalous. On the other hand, the unsupervised ML-based systems are just fed with a large amount of data that has not been previously classified, the system uses this data as a training set and learns to differentiate between standard and a fraudulent activity on the basis of transactions happening in digital space every day. 3. Customer Retention & Marketing Fintech companies collect a large amount of data from their customers which is often used by them for financial analysis. This information can likewise be utilized for enhancing the client base and expanding their lifetime value. Customer data right from their transactions, social media engagement, and personal information can be taken into consideration and used to offer them a better experience. For instance, by analyzing the previous products purchased by the customers’ algorithms can be created to predict their future choices. This knowledge can also be utilized to comprehend what items must be promoted among various age groups. FinTech companies may utilize client information to make thorough profiles of their clients

Kavika Roy
September 19, 2018
1 Comment

Easily Fixable Data Analytics Challenges Faced by Your Business Enterprise

Data analytics has become an indispensable part of the business world. Look all around and you will realize that everything is already data-driven. A bigger pool of organizations is moving towards executing this practice on their premises also. However, as per a 2016 report from Gartner, it was discovered that lone 15 percent of the aggregate businesses who attempt to execute data analytics, win the battle, and the rest stall out in the pilot phase of the venture. After running a background check on this problem, it was understood that there is a set of common issues that every one of the firms is confronting. In this article, you will discover the 10 most regular concerns upsetting the execution of data analytics ventures and the approaches to effectively resolve them. 10 Data Analytics Challenges 1. Large Volume of Data to Store The first and foremost challenge faced by the companies implementing data analytics is associated with data storage and analysis. Higher traffic websites such as the New York Times and Amazon may generate petabyte data or more in a single month. IDC in its Digital Universe reported estimated that the information stored in the IT systems of the world is doubling every two years. Another issue with all this immense data is that a major chunk of it is in the unstructured form. Documents, videos, audios, and photos are comparatively difficult to search, analyze and occupy a lot of space. To deal with these data problems, organizations are turning to various types of technologies. Technologies like tiering, compression and deduplication are being utilized to reduce the amount of space required to store the data. To manage the analysis part, firms use tools like Hadoop, NoSQL databases, Spark, BI applications, big data analytics software, ML, and AI to dig out the insights that they want. Data literacy is the solution to this challenge. Instead of collecting any data available from various sources, enterprises need to work on collecting meaningful data. Hiring data analysts and training employees to understand data literacy will help businesses collect data that is useful for decision-making. Another method to overcome the challenge is to scale the data warehouses/ data lakes in stages rather than going for a complete upgrade. This allows enterprises to manage the incoming data without spending billions of dollars at once. 2. Timely Generation of Insights The data doesn’t have to be just stored, it has to be used to achieve the business goals. As per the NewVantage Partners Survey, there are some common goals that are shared by almost every organization that deals with data analytics. Some of which include All these goals when achieved help businesses gain an edge over others in the market. However, the success of which usually depends on how quickly the generated insights are being acted upon. In case, the action time is less the data and insights tend to lose their value in the market. In order to achieve faster speed, some companies are looking forward to using new generation analytics tools and at the same time investing in real-time analytics that will dramatically reduce the time taken to generate reports. Real-time analytics are ruling the industry, thanks to powerful tools like Tableau, Power BI, Qlik, etc. The best way to generate timely insights is to choose the right tools for data storage and analytics. Where should a business store the data? In-house servers or cloud solutions like Microsoft Azure? Which analytical tools can easily handle big data and deliver real-time results? Talking to an expert will help businesses choose the right tools and customize them for their requirements. 3. Less Understanding of Analytics Data analytics has the ability to bring in precise and accurate decisions for the organizations that tend to use it. It helps them in managing their finances, launching new products, understanding their customers and much more. However, there is still a lot that needs to be done so that people have a clear picture of data analytics and its importance in today’s world. NewVantage found that only 27% of organizations in 2020 called their businesses data-driven. Moreover, 73% of businesses felt that big data management is an ongoing challenge. Seminars, small workshops on the office premises, discussions, and real-life examples are some of the ways that organizations are using to improve the understanding of data analytics among their staff. Training and empowering employees is vital to getting desired results from the data-driven model. It’s not sufficient if the top management and C-level executives understand the need for analytics. Every employee in the organization who needs to work with the new tools and systems has to realize the importance of quality data and accurate insights. 4. Recruiting Skilled Talent Organizations find it a challenging task to both retain and recruit talent that can handle their data and utilize it to derive useful insights. The 2017’s Robert Half Technology Salary Guide has suggested huge pay raise for the positions of data scientists and business analysts all over the globe. Companies are also trying to train their staff to learn some of the tools and techniques that can help them handle their data needs. But, there is still a large gap in the understanding of this field. The trend is continuing even in 2022, with Revenue Cycle Analyst and Database Administrator being the top two positions with the highest pay increase. Also, there are many firms that solely deal with data analytics and all the related operations. In case, the organizations are unable to find a suitable recruit for their firm, they can consult the professionals and get their data needs satisfied. These data analytics firms have all the expertise that is required to accomplish the given task. As an added advantage, outsourcing the work to another firm proves to be more economical than setting up a whole new section in an already established company. Hiring offshore solution providers and dedicated teams to manage data analytics for the business is a cost-effective solution. 5. Integrating

Kavika Roy
August 27, 2018
No Comments

AI in Pharma: How Pharma Industry is Getting Smarter Today

Artificial Intelligence or AI in the pharma industry presents various opportunities to substantially improve the pace of drug discovery and distribution process. The current protocol followed needs to be upgraded in order to meet the rising demand for medicine and that too without compromising its quality. Advanced AI solutions will help pharma companies to process structured and unstructured data in order to derive useful and actionable insights. The application of machine learning and AI to drug discovery will not only accelerate the process but also help companies to spawn a higher return on investment. It will make it easier for scientists to find potential targets and for the manufacturers to ensure its timely delivery. McKinsey estimates that machine learning and big data can help to generate a profit of around $ 100 billion for the pharma industry. The insights produced with the help of analytics would help the pharma companies to make better decisions, improve the efficiency of clinical trials, advance the shipping process and ultimately achieve greater commercial success. What is Artificial Intelligence in Pharmaceutical Industry? AI in the pharma industry is the use of algorithms, computer vision technologies, and automation to speed up tasks that were traditionally performed by humans. The pharma and biotech industry saw huge investments in artificial intelligence in recent times. From market research to drug development and cost management, AI is playing a vital role in modernizing the pharma industry and bringing new drugs faster into the market. Big data and AI-based advanced analytics have brought a radical change in the pharma sector. Faster innovation, increase in productivity, and building comprehensive supply chain systems are possible with artificial intelligence. According to a study conducted by the Massachusetts Institute of Technology (MIT), less than 14% of the new drugs pass clinical trials. Moreover, the pharma company has to pay billions to get the drug approved by the government authorities. By using artificial intelligence in pharmaceutical research and development, pharma companies can increase their success rate. The data from clinical trials are collected and processed using AI and ML systems to derive insights about the drug and its reactions to the test subjects. The positives and side effects are carefully observed and analyzed to make the necessary changes to the drug’s composition. This will result in drugs with a better curing capacity and fewer side effects. The pharma industry requires billions to keep up the R&D. The company spends huge amounts of money at every stage to ensure that the drug is made using quality materials and in hygienic and sterile conditions. The warehouse for storing inventory should have a temperature control facility to maintain the necessary conditions for the drugs to retain their original composition. By adopting artificial intelligence software apps and integrating them with systems in the pharma company, the management can streamline the process from start to finish. This will reduce operational costs and minimize the risk of damaging the drugs. Let’s take Novartis as an example. The pharma company is investing in AI and ML to find ways to speed up the treatment processes and help patients become healthier. The company is working on classifying digital images of cells based on how they are responding to treatment (compounds). The ML algorithms collect the research data and group cells with similar responses to the compounds used for the treatment. This information is then shared with the research team to help them use the insights and their experience in understanding the results. Novartis uses the images developed by machine learning algorithms to run predictive analytics and identify cells that may not respond to the treatment. The ML algorithms make it easier to study large amounts of data and identify the patterns of different diseases, their impact on the cells and organs, the symptoms, and the possible treatment methods/ drugs that can cure the diseases. A pharma company that invests in adopting artificial intelligence at each level (R&D, production, supply chain, etc.) will have an edge over competitors and can provide expensive drugs for cost-effective prices to make treatment affordable for more patients. AI in Pharma Industry: The Transformation Look how ML and AI models are transforming the pharma industry and making it even better than before. Supply Chain Management Optimization of the supply chain across pharmaceutical industries has always been a challenge for the owners. However, with the advent of AI and ML, the process is becoming smoother. The big data generated helps companies to reach out to their prospective clients and understand their needs, which in turn ensures the number of drugs to be produced by the companies. Also, predictive analytics insights generated with the help of big data allow the companies to foresee the demand pattern and hence manufacture only the required quantity of medicines. The drugs today are being increasingly customized for even small populations with particular genetic profiles. Finding out a way to deliver a medicine that is relevant only to a small bunch of 1000 people is more difficult than delivering medicines across the world. This venture requires proper utilization of resources so that there is no delay in delivery and loss to the company. An expert at “LogiPharmaUS Conference” in 2017 said that “Instead of executing one supply chain a thousand times, we should get ready to execute a thousand supply chains, one at a time.” This act will not only ensure timely drug delivery but also safeguard the hassle of re-execution every time. Machine learning and AI algorithms can help to automate this process and make it more robust. Now, when we talk only about shipping drugs, there are many medicines that are expensive and require very specific conditions to be transported. Billions and trillions of money are spent by the pharma companies to deal with the transportation process. With the application of ML and AI pharma, companies will be able to forecast demand and distribute products efficiently. Also, many key decisions will become automated allowing the companies to cut down their labor costs and make more profit.

Kavika Roy
August 16, 2018
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Why is Building a Data Strategy Important For Your Business Growth?

We all know that an immense amount of data is generated with every passing second. From our Uber ride to ordering a burger at McDonald’s or every transaction that we make at the ATM, everything is recorded and stockpiled for further analysis. In the past, data was perceived as nothing but a by-product of business activity, but today it has a value and is considered more of an economic asset. All the big enterprises generate numerous data, which they want to utilize for the benefit of their company but still struggle in managing, sharing, and turning it into useful information. If you are among one those business owners who are looking forward to utilizing the data that has been just stored in your systems, you have come to the right place. In this article, you will know all about why is data strategy important, how to strategically manage your data, and whom to consult by uncovering data strategy. What Are the Advantages of Implementing a Data Strategy? Listed below are some of the benefits of implementing a data strategy in your organization’s operations:- What is Data Strategy? In this modern world, we are bombarded with a continuous flow of data in our lives. The same can be said for business houses as well. But, having said so, the raw data will render useless unless we cleanse, sort, process, and churn out the insights out of it. Though we all understand the importance of it, unfortunately, most organizations are unable to leverage the benefit of the most powerful weapon in their arsenal – their data set. As per research, less than half of the organizations can leverage their data for decision making and only 1% of the unstructured data is being utilized as of now. Organizations need a proper data strategy to smoothen the operational flow. Data Strategy can be defined in simple terms as a complete and comprehensive strategy for collating, governing, analyzing, and identifying the relevant intel out of the raw data and putting it to use for making business decisions in a data-driven manner. Data strategy is inherently driven by the organization’s goal and overall business strategy. Whether it is better decision making, understanding the pain areas of the customers, or designing a product–data strategy can make a paradigm shift in the organizations’ business approach. A well-defined data strategy will comprise of – In this fiercely competitive world, having a well-defined data strategy puts a business in a better position than its competitors. A well-round strategy defines all the aspects and considers all the factors so the management can make effective data-driven decisions to drive the organization. What is a Data Strategy Framework? Any strategy can be only defined when put in a proper and systematic framework. A framework goes by as the supporting structure underlying the concept or strategy. The entirety of data strategy success is solely dependent on how properly defined the framework is. With sophisticated platforms and methodology for data retention, the organizations do get the half job done but the other half is completely reliant on the tactical and strategic understanding of the 360-degree data strategy. A framework comes here for the rescue. A properly defined data strategy framework covers multitudes of disciplines from data management. It comprises five core components that collaboratively work together as the building block for the comprehensive data management strategy – Identify, Store, Provision, Process, and Govern. Identify No matter how many terabytes of data we possess, none of this would matter much if we don’t know the proper identification and representation of the relevant content. Whether it is structured or unstructured, modifying and processing wouldn’t be possible if the data doesn’t cater to a properly defined format and value representation. Identification comprises the establishment of pertinent data element naming and proper value conventions. Having precise and accurate metadata (data about data) for identifying and referencing purposes is the sole essence of this first stage. Store Once the data is identified, the data needs to be stored somewhere safe and in a secure manner. In simple terms, putting data in a proper structure and safe storage so it can be retrieved, accessed, and analyzed whenever in need in the future, is the main agenda of data storing. Many organizations do effectively define the storage mechanism, but in practicality, there is a lot of scope for improvement that organizations need to focus on. Provision Previously organizations used to store data in silos and whenever needed, they used to retrieve the data for an individual business need. But now there is a complete shift in the business management process. Having data always ready for retrieval and usage is not an add-on capability, rather it is the need of the hour. Provision is defined as the packaging of data systematically so it can be shared and reused. Also, it provides the appropriate rules and access as the guideline for data usage. Process All other steps will fall apart if the processing of raw data into meaningful information is not done properly. Processing is the most complex part. From data cleansing to data formulating – it takes care of all the steps required to provide a unified data view. It hides all the complexities in the back end and gives a complete viewpoint for the users. Govern The last part lies with governance to ensure the efficacy and usability of the data remain high. It constitutes multiple steps such as managing data security, establishing data correction logic, setting up new data management rules, and many more. Data governance ensures that the data is consistently usable and adheres to standard data policies. Data Strategy Roadmap Once we understand what data strategy is, putting all the points together and making an actionable plan is what a data strategy roadmap does. It is the culmination of operation and strategy. Roadmap collates all the activities and puts a proper structure around them. In the initial phase, all activities look equally important, but it is crucial from