Page 45 – DataToBiz

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
No Comments

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

Kavika Roy
July 31, 2018
1 Comment

Marketing Analytics: The Secret To Your Business Success

Monitoring websites to track the success of a business is a culture and has since become a practice. It is usually interpreted that if the graph is good, the business is on the road to success and this somehow leads to significant aspects being neglected. Now, you must be wondering why so? This is a fact though, that simply by analyzing your website, you might just never be able to reach potential leads or evaluate the conversion rate of your potential customers or maybe even understand what is actually desired of your product to fulfill the demands of the market. Marketing analytics fills in the blank here and is the answer to all the forecasting you have been doing to date based on the evidence only. Implementing marketing analytics is what is to be learned now. When it comes to analytics there are people who will behave as if it doesn’t matter. But it sure does. Smart people across the globe are investing in this science so as to improve campaigns and gain insights. Data from HBR (2015) shows various ways in which marketing analytics is being used by different companies. Given the importance of marketing analytics for business growth, let me take you through it so we can see if it actually does matter to make it a part of your everyday business operations. Customer Behaviour Prediction: The key to all questions Prediction of customer behavior has been a significant part of major e-commerce firms like e-bay and Amazon. It gives them an edge over other businesses and allows them to predict the demands and needs that may arise in the future. According to a Silicon Valley-based predictive marketing company, AgilOne, these three principal classes of predictive models are recognized: Segmentation models Algorithms for grouping customers on basis of factors like the type of products they buy or the amount of money they spend etc. Prediction models Insights like customer conversion ratio (the number of people who visited the website to the ones who got influenced by the marketing campaign and bought products from the company), the likelihood of engagement, propensity to churn, the probability to buy etc. are derived from these models. Prediction models built on customer analytics can make a significant impact on any business. Recommendation models Offering recommendations about the product that the customers may like depending upon their previous purchases. All these models collectively utilize all the data from the present as well as the past and transform it into useful insights allowing businesses to improve in lucrative terms like sales and the RoI (return on investment). Prioritize and Qualify Leads Ranking your leads based on the likelihood of the actions they might end up taking is another front marketing analytics could help you peep into. This further assists in identifying prospects with similar attributes. The leads thus generated become prospects that can be turned into permanent customers. In this scenario, it is important to consider that, to generate optimum results a large number of data sets are needed. This gives bigger firms an edge over the ones that might not own such huge data quantities, by improving their return on investment. But with the right approach and help, smaller industries may also be able to gain this insight as per their needs. Driving Better Products in Market To launch a product that will suit the present demands and patterns of the market, a deep and well-predicted insight is a must. Again, marketing analytics is the answer here, the predictive algorithms allow the companies to improve the new product based on customer data piled up while taking sales and feedbacks into consideration. All these factors integrate to form a part of the bigger marketing strategy and allow the business to enhance its earnings. Creating Better Marketing Campaigns We all are proof that marketing campaigns via emails or social media allow the firms to improve their customer base. Marketing analytics gives companies the power to monitor and optimize these campaigns so that sales and ROI can see growth. The integrated tools and techniques help rule out the lacuna that might hamper the campaigns and their implementation as decided. Also, it becomes possible to monitor the current outcome of any campaign, whether it works properly or needs to be altered for the future. Taking Wiser Decisions A company may find out that digital, print, TV and radio marketing takes up around 85% of its market spending. Now amongst these many of the activities can be directly measured but spending on digital marketing can be refined using marketing analytics tools. The company can then use the results to optimize its strategy. Using these types of approaches allows the marketers to track marketing performance on real-time marketing analytics metrics and make wiser decisions for its better implementation. How Is Data Analytics Used in Marketing? Data analytics helps the marketing teams to combine structured and unstructured data and derive real-time insights about customer behavior, purchase patterns, sales graph, market trends, and more. It helps enterprises with the following: Customer Acquisition A study said that 42% of marketers use data analytics to make decisions about customer acquisition. Customer acquisition is a technique of winning new customers and convincing them to buy the products by the brand. The process includes several stages and is represented in a funnel-shaped design. It consists of targeted promotions to reach prospective customers and then provide them with relevant information to spark interest. Attracting the audience, engaging them through promotional material, convincing them to buy, and keeping them happy with customer service can be called the customer acquisition flywheel. Data analytics helps marketers by providing actionable insights to attract customers. Customer Insight Customer insight is the process of understanding and interpreting the purchase behavior of customers. This helps in knowing which products are more popular and why. Businesses can suggest products to customers based on customer insight and increase sales. Data analytics processes the unstructured data from the World Wide Web to share reports and data visuals about customers’ preferences.

Kavika Roy
July 23, 2018
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Customer Analytics – Win Your Customers and Increase Revenue

Corporations across the globe are trying their best to look at the business from a customer-centric view. This exercise opens for them a window to peek into the interests of their clientele and create policies accordingly. But in today’s volatile business environment judgments built simply from past experience or intuition is increasingly unreliable. Customers today are more connected and empowered. Access to the internet all the time has allowed them to become more specific about their needs. They are aware of everything that is trending in the market. In such a scenario it becomes important for a business owner to predict a customer’s response with respect to his organization. The deeper businesses understand their customers’ preferences and lifestyle habits, the more they are able to attract potential buyers. However, it is not as simple as it seems. It is a big challenge for organizations to understand customer feedback, behavior and needs, well enough so as to make data-driven decisions about what customers are likely to respond to or what they are likely to purchase. Customer analytics or customer data analytics is that significant insight gained with the help of data science, that allows businesses to use customer data in order to make key business decisions. The information obtained from the process is used for designing effective marketing campaigns, site selection, customer relationship management, and secure decisions for the future. Insights pertaining to the customer’s feedbacks and responses drive the organizations to directions that help them outperform their competitors. Strategizing everything beginning from their production to their supply far before the demand arises, helps them improve their key performing metrics. Why Do We Need Customer Analysis? We already know how important customers are to any business. But knowing how to attract more customers and retaining existing ones is easier said than done. Using customer analysis helps get a better insight into what a customer wants and how we can keep them happy. Increase Customer Retention Rate Around 82% of businesses said that retaining customers is cheaper than acquiring new ones. Moreover, 65% of sales come from existing customers. Having repeat customers is good for the business. But, for this to happen, providing the customers with what they want is important. You have to understand their product preferences, their expectations from your business and find ways to prevent them from going to your competitors. Customer analysis helps answer these questions and increase the retention rate. The more you know your customers, the better you can meet their expectations. Better Customer Segmentation Segmenting customers and grouping them into different categories helps in targeted campaigning. There are different ways to segment customers- Segmenting customers into neat categories is possible only when you know enough about them and their preferences. Consumer data points are vital metrics that provide insights into customer preferences and behavior. The data points give enterprises a picture of the products preferred by customers, the frequency of purchase/ usage, and most used features/ functionalities, and so on. Customer analysis gives you the insights needed to know your customers. Develop Personalized Marketing Strategies Once you segment customers and prospective leads, you can plan a comprehensive marketing campaign for each segment. For example, sending emails to a customer who is old-fashioned and doesn’t check emails every day is not an effective marketing strategy. From choosing the marketing channel to determining the type of approach, customer analysis can help your sales and marketing teams fine-tune promotional tactics to increase market reach, sales, and returns. It also helps in understanding the market trends in relation to customer preferences. Accurately Predict Customer Behavior Customer behavior is hard to predict without using historical and real-time data. Customers decide whether or not to buy a product based on several factors. Customer behavior is broadly classified into the following- Reduce Customer Acquisition Costs As already discussed, acquiring a new customer is an expensive affair. However, knowing what the target audiences want can help reduce the acquisition costs. This is very useful for SMEs as they tend to have limited budgets for marketing and promotions. Acquiring customers means you need to spend on building a brand image that aligns with the preferences of your target audiences. Customer analysis provides you with the necessary information. For example, sustainability and eco-friendliness are being given more importance by some customers. If the target customer base is eco-conscious, emphasizing transparency in the supply chain and highlighting the use of sustainable resources will help build the brand image as an eco-friendly business. Enhance Customer Services Almost 90% of companies say that customer experience is the key to customer retention. Contented customers are more likely to stay with the business or come back even if they exhibit variety-seeking behavior. Even if your products are of good quality and match customers’ tastes, but your customer service doesn’t meet their expectations, you are at a high risk of losing your consumer base. Customer analysis allows you to streamline your customer service department and empower the agents to be more efficient at work. The insights derived from customer feedback will tell you exactly what is annoying them or what they expect for your customer service agents. You can use this information to hire more agents or train them to effectively deal with customers’ queries and complaints. Accurate Sales Forecasting When you acquaint yourself with your customers thoroughly, you can accurately predict the percentage of sales a product will generate. This helps in manufacturing, maintaining inventory levels, and calculating profits. Sales forecasting usually uses data from CRM systems, sales teams, and finance departments to get the complete picture. Moreover, enterprises can save money when making large investments in a product or service. Revamped Product Portfolio What if your customers want more features from your products? Could that be the reason they aren’t buying your products? Maybe your competitor offers more choices in terms of color, sizes, designs, etc. Customer analysis also helps in making changes to your products and revamping your product portfolio. The customer analysis reports can aid your R&D teams

Kavika Roy
July 17, 2018
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11 Insane Machine Learning Myths Debunked for You!

The world is becoming smart, smarter than ever before. There are homes that know how to turn on the lights by judging their intensity and there are cars that can drive themselves. Isn’t it something like living in a sci-fi world? Everything that was imagined is turning into reality. Among all that we hear about the upcoming technology, machine learning (ML) is a common term being associated with almost all of them. The term has been more misinterpreted than understood and there has been a considerable measure of hype buzzing around it. With more gadgets and technologies being launched every day, customers are keen to know what is it that is making them smarter? They are curious to discern the tech running behind the smartness and understand how it can benefit them in their personal as well as business ventures. This inquisitiveness towards the “working” has lured people to read and question about the same, however, the responses have not been palatable. For instance, you may often see mobile companies using the terms artificial intelligence and machine learning interchangeably for their products, now this is how a misperception is shaped. The customers do not understand the difference between the two and start treating them as synonymous with each other. The aim here is to make you understand the similarities and differences between “machine learning” and the terms it is confused with. this write-up shall provide you with a clear insight so that you can differentiate between the hype and the reality. It is important because machine learning forms an integral part of almost all data-driven work. In the event that you intend to consolidate it into your business, you should discern what it may or may not be able to do for you. Having a clear perspective will ensure that you develop a strategy that fits into your business module and helps you accomplish the set objectives. Removing the Misconception You know how they say in school that if your basics are clear, you will understand each and every concept and if not then surely there will be trouble. This concept will hold true in your entire life and therefore if you recognize the simple notion of machine learning you’ll never be influenced by the related hysterias. The figure below describes machine learning in its most naive form. There is a lot of reality and there is a lot of hype pertaining to machine learning. But with the above-illustrated diagram, it should be clear that machine learning is, training a machine by giving it a large amount of data and then letting it perform based on that learning. Exposing the Machine Learning Myths Machine learning is currently going through a phase of inflated expectations. Along with ongoing machine learning developments around the globe , there are still a lot of organizations looking forward to conceptualizing and running ML projects without even exploring the power of basic analytics. How do you expect them to meet their goals when they do not know what ML can or cannot do? In such a scenario it becomes imperative to know the myths and truths related to the subject. #1 Machine Learning and Artificial Intelligence Are Same One of the most common misconceptions is between artificial intelligence and machine learning. Both the terms are not only different in words but are two different fields belonging to a bigger pool of data science. In order to understand the difference consider this example – You wish that the camera of your phone should recognize a dog. Now in order to do that you provide it with a huge amount of data that contains pictures of all the types of dogs present in the world. With the help of these images, the camera is able to create a pattern that resembles a dog. Now whenever you point the camera toward the dog, it matches the pattern and that is how you get a positive hit. On the other hand, pointing the camera toward a cat doesn’t identify it as anything. This is a machine-learning process where the machine is trained to accomplish a particular task. Artificial Intelligence on the other hand is a broader concept, where the machines are trained in such a way that they can make their own decisions just like the human brain. If you put a cat in front of a camera that works on an AI technology, it will use it as another input and further reuse it to train itself. This training would help the AI-enabled phone to tell that isn’t a dog but it may be something else that can be explored. #2 Hiring the Best ML Talent Is Sufficient to Resolve Business Issues Business firms are spending a lot of money in gathering the best machine learning talent which can analyze their data and offer useful insights. What they forget in the process is that machine learning is just one part of an effective strategy, the basics are to have the right type and amount of data. If there is no one who can fetch the data, what will the professionals work upon? Therefore, businesses do not need a staff good in one field but someone who knows how to work from the scratch. There are data science firms all over the globe that can help businesses develop a correct approach and provide the useful insights they have been looking for. #3 ML Implementation Requires Humongous Infrastructure Machine learning sounds scientific and complicated that many presume it is not meant for their business. After all, what will an ordinary business do with advanced technology? Not every SME hires AI experts, isn’t it? That’s where we are wrong Years before it was said that if you wish to carry our ML operations on your premises, you’ll need to invest a large amount in infrastructure. The scenarios have changed now. Since data science and data analytics has become such an integral part of the business world, there are professionals who are