Data Engineering Archives - Page 9 of 11

11 Most Common Issues with Data Integration [Solved]

Data integration is a much familiar term you might hear these days. It is the basis to build a data-driven business process in your enterprise. Furthermore, data integration helps you adopt new technologies, create successful business models, and make better decisions. However, data integration is neither easy nor simple. It comes with various challenges that can result in the opposite of what you want for the enterprise. For data integration to be successful, you will need the right tools, strategies, and talent. These need to be aligned for the ultimate goal of taking your decision using a data-driven model/approach. You can take on the services of a data science consulting company to provide you with a comprehensive data integration plan. The aim is to overcome the data integration challenges using the expertise of the best industry professionals. What is Data Integration? We’ve already talked about data integration several times. But what exactly is data integration? The process of consolidating data from distinct sources is known as data integration. This is the simplest of definitions. Data integration is the first step necessary to work on data analytics, reporting, and forecasting. Data from all corners of the enterprise and across various online platforms is collected and stored in a central database. This is usually termed a data warehouse. You will need a warehouse that can hold a vast amount of information as you will keep adding more data every day. Also, you need to see that data from each source will be in a different format. You are essentially dealing with raw and unstructured data that needs to be brought together for further processing. A data integration example is collecting and processing data in the retail sector to manage inventory, supply chain, and customer satisfaction. The online and offline sales records are brought together to create a central database. This allows the management to decide how much inventory to hold, when to order more stock, and when to move the goods from the warehouse to the stores. It provides more control over business operations. Two Main Types of Data Integration Data integration is divided into the following- Enterprise Data Integration: EDI is the set of technological instructions that should be followed to manipulate data over more than two sets. It is the process of acquiring data from different business systems to help perform numerous business activities. Customer Data Integration: CDI is a part of the data integration solutions that deal with customer data. This data is used to identify leads, understand customer behavior, and provide personalized customer services. Need for Data Integration Data integration is necessary for a simple reason that SMEs need to know which decisions will work in their favor and which will backfire. Once a huge amount of data is collected, it is processed to derive meaningful insights. This process of converting raw data to useful and structured data is known as data mining. With such an extensive quantity of data available, issues during data integration in data mining like data latency, duplication, etc., can put roadblocks in your progress. But these can be overcome with proper understanding and planning. Before we look at the most common challenges of data integration and how they can be solved, let’s have a quick read about why data integration is needed for business organizations. The Most Common Data Integration Challenges 1. Data is Not Available Where it Should Be One of the most common business integration challenges is that data is not where it should be. When data is scattered throughout the enterprise, it gets hard to bring it all together in one place. The risk of missing a crucial part of data is always present. It could be hidden in secret files. An ex-employee could have saved data in a different location and left without informing the peers. Or it could be any other reason that results in the data being elsewhere. It is suggested to use a data integration platform to gather and compile data in one place to overcome the problem of not finding data where expected. Asking developers to work on it is time-consuming, which leads to the next issue. 2. Data Collection Latency and Delays In today’s world, data needs to be processed in real-time if you want to get accurate and meaningful insights. But if the developers manually complete the data integration steps, this is just not possible. It will lead to a delay in data collection. By the time developers collect data from last week, there will be this week’s left to deal with, and so on. Automated data integration tools solve this problem effectively. These tools have been developed to collect data in real-time without letting enterprises waste their valuable resources in the process. 3. Wrong and Multiple Formats Another of the common challenges of system integration is the multiple formats of data. The data saved by the finance department will be in a format that’s different from how and sales teams present their data. Comparing and combining unstructured data from different formats is neither effective nor useful. An easy solution to this is to use data transformation tools. These tools analyze the formats of data and change them to a unified format before adding data to the central database. Some data integration and business analytics tools already have this as a built-in feature. This reduces the number of errors you will need to manually check and solve when collecting data. 4. Lack of Quality Data We have an abundance of data. But how much of it is even worth processing? Is all of it useful for the business? What if you process wrong data and make decisions based on it? These are some challenges of integration that every organization faces when it starts data integration. Using low-quality data can result in long-term losses for an enterprise. How can this issue be solved? There’s something called data quality management that lets you validate data much before it is added to the warehouse. This

How to Convert Your Big Data Into Actionable Intelligence? (7 Easy Steps)

In today’s world, access to data is no longer a problem. There are such huge volumes of data generated in real-time that several businesses don’t know what to do with all of it. Unless big data is converted to actionable insights, there is nothing much an enterprise can do. And outdated data models no longer help in processing big data to derive insights. When a business fails to gain actionable analytics and implement the data-driven model to improve decision-making, it ends up losing to competitors in the market. Despite having access to real-time data, the business will continue to be stagnant and passive because it doesn’t have the necessary setup to convert big data into actionable intelligence. Many big data consulting companies offer offshore services to help SMEs and large-scale enterprises implement the data-driven model in their business by investing in advanced data analytics. Let’s read about big data, how it works, actionable intelligence and its benefits, and steps of converting big data into actionable intelligence. What is Big Data? Big data is a trending technology that helps to handle a large amount of data that is complex to categorize and process using traditional data management systems. The Five V’s define the nature of big data: Volume- Big data is huge and is constantly increasing in volume. It needs to be stored in data lakes or on the cloud. Velocity- Big data is collected in real-time and is generated at a rapid pace. IoT, data streams, smart meters, etc., are always collecting data. Variety- Big data is raw data and comes in structured, semi-structured, and unstructured formats. It can be images, text, audio, video, graphs, and much more. Veracity- Since big data comes from multiple sources, it needs to be cleaned and processed before it can make sense to the end-user. Variability- Markets are volatile, and data flows cannot be predicted. It is important to know how data is impacted by the changes and how that can, in turn, impact business decisions. How Big Data Works Big data analytics help you in deriving accurate insights. But for that to be possible, we need to know big data works. The Need for Big Data and Big Data Analytics Analyzing big data helps you to understand the market conditions, consumer behavior, the financial position of the enterprise, and several other vital factors that play a role in shaping the future of your brand. Due to the vast amount of data available, you cannot rely on manual data analytical procedures to gain insights. The following are some reasons why every organization needs to invest in big data: What is Actionable Intelligence? Actionable intelligence is an insight or prediction that can help you gain a competitive edge over competitors. It helps in making future decisions to improve the overall performance of the enterprise and keeping it ready to face the competition. Actionable intelligence is one step ahead of business intelligence. It doesn’t stop at providing data insights. It provides you with a comprehensive plan to get the best possible results from the insights. Benefits of Using Actionable Intelligence Actionable intelligence is derived using big data analysis. It is mostly used for competitor analysis to understand how you can do better than them. At the same time, you also have to know where to draw a line. Aggressive data gathering attempts to know more and more about your competitor can be termed illegal and come under corporate espionage (corporate spying). So, apart from the competitor analysis, what are the other benefits of using actionable intelligence? However, for you to successfully get actionable intelligence from big data, you will need to hire a trustworthy consulting company to help you establish the setup in your business. The success of your decision to use big data analytics will work when experts handle the job. Steps to Convert your Big Data into Actionable Intelligence Converting big data into actionable intelligence needs proper planning and approach. You need to work with the consulting company to first understand what you need for your business. Only then can you find the best way to make it possible. Step 1: Know What You Want in the Long Term Don’t let the traditional systems hold you back and limit the insights you can gain. Start fresh without excess baggage from the past and be ready to adopt new tools. However, it is also necessary to have a clear long-term plan for your business. Unless you know what you ultimately want for your enterprise, you cannot choose the necessary tools and software to reach the goal. Artificial intelligence-based tools are used to process big data. But that doesn’t mean any such software will do. In addition, it does not mean that you have to invest in a company-wide adoption for the system to undergo a complete change. All these come later when you know what your business should achieve in the next five years or so. Set a tangible target and start creating a pathway to reach this target. Focus on the most important goals instead of having too many targets. Step 2: Identify the Factors that will Result in the Required Outcome Since you know what you want to achieve, it’s time to identify the factors that will help you get the expected results. It is one of the trickiest parts of the process. Going wrong here would mean that your entire plan of action would be wrong. For instance, if you wish to increase your customer base by 10%, you need to know what factors can help you achieve this. Should you target a new market or work on existing ones? Should you reach out to a different target audience? If yes, what changes do you need to make to the marketing strategy to attract new audiences? Can all these be aligned and mapped together to become a part of a single process? Which step should come after which one? How can the factors be executed, and how many resources do you need to spend on it? If

How Advanced Data Analytics help to Achieve Organization’s Goals?

Data analytics is not a new phenomenon. With vast amounts of data being generated every day, the time has come for SMEs to make the most of it. Raw data is of little use if an enterprise doesn’t know what to do with it. Manual processing of such volumes of data is near impossible. But many small and even large organizations have been hesitant to invest in Advanced Data Analytics. They felt it was a time-consuming and cost-intensive process, without understanding how it could help their business. Data-driven business processes were not a priority. But things are changing fast. During the last year or so, more and more enterprises realized the importance of becoming a data-driven business to survive the competition and retain the market share. Advanced data analytics, predictive analytics, descriptive analytics, etc., became prominent as the focus shifted to building an analytics-driven organization. However, there are still questions and doubts about how advanced data analytics can contribute to achieving organizational goals. Will building a data analytics team or taking the assistance of a data analytics company really help SMEs? Let us try and answer these questions. We will first start by understanding what advanced data analytics is and why it is important for every enterprise. What is Advanced Data Analytics? Advanced data analytics is one step ahead of data analytics. It enables optimization and innovation by using mathematical and statistical algorithms to generate new and accurate information, identify patterns, and predict outcomes for various probable scenarios. It helps enterprises create better plans and strategies to develop, release, and market new products and services. Segmentation of data into different categories or groups Identify the correlation between various events Classify the unclassified elements and factors Detect the patterns and relationship between the elements and how they influence each other Forecast future trends and insights The Importance of Advanced Data Analytics Advanced data analytics is also known as advanced analytics (called AA, in short). It helps enterprises effectively manage (collect, store, process, and analyze) large amounts of data. The use of the latest software tools and modern interfaces allows for better representation of data. Data visualization is a part of the process. Gaining insights from historical and real-time data becomes easier, resulting in better decision-making abilities. Prediction of short-term and long-term trends helps organizations be better equipped to deal with the changing market conditions. Advanced analytics helps the top management implement the necessary cultural changes for building an effective analytics organization to increase productivity. The management can save time and money by streamlining the business processes to provide an enhanced user experience. Organizational Structure for Data Analytics and Big Data For advanced analytics to successfully contribute to the growth and development of an organization, changes have to be made to the structure, work culture, and systems within the business. Only when all the systems and processes align with each other is that the organization can achieve its goals. Decentralized, Centralized, or Hybrid: You will need to choose between decentralized and centralized structuring teams. Though opting for one model doesn’t mean you have to continue with it throughout. Many organizations start with a decentralized model and end up with a hybrid version with elements of decentralized and centralized models. In-house or Third-party Data Analytics Services: Do you want to build an in-house team (either by training the existing employees or hiring new ones)? Do you want to rely on a third-party data analytics company to provide outside support? Location of the Center of Excellence (CoE): Where do you want the team to be? The ideal method would be to have a fully functional, enterprise-wide setup that can be accessed by every department within the business. Setting up CoE in an individual functional unit can limit its role in the organization. You can read more about data & analytics organizational models, roles, and responsibilities on our blog about how to build the right data science team for an enterprise. Contribution of Advanced Data Analytics 1. Businesses can get Accurate Insights from Data One reason organizations didn’t want to invest in advanced analytics was that they felt the insights were not accurate enough. The reason for this could have been anything. Some enterprises indeed failed to get the right kind of results from data analytics. However, it is crucial to understand that the reports generated by advanced analytics are based on the data input. When the data analytics team enters the correct parameters to process data, the insights will naturally be accurate. In fact, using AI tools had led to an increase in the accuracy of data analytics predictions. 2. Easy to Access Data Through the Cloud Cloud storage has been here for more than a while now. From only the giants like Google and Microsoft offering cloud services to private players creating cloud platforms, there has been tremendous growth in this area. Most SMEs have migrated their business systems to the cloud to cut down operational and maintenance costs. When data is stored on the cloud, it becomes easier for employees to access the required information. This cuts down the time taken to process access requests and sharing of data through emails. Employees at every level can make faster decisions and complete the work in less time. 3. Helps Automate Business Operations One way to achieve the business goals faster and with increased effectiveness is by automating the time-consuming recurring processes and tasks. This lets employees become more productive as they can complete larger amounts of work in less time. The risk of human error is also reduced thereby, increasing the overall quality and efficiency of the business operations. Data analytics teams build business models that help SMEs adopt new technology and processes into the business systems and empower employees. 4. Collaborations are Now More Effective using Data Analytics Tools Collaborations are now an inherent part of businesses. Remote collaborations have increased during the last year due to the pandemic lockdown and restrictions. The organizational structure of business analytics allows the teams in different functionalities to work with each

11 Advantages of Having Predictive Analytics in Healthcare Industry

The healthcare industry is almost always under pressure to deliver better than before. Doctors, nurses, staff, etc., need to be accurate every single time. They are expected to make no mistakes but we also know that this is not possible. Knowledge and experience have their limitations. But technology using artificial intelligence and machine learning can support and help the healthcare industry to be one step ahead at all times. Healthcare predictive analytics is one such technology that is assisting doctors in offering better treatment to patients. Hospitals can streamline business operations and manage staff effectively. Predictive analytics in healthcare is helping doctors be proactive instead of reacting when a crisis occurs. The aim here is to avoid and avert the crisis rather than minimize the damage once it happens. What is Predictive Analytics? It sounds fascinating to know that artificial intelligence, machine learning, and the Internet of Things (IoT) can improve medical care and empower the medical teams to deliver exceptional performance. But what exactly is predictive analytics? As the name suggests, predictive analytics is a branch of advanced analytics that predicts future events by analyzing historical data. AI services, deep learning, machine learning algorithms, data mining, and statistical modeling are used to analyze this historical data and come up with insights for the future. Unstructured data is arranged in an easy-to-understand format for data processing and extraction. Predictive analytics in healthcare is used to identify at-risk patients in their homes so that timely treatment can be provided to prevent their re-admission. Similarly, it is also used to track the recovery progress of patients in ICU to detect if there are any signs of relapse or health deterioration. How is Healthcare Predictive Analytics used? Predictive analytics in healthcare includes processing historical and real-time data. It helps detect trends and find ways to contain the spread of diseases. Using predictive analytics in healthcare can improve the quality of healthcare, collect more clinical data for personalized treatment, and successfully diagnose the medical condition of individual patient. It also helps in keeping a tab on population health management. Why is Predictive Analytics in Healthcare Industry Important? A data-driven system is efficient in delivering quality care for patients. Whether it is reducing the waiting time or bringing down the percentage of readmission, predictive models in healthcare can help understand a patient data and provide accurate treatment. Advantages of Predictive Analytics in Healthcare There are various advantages of implementing predictive analytics in healthcare using machine learning tools and techniques, be it improving business efficiency or assisting doctors in providing health care services to each patient. 1. Selecting the Right Location to Set up New Clinics and Hospitals Setting up a new clinic or a medical center is no small feat. The first step is to pick the right location for the establishment. If the management makes an erroneous judgment here, it could affect everything else and lead to losses. For a hospital to provide valuable services to the public, it needs to be closer to the target audience, easily reachable, and carve a place or itself among the competitors. Predictive analytics can help the management in evaluating the prospective sites based on various factors. By studying how competitors are doing and analyzing the accessibility of the place (along with other details), predictive analytics in healthcare can give you the pros and cons of setting up the clinic at a particular location. 2. Improving the Business Operations for Seamless Hospital Management Hospital management is probably the hardest of all. Even the smallest of mistakes and miscommunication could lead to life-threatening situations. Everything has to be in sync and streamlined to perfection. But it is easier said than done. However, using advanced technology can make it possible. Especially predictive analytics in healthcare insurance has led to the patients, hospitals, and insurance companies working in tandem to process claims and avoid complications. Delays in processing and approving claims can be reduced to help patients get faster treatment. Healthcare centers can have a stress-free work environment where recurring tasks are automated, allowing the staff to focus on delivering friendly and efficient customer service to the patients. 3. Effectively Managing Staff to Increase Productivity and Patient Satisfaction If you are planning a new hospital, how many staff members do you wish to employ? Which roles and responsibilities are they going to fill? How many specialists should you have onboard, and how many visiting doctors can you hire? And if the healthcare center is already established, there are another set of questions to answer. What are the productivity levels of the existing staff? Can you cut down the number of employees you have? Do you need to hire more? Predictive analytics will help find answers to these questions and more. You can create a work culture that empowers the staff to be more productive. This lowers the risk of error and increases customer satisfaction. Patients who find the process hassle-free will naturally prefer your healthcare center over your competitors. 4. Identifying the Right Target Audiences to Promote the Clinic As we already mentioned, promoting the clinic is just as important as delivering quality services. The primary step here is to identify who the target audiences are. Predictive analytics in healthcare organizations are recreating their marketing strategies to target those families and audiences who are more likely to respond to the ads. For example, a child healthcare center should attract parents with young kids rather than senior citizens. The marketing and advertising strategies should be crafted to reach as many young parents as possible in the region. Instead of creating a blanket campaign to target all kinds of audiences, the healthcare center should create individual strategies to reach out to different types of audiences. Predictive analytics will help in formulating this strategy by showing which one is more likely to deliver the expected results. 5. Understanding Market Opportunities for Growth Real-time predictive analytics in healthcare should not be limited to helping doctors and specialists. For a hospital or a clinic to be

10 Best Data Visualization Tools for Massive Business Growth (You Shouldn’t Ignore This!)

Big data, data science, artificial intelligence, etc., are some terms we hear quite often these days. Though they are all different from each other, the common point for all the concepts is data, information, and database. Collecting, processing, and analyzing data is the core aspect. With access to volumes of data every second, enterprises are now under the pressure of excess data and overflowing databases. This earlier led to delay in making decisions when it should have been the opposite. But raw data on its own is of little or no value. We can hardly understand anything from it. One famous method of understanding a vast amount of raw data is known as data visualization. Visualizing complex data becomes crucial when it comes to understanding and detecting patterns that are otherwise not easily found. And then… Data Visualization tools come into play. What is Data Visualization? In simple terms, presenting data and information in an easy-to-understand visual or graphical format is known as data visualization. Of course, it is not as limited in its usage and helps with more than visualizing data. Data visualization tools provide a range of features and can be used for multiple purposes. A few data visualization examples include- presenting data in various charts, graphs, infographics, maps, etc., in highly customizable interactive dashboards to facilitate a better understanding of the derived insights. It is a form of visual storytelling for business purposes. It is all about how effectively you can analyze and present the data in real-time to derive accurate and meaningful insights. These insights allow you to make better decisions to expand the business, grab new opportunities, enhance customer satisfaction, and increase ROI. These visualization techniques effectively present the analytics for easier understanding. Types of Data Visualization The types here are nothing but the numerous ways in which data can be presented in a visual format. There are as many as 67 types of data visualizations to choose from. Nevertheless, these 10 are the most popular choices for enterprises around the world. Tree graphs, heat maps, dot maps, networks, and text tables are a few more types of data visualizations we see often. You can design any type of charts and graphs to generate reports. In fact, most enterprises rely on more than one type to process data efficiently and create better presentations. This ultimately results in better decisions. But how are such charts, graphs and effective visualizations created? Employees can’t spend days and weeks toiling on data, right? By the time they manually create a chart, the data would be outdated, and the market trends would have changed. The charts and graphs become unworthy and end up as an additional expense for the business. Even though historical data and real-time data are necessary for decision-making, using past data alone will not be enough. Investing in tools for data visualization is the right way to process historical and real-time data in less time and investing in data visualization software will aid your business in many ways. If using data connectors and working on data exploration seems hard, hire offshore professionals and data analysts. They manage data files and data integration to simplify complex data. They create interactive maps, charts, and transform large data sets into interactive data visualizations. Outsourcing experts is a great way to learn and empower your business. You can compare values and get the gist of what’s better and what’s not just by looking at the charts. You can identify the patterns in huge volumes of data to see how a decision or a change has impacted different aspects of your business. Data visualization techniques make the process effective. The information you own will have a new and effective meaning. It becomes easier to identify emerging trends in the industry and plan in advance to make the most of the changes. This fetches you a definite edge over your competitors. You can ensure your success by investing in visualization capabilities that process data from multiple data sources and present the analysis through customizable dashboards. There are times when we want to see how certain variables can change the course of the business plan. Or, we might want to correlate two or more types of data visualizations to identify common variables. The data visualization tools and online applications can assist us in finding what we are looking for. These can be used through the cloud, through browsers, and/ or by installing in-house servers. You can also find open-source data visualizations and applications. 10 Best Data Visualization Tools 1. Tableau Tableau is counted among the best data visualization software. Since it was released in 2013, many small, medium, and large data analytics consulting firms have started working on the software and became Tableau Alliance Partners. You can find a lot of firms offering Tableau capabilities to manage big data. You can work on the Tableau desktop app, via servers, or hosted online through the cloud, or start with the Tableau public version available for free and proceed to buy the paid version. Data from CSV, Salesforce Einstein analytics platform, Google Analytics, and various other sources can be processed and presented on the interactive dashboard. You can also try the Tableau mobile version. The rich gallery of templates for infographics is a delight to every Tableau consultant. With such extensive options and components included, it’s no surprise that Tableau is an impressive and important business intelligence storytelling app. The tutorials shared on the website guide consultants in brushing up their skills and becoming efficient in using Tableau software for data visualization. Robust performance and scalability are other advantages of using this software. Data privacy is another advantage of investing in Tableau. However, the free public version doesn’t allow you to keep your analytics private as the Tableau server does. 2. FusionCharts FusionCharts is a JavaScript-based data visualization tool famous for offering around 1000 types of maps and 150 types of charts and graphs to present the data in a visual format. The aim of this tool is to create

12 Great Facts About Analytics for Retail Price Optimization

What is one of the most important factors a retailer should always monitor? It’s the price of products they sell. We all know that the selling price of a product is pre-fixed. But that doesn’t stop some retailers from offering discounts and giveaways. In fact, customers today expect you to offer products at competitive prices. With so many stores mushrooming (online and offline), it has become necessary to have a flexible pricing policy. Guesswork won’t help you in the long run. It is a sure way to end up in losses. Why not give a shot to analytics for retail price optimization? What is Retail Analytics? In simple words, analyzing retail business & customer behavior data to help retailers make better decisions is known as retail analytics. Of course, it is hardly that easy in real life. Retail analytics is the process of using AI tools to collect and analyze historical and real-time data to derive in-depth insights. It allows you to make better decisions based on these insights. This will be a win-win situation for you and your customers. Before we read more about analytics for retail price optimization, let us answer the following questions. These are some key factors that you must consider as a part of your retail analytics. How willing are Customers to Pay for a Product? Also known as price sensitivity, this factor deals with the maximum amount a customer would pay for a product. Unless you know this, you cannot make adjustments to the prices of products you sell in your retail stores. What is the Average Revenue Generated per User? How much does each customer contribute to your revenue each month? Knowing the answer to this question will help you understand who your most valued customers are and which products they are buying. What Makes a Product Famous Among the Customers (Product Value)? It can also be termed as feature value analysis, where you try to identify the most liked and least liked features of a product. Depending on the number of features/ aspects customers like or prefer in a product, they will fix a price for it in their minds. If you set your selling price over that amount, it will affect your sales as they may not wish to pay as much. How do Customer Acquisition Costs and Customer Lifetime Value Affect Your Pricing Decisions? You need to know how much you can invest in a customer. There is no point in running an extensive campaign if the customer doesn’t buy from your retail stores, right? Retail intelligence and analytics for retail price optimization give an insight-based understanding. Here’s how retail analytics can optimize your pricing strategies, streamline your business operations and promotional plans, and help you become a leading retailer in the market. 1. You can Get Immediate Returns on Your Investment Getting faster returns is the dream of every retailer. Who would want to wait for months and years as the interest accumulates and reduces the profit margin? Computer vision solutions for retail allow you to come up with a pricing policy that can be changed in real-time. Each time you adjust your short-term goals, you can tweak the prices accordingly. That too, without worrying whether the customer will pay as much or not. A study by PwC found that 60% of the customers decide whether to buy a product or not solely based on its price. Fixing the price without knowing how things stand could lead to losses instead of increasing ROI. 2. You can Understand Your Customers’ Purchasing Behavior It is eventually up to the customers to buy a product, isn’t it? Even the best discount offers don’t result in sales at times. This could be because- Customers don’t prefer that product The timing of the discount was wrong The offer didn’t reach the target audience How can you make sure such mistakes don’t happen? By using retail analytics to get insights about customers’ purchase behavior and interests. What drivers influence customers to buy a product? The answer to this question can help you optimize the price to increase sales and profits. 3. Automate Your Business Operations to Gain Competitive Edge Who said automation is not meant for retailers? Why spend your precious time calculating and analyzing the market trends, measuring the price changes, and monitoring customer demands? Let technology do it on your behalf. It would also reduce the risk of human error and give you more time to focus on implementing the pricing and promotional strategies. Machine learning algorithms can help automate pricing. It can be integrated with other retail applications you use to streamline your business operations. You can stay up to date with the latest changes in the market (check the next point). 4. Be Ready to React to the Changes in the Market A successful retailer is the one who can make fast and accurate pricing changes in real-time. Effectively managing both offline and online sales is becoming more of a necessity in recent times. When you know that the demand for a product will increase or decrease in the coming days, you can plan your pricing strategies to attract more customers and increase your return on investment. 5. Make Use of the Feedback to Correct Your Pricing Strategies The feedback here comes from the retail analytics software you use. The regular reports generated by the software will tell you whether the current pricing plan is effective or if changes have to be made. Real-time insights are derived from the latest information available. This helps you make quick changes to the prices and adjust them immediately to suit the customers’ demands. Instead of taking feedback through surveys, you can get the required reports from the software. The constant feedback will keep you at the top of your game and ahead of your competition. 6. Support Your Decisions with Processed Data A wrong decision could prove to be very costly for a retailer. While gut feeling cannot be ignored, relying entirely on it is a risky

How is Vision Analytics Retransforming Modern Industries?

Vision analytics has always been considered a game-changer in the industry. It was expected to revolutionize the way security tasks were performed. Improving operational efficiency was another aim of vision analytics. Both the public and private entities are leaning towards computer vision analytics to revamp their business processes and gain the top position in the markets. Artificial intelligence, machine learning, deep learning, 3D imaging, etc., are some terms we often hear when people talk about vision analytics. We often read about vision analytics retransforming modern enterprises and SMEs. Before we see more about what these mean, let’s understand what computer vision analytics is. The process of analyzing digital image/video signals to understand the visual world using the latest technologies in place of the human eye is known as vision analytics. Identifying intruders & impostors, recognising & tracking objects, identifying behavioral patterns etc.. are some examples of vision analytics. The global computer vision market anticipates having a CAGR (compound annual growth rate) of 7.6% from 2020 to 2027. There has been a significant escalation in the demand for computer vision services during the last year due to the COVID-19 pandemic. Taking the increasing adoption of vision analytics into account, we can say that the following trends are going to rule the industry in the coming days. Latest Trends in the Vision Analytics Industry Artificial Intelligence AI has made it possible to analyze vast amounts of data in less time. Data can be in any form- text, images, or videos. Artificial intelligence in vision analytics is used to examine videos and detect patterns. It helps to identify and predict events based on existing data. The systems can communicate with each other and alert the user about a potential change in the pattern. For example, AI in the security department is used to analyze videos and identify suspicious activity such as trespassing, sneaking, breaking in, etc. Vision analytics can help detect the change before the actual event takes place and alert the concerned authorities. In the retail sector, AI in vision analytics is used to identify customer behavior patterns and purchasing trends. Deep Learning and Machine Vision Even though machine vision and deep learning are two independent elements, they complement each other and have abilities that overlap. Deep learning has given machine vision a new dimension. Neural networks are an example of deep learning that works well with machine vision. It helps identify the presence in an image/ video frame. It determines if the presence is good news or bad news. We can call them image-classifiers. Deep learning also helps in increasing the speed of a business process by improving operational efficiency. Many machine vision consulting services include artificial neural networks (ANNs) to provide a comprehensive system for automation in the manufacturing industry. Thermal Imaging Thermal imaging is the process that uses infrared and heat radiation to detect objects in the dark. The thermal cameras can distinguish the difference in temperatures so that we can detect the warmer objects/ beings. It becomes easy to identify the presence of a person or an animal against the cold and dark background. When terminal imaging is used with vision analytics, it sends alerts only for a fixed range of temperature levels. For example, the movements of trees, winds, vehicles, etc., are usually false positives when you want to find a human presence. This is especially useful for security purposes. The percentage of false security alerts can be reduced, thereby improving the efficiency of the security system. 3D Imaging Do you know that the 3D vision market is estimated to have a CAGR of 9.4% from 2020 to 2025? It is the next big thing in the market as the demand for quality inspection of the end products is touching the skies. With SMEs and large-scale enterprises wanting to automate their business, they are turning to 3D vision analytics for high-speed imaging, vision-guided robotic systems, and surface profiling. 3D imaging and vision analytics are also important as the industry is shifting from standard products to personalized products based on customer requirements. 3D smart cameras are said to rule the industries in the coming years. 3D imaging also helps in logistics for autonomous navigation via object detection, self-localization, etc. Use of Liquid Lenses for Vision Analytics Liquid lenses are single optical elements but with an optical liquid material that is capable of changing its shape as and when required. They are used in smart cameras and smart sensors though now we can find them being used in various fields such as biometric recognition and data capturing, reading barcodes, digital photography, and more. Heavy industries are investing more in liquid lenses to help with various manufacturing applications. The lenses have great focus and adjust to the changes in the voltage and current automatically. Apart from industries, public spaces are also going to be monitored using liquid lenses to track if people are following the safety norms or not. Embedded Vision In simple terms, embedded vision is the integration of a camera and a processing board. Instead of having more than one device to stay connected and deliver us the results, embedded vision systems directly work with algorithms. When an embedded system (a microprocessor-based unit) is combined with computer vision technology to digitally process the images/ videos and use machine learning algorithms to share the information with other cameras and systems in the network, it is known as embedded vision. The main reasons for embedded vision systems to become popular are low cost, lesser energy consumption, smaller in size, and lightweight. Embedded computer vision consulting services are used for robotics in the manufacturing industry (for factory automation), the healthcare sector (for medical diagnosis), gesture recognition (for transportation and logistics), the famous facial recognition systems and many more. Several multinational organizations and public sector industries have adopted vision analytics to retransform their operational processes. Vision Analytics and Retransformation of Modern Industries Below are some ways to see vision analytics retransforming modern industries in the global market. Public and Workplace Safety

How Data Analytics Helps Respond Covid Impact?

Regardless of the coronavirus disease (COVID-19) consequences in society and our workplaces, we are all working in extraordinary times. The sheer fluidity of transition has forced us to deal with this alone in March seems unreal. It is bewildering to think that a relatively isolated number of cases announced to the WHO on 31 December in Wuhan, China, meteorically increased to nearly 330k confirmed cases and 14.4k deaths in over 180 countries as of 22 March 2020. While society struggled with the public health and economic problems manifesting in the aftermath of COVID-19, corporations scrambling to realign themselves to this new paradigm are finding technologies to help. In particular, data analytics proves to be an ally for epidemiologists as they join forces with data scientists to address the severity of the crisis. The spread of COVID-19 and the public’s desire for information has sparked the creation of open-source data sets and visualizations, paving the way for a pandemic analytics discipline introduced. Analytics is aggregating and analyzing data from multiple sources to gain information. When used to research and global counter diseases, pandemic analytics is a new way of combating an issue as old as civilization itself: disease proliferation. To Craft The Correct Response – Data Analytics In COVID-19 In the early 1850s, London fought a widespread rise in the number of cholera cases, John Snow – the father of modern epidemiology – discovered cluster clusters of cholera cases around water pumping. For the first time, the discovery allowed scientists to exploit data to counter pandemics, drive their efforts to measure the danger, identify the enemy, and formulate a suitable response strategy. That first flash of genius has since advanced, and 170 years of cumulative intelligence have demonstrated that early interventions are disrupting disease spread. However, analysis, decision-making, and subsequent intervention can only be useful if it takes all the information into account first. Healthcare managers at Sheba Medical Center in Israel use data-driven forecasting to improve staff and resources distribution in anticipation of possible local outbreaks. These solutions are powered by machine learning algorithms that provide predictive insights based on all available disease spread data, such as reported cases, deaths, test results, contact tracing, population density, demographics, migration movement, medical resource availability, and pharmaceutical stockpiles. Viral propagation has a small silver lining: the exponential development of new data from which we can learn and act. With the right analytics tools, healthcare professionals can address questions such as when the next cluster will most likely appear, which population is most susceptible, and how the virus mutates over time. Ohn Snow, the founder of modern epidemiology, noticed cluster patterns of cholera cases around water pumps in the early 1850s, as London battled a rampant rise in the number of cholera cases. For the first time, this discovery enabled scientists to leverage data to combat pandemics, drive their efforts to quantify the risk, identify the enemy, and devise a suitable response strategy. That first flash of genius has since advanced, and 170 years of cumulative intelligence have demonstrated that early interventions are disrupting disease spread. However, analysis, decision-making, and subsequent intervention can only be useful if it is taken into account first. Accessibility of trusted sources of data has resulted in an unprecedented sharing of visualizations and messages to educate the general public. Take, for example, the dynamic world map created by the Center for Systems Science and Engineering at Johns Hopkins, and these brilliantly simple yet enlightening Washington Post animations. These visualizations quickly inform the public how viruses spread, and which human behavior can support or hinder the spread of viruses. The democratization of data and analytics software, combined with the vast capacity to exchange information over the internet, has allowed us to see the incredible power of data being used for good. To See The Unseen (Data Analytics) Accessibility of reliable sources of data has resulted in an unparalleled exchange of visualizations and messages to inform the general public. For example, take the interactive world map created by the Center for Systems Science and Engineering at Johns Hopkins, and these beautifully simple but enlightening Washington Post animations. These visualizations quickly show the public how viruses spread, and which human behavior can support or hinder the spread of viruses. The democratization of data and analytics software, combined with the vast capacity to exchange information over the internet, has allowed us to see the incredible power of data being used for good. In recent months, companies have taken an in-house collection of pandemic data to develop their proprietary intelligence. Some of the more enterprising enterprises have even set up internal Track & Respond Command Centers to guide their employees, customers, and broader partner ecosystems through the current crisis. Early on in the outbreak, HCL realized that it would need its COVID-19 response control center. Coordinated by senior management, it gives HCL data scientists autonomy to develop innovative and strategic perspectives for more informed decision-making. For example, the creation of predictive analytics on potential impacts for HCL customers and the markets where HCL services are provided. We employed techniques such as statistics, control theory, simulation modeling, and Natural Language Processing ( NLP) to allow leadership to respond quickly during the development of the COVID-19 situation. For simplicity, we are going to categorize our approach under the umbrella of Track & Respond: TRACK the condition to grasp its significance, both quantitatively and qualitatively. Perform real-time topic modeling across thousands of international health agency publications and credible news outlets; automate the extraction of quantifiable trends (alerts) as well as actionable information relevant to the role & responsibility. Policymakers, public agencies, and other institutions worldwide have used AI systems, Big Data analytics, and data analysis software. All of these are used to forecast where the virus may go next, monitor the virus spreading in real-time, recognize drugs that could be helpful against COVID-19, and more. People who work at the sites of the disease outbreak gather critical COVID-19 data such as transmissible, risk factors, incubation time,

Unraveling The Meaning From COVID-19 Dataset Using Python – A Tutorial for beginners

Introduction The Corona Virus – COVID-19 outbreak has brought the whole world to a stand still position, with complete lock-down in several countries. Salute! To every health and security professional. Today, we will attempt to perform a single data analysis with COVID-19 Dataset Using Python. Here’s the link for Data Set available on Kaggle. Following are the the Python Libraries we’ll be implementing today for this exercise. What Data Does It Hold The available dataset has details of number of cases for COVID-19, on daily basis. Let us begin with understanding the columns and what they represent. Column Description for the Dataset: These are the columns within the file, most of our work will working around three columns which are Confirmed, Deaths and Recovered. Let Us Begin: Firstly, we’ll import our first library, pandas and read the source file. import pandas as pddf = pd.read_csv(“covid_19_data.csv”) Now that we have read the data, let us print the head of the file, which will print top five rows with columns. df.head() As you can see in the above screenshot, we have printed the top five rows of the data file, with the columns explained earlier. Let us now get into some dept of the data, where we can understand the mean and standard deviation of the data, along with other factors. df.describe() Describe function in pandas is used to return the basic details of the data, statistically. We have our mean, which is “1972.956586” for confirmed cases and Standard Deviation is “10807.777684” for confirmed cases. Mean and Standard Deviation for Deaths and Recovered columns is listed, too. Let us now begin with plotting the data, which means to plot these data points on graph or histogram. We used pandas library until now, we’ll need to import the other two libraries and proceed. import seaborn as snsimport matplotlib.pyplot as plt We now have imported all three libraries. We will now attempt to plot our data on a graph and output will reflect figure with three data points on a graph and their movements towards the latest date. plt.figure(figsize = (12,8)) df.groupby(‘ObservationDate’).mean()[‘Confirmed’].plot() df.groupby(‘ObservationDate’).mean()[‘Recovered’].plot() df.groupby(‘ObservationDate’).mean()[‘Deaths’].plot() Code Explanation: plt.figure with initial the plot with mentioned width and height. figsize is used to define the size of the figure, it takes two float numbers as parameters, which are width and height in inches. If parameters not provided, default will be scParams, [6.4, 4.8]. Then we have grouped Observation Data column with three different columns, which are Confirmed, Recovered and Deaths. Observation goes horizontal along with the vertical count. Above code will plot the three columns one by one and the output after execution will be as shown in following image. This data reflects the impact of COVID-19 over the globe, distributed in three columns. Using the same data, we can implement prediction models but the data is quite uncertain and does not qualify for prediction purpose. Moving on we will focus on India as Country and analyze the data, Country Focus: India Let us specifically check the data for India. ind = df[df[‘Country/Region’] == ‘India’]ind.head() Above lines of code will filter out columns with India as Country/Region and place those columns in “ind” and upon checking for the head(), it will reflect the top five columns. Check the below attached screenshot. Let’s plot the data for India: plt.figure(figsize = (12,8))ind.groupby(‘ObservationDate’).mean()[‘Confirmed’].plot()ind.groupby(‘ObservationDate’).mean()[‘Recovered’].plot()ind.groupby(‘ObservationDate’).mean()[‘Deaths’].plot() Similar to earlier example, this code will return a figure with the columns plotted on the figure. Output for above code will be: This is how Data is represented graphically, making it easy to read and understand. Moving forward, we will implement a Satterplot using Seaborn library. Our next figure will place data points, with respect to sex of the patient. Code: Firstly we’ll make some minor changes in variables. df[‘sex’] = df[‘sex’].replace(to_replace = ‘male’, value = ‘Male’)df[‘sex’] = df[‘sex’].replace(to_replace = ‘female’, value = ‘Female’) Above code simply changes the variable names to standard format. Then we’ll fill the data points into the figure, plotting. plt.figure(figsize = (15,8))sns.scatterplot(x = ‘longitude’, y = ‘latitude’, data = df2, hue = ‘sex’, alpha = 0.2) Code Explanation: The “x and y” defines the longitude and latitude. data defines the data frame or the source, where columns and rows are variables and observations, respectively. The hue defines the variable names in the data and here these variables will be produced with different colors. alpha, which takes float value decides the opacity for the points. Refer the below attached screenshot for proper output. Future Scope: Now that we have understood how to read raw data and present it in readable figures, here the future scope could be implementing a Time Series Forecasting Module and getting a Prediction. Using RNN, we could achieve a possibly realistic number of future cases for COVID-19. But at present, it could be difficult to get realistic prediction as the data we posses now is too uncertain and too less. But considering the current situation and the fight we have been giving, we have decided not to implement Prediction Module to acquire any number which could lead to unnecessary unrest. Contact us for any business query

20 Mistakes That Every Data Analyst Must Be Aware Of!

Computer Science is a research that explores the detection, representation, and extraction of useful data information. It is gathered by data analyst from different sources to be used for business purposes. With a vast amount of facts producing every minute, the necessity for businesses to extract valuable insights is a must. It helps them to stand out in the crowd. Many professionals are taking their founding steps in data science, with the enormous demands for data scientists. Despite a large number of people being inexperienced in data science, young data analysts are making a lot of simple mistakes. What Is Data Analytics? The concept of data analytics encompasses its broad field reach as the process of analyzing raw data to identify patterns and answer questions. It does, however, include many strategies with many different objectives. The process of data analytics has some primary components which are essential for any initiative. A useful data analysis project would have a straightforward picture of where you are, where you were, and where you will go by integrating these components. This cycle usually begins with descriptive analytics. That is the process of describing historical data trends. Descriptive analytics seeks to address the “what happened?” question. It also has assessments of conventional metrics like investment return (ROI). Of each industry, the metrics used would be different. Descriptive analytics does not allow forecasts or notify decisions directly. It focuses on the accurate and concise summing up of results. Advanced analytics is the next crucial part of data analytics. This section of data science takes advantage of sophisticated methods for data analysis, prediction creation, and trend discovery. This data provides new insight from the data. Advanced analytics answers, “what if? “You have concerns. The availability of machine learning techniques, large data sets, and cheap computing resources has encouraged many industries to use these techniques. Big data sets collection is instrumental in allowing such methods. Big data analytics helps companies to draw concrete conclusions from diverse and varied data sources that have made advances in parallel processing and cheap computing power possible. Types Of Data Analytics Data analytics is an extensive field. Four key data analytics types exist descriptive, analytical, predictive, and prescriptive analytics. Each type has a different objective and place in the process of analyzing the data. These are also the primary applications in business data analytics. Descriptive analytics helps to address concerns about what happened. These techniques sum up broad datasets to explain stakeholder outcomes. Such methods can help track successes or deficiencies by creating key performance indicators ( KPIs). In many industries, metrics like return on investment ( ROI) are used. Specific parameters for measuring output are built in different sectors. This process includes data collection, data processing, data analysis, and visualization of the data. This process provides valuable insight into past success. Diagnostic analytics help address questions as to why things went wrong. These techniques complement more fundamental descriptive analytics. They are taking the findings from descriptive analytics and digging deeper for the cause. The performance indicators will be further investigated to find out why they have gotten better or worse. That typically takes place in three steps: Predictive analytics aims to address concerns about what’s going to happen next. Using historical data, these techniques classify patterns and determine whether they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future, and their methods include a variety of statistical and machine learning techniques, such as neural networks, decision trees, and regression. Prescriptive analytics assists in answering questions about what to do. Data-driven decisions can be taken by using insights from predictive analytics. In the face of uncertainty, this helps companies to make educated decisions. The techniques of prescriptive analytics rely on machine learning strategies, which can find patterns in large datasets. By evaluating past choices and events, one can estimate the probability of different outcomes. Such types of data analytics offer insight into the efficacy and efficiency of business decisions. They are used in combination to provide a comprehensive understanding of the needs and opportunities of a company. 20 Common Mistakes In Data Analysis It should come as no surprise that there is one significant skill the modern marketer needs to master the data. As growth marketers, a large part of our task is to collect data, report on the data we’ve received, and crunched the numbers to make a detailed analysis. The marketing age of gut-feeling has ended. The only way forward is by skillful analysis and application of the data. But to become a master of data, it’s necessary to know which common errors to avoid. We ‘re here to help; many advertisers make deadly data analysis mistakes-but you don’t have to! 1. Correlation Vs. Causation In statistics and data science, the underlying principle is that the correlation is not causation, meaning that just because two things appear to be related to each other does not mean that one causes the other. It is the most common mistake apparently in the Time Series. Fawcett gives an example of a stock market index, and the media listed the irrelevant time series Amount of times Jennifer Lawrence. Amusingly identical, the lines feel. A statement like “Correlation = 0.86” is usually given. Note that a coefficient of correlation is between +1 (perfect linear relationship) and -1 (perfectly inversely related), with zero meaning no linear relation. 0.86 is a high value, which shows that the two-time series statistical relationship is stable. 2. Not Looking Beyond Numbers Some data analysts and advertisers analyze only the numbers they get, without placing them into their context. If that is known, quantitative data is not valid. For these situations, whoever performs the data analysis will ask themselves “why” instead of “what.” Fallen under the spell of large numbers is a standard error committed by so many analysts. 3. Not Defining The Problem Well In data science, this can be seen as the tone of the most fundamental problem. Most of the