Data Engineering & Analytics Archives - Page 8 of 9

Sulaksh More
May 30, 2020
9 Comments

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

Vaishali
May 26, 2020
4 Comments

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

Vaishali
May 20, 2020
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Predictive Analytics & Distribution | Know Its Impact!

From large companies to smaller companies, predictive analysis and analytics tools offer unparalleled benefits. Predictive tools will clarify what’s coming with unparalleled precision through the ingestion and application of different data points. They can also disperse massive information troves to reveal hidden insights, potential opportunities, and more. It’s no wonder that forecasts put the global market valuation at $10.95 billion by 2022, with predictive modeling being so useful. The impact does, of course, differ slightly from business to business. For example, how it works and what it might demonstrate in marketing is entirely different from what it could display in the delivery process. How Do Predictive Analytics Tools Affect Distributors? Following are some of the ways in predictive tools affect distributors: Enables Real-time Prediction For Predictive Analytics Techniques In most cases, real-time is a buzzword, but it applies here wholeheartedly. Intrinsically, real-time data comes from an up-to-date and endless stream of information. The streaming data is on the cutting edge, and it offers a clear image of what’s going on in the frontline. In delivery, real-time sources allow for the ability to communicate and make decisions that impact the future — in a split second. For example, development may be instantly scaled up or down to respond to changes in demand. It makes unparalleled production output that anticipates the demand, and not just returns to it. Data is the lifeblood of every productive company and provides continuous sources of real-time solutions. It is no small feat to incorporate the raw data into ongoing operations seamlessly. It is essential to develop foundationally not only the tools but additional services, like supporting teams that can take the ideas and bring them into practice. Swapping significant systems, for example, to IoT-powered tech, will not occur overnight. And the data that such an accomplishment will generate is almost infinite, so yes, the effort is worth it. The Competitive Advantage Under Predictive Analytics Techniques Organizations that use predictive analytics have a considerable advantage over competitors, particularly when it comes to market trends and preparation. Predictive analytics offer insight into what’s happening through data ingestion, which already happens in many cases. Most businesses gather an almost infinite supply of digital content. But analytics tools are learning it and making use of it — they make it more realistic. By tapping into not only customer data but also market and company performance insights, distributors can gain a leg up at any given time on what is happening. Organizations can detect real-time shortages, supply chain challenges, and demand changes. Helps In Identifying Fraud As Predictive Analytics Techniques Distributors process fraud and counterfeit goods regularly. Theft is another primary concern, particularly regarding global operations. Fortunately, predictive analysis can fight fraud by putting the abnormal behavior and events in the spotlight. Incoming data is analyzed to give a clear picture of behaviors and events in full. Spotting unwieldy patterns is much simpler, which shows that fraud or theft is going on in the course of a trip. For example, retailers may see exactly where an item is missing, and how much of a product or supply is affected. The outcome is an ideal source of insights helping organizations to reduce fraud, theft, and other erroneous issues. Through applying unusual results to real business insights, companies will discover not just who is responsible but also ways to avoid future occurrence of these events. Commercial Planning Of Predictive Analytics In Big Data It’s no secret that certain events happen in the distribution world that can directly affect a company’s performance and revenue. For example, mergers and acquisitions can set a significant dent in customer relationships. A former partner may not be viable anymore, and this is a transition that can happen almost without notice. That is, without the statistical tools in place for analytics. Predictive analytics can also predict how a partnership with prospective partners could be playing out, revealing when an acquisition might be problematic. The tools may illustrate risks associated with a business partnership, and even identify or suggest new opportunities for partners. Reveals Future Events The novel coronavirus is an excellent example of current events that have a significant impact on the supply chain and the broader market. One of the most instrumental advantages of predictive tools is that they not only help to understand but also to estimate what will happen over a given period. Before this particular case, almost no one could have expected that toilet paper would be such a product, unless, of course, they used trending data when it first began. The strength of predictive models is that they can prepare for and provide the details required to deal with these incidents well before they play out. In other words, predictive analytics may use current performance data, market trends, and human behavior to build a model or scenario. It can influence current events and help distributors prepare for what is to come, far outside the boundaries of what is considered natural. Predictive Analysis Is Essential. Undoubtedly, tools and solutions for predictive analytics are “mission-critical” and essential to achieving success in the ever-evolving world of today. Specifically, in the area of distribution and supply chain, they will have a great many perspectives to tackle industry and customer dynamics, potential issues, and much more. They also offer a robust and reliable method to handle fraud and theft. Predictive Analysis In Today’s World Important sectors where predictive analysis is useful in today’s world are: Banking and financial services With massive amounts of data and money at stake, the financial industry has long embraced predictive analytics to detect and minimize fraud, assess credit risk, optimize cross-sell / up-sell opportunities, and maintain valuable clients. Commonwealth Bank uses analytics to determine the probability of fraud in any transaction until it is approved-within 40 milliseconds of the start of the transaction. Retail Since the now infamous study that showed men who buy diapers frequently buy beer at the same time, retailers everywhere use predictive analytics for merchandise planning and price optimization,

Vaishali
April 11, 2020
2 Comments

Retail Analytics Helps You Grow Your Sales (Everything You Should Know)

Retail analytics focuses on providing insights into revenue, inventory, clients, and other critical factors that are essential to the decision-making process for merchants. The discipline covers many granular fields to build a full image of the health of a retail sector, and sales alongside overall areas for development and strengthening. Mainly, retail analytics are used to help make smart decisions, operate companies more effectively, and provide smart analytics of customer service. In addition to superficial data analysis, the field of retail analysis uses techniques such as data mining and data exploration to sanitize datasets to generate actionable BI insights that can be implemented in the short term. Also, businesses are using these tools to create accurate snapshots of their target demographics. Retailers may classify their ideal customers according to different categories such as age, tastes, purchasing habits, location, and more by using sales data analysis. The field focuses not just on interpreting data, but also on determining what information is required, how best to collect it and, most importantly, how it is to be used. Through prioritizing the fundamentals of retail analytics that concentrate on the process and not merely on the data itself, companies will uncover better insights and be in a more competitive position to excel in predicting market and customer needs. There are some excellent examples of retail analytics applicable to several businesses. One of the most significant benefits that the sector offers to companies is to maximize their production and procurement. Businesses may use historical data and pattern analysis to decide which items they will order, and in what amounts, rather than depending solely on past orders, thanks to statistical instruments. Also, they will improve inventory management to accentuate consumer demands for goods, reducing unused space, and related overhead costs. Aside from procurement operations, other retailers use analytics by integrating data from various areas to identify consumer patterns and adjust preferences. By combining sales data with several variables, companies can help recognize and predict emerging trends. It is closely related to marketing functions that benefit from analytics as well. Companies can use retail analytics to improve their marketing strategies by creating a deeper understanding of consumer tastes and gleaning more granular insights. Companies may build campaigns that focus on consumers and show higher success rates by combining demographic data with details such as shopping patterns, interests, and purchasing history. What drives the retail industry in a highly competitive market is in-store conversion, i.e., the number of shop customers vs. no people who left with a purchase. With customers becoming increasingly flexible in their purchasing habits and switching seamlessly between in-store and online, knowledge and observations are becoming crucial to understanding essential business factors such as inventory, supply chain, demand for goods, customer behavior, etc. More than 35 percent of the top 5000 retail firms struggle to do so, according to some reports. Retail analytics plays a vital role in this. Benefits of Retail Analytics Although there are multiple benefits that retail analytics can bring in, let’s look at how retail analytics tools help improve sales in-store. 1. Better Analyze Your Customers Customers are the backbone of your retail business; they are the ones that come into your shop, visit your online store, and determine what to buy. They perform conversions. And, how do you get to learn their purchasing habits, why they buy a product and why they don’t. It is where market analytics allows you to better understand your customers based on customer segments and consumer loyalty, which will enable you to improve sales. 2. Optimize Your Spend On Marketing Budgets A retail company needs to target a customer accurately. Marketing plays a leading role in advertising and targeting the right consumers, and again retail analytics tools that support maximizing marketing spending can help you plan consumer awareness, evaluate advertising effectiveness, and calculate marketing returns. 3. Target Customers Using Hyper Location Customers are attached to a familiar place where they often work, live, and transfer their closest buying centers. With the percolation of social media and its convergence with the web, targeted web-based advertising is becoming relevant and easy to reach local consumers at specific times on specific social media. However, analyzing this big data about your retail company is difficult, and retail analytics solutions now have a feature to get to the hyper location path. 4. Improve Your Product Offerings Sortiments or product lines are crucial to sales because the size at which the goods are available, the scope and depth of the offerings are critical for consumers to assess a product, try it and decide to purchase it. For a multitude of businesses and their items going through the market, it makes it difficult to know which items consumers want more and which ones need to be put in the store’s prime locations. The optimization of variety comes into play here. Retail analytics tools bring with them significant advantages in knowing product attributes for performance, carrying out replenishment analysis and maximizing package size, etc. 5. Price Analytics Sortiments or product lines are crucial to sales because the size at which the goods are available, the scope and depth of the offerings are critical for consumers to assess a product, try it and decide to purchase it. For a multitude of businesses and their items going through the market, it makes it difficult to know which items consumers want more and which ones need to be put in the store’s prime locations. The optimization of variety comes into play here. Retail analytics tools bring with them significant advantages in knowing product attributes for performance, carrying out replenishment analysis and maximizing package size, etc. 6. Inventory Analytics To retailers, getting the right product in the right location at the right time may sound like a major cliche. But this is the critical slogan for every retail company to succeed. According to IHL Group, a multinational consulting company, retailers are always trying to enhance the inventory management process, assign the correct inventory to customers, and

Vaishali
April 09, 2020
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Data Analytics Helping Accountant Excel! Role Of Data Science In Accounting.

If the C-suite were to shape a rock band focused on the standard positions, the guitarist would be the ambitious CEO, and the resourceful COO would play lead guitar. The level-headed CFO will possibly be positioned as the guitarist, a significant band member, but put in the background and tasked primarily withholding the band on track to help the other members shine. This perception of the CFO as a back-office number cruncher who controls schedules monitors costs and maintains the lights on might have been accurate in the past, but the new CFO is squarely at the heart of the corporate strategy. Data’s core position in today’s business climate is the impetus for this transition. Today the CFO is the company’s co-pilot, finding the most successful clients, evaluating risk through scenario preparation, measuring customer loyalty through data collection, and designing new KPIs. Corporate boards are continually considering a future CFO in terms of whether he or she will take over as CEO eventually. CFOs should have global and diverse experience, be up-to-date on technology, be able to recognize and recruit the right talent and, most importantly, know how to lead, as per one of the KPMG Global CEO Survey. The study also found that 85 percent of CEOs agree that the most significant strategic advantage a CFO can bring to a company is to use financial data to achieve sustainable growth. CFOs need new enterprise performance management (EPM) tools to serve this strategic role— and many see the cloud’s ability to unleash the power of their data and turn their business into an analytics powerhouse. CFOs and the finance department will need a live view into all business areas, with resources that allow them to provide real-time analyzes of changing situations, suggest actions, and offer effective strategic planning and forecasting. In a recent CFOs survey of Oracle as well as other business leaders, 90 percent of the executives said that the ability to create data-based insights is very crucial to the success of their organization. Still, more than half questioned the strength of their organization to handle large data inflows. So the more data an organization uses, the more reliable the research will be. So, almost half of the financial decision-makers in Europe and the Middle East, for example, expanded the number of data sources they evaluate to better understand the effect of this surprising change after the Brexit vote. How To End The Tyranny Of The Spreadsheet (Data Science In Accounting) In business, where you always stand depends on where you are seated, and the finance department is well placed to offer a holistic view of the company. The CFO’s ability to link key areas around the enterprise— marketing, supply chain, manufacturing, services, and human capital management — to build a holistic, real-time business image is vital to risk management and value creation. That calls for the right resources. The ubiquitous spreadsheet is one adversary of such real-time analytics. Consider how an annual budget is produced by the finance department of the business or any department within the organization. The budget process is mostly done through a series of spreadsheets that are sent to various stakeholders, with the usual concerns: Is this the latest version? Who made the most recent alterations? Was the data correct, or have the consolidation process made mistakes? Usually, the finance department spends most of its time tracking down and checking the data— and not enough time evaluating it. Due to the many data systems and reporting tools acquired over the years, organizations rely heavily on spreadsheets and Data Analytics in Finance to organize the information. Because data is siloed in their respective units, to build budgets and strategies, LOB members must first dig into the data. Finance then spends massive amounts of time testing and rolling this unconnected data into more detailed predictions and plans. Finance teams with Data Analytics in Finance need to build better models for financial and organizational improvements if businesses are to stay ahead of the market. Today’s digital finance team is moving from simple, traditional transaction analysis to more sophisticated predictive analysis, such as statistical-based modeling, dynamic market management, and risk-adjusted business simulations. To do so, they need access to a centralized data system that drills both intensely across transactional data, and broadly through core functional divisions of the organization. Finance companies need to use analytics that interacts with cross-functional drivers such as customer loyalty, process management, and business decision-making. And, unlike in the past, these observations are obtained in real-time, not just at daily reporting times— providing a continuous view of the company’s birds. Agile CFOs Measure Non-Financial Data, Too In addition to having a profound impact on existing business models, digitization and globalization have also changed the way we evaluate business performance. Today, intangible assets like brands, customer relations, intellectual property, and expertise have become the primary drivers of the overall success of a business. Measuring the success of a company in all of these fields involves data from around the organization. It is a challenge for finance to track these non-financial key performance indicators (KPIs) with the same degree of methodological rigor it gives to financial metrics— like productivity and return on investment. A new report by the American Institute of CPAs and the Chartered Institute of Management Accountants on financial leaders found that the most forward-thinking CFOs are more likely to monitor non-financial KPIs such as talent pool, customer experience, business process performance, brand credibility, and competitive intelligence; Therefore, sustainability and social responsibility are also increasingly relevant for consumers, workers, and the result, and are steps that CFOs will recognize What’s unique in monitoring this information is not just that the data is non-financial; it’s unstructured too. Many of the data regarding brand credibility and consumer loyalty may come from social media, for example. CFOs need to rapidly track, analyze, and evaluate unstructured data and collaborate with organization-wide subject matter experts to develop new performance metrics that incorporate this data. As a result, KPI

Vaishali
April 06, 2020
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Converting Big Data To Smart Data | The Step-By-Step Guide!

Over the last few years, Big Data has become one of the biggest buzzwords for businesses worldwide. With data of all sorts being generated in record amounts each year, capturing and analyzing this knowledge would give businesses greater visibility into their clients and their markets than ever before, and maybe even encourage them to foresee what may happen in the future. Here are just one of many amazing big data stats: They submit 204 million emails per minute, upload 2.5 million pieces of content on Twitter, send 277,000 tweets, and publish 216,000 photos on Instagram. There is a massive amount of data out there, just enough to learn. But it can be time-consuming as well as challenging to make sense of millions (maybe billions) of data points without powerful technology, particularly when this data becomes unstructured. That is often the case for digital online data in the form of news stories, social media messages, feedback from blogs, and much, much more. Besides, such is the difficulty of this cycle that a reaction towards big data has been somewhat current. Now there are concerns about the value of big data being overstated because it is too “huge” and unruly. There are two primary forms of Smart Information, which are often addressed by industry experts. Another type is information collected by a sensor, then sent to a neighboring collection point, and acted on before being sent to a database for Analytics. Such data comes from Intelligent Sensors, in particular within the Industrial Things Internet (IIoT) networks. The other kind of Smart Data is the Big Data stored and waiting to be translated into actionable information. Data heading to and from a Smart Sensor is “sensor data” for this report. The word, Smart Data, would apply to Big Data which was tested for useful information. Consumer Journey Analytics weaves hundreds of communications through multiple channels from the company internet. It incorporates thousands of activities to create a journey for the customers of a company. It is a data-driven methodology that is used to identify, interpret, and impact the experience of consumers. However, if the input is “false,” it is both annoying and offensive. Further, it may result in the loss of a client. The Customer Experience Assessment (or Customer Analytics Voice) utilizes tools and techniques to collect the perceptions, thoughts, and feelings of the customer. Consumer Analytics speech stresses the customers’ mental state. Machine Learning Smart Data Machine learning is often a method of preparation with Artificial Intelligence applications but can also be used as a system of understanding and decision making. While Smart Data’s use and prominence has grown, it has also been used with Machine Learning algorithms designed to find Business Intelligence and insights. Machine Learning allows companies to process data lakes and data centers, thus generating smart results. Traditionally, companies pursuing Big Data Business Intelligence have used Data Scientists who spend time searching for trends and correlations within the databases of an organization. Artificial Intelligence and Smart Data Decisions are made during the scanning and filtering process of creating Smart Data as to which data should be filtered and which should be released. During this method, Machine Learning and Artificial Intelligence (AI) employ specific criteria. AI is a continuous attempt to create wisdom inside computers, allowing them to function and act like human beings. Artificial Intelligence has provided autonomy and can address specific goals. Financial services companies, for example, can use AI-driven Smart Data for consumer identification, fraud detection, market analysis, and enforcement. Collecting Data Organizations with less knowledge of Big Data often gather everything and then archive it in a Data Warehouse, Data Lake, or often what a Data Swamp is. We obtain Big Data intending to use it “until we decide to use it.” While these companies may believe they are gathering quantitative data for years, the data may lose quality or quantity or may even be in the wrong format. Their money would be best used to collect data appropriate for their company. An enterprise can be knowledgeable about the data it collects and retains in a Data Lake. Data takes time and money to collect, compile, and manage. Collecting Intelligent Information can be an effective strategy for small and medium-sized organizations, rather than “pure” Information. The emphasis on Smart Data collection helps a company to use cost-effective solutions to handle it. Collecting only the essential data will minimize the use of Self-Service BI systems, preventing workers from getting lost in the mass of irrelevant data. Smart data collection is not just about removing the excess data. Smart data can come from various outlets, and an agile enterprise can combine these resources to develop a highly focused business intelligence model. The point of view is right away. Big data is unusable, lacking order. It is just a collection of random knowledge that would take years to absorb, and may not provide any results even then. But when the form can be easily overlaid and evaluated, big data tends to become smart data. At Talkwalker, we have a way to explain just how this is going to happen, and how it can be a little like searching for a partner in life. From a machine, all social data is just words on a page through different sources, including Twitter posts, Facebook posts, news articles, websites, and discussion sites. The first step, as you would on Google, is to search for a particular subject in that data. Let’s claim we’re typing “Talkwalker” into our framework for social data analytics. At this point, we would have a very long list of URLs or post titles in no particular order, without any other criteria or filters. With such a narrow filter, the knowledge that we can obtain from such details is, as you can guess, also quite restricted. All we’d ever say is how many times a specific word has been listed online. The detail is by no way meaningless. It may, in turn, be important Information for businesses looking

Vaishali
March 22, 2020
1 Comment

Integrating Data Analytics At Every Level Of Your Organization! professionals’ Guide.

What is data analytics and how it is used by large organizations to support strategic and organizational decisions? Senior leaders offer insight into the problems and opportunities involved. Most data and analytics (D&A) conversations begin by focusing on technology. It is critically important to have the right resources, but executives too often ignore or underestimate the value of the people and organizational components needed to create a productive D&A process. When that happens, D&A initiatives that fail — not deliver the insights required to move the organization forward, or inspire trust in the actions necessary to do so. The stakes are high with International Data Corporation predicting global D&A market spending to surpass $200 billion a year by 2020. A stable, efficient D&A feature encompasses more than just a stack of technology, or a couple of people isolated on one level. D&A will be the organization’s heartbeat, integrated into all leading sales, marketing, supply chain, customer service, and other core functions decisions. Why can I develop successful D&A capabilities? Start by creating an enterprise-wide plan that provides a clear picture of what you’re trying to achieve and how progress will be evaluated. One of America’s prominent sports leagues is a perfect example of an organization making the most of its D&A feature, applying it to cost management plans, for example, reducing the need for teams to fly back-to-back nights from city to city for games. Throughout the 2016–2017 season, thousands of travel-related restrictions, player exhaustion, ticket sales, arena capacity, and three major TV networks had to be taken into account. With 30 teams and 1,230 regular season games stretching from October through May, there were trillions of scheduling choices available. Companies should follow the lead of the league by understanding first that good D&A starts at the top. Make sure the leadership teams are entirely engaged in the company in identifying and setting goals. Avoid allowing the setting of objectives and decision-making to take place in organizational silos that can generate shadow technologies, conflicting versions of the reality, and paralysis of data analysis. Ask: Is the aim to help boost the company output before launching some new data analysis initiative? System jump start and cost efficiency? Drive policy, and speed up change? Growing market share? More successful innovation? Any of that? Leadership teams must understand that it takes bravery to be successful because, as they embark on the journey, data analytics observations will always point to the need for decisions that may entail a course correction. The leaders need to be frank about their ability to integrate the findings into their decision-making. Further, they should keep themselves and their teams accountable for that. Consider a large global life sciences company that spent a huge amount of money to develop an advanced analytics platform without knowing what it was supposed to do. Executives allowed their development team to buy a lot of items, but none understood what the developed tools were meant to do or how to use them. Luckily, before it was too late, executives identified the issue, undertaking a company-wide needs assessment and restoring the platform in a manner that inspired trust in its ability to drive productivity and promote business transformation. In another instance, a global financial services company, focused on stakeholder expectations, has developed a robust development infrastructure. But executives soon discovered after creating it, that they lacked the organizational structure and resources to use the platform effectively. When these requirements were met, the organization was able to use a great platform to generate substantial operating cost savings. Data analytics is the most in-demand technology skill for the second year running, according to KPMG’s 2016 CIO Survey. Still, almost 40 percent of IT leaders claim they suffer from skill shortages in this critical sector. Formal, organized structures, procedures, and people committed to D&A can be a competitive advantage, but this significant opportunity is lacking in many organizations. Companies who develop a D&A infrastructure to meet their business needs have in our experience teams of data and software developers who are experienced in using big data and data scientists who are entirely focused on a D&A initiative. Although processes vary, the team should be integrated seamlessly with existing D&A suppliers and customers in the sector, working in collaboration with non-D&A colleagues — people who understand both the market problems and how the business analytics functions — to set and work towards practical and specific strategic objectives. The teams will need full executive leadership support, and their priorities should be aligned entirely with the company plan. In an era in which data is generated on a scale well beyond the capacity of the human mind to process it, business analytics leaders need D&A that they can trust to inform their most important decisions— not just to cut costs but also to achieve growth. And the best would use D&A to predict what they want or need from their customers before they even know they want or need it. Volatility, sophistication, and confusion can better characterize today’s business analytics decisions governing the macroclimate. In this uncertain climate, forward-thinking companies are identifying and exploiting data as a strategic tool to improve their competitive edge. Data Analytics facilitates proactive decision-making by offering data-driven insights into products, consumers, competitors, and any aspect of the market climate. Today analytics are applied on a need-based basis in most organizations. Although most companies are still considering making investments in data analytics and business intelligence, they need to realize that the process of incorporating advanced analytics into the corporate structure requires far more than investing in the right people and resources. A data-driven culture is the core of this framework and is a crucial factor for the effective introduction of analytics into the organizational system. The integration cycle begins with a data-driven resolve. Big data analysis and advanced analytics must be accepted at the corporate level as an operational feature to be powered by the data. Projects and assignments undertaken must be analyzed from an analytical

Vaishali
February 26, 2020
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Don’t Fall For Frauds | Here Is How To Hire AI & Data Analytics Company?

Data Science, AI, and Machine Learning have now, become an integral part of the technology revolution in all industries. Capabilities of predictive analytics for all kinds of businesses have led it to become a hot topic of discussion. With more and more discussion going on about AI & Data Analytics, it has been attracting several business owners to hire AI & data analytics companies to help them get the best solution to their data-related problems. However simple it seems, it indeed is one of the important decisions for a business as they will provide access to all their data to the data analytics consulting company they hire. Before you decide to hire a data science company, you must understand what you need them for. This question can be answered by a simple consultation with experts, which every good data science company like DataToBiz provides for free or you can use a technique of isolating your question to figure out a specific problem you need to be resolved. This way you will know exactly what you want from a data science & AI consulting company for your business. To make it more simple, we are here sharing all the things you should consider before hiring an AI & data analytics company. Points To Check Before Hiring Data Analytics Company Being data analytics experts, we are here to share in detail the points to consider before you select a data analytics company. So, let’s start with the list of points to consider. 1. PinPoint The Problem & See If They Provide Possible Solution When it comes to data science, it is all about gathering useful information out of the collected data. There are many things for which a data analytics company is hired for. Some hire them to build products that use machine learning, for example, the product that helps an application to transform speech to text, etc while some might need to develop a custom analytical as well as visualization platform to make strategic decisions on the basis of insights. This is not all, you can also hire the data science experts like DataToBiz to gain insights about the business you do and use those insights to further improve the business operation. In addition to all this, you can also hire data science and AI experts to develop AI-based applications for your customers. Where the former is for the business end there, there the later one is developed for the customer end. Let’s discuss both these ends one by one. Business & Statistical Analytics For those who don’t know what is business analytics, you will get to know now. BA that is business analytics is a process of exploring the data using statistical & operational analysis. What is the purpose of Business Analytics? Business Analytics is a process designed for the purpose of monitoring the business processes and using the insights from data that can help you make a well-informed decision. What Are The Best Business Analytics Techniques You Should Know About? There are two groups of business analytics techniques that every efficient data analytics company like DataToBiz must know about. These two groups include – business intelligence and statistical analysis. The AI and data analytics company with expertise in business intelligence work efficiently on analyzing and reporting the historical data insights which in turn help companies make informed strategic decisions regarding current business operations and developments. However, the companies with specialties in statistical analytics bring on table more elaborate digging. Where Can You Use Business Analytics? Before you hire a data science company, you must know where business analytics can be helpful. Below is the list of issues where business analytics might come in handy. Types of Business Analytics include – Prescriptive Analytics, Predictive Analytics, Descriptive Analytics, and Diagnostic Analytics. So, before you hire data analytics & AI consulting company, you must know the basics of what business analytics is about. Customer End Applications & Fraud Detection Mostly every customer end application is powered by the machine learning algorithms and is designed with the sole purpose of providing a solution to any of the problems faced by customers. Every good AI and Data Analytics company must have knowledge of what customer-end applications need. Some of the Cases In Which You Might Need Customer Facing Solutions – Along with these applications, the customer end data analytics can also be used in fraud detection systems. 2. Check For The Off-The-Shelf Solutions Or Products Before Hiring Data Analytics Company! Before you start hunting for the best data analytics company make sure that you have gone through every possible off-the-shelf solution for the problem you need to be resolved. There are several websites and platforms that list analytics as well as SaaS solutions like KDnuggets & PCMag. In some cases where one uses CRM systems to collect customer insights, you must check with the vendor if they provide additional modules to resolve your problem. What Is The Catch? The catch in this off-the-shelf solution is that most of them do not support the functionality that you might need. This is where data science and AI companies jump in. 3. Check The Company’s Portfolio & References! Once you have shortlisted the company, you must check out for the portfolio of the AI & data analytics consulting company. Note that the data science consultancy company that vouches on having the domain knowledge not just delivers a solution but can also refer to product development and doesn’t need a huge time to study and figure out the problem. References – When you decide on hiring someone, it should be based on the references they get from their present and past clients. Not only this, the news articles, and press releases can also help you gather insight on how good the data science consultancy company is. 4. One-on-One Interview With Data Science Consultancy Company Finally when the data analytics and AI company has crossed all these aspects, what you have to do is have a one-on-one conversation

Vaishali
February 25, 2020
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5 Awesome Benefits of Big Data in Business Invoicing System

Invoicing system has undergone some major changes since the introduction of big data in them. We, being a big data analytics company with expertise in big data, data science, and machine learning are here to share how you can improve your invoicing system. Along with that in this piece of article, we are going to share how big data has helped improve the invoice system. There are many ways in which big data has improved invoicing applications which you can check in the detailed report by Spend Matters. Big Data Revolutionizing The Invoicing Software Applications Before the invoicing system was upgraded using big data, there was a debate on its application between many SME owners. The reason for this debate is that it is considered not very challenging to handle it manually. However, after running the invoicing system, software or application, everyone regrets not using it from the very start. There are many reasons to support why the invoicing system or software is perfect for businesses. Most of these reasons are because of the introduction of big data in them. Benefits of Switching To Invoicing System With Big Data Below are some of the advantages of using the Invoicing system rather than going with the traditional invoice template system. 1. Save Time & Money We have seen that the invoice template works fine for many businesses, however, there are many features and functionalities that are missing when it comes to this old invoicing system including the fact that these systems don’t ensure that you get paid or not. Using the invoicing software resolves this issue. Almost every invoicing system uses big data to connect clients and payment providers. This, in turn, streamlines the payment process for the companies. What is even more interesting is that this software also provides multiple payment gateways to pick from which takes only a few clicks. In addition to this, with the help of invoicing software, the receipts and accounts are automatically updated. 2. Can Be Used From Anywhere The following reason is that it is time-saving, these invoicing systems designed using big data can be used on the go. Thanks to this software, you do not have to sit in front of your computer to send the invoice. This feature comes in handy for those who find it difficult to spare time to process invoices. This robustness of the invoicing applications has made them more efficient and useful. Big data has helped advance the invoicing system to introduce this feature which allows you to not only send the invoice while you are on the go but can also allow clients to pay from wherever they are at that moment, making the entire system more efficient. 3. Customization Feature In addition to the above two points, the most important benefit these big data modified invoicing systems include is the option of customization. When you are using the invoice template method, there is no option for customization. However, big data is all about personalization. With invoicing software, you can easily customize the invoices as per the customers or and clients with simple settings, making the entire system flawless. 4. Detailed Reporting In Invoicing System The best part of these big data-modified invoicing systems is their ability to track all your financial transactions with every client. Not only this, but this software also includes the feature of generating detailed reports on what has been paid/received and when exactly to which client. So, instead of following up with every client, with the modified invoicing system, you can easily track payment history using an automated for you. With this reporting system, you can not only make your life simpler but can also ensure that your clients pay and on time. 5. Multiple Invoicing System When it comes to discussing the advantages of the invoicing system, multiple invoicing definitely comes up. Unlike the traditional invoice template method where one has to send a ton of invoices, through this software you can send multiple invoices for different services simply using the software feature. In all these points, it is clear that big data has helped dynamically improve the invoicing system for business owners. Every point we have discussed above makes it obvious why one should opt for the invoicing system. Through this blog, you have understood the functioning and benefits of big data in invoicing systems. Implementing these technologies can not only improve your revenue but also increase the efficiency of your business operations. Partner with leading big data analytics companies like DataToBiz to leverage big data to turbocharge the business operations. Talk to an expert today!

Vaishali
February 22, 2020
1 Comment

Revealing The Success Mantra Of Netflix! Role of Big Data & Data Analytics.

Today, Netflix is one of the most loved streaming apps in the market. With the number of users increasing every second from 115 million users, there is no doubt that this streaming channel has won the hearts of millions of people becoming the kind of streaming world today. Most of you must be thinking about how they have managed to be this successful, and we are here to reveal their secret today. You can also become a new rising star in the streaming world with our data analytics services. It has been established that Netflix has taken over the entire Hollywood which indeed is raising huge questions on how? The answer is simple, the secret is “Big Data”. As per the Wall Street Journal, Netflix has been using Big Data Analytics to optimize the overall quality and user experience. Through big data analytics, Netflix is targeting users through new offers for shows that will interest them. Not only this but through big data analytics, they also are playing the ground with relevant preferences. All these efforts all together have led to the success of the Netflix streaming platform. The Secret Behind Netflix, The Streaming Platform By now, we have established that Netflix has become one sensational streaming platform of today that has millions of subscribers from all across the world. Now, if we go deep, these million subscribers derive a humongous amount of data that can and has been used by Netflix to grow even more. Although there are many challenges that one faces when it comes to including data analytics in your business, still after reading this, you will understand how important it is. Right from the prediction of the type of content to recommending the content for the users, Netflix does it all through big data analytics. Netflix started collecting data from the time they were distributing the DVDs which later when they started their streaming service in 2007 shaped into something more. It took them 6 years to gather proper data to analyze find the result-driven data from it and use it. This big data analytics lead to the launch of their first show – “House of Cards” which they estimated to be a success through data analysis, proving how beneficial big data analytics has been for them. This also gives another reason why you should consider adding big data analytics to your business. Thankfully, there are many experts in the market like us at DataToBiz, who can help you through it. Netflix also invested a million dollars in the development of the algorithm for data analysis to improve the efficiency and accuracy of the process, helping then increase the retention rate. Why Has Netflix Become so Popular? Netflix has worked on a combination of factors to reach the current stage of being at the top. And now, why is Netflix so successful? Because it worked on its core aspects of providing users with content they want to watch and kept the pricing at an affordable range. Moreover, Netflix has such a vast collection of shows, movies, documentaries, etc., that users could keep watching and never worry about running out of content to consume. How Netflix Uses Big Data Analytics to Ensure Success Around 80% of the content streamed on Netflix comes from the recommendation engine. The platform has developed a series of algorithms that consider an array of factors to deliver personalized recommendations to every user. Netflix built new data pipelines, worked on complex datasets, and invested in data engineering, data modeling, heavy data mining, deep-dive analysis, and developing metrics to understand what the users want. Netflix innovation relies on- Netflix hasn’t limited the use of big data analytics only to curate content for users. It uses algorithms to estimate and predict how much a new project would cost and find alternate ways to optimize the production and operations. By reducing bottlenecks in daily operations, Netflix could streamline the workflow and make better decisions about the projects. This is how Netflix used big data and analytics to generate billions and has won 22 Golden Globe awards in 2021 while having 42 total nominations. Make Sure What Users Need! With the help of Big data analytics, Netflix knows what you want and what you would like to watch next. Now, this might seem scary but the science behind it really simple. Knowing and understanding the preferences of the users have proven to be the two pillars of success for Netflix. With the help of which they understood the viewing habits of viewers which help the prediction system that is powered by the algorithm designed by the developers. In short, big data analytics helped Netflix to gather insights which further helped in the optimization of the algorithm and then adjust the algorithms. In addition to studying the behavior of the users, Netflix also uses tagging features that allow consumers to suggest as well as recommend different movies and series they think a user will enjoy. This feature encourages more views, clicks, and raises engagements. This magic formula took 6 years of Netflix which has paid off really well as it has become the no.1 streaming app today. What Makes Netflix Different from its Competitors? Netflix has around 231.6 million paid subscribers around the world in the third quarter of 2021. The maximum of them come from the US, with Canada next in the line. There are around 5 million Netflix subscribers from India (as of Jan 2021). But why is Netflix a great product? How has it set itself apart from its competitors? Aggressive data mining has helped Netflix offer customers the exact kind of shows, movies, etc., they prefer to watch. The data is analyzed to sort through the genres, most-watched episodes, most-searched-for shows/ movies, and so on. Another advantage Netflix has created for itself is the pricing. With a flat fee per month, users have access to unlimited content streamed on the platform. Netflix also provides the first month free for subscribers. Even though Netflix