Page 40 – DataToBiz

Vaishali
May 21, 2020
11 Comments

Impact Of AI In Market Research | How It Is Being Improved

To understand the effect that artificial intelligence (AI) can have on market research. First, it is essential to be clear about what exactly is AI and what it is not. Artificial intelligence is the machine-displayed intellect, which is often distinguished by learning and adaptability. It is not quite the same as automation. Automation is now commonly used for speeding up a variety of processes in the insights field. Automation is essentially the set of guidelines from recruitment to data collection and analysis that a computer follows to perform a function without human assistance. When complex logic and branching paths are introduced, differentiation from AI can be difficult. But, there is a significant difference. Except in the most complex of ways, software follows the instructions it has been given when a process is automated. Every time the cycle runs, the program (or machine) makes no decisions or learns something new. Learning is what makes artificial intelligence stand out from automation. And this is what gives those who accept it the most significant opportunities. Examples Of AI Today With AI Market Research Companies There is already a range of ways in which artificial intelligence can provide researchers with knowledge and analysis that weren’t possible before. Of particular note is the ability to process massive, unstructured datasets. Processing Open End Data In AI-Driven Market Research Dubbed Big Qual, the method of applying methods of statistical analysis to large quantities of written data aims at distilling quantitative information. The natural language API in Google Cloud offers an example of this in practice. The program recognizes “AI” as the most prominent entity in the paragraph (i.e., the most central one in the text). It can also know the category of text, syntactic structure, and provide insights into feelings. In this situation, there was a negative tone in the first and third sentences, while the second was more positive overall. It can reduce the time it takes to evaluate qualitative responses from days to seconds when implemented on a large scale, particularly in the case of open-ended results. How Artificial Intelligence Will Change The Future Of Marketing: Artificial Intelligence In Marketing Analytics? Following is the way in which artificial intelligence change the future of marketing: Proactive Community Management A second direction that artificial intelligence is being used in group management today can be observed. As every group manager can attest, participant disengagement is one of the most significant challenges to a long-lasting society. It can result in a high turnover rate, increased management effort, and outcomes of lower quality. Luckily, AI-driven automated market research behavioral forecasts increased the chance of disengagement. Behavioral predictions include evaluating a vast array of group members’ data points such as several logins, pages viewed, the time between logins, etc. to construct user interaction profiles. When designed against disengaged members and measured, the AI can classify the members are at risk of disengagement. It allows community managers to provide these individuals with additional support and encouragement, thus reducing that risk. Machine Making Decisions Give enough details to a computer, and it’ll be able to make a decision. And that’s precisely what Kia did over two years ago when the company used IBM’s Watson to help determine which influencers on social media would better endorse its Super Bowl commercial. Using Natural Language Processing (NLP), Watson analyzed social media influencers’ vocabulary to recognize which characteristics Kia was searching for – openness to improvement, creative curiosity, and striving for achievement. Perhaps the most exciting thing about this example is that Watson ‘s decisions are those that would be difficult for a human to make, demonstrating the possibility that AI for market insights might better understand us than we can. Future Of AI In Market Research Progress, of course, never ends. We are still very much in the absolute infancy of artificial intelligence. In the years to come, it is a technology that will have a much more significant effect on market research. Although there is no way to predict precisely what the result would be, the ideas outlined here are already being formulated – and that arrive sooner than we expect. Virtual Market Research It’s expensive to hire. It can quickly eat away on a research budget, depending on the sample size and the length of a task. One proposed suggestion to further reduce this expense and extend insight budgets is to create a virtual panel of respondents based on a much smaller sample. The idea is that sample sizes inherently restrict the ability of a company to consider every potential customer and client’s behavior. Hence, taking this sample, representing it as clusters of behavioral traits, and building a larger, more representative pool of virtual cluster respondents offers a more accurate behavior prediction. This method has abundant limitations, such as the likelihood that in the first instance, the virtual respondents will be limited to binary responses. But this still has value – particularly when combined with the ability to run a large number of virtual experiments at once. It may be used to determine the most suitable price point for a product or to understand how sales could be affected by reaction to a change in product attributes. Chatbots As Paul Hudson, CEO of FlexMR, emphasized in a paper presented at Qual360 North America, a question still hangs over whether artificial intelligence could be used to gather on-scale qualitative conversational research. The research chatbots of today are restricted to pre-programmed questions, presented in a user interface typical of a conversation online. However, as developments in AI continue to grow, so will these distribution methods for online questioning. The ultimate test would be whether such a tool could interpret responses from respondents in a way that allowed tailoring and sampling of interesting points following questions. It will signal the change from question delivery to virtual moderator format. The resource is a natural limitation to desk investigation. While valuable, desk research can be time-consuming, meaning that insight does not always reach decision-makers’ hands before a decision

Vaishali
May 21, 2020
13 Comments

Automated Machine Learning (Automl) | The New Trend In Machine Learning

The digital transformation is driven primarily by the data. So today, companies are searching for as many opportunities to gain as much value from their data as they can. In reality, in recent years, machine learning (ML) has become a fast-growing force across industries. ML ‘s effect on driving software and services in 2017 was immense for companies like Microsoft, Google, and Amazon. And the utility of ML continues to develop in companies of all sizes: examples include fraud prevention, customer service chatbots at banks, automated targeting of consumer segments at marketing agencies, and suggestions for e-commerce goods and retailer personalization. Although ML is a hot subject, there is another popular trend: automated machine learning platform (AutoML). Defining AutoML (Automated Machine Learning) The AutoML field is evolving so rapidly, according to TDWI, there’s no universally agreed-upon definition. Basically, by adding ML to ML itself, AutoML gives expert tools to automate repetitive tasks. The aim of automating ML, according to Google Research, is to build techniques for computers to automatically solve new ML issues, without the need for human ML experts to intercede on each new question. This capability will lead to genuinely smart systems. Furthermore, possibilities are generated thanks to AutoML. These types of technologies, after all, require professional researchers, data scientists and engineers, and worldwide, but such positions are in short supply. Indeed, those positions are so poorly filled that the “citizen data scientist” has arisen. This complementary position, rather than a direct replacement, hires people who lack specialized advanced data scientist expertise. But, using state-of-the-art diagnostic and predictive software, they can produce models. This capability stems from the emergence of AutoML, which can automate many of the tasks that data scientists once perform. To counter the scarcity of AI/ML experts, the AutoML example has the potential to automate some of ML’s most routine activities while improving data scientists’ productivity. Tasks that can be automated include selecting data sources, selecting features, and preparing data, which frees marketing and business analysts time to concentrate on essential tasks. For example, data scientists can fine-tune more new algorithms, create more models in less time, and increase the quality and precision of the model. Automation And Algorithms Organizations have turned toward amplifying the predictive capacity, according to the Harvard Business Review. They’ve combined broad data with complex automated ML to do so. AutoML is marketed as providing opportunities to democratize ML by enabling companies with minimal experience in data science to build analytical pipelines able to solve complex business problems. To illustrate how this works, a current ML pipeline consists of preprocessing, extraction of features, selection of features, engineering of features, selection of algorithms, and tuning of hyper-parameters. But because of the considerable expertise and the time it takes to enforce these measures, there is a high barrier to entry. One of the advantages of AutoML is that it removes some of these constraints by substantially reducing the time it takes to usually execute an ML process under human control, while also increasing the model’s accuracy as opposed to those trained and deployed by humans. Through enacting this, it encourages companies to join ML and free up ML data practitioners and engineers’ resources, allowing them to concentrate on more difficult and challenging challenges. Different Uses Of Automl About 40 percent of data science activities should be automated by 2020, according to Gartner. This automation would result in a broader use by citizen data scientists of data and analytics and improved productivity of skilled data scientists. AutoML tools for this user group typically provide an easy-to-use point-and-click interface for loading ML models for data building. Most Automl tools concentrate on model building rather than automating a whole, particular business feature, such as marketing analytics or customer analytics. However, most Automl tools and ML frameworks do not tackle issues of ongoing data planning, data collection, feature development, and integration of data. It proves to be a problem for people who are data scientists, who have to keep up with large amounts of streaming data and recognize trends that are not apparent. They are still not able to evaluate the streaming data in real-time. And poor business decisions and faulty analytics can arise when the data is not analyzed correctly. Model Building Automation Some businesses have switched to AutoML to automate internal processes, especially building ML models. You may know some of them-Facebook and Google in particular. And Facebook is widely on top of every month’s ML, training, and testing around 300,000 ML models, essentially building an ML assembly line to handle so many models. Asimo is the name of Facebook’s AutoML developer, which produces enhanced versions of existing models automatically. Google also enters the ranks by introducing AutoML techniques to automate the process of discovering optimization models and automating machine learning algorithm design. Automation Of End To End Business Process In certain instances, it is possible to automate entire business processes once the ML models are developed, and a business problem is identified. It needs the data pre-processing and proper function engineering. Zylotech, DataRobot, and Zest Finance are companies that primarily use AutoML for the entire automation of different business processes. Zylotech was developed for the entire customer analytics automation process. The platform features a range of automated ML models with an embedded analytics engine (EAE), automating customer analytics entering the ML process such as convergence, feature development, pattern discovery, data preparation, and model selection. Zylotech allows data scientists and citizen data scientists to access full data almost in real-time, allowing for personalized consumer experiences. DataRobot was developed for predictive analytics automation as a whole. The platform automates the entire lifecycle of modeling, which includes transformations, ingestion of data, and selection of algorithms. The software can be modified, and it can be tailored for particular deployments such as high-volume predictions, and a large number of different models can be created. DataRobot allows citizen data scientists and data scientists to apply predictive analytics algorithms easily and develop models fast. ZestFinance was primarily developed for 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 27, 2020
1 Comment

What is Data Science? How Do Data Scientists Help Businesses

Ever wondered what is data science? Do you know what a data scientist does? Here is something to help you. Data science is advancing as one of the most motivating and after sought vocation ways for all the gifted experts. Today, effective data experts comprehend that they should progress past the conventional aptitudes of breaking down a lot of data, data mining, and programming abilities. To reveal helpful knowledge for their associations, data researchers must ace the full range of the data science life cycle and have a degree of adaptability and comprehension to amplify returns at each period of the procedure. Data science or data-driven science empowers better dynamic, prescient examination, and example disclosure. It lets you: Locate the primary source of an issue by posing the correct inquiries Perform exploratory investigation on the data Model the data utilizing different calculations Convey and picture the outcomes using charts, dashboards, and so forth. By and by, data science is as of now helping the carrier business foresee disturbances in the movement to lighten the torment for the two aircraft and travelers. With the assistance of data science, carriers can streamline activities from numerous points of view, including: Plan courses and conclude whether to plan direct or corresponding flights Fabricate prescient investigation models to estimate flight delays Offer limited customized time offers dependent on clients booking designs Choose which class of planes to buy for better generally speaking execution In another model, suppose you need to purchase new furniture for your office. When looking on the web for the best choice and arrangement, you should address some necessary inquiries before settling on your choice. What is Data Science? (Understanding Data Science Before Becoming Data Scientist) In the previous decade, data researchers have become vital resources and are available in practically all associations. These experts are balanced, data-driven people with significant level specialized abilities who are fit for building complex quantitative calculations to sort out and incorporate a lot of data used to respond to questions and drive methodology in their association. It is combined with the involvement with correspondence and administration expected to convey substantial outcomes to different partners over an association or business. Data researchers should be interested and result-arranged, with extraordinary industry-explicit information and relational abilities that permit them to disclose profoundly specific outcomes to their non-specialized partners. They have a solid quantitative foundation in measurements and straight variable-based math just as programming information with centers in data warehousing, mining, and displaying to assemble and dissect calculations. Why Become a Data Researcher? As expanding measures of data become increasingly available, large tech organizations are never again the main ones needing data researchers. The developing interest for data science experts across businesses, of all shapes and sizes, is being tested by a deficiency of qualified applicants accessible to fill the open positions. The requirement for data researchers does not indicate easing back down in the coming years. LinkedIn recorded data researchers as one of the most encouraging occupations, alongside various data-science-related abilities as the most sought after by organizations. How Data Scientist At Big Companies Use Data Science? IT associations need to address their complex and extending data conditions to distinguish new worth sources, misuse openings, and develop or improve themselves, productively. Here, the integral factor for an association is ‘the thing that esteem they extricate from their data store utilizing investigation and how well they present it.’ Beneath, we show the absolute greatest and best organizations that are enlisting Data Scientists at first-rate pay rates. Google is by a long shot, the most significant organization that is on an enlisting binge for prepared Data Scientists. Since Google is generally determined by Data Science, Artificial Intelligence, and Machine Learning nowadays, it offers perhaps the best data science compensation to its representatives. Amazon is a worldwide online business and distributed computing monster that is procuring Data Scientists on a significant scale. They need Data Scientists to discover client outlook and improve the topographical reach of both online business and cloud areas, among different business-driven objectives. Data Science Life Cycle Data Revelation The primary stage in the Data Science life cycle is data revelation for any Data Science issue. It incorporates approaches to finding data from different sources, which could be in an unstructured configuration like recordings or pictures or an organized arrangement like in content documents, or it could be from social database frameworks. Associations are likewise peeping into client web-based life data, and so forth, to comprehend client attitude better. Right now, as Data Scientists, our goal is to help the deals of Mr. X’s retail location. Here, factors influencing the deals could be: Store area Staff Working hours Advancements Item position Item valuing Contenders’ area and advancements, etc Remembering these components, we would create clearness on the data and get this data for our examination. Toward the finish of this stage, we would gather all data that relate to the components recorded previously. Data Preparation When the data revelation stage is finished, the following step is the data arrangement. It incorporates changing over divergent data into a typical configuration to work with it consistently. This procedure includes gathering clean data subsets and embedding appropriate defaults, and it can likewise include increasingly complex strategies like recognizing missing qualities by displaying, etc. The following stage is to coordinate. And further, make an end from the dataset for examination when the data cleaning is done. It includes the coordination of data which incorporates blending at least two tables of similar items, yet putting away extraordinary data, or condensing fields in a table utilizing accumulation. Mathematical Models Do you know, all Data Science ventures have specific numerical models driving them. These models are arranged. They are further worked by the Data Scientists to suit the particular need of the business association. It may include different zones of the digital space, including measurements, strategic and direct relapse, differential and indispensable analytics, and so forth. Different instruments and mechanical assembly utilized right now are

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

Sulaksh More
April 05, 2020
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Computer Vision in Healthcare – The Epic Transformation

Before discussing futuristic applications of computer vision in healthcare, let us talk a little about how computer vision works. Although the ability to make machines “see” a still image and read it, is related to human’s ability to see, the machines see everything differently. For example, when we see a picture of a car, we see car doors and windows and glasses, color, tires, and background, but what a machine sees is just a series of numbers, that simply describes the technical aspects of the image. Which does not prove that it is a car. Now, to filter out everything and to arrive at a conclusion that it is a car, is what Neural Networks do. Various Neural Networks and Advanced Machine Learning Models are being developed and tested over the period, a massive amount of training data as being fed and machines, now have achieved a level of accuracy. How AI could benefit Health Care Industry: There have been discussions on how AI could help various industries and Health Care is one of the most talked. There are many ways AI could support the industry. AI is a vast field and can be confusing on what specific model to use. There have been continuous discussions and multiple methods approached and improvised. Support Vector Machines For the purpose of classification and regression, Support Vector Machines can be implemented. Here support vectors are data points, which are closest to the hyperplane. To diagnose cancer and other neurological diseases, SVMs are widely used. Natural Language Processing We now have a large amount of data which is composed of examination results, texts, reports, notes and importantly, discharge information. Now, this data could mean nothing for a machine that has no particular training for reading and learning from such data. This is where NLP could be of use, by learning about keywords related to disease and establishing a connection with historical data. NLP might have many more applications based on needs. Neural Networks Implementing hidden layers to identify and establish a connection between input variables and the outcome. The aim is to decrease the average error by estimating the weight between input and output. Image Analysis, Drug Developments and a few, are the fields where Neural Networks are harnessed. As Always, CNNs are the Best: Convolution Neural Networks, over time, has rapidly been developed and currently is one of the most successful computer vision methods. “CNNs simply learns the patterns from the training data set and tries to see such patterns from new images.”. This is the same as humans learning something new and implying the knowledge but what all these models know is a series of ones and zeros. With an accuracy of 95%, a CNN trained at the University of South Florida, can quietly easily detect small lung tumors, often missed by the human eye. Another research paper suggests that cerebral aneurysms can be detected using deep learning algorithms. At Osaka City University Hospital, they detected cerebral aneurysms with 91-93% of sensitivity. RNNs, which are Recurrent Neural Networks are also popular and could be of great use as they are neural networks but with information in sequence. Performing the same task for multiple elements and composing output based on the last computation. How Google’s DeepMind sets new milestones: Acquired by Google in 2014, DeepMind has outplayed many players and has set a new record in AI for the Health care Industry. Protein Folding is something they have been working on and reached a point where predicting the structure of the protein, wholesomely based on its genetic makeup, is possible. What they did was rely on Deep Neural Networks, which are specifically trained to predict protein properties based on the genetic sequence. Finally, they reached a point where they had the model predict the gap between amino acids and the angles connecting the chemical bonds which connect earlier mentioned amino acids. This could also help in understanding the underlying reasons for how genetic mutation results in disease. Whenever the problem with Protein Folding will be solved, it will allow us to pace up our processes like drug discovery, research, and production of such proteins. How could this help in tackling COVID-19 It is not a new discovery that machine learning can fasten the Drug Development Process for any disease or virus. There are very few datasets available related to Corona and has a lot to tackle in order to establish a conclusion. Recently, there have been developments involving AlphaFold, which is a computational chemistry-related deep learning library. FluSense using Raspberry Pi and Neural Computing Engine: Starting with Lab tests, FluSense is now growing to identify and distinguish human coughing from any other sound, in public places. Idea is to combine the coughing data with people present in the area, which might lead to predicting an index of people affected by the flu. This is a perfect use case of computer vision in healthcare considering the recent pandemic of covid-19. Conclusion Though there have been tremendous developments and many new algorithms are been developed, it would be too early to completely rely on a machine’s output. Efficiently detecting minor diseases around the lungs is a great step, but still, a small error could lead to catastrophic events. Few more steps towards better models and we can improve health care, until then we can rely on image analysis systems as an assistant. DataToBiz has been working with a few healthcare startups in shaping up their computer vision products/services. It has been judged time and again as one of the top AI/ML development companies in the industry. Contact our expert and avail of our AI services.

Vaishali
March 22, 2020
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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
March 04, 2020
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25+ Data Mining Tools You Must Know About

In this article, you are going to learn what data mining is, what are its benefits and what are the best data mining tools that you must know about. So, if you are looking for the data mining tools, we hope you get the answer after reading our piece. What is Data Mining? Data mining is a method that businesses use to turn raw data into useful information. Businesses may understand more about their clients by using algorithms to scan trends in large batches of data, and create more effective marketing campaigns, raise revenue, and decrease costs. Data mining relies on efficient data collection, storage, as well as computer processing. To collect concrete patterns and trends, data mining includes investigating and evaluating large blocks of knowledge. It can be used in a variety of ways, such as selling the site, handling credit risk, identifying theft, screening spam messages, or even discerning consumer preferences or views. The method of collecting data breaks down into five phases. Secondly, companies are collecting data and putting it into their data stores. Next, they store and maintain the records, either in-house or cloud servers. Business analysts, management teams, and IT experts access the data to decide whether they want it to be structured. Data Mining applications evaluate data interactions as well as the trends depending on what consumers are looking at. For instance, a company may use data mining software to create knowledge groups. For example, consider a restaurant wanting to use data mining to assess when specific specials should be served. It looks at the knowledge it has obtained, then generates classes based on when and what clients are doing. In other instances, data miners may consider knowledge clusters based on logical connections or will look through correlations and temporal patterns to conclude consumer behavior trends. Benefits of Using Data Mining Tools Data Mining Tools Help to Identify Shopping Pattern Most of the time, one might encounter some form of unexpected problems when designing some shopping patterns. And it can be useful to resolve so find out the real purpose behind the data mining. One of the techniques of data mining is to learn all the knowledge about those buying habits. This method of data mining creates a space that decides all of the unforeseen buying habits. Such data mining can, therefore, be useful when detecting shopping habits. Website Optimization Can Be Done With The Help of Data Mining Tools It allows us to learn all sorts of information about the hidden components according to the purpose and interpretation of data mining. Then contributing to that data mining allows refining the platform further. Similarly, as most main website optimization considerations deal with information and analysis, this mining offers such details that data mining techniques can be used to improve website optimization. Companies Use Data Mining Tools For Marketing Campaigns More notably, all data mining aspects are concerned with the exploration of knowledge and also in the way it is summarised. It is also useful for marketing campaigns, as it helps define the reaction of the consumer over certain items available in the market. Thus, through the marketing campaign, all the operating structure of these data mining processes recognizes the consumer reaction, which can execute benefits for the growth of the company. Data Mining Tools Help In Determining Customer Groups As explained earlier, data mining frameworks help to provide marketing campaign answers for customers. And it also includes information assistance when assessing classes of consumers. Through some kind of surveys, these new customer segments can be introduced, and these surveys are one of the ways of mining where various types of knowledge regarding unknown products and services are collected with the aid of data mining. Measure Profitability Factors The data mining system provides all manner of consumer answer details and customer category determinations. It can, therefore, be useful when calculating all of the profitable business considerations. As these forms of data mining operating conditions, one can better understand the actual calculation of the company’s productivity. Moreover, these methods in data mining discern critical factors between the market components’ profit and loss. Data mining is finding secret, real, and all conceivable useful correlations in data sets of large sizes. Data Mining is a technique that helps you find unsuspected relationships among the company, which gains the data. Data Mining Tools There are many useful Data Mining tools available. The following is a compiled collection of top handpicked Data Mining tools with their prominent features. The reference list includes both open source and commercial resources. 1. SAS Data Mining Tools The program of Statistical Analysis is a result of SAS. It was created for data management and analytics. It offers non-technical consumers with a streamlined UI. Features: 2. Teradata Teradata is a massively parallel distributed processing system developed to create large scale systems for data warehousing. Teradata can operate on Database server Unix / Linux / Windows. Features of this data mining tool: 3. R-programming | The Most Famous Data Mining Tools R is a Mathematical Computer and Graphics language. It is also used for processing big data. It offers a wide array of statistical tests. Features: 4. BOARD The panel is a Toolkit for Handling Intelligence. It blends business intelligence and corporate performance management functions. Business intelligence and business analytics are provided in a single package. Features: 5. Dundas Data Mining Tool | Know All About It! Dundas is a data mining platform designed for the business that can be used to create and display virtual dashboards, reports, etc. Dundas BI can be installed as the organization’s primary data repository. Features: 6. Ineytsoft | Features & More! Intelligence type Data Mining technology from Inetsoft is a powerful forum for data mining and intelligence. It enables data to be processed quickly and flexibly from various sources. Features: 7. H2O, Data Mining Tools H2O is another outstanding Data Mining method for open-source software. It is used by cloud computing technology frameworks to do data analysis on the