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Enhance Your Business with Data Modeling Techniques

Bineesh Mathew | January 24, 2022

Data Modeling Techniques

Introduction

Data modeling is the study of data objects and their interactions with other things. It's used to research data requirements for a variety of business requirements. The data models are created to store the data in a database. Therefore, instead of focusing on what processes we must conduct, the data modeling methodologies focuses on what data is required and how to organize it.

Data modeling techniques facilitate the integration of high-level business processes with data structures, data rules, and the technical execution of physical data. Data modeling best parctices bring your company's operations and data usage together in a way that everyone can comprehend.

  • As 2.5 quintillion bytes of data are created every day, enterprises and business organizations are compelled to use data modeling techniques to handle them efficiently.
  • Data modeling for businesses reduces the budget for programming by up to 75%.
  • It typically consumes less than 10% of a project budget.

“The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – is going to be a hugely important skill in the next decades.”

- Hal Varian, Chief Economist, Google

Top Techniques to Enhance Your Data Modeling for Business

Data modeling methodology helps create a conceptual model and establish relationships between objects. The three perspectives of a data model are dealt with in the primary data modeling techniques. And they are conceptual, logical, and physical data models.

Let us look into some essential data modeling techniques to accelerate your business.


Have a Visualization of the Data You're Going to Model

It's unconvincing to think that staring at endless rows and columns of alphanumeric entries will lead to enlightenment. On the contrary, most people are significantly more comfortable inspecting and joining data tables using drag-and-drop screen interfaces or looking at graphical data representations that make it quick to spot any irregularities.

These types of data visualization techniques assist you in cleaning your data so that it is comprehensive, consistent, and free of errors and redundancies. They also help you identify distinct data record types that correspond to the same real-life entity, allowing you to change them to use standard fields and formats, making it easier to combine data sources.


Recognize the Business Requirements and Desired Outcomes

The purpose of data modeling best practices is to improve the efficiency of an organization. As a data modeler, you can only collect, organize, and store data for analysis if you understand your company's requirements.

Obtain feedback from business stakeholders to create conceptual and logical data models tailored to the company's needs. Collect data requirements from business analysts and other subject matter experts to aid in developing more comprehensive logical and physical models from the higher-level models and business requirements. Data models must change in response to changes in business and technology.

As a result, a thorough grasp of the company, its needs, goals, expected outcomes, and the intended application of the data modeling mission's outputs is a critical data modeling technique to follow.

According to IBM, “Data models are built around business needs. Rules and requirements are defined upfront through feedback from business stakeholders so they can be incorporated into the design of a new system or adapted in the iteration of an existing one.”


Distinguish Between Facts, Dimensions, Filters, and Order when Dealing with Business Enquiries

Understanding how these four parts characterize business questions will help you organize data in ways that make providing answers easier. For example, you may make locating the top sales performers per sales period easier and answer other business intelligence queries by structuring your data using different tables for facts and dimensions.


Before Continuing, Double-Check Each Stage of your Data Modelling.

Before going on to the next stage, each action should be double-checked, beginning with the data modeling priorities derived from the business requirements. For example, a dataset's main key must be chosen so that the primary key's value in each record may be used to identify each in the dataset uniquely. The same data modeling technique can check that joining two datasets is either one-to-one or one-to-many and avoid many-to-many interactions that lead to too complicated or unmanageable data models.


Instead of Just Looking for Correlation, Look for Causation

Data modeling best practices offers instructions on how to use the modeled data. While allowing end-users to access business intelligence on their own is a significant step forward, it's equally critical that they don't make mistakes.

They may notice, for example, that sales of two different products appear to grow and fall in lockstep. Are sales of one product driving sales of the other, or do they rise and fall in lockstep due to another factor like the economy or weather? Confusing causality and correlation could lead businesses to lose resources by focusing on the wrong or non-existent possibilities.

Summing Up

Data modeling can assist companies in quickly acquiring answers to their business concerns, improving productivity, profitability, efficiency, and customer happiness, among other things. Linking to corporate needs and objectives and employing tools to speed up the procedures in preparing data for replies to all inquiries are critical success elements and part of data modeling techniques.

Once these prerequisites are met, you can anticipate your data modeling to provide significant business value to you and your company, whether small, medium, or large.

Frequently Asked Questions


What are some of the crucial data modeling techniques?

There are many crucial data modeling techniques in the business. Some of them are:

  • Hierarchical data model
  • Network data model
  • Relational data model
  • Object-oriented data model
  • Entity-relationship data model
  • Data model with dimensions
  • Data model based on graphs


What are data modeling techniques?

Data modeling is optimizing data to streamline information flow inside businesses for various business needs. It improves analytics by formatting data and its attributes, creating links between data, and organizing data.


Why is data modeling important?

Data modeling is essential as a clear representation of data makes it easier to analyze it correctly. Also, it helps stakeholders to make data-driven decisions as data modeling improves data quality.

Spotlight

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OTHER ARTICLES
DATA SCIENCE

Data Mining Techniques and How Businesses Implement Them

Article | March 31, 2022

Businesses have more data than ever in contemporary times because of rapidly evolving technology. Data is one of the most valuable resources available to any business or entrepreneur. Businesses today are overflowing with data from a wide range of sources, including websites, mobile devices, social media, and other digital channels, but they don’t know where to start. It doesn't matter if you have massive data; what matters is what you do with it. This is where data mining comes into play. Turning unstructured data into insights helps companies accomplish their goals and improvement strategy for the future. These days, data mining techniques are widely used by organizations from different industries. “With data collection, ‘the sooner the better’ is always the best answer.” - Marissa Mayer, Ex-CEO of Yahoo! Let’s explore some of the top data mining techniques used by businesses. What are the Must-Have Data Mining Techniques? Data mining is a successful process that uses a variety of data mining approaches. The problem is determining which data mining techniques are most appropriate for your situation and business. Even though many data mining techniques are often required to uncover insights hidden inside massive datasets, it's wise to use more than one. Data Cleaning When it comes to data mining, data cleaning is a must-have approach. For raw data to be used for various sorts of analytical techniques, it must first be cleaned, formatted, and analyzed. This data mining technique is used in data modeling, transformation, aggregation, and migration processes. Clustering Analysis Clustering analysis is the recognition and clustering of related data from an big data collection. Clustering analysis can assist an organization in evaluating the differences and similarities in data from the perspective of a company. This can help them develop customer personas, similar segment leads, and others. Association Rule Discovery This is a technique for discovering interesting connections and interdependencies among variables in big datasets. This data mining technique can help decipher hidden data patterns that would otherwise go unnoticed. Association rule discovery a term that's frequently used in machine learning. Classification Analysis The technique of extracting information about the data is called classification analysis. It is the most complicated data mining technique. Data classification involves splitting data into categories that have similarities in their context. As a result, classification analysis is useful in combination with clustering analysis. The structure or recognition of the data is known as classification. Data Visualization Data visualization uses real-time graphs and charts to provide users with extra insights into their data and help them better grasp performance targets. Data visualization is a popular data mining technique because it can get data from any source, such as file uploads, database queries, and application connections. Top Industries Using Data Mining Data mining in business can help you manage risk by detecting fraud, errors, and discrepancies that can result in revenue loss and reputation damage. Data mining is used in various industries to gain a deeper understanding of their customers and businesses. Many brands in various industries perfectly portray how data mining is used in business. Finance and Banking Using data mining approaches, financial organizations gather information about loans and credit reports. Financial institutions can evaluate if a lender has a good or bad credit score by using an analysis model based on historical financial data. Banks can also keep a watch on suspicious or fraudulent transactions with the help of data mining techniques. E-commerce E-commerce platforms are among the most well-known examples of data mining and business analytics. Many e-commerce websites utilize data mining and business intelligence to provide cross-sells and up-sells. Amazon is, of course, one of the most popular users of data mining and business intelligence. Retail Customers are segmented into 'recency, frequency, and monetary' (RFM) categories by retailers, focusing on marketing to those segments. A consumer who spends little but frequently and recently will be treated differently from one who spends a lot just once, which was some time ago. Loyalty, up-sell, and cross-sell offers may be made to the frequent buyer, while the big spender may provide a win-back deal. Top 3 Companies Leveraging Data Mining Techniques Businesses use data mining to boost revenue, save expenses, locate consumers, improve customer experience, listen to what others have to say, and conduct competitive intelligence. These are just a few ways of using data mining techniques. Here are the top three examples of data mining in business. Amazon Amazon is gathering competitive intelligence and pricing data from its competitors. Consumers who use the Amazon Price Check Mobile App to scan items in-store, capture an image of the product, or do a text search to find the best price will get a $5 discount. The application also encourages customers to report the in-store price. Netflix House of Cards – the American thriller series was an ideal entertainment experience for creating data models and discovering what makes a show or movie successful among viewers based on the insights data gathered. They went all out for the license, winning a bidding battle with rival businesses and instantly scheduling two seasons before presenting a single episode. It was a super hit, and the best thing is that they had a pretty good idea of what it would be. Walmart The current search engine of Walmart contains semantic data. Polaris, an in-house platform, uses text analysis, machine learning, and even synonym mining to provide relevant search results. Walmart says that integrating semantic search has led to a 10% to 15% increase in the number of people who buy things online. Conclusion Data mining's ultimate significance for data-driven growth and progress cannot be overstated. Using the right data mining technique will give you unparalleled insight into your massive data. Data mining will only get better as technology improves, which will allow for more in-depth analysis. FAQ What are the 3 types of data mining? Pictorial data mining, text mining, social media mining, online mining, and audio and video mining are only a few examples of data mining. What are the 6 processes of data mining? Data mining is both an analytical process and a collection of algorithms and models. The CRISP-DM process model has been broken down, just like the CIA Intelligence Process. These parts are business understanding, data understanding, data preparation, modeling, evaluation, and deployment. What are the most commonly used data mining processes? Clustering, data cleansing, association, data warehousing, machine learning, data visualization, classification, neural networks, and prediction are just a few of the important data mining techniques to consider when starting out in the industry.

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BUSINESS INTELLIGENCE

Power up Your Game with Robust BI Tools & Techniques

Article | March 30, 2022

With more data at our fingertips, it’s difficult to focus on the relevant information and present it in an actionable way. From sales executives to the C-suite, everyone wants to use data to their advantage. According to Sigma, 88% of executives feel the urgency to invest in big data. Business intelligence tools make it easier to gather the right data and visualize it in a manner that helps understand its meaning. Business intelligence (BI) deployment also brings additional value to the business in every vertical. BI tools provide insights from structured data for data-driven decisions. According to Google's Head of Marketing, Nic Smith, "BI is about providing the right data at the right time to the right people so that they can make the right decisions." Selecting an accurate business intelligence tool is tough. With so many BI tools competing for attention, even the most tech-savvy can become paralyzed. 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Mobile Business Intelligence Mobile business intelligence makes BI apps and dashboards accessible on smartphones and tablets. Mobile BI tools are often developed keeping in mind the ease of use, which is more about displaying data than analyzing it. Software-as-a-Service BI SaaS BI tools are also known as cloud BI tools. They use vendor-hosted cloud computing platforms to provide customers with data analysis tools in the form of a subscription-based service. Online Analytical Processing (OLAP) Tools One of the oldest BI technologies, the OLAP tool, helps users analyze data across multiple dimensions, specifically tailored to complex queries and calculations. 3 Industries That Have Benefited by Business Intelligence tools and Techniques As business intelligence technology advances, more BI tools will become available, resulting in broad use across industries. The industries listed below have established themselves and are now at par with companies that are already reaping benefits from BI tools on a regular basis. E-commerce E-commerce is one industry that has greatly benefited from business intelligence tools and techniques. To improve their supply chain, e-commerce giants like Amazon must constantly monitor and analyze data. Amazon, in particular, has a huge supply chain that involves 11 marketplaces and sells over 3 billion products. Retail Business intelligence is used by players in the retail industry to help them recognize and target potential consumers and strengthen their relations with existing customers. Retail businesses can use business intelligence tools to combine data from CRMs, ERPs, and other systems to get a comprehensive, and clear idea of their consumers. Entertainment Today, companies in the media and entertainment industries help you narrow down your search for TV shows, movies, music, and other media by making intelligent suggestions. Business intelligence is used by media streaming giants like Netflix and Spotify to generate a list of recommended movies, shows, and songs based on the customer's preferences and streaming history. Top Business Intelligence Tools Choosing the best business intelligence tools is a personal decision based on your company's requirements. Are you looking for a business intelligence tool that lets you make interactive data visualizations or a tool that lets you do in-depth financial data analysis? While each business's requirements are different and unique, there are several business intelligence tools that work well across a wide range of businesses and industries. Here we have made a list of business intelligence tools named in Gartner’s Magic Quadrants 2021. 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Is there a need to modify existing IT systems to integrate them with BI tools? The implementation of a BI system does not require the intervention of any existing systems. Some systems need more configuration to connect them to business intelligence tools. What is the time frame required to implement a business intelligence system? It depends on the scope of the project. Implementing business intelligence tools might take anywhere from a few weeks to several months. Many companies begin to see the benefits and decide to expand their system by adding new areas and features.

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BUSINESS INTELLIGENCE

7 Reasons Why Business Intelligence (BI) is Crucial

Article | March 30, 2022

In today’s digital and customer-centric world, businesses are facing stiff competition. Most of these businesses are bombarded with information and are actively exploring ways to derive significant insights and control from the data gathered. For businesses to resolve the issue of data overloading, obtain a competitive edge in the market, and make informed decisions, there is a need to adopt business intelligence. Unfortunately, even with the long list of benefits and the increasing number of users, most companies are very slow in adopting it. Business intelligence empowers you to combine the power of technology and business expertise to make informed decisions and outplay competitors. According to Techjury, more than 46% of businesses are already using a business intelligence tool as a core part of their business strategy. Swain Scheps rightly highlights the importance of business intelligence in his quote: “Business intelligence is essentially timely, accurate, high-value, and actionable business insights, and the work processes and technologies used to obtain them.” Business Intelligence VS Business Analytics Business intelligence and business analytics are often considered synonyms with the same meaning, definition, and method of working, but that's not the case. Business intelligence refers to technologies and strategies developed by enterprise industries to analyze existing business data and provide historical, current, and predictive events for business operations. Present-day businesses are widely accepting business intelligence technologies. Business analytics is the process of technologies and strategies utilized to continue analyzing and extracting insights and performance from historical business data to drive successful future business planning. There is also a long list of the importance of business analytics. Common Challenges Faced by Today’s C-Suite The responsibility of the C-Suite and the CEO, in particular, is to accelerate the growth of a company and work towards achieving industrial excellence. They face immense pressure from various stakeholders who sometimes have theoretical expectations regarding the performance of the company and its results. Let’s check out some of the common challenges faced by the C-Suite. Expectations for Growth Acceleration Driving growth and achieving a significant increase in the profit margin annually are among the top challenges faced by today’s C-suite. In the event of continued failure in achieving this goal, CEOs can affect their record. Business intelligence solutions analyze all the company data and assist the C-suite in making informed decisions. They also help in accelerating the growth of the organization by optimizing internal business processes, enhancing operational efficiency, gaining a competitive edge, and others. By extracting important information from unstructured data and turning it into useful information, BI helps to speed up the process. Stakeholder’s Demands Stakeholders can sometimes demand theoretical or special reports and data. Failure to fulfill this demand can upset the stakeholders. While business intelligence tools may not help you meet the special demands of your stakeholders, but it will certainly help you analyse and explain why a particular target could not be achieved. Moreover, it also keeps track of all the activities, your decisions, and how the company has performed, which will reflect your efforts and incremental progress to the stakeholders. Budgetary Restrictions According to Betsy Burton, vice president and distinguished analyst with Gartner, the cost of BI tools is high, which limits their implementation in businesses with limited budget access, such as small to mid-sized companies. Despite the demand and need for business intelligence, often a minimum portion of the operating budget is allocated for the improvement and upgradation of data analytics and the business intelligence systems. As a result, progress is not made, benefits of business intelligence are not reaped, and the cycle of challenges continues in the C-suite. In this case, businesses can either explore adopting business intelligence tools in phases, or they can opt for self-service BI or embedded BI tools, which are more affordable and can be easily integrated with existing systems. How Can Business Intelligence Make a Difference? Not only enterprise companies, but even small, mid-sized, and large businesses can benefit from business intelligence. Adopting business intelligence technologies has numerous benefits. Here are the top seven reasons why having business intelligence (BI) is crucial. Gain Customer Insights With the help of business intelligence, businesses can analyze their customers’ buying patterns to obtain customer insights and create user profiles as per their behavior. Customer insights will help businesses create better products and enhance the product experience for their customers. Improved Efficiency Across the Organization Having an effective business intelligence system significantly improves the efficiency of the overall business processes and has a positive impact on revenue. In addition, access to meaningful insights reduces the waiting time for reports and increases team productivity. Gain Sales and Market Intelligence If you are a sales executive or a marketer, you probably keep track of your customers with the help of a CRM solution. A CRM solution aims to collect all the data and make sense of the data about your customers through charts and tables. Insights into Consumer Behavior One of the significant benefits of investing in business intelligence is that it increases the ability to analyze and understand customer behavior. It will highlight a customer’s buying behavior and highlight changes in behavioral patterns. Improved Business Operations Visibility Understanding the importance of business intelligence helps control business processes. It helps to assess what is going on in a business carefully. Active vigilance over processes and standard procedures can help to fix errors. Return on Investment (ROI) Business intelligence helps a company get a better return on its investment (ROI) by improving strategic awareness, speeding up reporting, cutting operating costs, and getting better quality data. Gives a Competitive Edge Apart from all the other benefits of business intelligence, having the potential to handle and analyze enormous amounts of data is in itself a competitive advantage. Furthermore, budgeting, planning, and forecasting are effective ways to keep up with the competition, go well beyond ordinary analysis, and are simple to implement with business intelligence tools. Final Thoughts Understanding the importance of business intelligence and having a great business intelligence system has become quite essential for businesses these days. Business intelligence is much more than just graphical representation. It is a set of tools that businesses can use to help their employees succeed. BI can change your business by providing the information required to make fast and informed decisions. FAQ Will my business data be secure? Any IT system must have data security and availability as their top priority. A business intelligence solution should provide the high standards of performance, reliability, and security. To keep the data safe, credible business intelligence solutions make use of existing security infrastructures. My business has already invested in CRM, Accounting, and Marketing Software. So, why should I also invest in Business Intelligence? While you may utilize a variety of line-of-business systems to administer your company, BI is about integrating data from numerous sources in an organized way to graphically represent information in a meaningful way. A constructive business intelligence solution should be able to connect to daily business software with ease. Why Is BI Reporting Better Than Conventional MIS Reports? Management reporting is only a small part of business intelligence. It gives you real-time, quick, and easy access to actionable business information about customers, goods, finance, and the market.

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BIG DATA MANAGEMENT

How is Big Data Analytics shaping up Internet of Things?

Article | March 26, 2022

The Internet of Things (IoT) is a new paradigm that has transformed the traditional way of living into a complete tech lifestyle. From smart phones to smart cities, everything has been transformed with the integration of the IoT. This new technology has made it possible to put more computer power into small devices, which can help the people who use them and also extract a lot of data in real-time. The data generated from IoT devices can be helpful if thoroughly analyzed. This is where data analytics comes into play. Businesses have realized the importance of big data in IoT, and by integrating data analytics, they can leverage the actionable insights to make informed decisions. Integration of big data analytics in IoT has some challenges too. Let’s begin by addressing them. Addressing the IoT Big Data Challenges An increasing number of brands are moving to IoT big data analytics to improve their company’s performance. However, evaluating such large volumes of data comes with some challenges. If these concerns are addressed, it can enhance the output of IoT analytics. Massive Data Management and Storage One of the major challenges of massive data is the effective storage and handling of large volumes of data. The amount of data stored in the company database is constantly increasing because of the growth and regular use of IoT devices. It becomes difficult to manage these data sets as they increase exponentially in real time. The majority of data is collected through sensors and gathered in an unstructured manner. Modern techniques are helping in the management of large volumes of data, and businesses are now understanding how these technologies have assisted them in overcoming this challenge. Deduplication helps remove duplicate and undesirable data from data sets using modern tools that condense huge data sets. It also ensures that all data is separated and stored in the most precise location. Data Reliability The entire system goes offline when there is no power supply or when the local internet service provider goes down. Data centres, which are required for most IoT systems to function properly, can also be damaged by natural disasters and other crises. Low-power and offline compatibility are two qualities that should be highlighted in the IoT market for such situations. However, in the business world, reliable devices and systems are required to perform even in unpredictable situations. Privacy Encryption protects the majority of online connections. Despite this, many companies continue to avoid intentionally utilizing encryption. It's risky to keep sensitive digital information such as user accounts, passwords, and personal information in plain text files. Unencrypted data can be collected, transmitted, and used by IoT platforms, making it vulnerable. Devices or systems designed and maintained by inexperienced developers are a huge risk. In this case, erasing data is one of the best ways to truly safeguard and respect the privacy of all parties involved. Another way to keep your data safe is to use strong encryption and keep a limited number of access points and gateways. How Integrating Big Data Analytics with IoT can Benefit Your Business? The role of big data in IoT has become important because it has helped businesses across industries to make more efficient and well-informed decisions. It also allows them to provide better services and products. Companies can use IoT with big data to analyze data, find trends, identify unseen data patterns, uncover hidden data correlations, and discover new information. Enable Personalization As customer awareness grows, internet penetration develops, and IoT big data analytics is adopted, businesses strive to deliver personalized products rather than a one-size-fits-all solution. As a result, businesses can now tailor their products to meet customer’s demands and preferences by reinventing the product creation process. Enhance Productivity Improving productivity is one of the ways that IoT data analytics can benefit your company. By deploying smart sensors and devices across your premises, you can gather employee engagement statistics, performance evaluations, and a variety of other work-related parameters. You can use this data to help simplify your organization's day-to-day business processes and make better use of staff energy and time. Product Improvement Opportunities The C-suite and entrepreneurs can use IoT analytics parameters to support them in creating the next generation of products. By putting smart devices into your products, you will be able to analyze your customers’ usage patterns and detect design flaws better. It allows you to make necessary improvements. Boost Your ROI In IoT big data analytics, the IoT enables businesses to extract information to gain better actionable insights. Better business insights assist in making better decisions that yield a high return on investment (ROI). The cost of implementing big data cloud storage has dropped because there has been a rise in the need for data storage. Big Data Analytics IoT Case Study Many businesses are switching to IoT big data analytics to obtain a competitive edge and unlock exceptional growth opportunities. Here we have mentioned a success story of Bayer Crop Science. Bayer Crop Science Uses AWS IoT Core Bayer Crop Science, a division of Bayer, offers a variety of products and services to help farmers worldwide maximize crop yield and practice sustainable agriculture. The company uses IoT devices on harvesting equipment to track agricultural attributes, which are then manually transferred to its data centers for processing over many days. Due to a lack of real-time data collection and analytics, Bayer was unable to resolve issues related to equipment calibration, jamming, or deviations in time to help with subsequent run routing plans. Bayer's IoT team, already an AWS client, opted to shift their data collection and analysis pipeline to AWS IoT Core. The business designed a new IoT pipeline to manage the gathering, processing, and analysis of seed-growing data. During sowing or harvest season, the new solution takes numerous gigabytes of data from the company's research fields globally, at an average of one million attributes every day. This data is supplied in near real-time to Bayer's data analysts. The AWS IoT solution also includes a powerful edge processing and analytics platform that can be scaled across many IoT use cases and projects. Conclusion Due to the rising importance of big data in the IoT, organizations are becoming more enthusiastic about IoT big data analytics. IoT and big data have revolutionized how companies gain insights, make decisions, and transform their consumers' lives, making them faster and smarter. Even though IoT and big data analytics still have certain challenges to overcome, brands are using them because of their features. FAQ How much data do IoT sensors collect? Sensors gather data about the physical environment. Devices can share data with centralized systems and other devices. According to IDC, by 2025, IoT devices will generate 79.4 zettabytes of data. What are “big data sensors”? Big data sensing is a new concept and a future technology trend. It impacts sensor-based applications such as smart cities, disaster control, health care, environmental protection, and climate change research. Which is better: IoT or cloud computing? The cloud supports the implementation of IoT applications by increasing efficiency, accuracy, and speed. IoT application development is facilitated by cloud computing, but IoT is not cloud computing.

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DATA SCIENCE

Data Mining Techniques and How Businesses Implement Them

Article | March 31, 2022

Businesses have more data than ever in contemporary times because of rapidly evolving technology. Data is one of the most valuable resources available to any business or entrepreneur. Businesses today are overflowing with data from a wide range of sources, including websites, mobile devices, social media, and other digital channels, but they don’t know where to start. It doesn't matter if you have massive data; what matters is what you do with it. This is where data mining comes into play. Turning unstructured data into insights helps companies accomplish their goals and improvement strategy for the future. These days, data mining techniques are widely used by organizations from different industries. “With data collection, ‘the sooner the better’ is always the best answer.” - Marissa Mayer, Ex-CEO of Yahoo! Let’s explore some of the top data mining techniques used by businesses. What are the Must-Have Data Mining Techniques? Data mining is a successful process that uses a variety of data mining approaches. The problem is determining which data mining techniques are most appropriate for your situation and business. Even though many data mining techniques are often required to uncover insights hidden inside massive datasets, it's wise to use more than one. Data Cleaning When it comes to data mining, data cleaning is a must-have approach. For raw data to be used for various sorts of analytical techniques, it must first be cleaned, formatted, and analyzed. This data mining technique is used in data modeling, transformation, aggregation, and migration processes. Clustering Analysis Clustering analysis is the recognition and clustering of related data from an big data collection. Clustering analysis can assist an organization in evaluating the differences and similarities in data from the perspective of a company. This can help them develop customer personas, similar segment leads, and others. Association Rule Discovery This is a technique for discovering interesting connections and interdependencies among variables in big datasets. This data mining technique can help decipher hidden data patterns that would otherwise go unnoticed. Association rule discovery a term that's frequently used in machine learning. Classification Analysis The technique of extracting information about the data is called classification analysis. It is the most complicated data mining technique. Data classification involves splitting data into categories that have similarities in their context. As a result, classification analysis is useful in combination with clustering analysis. The structure or recognition of the data is known as classification. Data Visualization Data visualization uses real-time graphs and charts to provide users with extra insights into their data and help them better grasp performance targets. Data visualization is a popular data mining technique because it can get data from any source, such as file uploads, database queries, and application connections. Top Industries Using Data Mining Data mining in business can help you manage risk by detecting fraud, errors, and discrepancies that can result in revenue loss and reputation damage. Data mining is used in various industries to gain a deeper understanding of their customers and businesses. Many brands in various industries perfectly portray how data mining is used in business. Finance and Banking Using data mining approaches, financial organizations gather information about loans and credit reports. Financial institutions can evaluate if a lender has a good or bad credit score by using an analysis model based on historical financial data. Banks can also keep a watch on suspicious or fraudulent transactions with the help of data mining techniques. E-commerce E-commerce platforms are among the most well-known examples of data mining and business analytics. Many e-commerce websites utilize data mining and business intelligence to provide cross-sells and up-sells. Amazon is, of course, one of the most popular users of data mining and business intelligence. Retail Customers are segmented into 'recency, frequency, and monetary' (RFM) categories by retailers, focusing on marketing to those segments. A consumer who spends little but frequently and recently will be treated differently from one who spends a lot just once, which was some time ago. Loyalty, up-sell, and cross-sell offers may be made to the frequent buyer, while the big spender may provide a win-back deal. Top 3 Companies Leveraging Data Mining Techniques Businesses use data mining to boost revenue, save expenses, locate consumers, improve customer experience, listen to what others have to say, and conduct competitive intelligence. These are just a few ways of using data mining techniques. Here are the top three examples of data mining in business. Amazon Amazon is gathering competitive intelligence and pricing data from its competitors. Consumers who use the Amazon Price Check Mobile App to scan items in-store, capture an image of the product, or do a text search to find the best price will get a $5 discount. The application also encourages customers to report the in-store price. Netflix House of Cards – the American thriller series was an ideal entertainment experience for creating data models and discovering what makes a show or movie successful among viewers based on the insights data gathered. They went all out for the license, winning a bidding battle with rival businesses and instantly scheduling two seasons before presenting a single episode. It was a super hit, and the best thing is that they had a pretty good idea of what it would be. Walmart The current search engine of Walmart contains semantic data. Polaris, an in-house platform, uses text analysis, machine learning, and even synonym mining to provide relevant search results. Walmart says that integrating semantic search has led to a 10% to 15% increase in the number of people who buy things online. Conclusion Data mining's ultimate significance for data-driven growth and progress cannot be overstated. Using the right data mining technique will give you unparalleled insight into your massive data. Data mining will only get better as technology improves, which will allow for more in-depth analysis. FAQ What are the 3 types of data mining? Pictorial data mining, text mining, social media mining, online mining, and audio and video mining are only a few examples of data mining. What are the 6 processes of data mining? Data mining is both an analytical process and a collection of algorithms and models. The CRISP-DM process model has been broken down, just like the CIA Intelligence Process. These parts are business understanding, data understanding, data preparation, modeling, evaluation, and deployment. What are the most commonly used data mining processes? Clustering, data cleansing, association, data warehousing, machine learning, data visualization, classification, neural networks, and prediction are just a few of the important data mining techniques to consider when starting out in the industry.

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BUSINESS INTELLIGENCE

Power up Your Game with Robust BI Tools & Techniques

Article | March 30, 2022

With more data at our fingertips, it’s difficult to focus on the relevant information and present it in an actionable way. From sales executives to the C-suite, everyone wants to use data to their advantage. According to Sigma, 88% of executives feel the urgency to invest in big data. Business intelligence tools make it easier to gather the right data and visualize it in a manner that helps understand its meaning. Business intelligence (BI) deployment also brings additional value to the business in every vertical. BI tools provide insights from structured data for data-driven decisions. According to Google's Head of Marketing, Nic Smith, "BI is about providing the right data at the right time to the right people so that they can make the right decisions." Selecting an accurate business intelligence tool is tough. With so many BI tools competing for attention, even the most tech-savvy can become paralyzed. To choose an appropriate tool for business intelligence, you must first understand the types of tools available. Here is a list of the five most commonly used types of business intelligence tools. Types of Business Intelligence Tools Many existing business intelligence techniques and tools share similar features. The way business intelligence is used to improve decision-making is unique to each implementation. The types of business intelligence tools are mentioned below: Real-Time BI In a real-time business intelligence tool, data is analyzed as soon as it is produced, gathered, and processed so that users can get an up-to-date view of company operations, consumer behavior, financial markets, and other areas. Embedded BI Embedded business intelligence tools integrate BI and data visualization into business software. This allows business users to examine data within the systems that they use on a regular basis. Mobile Business Intelligence Mobile business intelligence makes BI apps and dashboards accessible on smartphones and tablets. Mobile BI tools are often developed keeping in mind the ease of use, which is more about displaying data than analyzing it. Software-as-a-Service BI SaaS BI tools are also known as cloud BI tools. They use vendor-hosted cloud computing platforms to provide customers with data analysis tools in the form of a subscription-based service. Online Analytical Processing (OLAP) Tools One of the oldest BI technologies, the OLAP tool, helps users analyze data across multiple dimensions, specifically tailored to complex queries and calculations. 3 Industries That Have Benefited by Business Intelligence tools and Techniques As business intelligence technology advances, more BI tools will become available, resulting in broad use across industries. The industries listed below have established themselves and are now at par with companies that are already reaping benefits from BI tools on a regular basis. E-commerce E-commerce is one industry that has greatly benefited from business intelligence tools and techniques. To improve their supply chain, e-commerce giants like Amazon must constantly monitor and analyze data. Amazon, in particular, has a huge supply chain that involves 11 marketplaces and sells over 3 billion products. Retail Business intelligence is used by players in the retail industry to help them recognize and target potential consumers and strengthen their relations with existing customers. Retail businesses can use business intelligence tools to combine data from CRMs, ERPs, and other systems to get a comprehensive, and clear idea of their consumers. Entertainment Today, companies in the media and entertainment industries help you narrow down your search for TV shows, movies, music, and other media by making intelligent suggestions. Business intelligence is used by media streaming giants like Netflix and Spotify to generate a list of recommended movies, shows, and songs based on the customer's preferences and streaming history. Top Business Intelligence Tools Choosing the best business intelligence tools is a personal decision based on your company's requirements. Are you looking for a business intelligence tool that lets you make interactive data visualizations or a tool that lets you do in-depth financial data analysis? While each business's requirements are different and unique, there are several business intelligence tools that work well across a wide range of businesses and industries. Here we have made a list of business intelligence tools named in Gartner’s Magic Quadrants 2021. Board Board International is made up of three tools, which work together. They are business intelligence, predictive analytics, and performance management. Domo Domo is a cloud-based platform that is easy to use and focuses on business-user-deployment dashboards. Microsoft Power BI Microsoft Power BI is one of the most popular BI tools available in the market. It’s easy for clients to use the Power BI app to analyze and visualize data from local or cloud sources, publishing their reports to the Power BI platform. Oracle Analytics Cloud Conversational analytics can be used in the Oracle analytics cloud to answer questions in natural language. It can also automatically generate natural language explanations to help people understand visualizations and trends. SAS Visual Analytics The SAS Visual Analytics tool aims to highlight critical correlations in datasets. In the new edition, there are automated suggestions for relevant factors, as well as visualizations and insights from natural language. Conclusion These business intelligence tools can help with a wide range of tasks inside a company. Off-the-shelf technologies necessarily focus on wide appeal instead of specialized features, since a single tool cannot accomplish all the task. There are several types of BI tools available on the market; businesses can explore and test them before making a final choice on which one to incorporate. Business intelligence is a flexible and effective tool that can be used in almost every industry. FAQ Will BI systems integrate with existing systems? Providers of business intelligence solutions understand the need to integrate data from many platforms. To connect directly with the existing systems and databases, they require a multitude of specialized drivers. Every new business intelligence system version adds to the list of accessible drivers. Is there a need to modify existing IT systems to integrate them with BI tools? The implementation of a BI system does not require the intervention of any existing systems. Some systems need more configuration to connect them to business intelligence tools. What is the time frame required to implement a business intelligence system? It depends on the scope of the project. Implementing business intelligence tools might take anywhere from a few weeks to several months. Many companies begin to see the benefits and decide to expand their system by adding new areas and features.

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BUSINESS INTELLIGENCE

7 Reasons Why Business Intelligence (BI) is Crucial

Article | March 30, 2022

In today’s digital and customer-centric world, businesses are facing stiff competition. Most of these businesses are bombarded with information and are actively exploring ways to derive significant insights and control from the data gathered. For businesses to resolve the issue of data overloading, obtain a competitive edge in the market, and make informed decisions, there is a need to adopt business intelligence. Unfortunately, even with the long list of benefits and the increasing number of users, most companies are very slow in adopting it. Business intelligence empowers you to combine the power of technology and business expertise to make informed decisions and outplay competitors. According to Techjury, more than 46% of businesses are already using a business intelligence tool as a core part of their business strategy. Swain Scheps rightly highlights the importance of business intelligence in his quote: “Business intelligence is essentially timely, accurate, high-value, and actionable business insights, and the work processes and technologies used to obtain them.” Business Intelligence VS Business Analytics Business intelligence and business analytics are often considered synonyms with the same meaning, definition, and method of working, but that's not the case. Business intelligence refers to technologies and strategies developed by enterprise industries to analyze existing business data and provide historical, current, and predictive events for business operations. Present-day businesses are widely accepting business intelligence technologies. Business analytics is the process of technologies and strategies utilized to continue analyzing and extracting insights and performance from historical business data to drive successful future business planning. There is also a long list of the importance of business analytics. Common Challenges Faced by Today’s C-Suite The responsibility of the C-Suite and the CEO, in particular, is to accelerate the growth of a company and work towards achieving industrial excellence. They face immense pressure from various stakeholders who sometimes have theoretical expectations regarding the performance of the company and its results. Let’s check out some of the common challenges faced by the C-Suite. Expectations for Growth Acceleration Driving growth and achieving a significant increase in the profit margin annually are among the top challenges faced by today’s C-suite. In the event of continued failure in achieving this goal, CEOs can affect their record. Business intelligence solutions analyze all the company data and assist the C-suite in making informed decisions. They also help in accelerating the growth of the organization by optimizing internal business processes, enhancing operational efficiency, gaining a competitive edge, and others. By extracting important information from unstructured data and turning it into useful information, BI helps to speed up the process. Stakeholder’s Demands Stakeholders can sometimes demand theoretical or special reports and data. Failure to fulfill this demand can upset the stakeholders. While business intelligence tools may not help you meet the special demands of your stakeholders, but it will certainly help you analyse and explain why a particular target could not be achieved. Moreover, it also keeps track of all the activities, your decisions, and how the company has performed, which will reflect your efforts and incremental progress to the stakeholders. Budgetary Restrictions According to Betsy Burton, vice president and distinguished analyst with Gartner, the cost of BI tools is high, which limits their implementation in businesses with limited budget access, such as small to mid-sized companies. Despite the demand and need for business intelligence, often a minimum portion of the operating budget is allocated for the improvement and upgradation of data analytics and the business intelligence systems. As a result, progress is not made, benefits of business intelligence are not reaped, and the cycle of challenges continues in the C-suite. In this case, businesses can either explore adopting business intelligence tools in phases, or they can opt for self-service BI or embedded BI tools, which are more affordable and can be easily integrated with existing systems. How Can Business Intelligence Make a Difference? Not only enterprise companies, but even small, mid-sized, and large businesses can benefit from business intelligence. Adopting business intelligence technologies has numerous benefits. Here are the top seven reasons why having business intelligence (BI) is crucial. Gain Customer Insights With the help of business intelligence, businesses can analyze their customers’ buying patterns to obtain customer insights and create user profiles as per their behavior. Customer insights will help businesses create better products and enhance the product experience for their customers. Improved Efficiency Across the Organization Having an effective business intelligence system significantly improves the efficiency of the overall business processes and has a positive impact on revenue. In addition, access to meaningful insights reduces the waiting time for reports and increases team productivity. Gain Sales and Market Intelligence If you are a sales executive or a marketer, you probably keep track of your customers with the help of a CRM solution. A CRM solution aims to collect all the data and make sense of the data about your customers through charts and tables. Insights into Consumer Behavior One of the significant benefits of investing in business intelligence is that it increases the ability to analyze and understand customer behavior. It will highlight a customer’s buying behavior and highlight changes in behavioral patterns. Improved Business Operations Visibility Understanding the importance of business intelligence helps control business processes. It helps to assess what is going on in a business carefully. Active vigilance over processes and standard procedures can help to fix errors. Return on Investment (ROI) Business intelligence helps a company get a better return on its investment (ROI) by improving strategic awareness, speeding up reporting, cutting operating costs, and getting better quality data. Gives a Competitive Edge Apart from all the other benefits of business intelligence, having the potential to handle and analyze enormous amounts of data is in itself a competitive advantage. Furthermore, budgeting, planning, and forecasting are effective ways to keep up with the competition, go well beyond ordinary analysis, and are simple to implement with business intelligence tools. Final Thoughts Understanding the importance of business intelligence and having a great business intelligence system has become quite essential for businesses these days. Business intelligence is much more than just graphical representation. It is a set of tools that businesses can use to help their employees succeed. BI can change your business by providing the information required to make fast and informed decisions. FAQ Will my business data be secure? Any IT system must have data security and availability as their top priority. A business intelligence solution should provide the high standards of performance, reliability, and security. To keep the data safe, credible business intelligence solutions make use of existing security infrastructures. My business has already invested in CRM, Accounting, and Marketing Software. So, why should I also invest in Business Intelligence? While you may utilize a variety of line-of-business systems to administer your company, BI is about integrating data from numerous sources in an organized way to graphically represent information in a meaningful way. A constructive business intelligence solution should be able to connect to daily business software with ease. Why Is BI Reporting Better Than Conventional MIS Reports? Management reporting is only a small part of business intelligence. It gives you real-time, quick, and easy access to actionable business information about customers, goods, finance, and the market.

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BIG DATA MANAGEMENT

How is Big Data Analytics shaping up Internet of Things?

Article | March 26, 2022

The Internet of Things (IoT) is a new paradigm that has transformed the traditional way of living into a complete tech lifestyle. From smart phones to smart cities, everything has been transformed with the integration of the IoT. This new technology has made it possible to put more computer power into small devices, which can help the people who use them and also extract a lot of data in real-time. The data generated from IoT devices can be helpful if thoroughly analyzed. This is where data analytics comes into play. Businesses have realized the importance of big data in IoT, and by integrating data analytics, they can leverage the actionable insights to make informed decisions. Integration of big data analytics in IoT has some challenges too. Let’s begin by addressing them. Addressing the IoT Big Data Challenges An increasing number of brands are moving to IoT big data analytics to improve their company’s performance. However, evaluating such large volumes of data comes with some challenges. If these concerns are addressed, it can enhance the output of IoT analytics. Massive Data Management and Storage One of the major challenges of massive data is the effective storage and handling of large volumes of data. The amount of data stored in the company database is constantly increasing because of the growth and regular use of IoT devices. It becomes difficult to manage these data sets as they increase exponentially in real time. The majority of data is collected through sensors and gathered in an unstructured manner. Modern techniques are helping in the management of large volumes of data, and businesses are now understanding how these technologies have assisted them in overcoming this challenge. Deduplication helps remove duplicate and undesirable data from data sets using modern tools that condense huge data sets. It also ensures that all data is separated and stored in the most precise location. Data Reliability The entire system goes offline when there is no power supply or when the local internet service provider goes down. Data centres, which are required for most IoT systems to function properly, can also be damaged by natural disasters and other crises. Low-power and offline compatibility are two qualities that should be highlighted in the IoT market for such situations. However, in the business world, reliable devices and systems are required to perform even in unpredictable situations. Privacy Encryption protects the majority of online connections. Despite this, many companies continue to avoid intentionally utilizing encryption. It's risky to keep sensitive digital information such as user accounts, passwords, and personal information in plain text files. Unencrypted data can be collected, transmitted, and used by IoT platforms, making it vulnerable. Devices or systems designed and maintained by inexperienced developers are a huge risk. In this case, erasing data is one of the best ways to truly safeguard and respect the privacy of all parties involved. Another way to keep your data safe is to use strong encryption and keep a limited number of access points and gateways. How Integrating Big Data Analytics with IoT can Benefit Your Business? The role of big data in IoT has become important because it has helped businesses across industries to make more efficient and well-informed decisions. It also allows them to provide better services and products. Companies can use IoT with big data to analyze data, find trends, identify unseen data patterns, uncover hidden data correlations, and discover new information. Enable Personalization As customer awareness grows, internet penetration develops, and IoT big data analytics is adopted, businesses strive to deliver personalized products rather than a one-size-fits-all solution. As a result, businesses can now tailor their products to meet customer’s demands and preferences by reinventing the product creation process. Enhance Productivity Improving productivity is one of the ways that IoT data analytics can benefit your company. By deploying smart sensors and devices across your premises, you can gather employee engagement statistics, performance evaluations, and a variety of other work-related parameters. You can use this data to help simplify your organization's day-to-day business processes and make better use of staff energy and time. Product Improvement Opportunities The C-suite and entrepreneurs can use IoT analytics parameters to support them in creating the next generation of products. By putting smart devices into your products, you will be able to analyze your customers’ usage patterns and detect design flaws better. It allows you to make necessary improvements. Boost Your ROI In IoT big data analytics, the IoT enables businesses to extract information to gain better actionable insights. Better business insights assist in making better decisions that yield a high return on investment (ROI). The cost of implementing big data cloud storage has dropped because there has been a rise in the need for data storage. Big Data Analytics IoT Case Study Many businesses are switching to IoT big data analytics to obtain a competitive edge and unlock exceptional growth opportunities. Here we have mentioned a success story of Bayer Crop Science. Bayer Crop Science Uses AWS IoT Core Bayer Crop Science, a division of Bayer, offers a variety of products and services to help farmers worldwide maximize crop yield and practice sustainable agriculture. The company uses IoT devices on harvesting equipment to track agricultural attributes, which are then manually transferred to its data centers for processing over many days. Due to a lack of real-time data collection and analytics, Bayer was unable to resolve issues related to equipment calibration, jamming, or deviations in time to help with subsequent run routing plans. Bayer's IoT team, already an AWS client, opted to shift their data collection and analysis pipeline to AWS IoT Core. The business designed a new IoT pipeline to manage the gathering, processing, and analysis of seed-growing data. During sowing or harvest season, the new solution takes numerous gigabytes of data from the company's research fields globally, at an average of one million attributes every day. This data is supplied in near real-time to Bayer's data analysts. The AWS IoT solution also includes a powerful edge processing and analytics platform that can be scaled across many IoT use cases and projects. Conclusion Due to the rising importance of big data in the IoT, organizations are becoming more enthusiastic about IoT big data analytics. IoT and big data have revolutionized how companies gain insights, make decisions, and transform their consumers' lives, making them faster and smarter. Even though IoT and big data analytics still have certain challenges to overcome, brands are using them because of their features. FAQ How much data do IoT sensors collect? Sensors gather data about the physical environment. Devices can share data with centralized systems and other devices. According to IDC, by 2025, IoT devices will generate 79.4 zettabytes of data. What are “big data sensors”? Big data sensing is a new concept and a future technology trend. It impacts sensor-based applications such as smart cities, disaster control, health care, environmental protection, and climate change research. Which is better: IoT or cloud computing? The cloud supports the implementation of IoT applications by increasing efficiency, accuracy, and speed. IoT application development is facilitated by cloud computing, but IoT is not cloud computing.

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Crowdcast is the leader in the new era of Business Intelligence: Social BI. We're in the business of helping you find out what your people really know about your business. Our revolutionary Social BI solutions bridge the gap between traditional business intelligence and enterprise social network applications. So, for the first time ever, companies can align their people with the purpose of the company-bringing all their insights, plans and experience together-to create insanely accurate business predictions and outcomes. It's all part of our mission to empower decision makers by leveraging the knowledge of their employees, customers and partners. Future business is now everyone's business. And the companies that take advantage of Social BI will be the ones that are going to succeed. Our integrated SBI solutions - a SaaS software platform, consulting services, and on-going support - deliver timely visibility into the true state of your business.

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