Page 38 – DataToBiz

Isha
July 26, 2021
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5 Computer Vision Strategies To Implement For Your Business

As humans, we can easily see, process, and act on something that could be considered a visual input. But how can that be replicated in machines? That is precisely what computer vision aims to do. While there may be limitations for a machine to act like humans, they are quite close when it comes to analyzing and acting as programmed to do. To cut it short, Computer Vision can be described as a process when a computer using artificial intelligence is able to identify and process visuals (like photos and videos), extract insights from them to create an appropriate output that makes the process of decision making simpler. According to this market analysis report conducted in 2020, the global computer vision market size was at a value of USD 10.6 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 7.6% from 2020 to 2027. Before going into the computer vision techniques that you can implement for your business, let us understand the basics. Understanding Computer Vision Computer Vision is enabled with the capability to read, identify, classify and verify objects. The recent developments were facilitated by ML or Machine Learning technology, especially the process that requires iterative learning, and significant updates in computing power, data storage, and high-quality yet inexpensive input devices. Before we look at how firms are making use of this technology through in-house computer vision services or by outsourcing it to an expert computer vision consulting firm, we need to understand what goes into making this technology so different. Computer Vision Process 1. Capturing an image When a digital device like a camera or CCTV captures an image, it is basically creating a digital file consisting of zeros and ones in the computer’s language. 2. Processing the image Algorithms are used to determine basic geometric elements to create images out of the acquired binary data. 3. Analyzing and taking action The final component that makes the process of computer vision application successful is the analysis of the data. The system then acts according to the way it is programmed and notifies the administrator or manager. Computer Vision Basics What is computer vision used for? Is computer vision and machine learning the same? A part of computer vision applies machine learning, and they are both spin-offs from AI. However, computer vision involves tasks like image identification and classification, object detection and tracking which is way different from what ML does. Who is making use of Computer Vision? A number of industries have been using Computer Vision to enhance customer experience, reduce costs and increase security. Some of the major players are retail, manufacturing, surveillance, and weather forecast. Top Computer Vision Techniques Used by Businesses As humans, we tend to start using our vision as soon as we are born. The way we process visuals and process them to understand and act is highly difficult to replicate in machines. And while the field of computer vision has been successful in overcoming challenges so far, there is still a lot that needs to be worked on and optimized. Recent developments in neural networks are deep learning initiatives that have greatly advanced the way these visual recognition systems perform. And while it’s easy to learn about the basics of computer vision, implementing it in the right way irrespective of the industry a business belongs to, is difficult. In such a situation computer vision consulting services seem like the best go-to option to implement this technology. And the top features of the technology that these firms make use of in their decision-making strategies are as follows- Image classification The fact that a computer system can identify, analyze, and act almost like a human sounds great. But for that to happen, the visuals that the input device captures must be classified into a particular category to take action. There are a good number of challenges associated with image classification in computer vision like viewpoint variation, scale variation, intra-class variation, image deformation, image occlusions, illumination conditions, and background clutter. To overcome these challenges, computer vision researchers have derived a data-driven approach to solve this. Instead of trying to specify what one type of image category looks like directly in code, they provide the computer with examples of each image class. Learning algorithms are then developed for the computer to learn about the visual appearance of each class so they can go with the classification easily. Object Detection The task to define objects within images involves placing bounding boxes and labels for individual objects. This differs from image recognition and classification in a way that detection would put objects in a particular box after the image classification has been done. Say for example, if there are multiple cars in an image, all cars need to be detected and put in a bounding box. For classification though, there are just two ways to do it- object bounding or non-object bounding. Object detection is important as it helps the system to understand the image or video and prepare for analysis. The major difference between image recognition and object detection is that the latter has the ability to locate objects within an image or other input visual. This can be applied in a number of ways by businesses or retail stores that implement professional computer vision services for crowd managing, self-driven cars, anomaly detection, face detection, and video surveillance. Object Tracking Object tracking involves estimating the state of the target object present in the scene from the information collected. The process involves two levels. First known as Single Object Tracking where the appearance of the target is tracked. And second, MOT or Multiple Object Tracking where a detection step is necessary to identify the targets that can leave or enter the scene. A major challenge in tracking multiple targets originates from the various interactions between objects that can sometimes also have a similar appearance. In recent years, due to the exponential rise in the research of deep learning methods, there has

Kavika Roy
July 23, 2021
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11 Most Common Issues with Data Integration [Solved]

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

Kavika Roy
July 22, 2021
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How to Choose the Right Data Warehouse Storage?

Do you know that 2.5 quintillion bytes of data are generated each day? It has been found that less than 0.5% of this data is used and the rest of it is just there, scattered around in enterprises. Even with more and more enterprises adopting data-driven technology, not all of them can make most of the data they have. A primary reason for this is the lack of proper data storage arrangements. Huge volumes of data have to be stored, cleaned, processed and analyzed to derive insights that help SMEs make correct decisions. But where and how should you store such vast amounts of data? Ordinary storage systems are no longer effective. That’s where data warehousing has begun. It is hardly a new concept but is gaining more popularity as enterprises are moving towards streamlined business systems. Read further to learn how to choose a data warehouse storage that would be apt for your business requirements. What is Data Warehouse? Simply put, a data warehouse is a place to store historical and real-time data, which is processed and analyzed to help the sales, marketing, customer service teams, and other departments make better decisions. The data warehouse is not the same as an operational database. It is more expansive and is not updated as frequently as the operational database. A data warehouse provides a long-range view of data from the past and present, and hence the analytics run on this data delivers more insights. It can be either an in-house storage system or a cloud storage system. So how do we pick the right data warehouse for the business? We’ll evaluate all the necessary factors in this post. But before we see more about these factors, let us read a little more about data warehouses. Reasons to Choose Data Warehouse What makes a data warehouse a necessary service for today’s enterprises? How does data warehousing help streamline business operations? How is Data Warehouse Used Within an Organization? How to Choose a Data Warehouse (4 Steps) Investing in a data warehouse doesn’t directly guarantee results unless you choose the right data warehouse for your business requirements. Whether it is choosing between the types of data warehouses or the service providers, you will first need to understand the business requirements. Hiring offshore data warehousing services from data analytics companies will help you get a complete picture of how to plan, adapt, and implement data warehousing in your organization. 1. Business Systems The first step is to understand your business systems. If you have a specific data administrator, you will need to choose a data warehouse that is compatible and can be integrated with it. Read the use cases shared by other companies. Ask the consulting agencies to analyze your business system and suggest the best suitable data warehouse. 2. Technical Specifications Data warehouses are usually designed to suit the varying needs of different SMEs across industries. However, you still need to ensure that the data retrieval speed, data storage speed, and flexibility you require can be provided in a data warehouse. 3. Billing Structure and Resources This point is important when you opt for cloud data warehousing. Each cloud provider follows a different billing structure. The cost of investment in both the short and long terms must be considered. 4. Security Specifications While all data warehouses promise data security, the actual security levels and encryption methods depend on the individual service providers. Does what they offer to match your security requirements? Evaluation Criteria Once you are fully aware of your business systems and what you need from the data warehouse, it’s time to consider the different factors that help you choose the right data warehouse for your enterprise. 1. Cloud vs. On-Premises We have been talking cloud data warehouse for a while now. It has been more popular in recent times when compared to on-premises data warehousing. However, that doesn’t mean cloud services are suitable for every business. For example, if majority of your data is stored in on-premises systems that are not fully compatible with cloud platforms, you will find it easier to invest in an in-house data warehouse. Of course, you can still migrate the entire business system to the cloud and upgrade your IT infrastructure. Companies like Oracle, Microsoft, and IBM offer on-premises data warehousing services. Microsoft has both on-premises and cloud data warehouses. 2. Type of Data What type of data do you plan to store in the data warehouse? Will it be structured or unstructured? Based on the type of data, you can choose between a relational database and a non-relational database. A relational database is suitable for structured data arranged neatly in the rows and columns of a spreadsheet. A non-relational database is ideal for large semi-structured data. Semi-structured data consists of emails, social media posts, demographic and geographical data, audios, videos, etc. What if you have unstructured data? In that case, a data lake might be an effective choice as it has been designed for the same. A data lake is a relatively new concept that promises to offer much more than a data warehouse. An in-depth comparison between a data warehouse and a data lake will give you a better idea about which one is the best for your organization. 3. Cost and Time Factors It can be quite a task to compare the costs of data warehousing services offered by different companies. The calculations are unique to each service provider, and unless you make a detailed comparison of what they offer and what they don’t, it can be hard to decide just by looking at the numbers. Remember that the cost here should also include the cost of implementation. If you hire data analytics companies to assist, you will need to pay them as well. Generally speaking, the cost of data warehousing depends on the storage, size of the warehouse, the resources required to run and maintain it, and the number of queries you run. If more than one team will access the

Kavika Roy
July 21, 2021
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How to Convert Your Big Data Into Actionable Intelligence? (7 Easy Steps)

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

Kavika Roy
July 16, 2021
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7 Ways Machine Learning Can Help You Handle Business Travel Risks

Traveling to meet clients and stakeholders is a part of the process for several organizations. A good percentage of day-to-day traveling are businessmen and women flying from one city/country to another for meetings and conferences. The industry has changed tremendously over the years. Air travel has become a preferred choice of most enterprises as it saves time compared to other modes of transportation. Allowing people to check-in from their mobile phones instead of waiting for long hours at the airport is one major development. However, the rise of the COVID pandemic in 2020 has disrupted all industries, and the travel sector is no exception. Flights have been canceled; the borders between countries were closed. Even the borders between the states were closed to prevent people from traveling and spreading the virus. While this led to substantial losses, it further pushed the industry to come up with new ways and promote safe travel for people. Technology has become an inherent part of this change. Be it self-serving machines, contactless transactions, or replacing customer care staff with virtual assistants to contain the spread of the virus, artificial intelligence and machine learning or ML can help you beat business travel risks on a large scale. Safety First When Traveling Employee safety has become one of the primary concerns for airports and airlines. The staff is at high risk of getting infected because of the nature of their work. This directly impacts productivity. At the same time, the health of the passengers traveling on the same flights is also a matter of concern. AI solutions are helping to manage these risks and facilitating safe travel while also ensuring that the staff is not exposed to the virus. Facial recognition systems, predictive maintenance, and contactless luggage screening are a few ways in which AI and ML are being used in the aviation industry. Masks, sanitizers, and face shields are an inherent part of the process to stay safe when traveling. But to arrange for safe travel, it is the technology that comes to the rescue. The Need for Technology – What is Machine Learning? Many airlines and airports are investing in ML to get back on track and increase their productivity without risking the lives of the people involved. But what exactly is machine learning? Is there a need to hire the services of a Machine Learning consulting company to adopt new technology? It is a subset of artificial intelligence. It is a method of data analysis that teaches computers to understand data and make a decision with minimum human interaction. This algorithm allows the system to learn from its mistakes and improve its efficiency to produce more accurate results. However, the vital point is that the airlines can be productive even in risky situations like the pandemic by using AI and ML services. Coming to the second question, it is not compulsory to hire offshore AI consulting companies to adopt the technology. However, revamping the business processes is not easy unless there is the right kind of support. This support comes from experienced consulting companies that offer personalized services to various organizations. Ways ML Can Help You Beat Business Travel Risks There are several ways in which machine learning can increase the productivity of airlines. We will first see how ML can reduce business travel risk and then read about how ML can be used in other areas of the travel industry. 1. Customization of the Trips Travel companies and airlines are now offering customized plans based on your travel history. Based on your previous flying experiences and preferences, a personalized travel program will be charted by the machine learning algorithm. There are already apps that use smart calendars to create a travel plan for the sales personnel. X.ai already allows similar functions. The apps have up-to-date information and can plan your entire travel program more effectively as they also consider the risk quotient, the delays in flights, the difference in time zones, and more. Machine learning can reschedule your itineraries based on your travel requirements and factors that affect your program. This is highly advantageous for business travel as it saves time and money for the enterprises who pay the expenses on behalf of the employees. It also helps streamline the trip to make it successful. 2. Self-Service Check-ins for Contactless Transactions Check-ins with as little contact as possible is a great way to prevent the virus from spreading, isn’t it? AI services let you check-in in advance, not just in the airports but also in hotel rooms. Some hotels in countries like Singapore are offering contactless check-ins. You can check-in via the app and also request any extra services you might need. You’ll be provided with a QR code that needs to be scanned at the hotel so that you can check into your room straight away using the kiosk. There is no need to talk to the reception staff or enter your details anywhere. There isn’t any need to sign your entry and risk touching items that could spread the virus. Singapore’s Changi Airport Terminal 4 is using automation, while the CitizenM hotel group is offering self-service check-ins for guests. 3. Humanoid Robots and Robotics to Replace Humans The Bangalore International Airport in India is planning to use humanoid robots in place of human employees. A local startup has been working on building the humanoid to assist passengers during travel. Sirena Technologies has also built the first Indian Humanoid Robot, Nino that enables a novel and modern approach to education. Similarly, airlines are starting to replace robot-driven aircraft tractors to take passengers to their flights. By reducing the involvement of employees, airlines can continue to fly planes and stay productive even in adverse conditions. There is an idea to use automated kiosks to check the temperature of the travelers along with their travel details, all in one place. This will eliminate the need for employees to handle the passports and other papers of the travelers. 4. Screening Luggage Using Artificial Intelligence Luggage

Kavika Roy
July 13, 2021
1 Comment

How Call Centers Are Being Modified Using Artificial Intelligence?

It has been more than two decades since call centers revolutionized the customer support system for various industries. While some static call scripts and one-size-fits-all strategies may remain unchanged in business outsourcing, technology has played a crucial role in drastically altering the way call centers function. Today, call centers have the unique ability to leverage all available data to drive each customer interaction. These data include various sources like the marketing campaign the customer has viewed multiple times, the type of transaction completed recently, or what a prospective customer looked for in their latest search. Of many technologies that call centers may have leveraged, artificial intelligence is one. Artificial Intelligence in call centers has allowed explosive growth owing to widely available cloud services and machine learning tools. AI in call centers There are two major types of AI in BPOs right now. The first can evaluate vast volumes of data and provide just-in-time insights for the agents to improve their performance on a call. This AI delivers the correct information during a call and ensures the agent or executive is on track with the right information when the caller asks for it. The second type of AI in business outsourcing is conversational AI. It analyses the speech of both the executive and the caller to identify emotions and, ultimately, intent. Conversational AI is mainly used to forecast the impact of a conversation based on vocal ticks, emotional state, and overall engagement level of both the caller and the agent. The sentiment analysis provides valuable real-time feedback on the emotional state of both the customer and agent and intervenes whenever required. Why is AI used in call centers? Ultimately, businesses want to create a personalized, positive experience for customers. And we are all well aware that when it comes to providing a good experience, the credits are always measured in sales, whereas a bad experience can have lasting effects. So how does AI change any of it? Here’s the answer. Automation: AI automatically captures data, routes calls to the suitable agent based on the input and mood from the analyzed data, and creates a profile for future reference that can be used in the call center and other business areas. Analysis: Call center AI provides in-depth analysis of individual calls making it easier for the managers and quality control executives to make decisions. Each call is measured and compared against performance benchmarks to provide a clear picture with an actionable insight of where the agent is performing and lacking. Support: AI is directly integrated with call center service workstations for agents providing immediate insight into the data being captured, the probable outcome of the call, and much more. The result is faster response times, a higher call resolution rate, and happier and motivated call center agents who are now empowered with tools to help them perform better. How is the call center scenario changing with AI? It is no news that call centers strive to provide a seamless and easy experience to customers since they constantly have to face the risk of losing out to a competitor. This report from a survey conducted by American Express found out that prospective or existing customers have bailed from a current purchase because of a poor service experience. To ensure that does not happen, call centers have turned to AI and machine learning solutions to help them take the following best action, turn leads into customers, increase retention rates, propensity product purchase, and much more. Business Process Outsourcing or BPO firms tend to work a lot around data. That is why data-driven call centers look forward to implementing AI solutions to improve customer experience. Here are a few practices for data-driven call centers with AI and big data to enhance selling via a good customer experience. 1. Intelligent call routing One of the significant advantages AI brings to call center finances is saving on human resources costs. Call routing through AI helps get the right customer to the right representative, taking into account the reason for the call and lifetime value and call complexity. A good number of call centers also opt to use skills-based call routing techniques to respond to a promotion. Say, for example, if one team is striving for promotion A and the other for B, AI can quickly identify and analyze the call and route it to the appropriate team. Layering in AI to skills-based call routing also ensures that the customer arrives at the right agent who can guide them in the best possible way. 2. Better analysis for caller feedback Traditional call monitoring cannot pull data from multiple sources in real-time. For example, the performance of call center executives or agents is currently monitored according to human observation. Calls are listened to and analyzed by a small number of ‘human’ managers who may or may not have their own biases that could impact evaluations. The analysis can be inaccurate, directly moving the scoring of agents. If you have ever called a customer support center of an e-commerce firm or any business, you’d be well aware of how they inform you before the issue that the call may get recorded for quality purposes. This indicates that agents receive feedback and support only after the ring, and there is probably no automation or real-time assistance implemented to guide the agent. What gets even more annoying for the customer is that they may have to speak to several agents or keep dialing digits without receiving any fruitful outcome — the result- poor feedback for the agent. AI improves this situation by performing those essential functions like monitoring, analysis, and support in real-time. Much of this function is performed by its branch, machine learning. ML services help in analyzing the mood and response of both the customer and agent on call. When used as feedback, the data enables the agents to respond more effectively, making them both happier, as compared to previous experience. 3. Personality profiling While the main objective of

Yashica Chopra
July 09, 2021
1 Comment

10 Ways the Advanced BI Tools Help in Decision Making

Business intelligence is a data-driven process that transforms raw and unstructured data from across the enterprise into structured data to gather meaningful insights. It is the process of analyzing and presenting data to understand how the business is performing and what needs to be done to improve the business. Before we know how the Advanced BI tools help in Decision Making, here’s the brief of what they actually do: Various tools and software are used to collect, process, analyze, and present data to derive insights. These tools play a crucial role in the decision making process of SMEs. Instead of relying on instinct and outdated data, organizations use real-time insights to understand the current and future market conditions. This allows the management to have more control over their decisions. In this blog, we will learn how advanced BI tools can empower enterprises to make the right decisions for their business. Functionalities of Business Intelligence Business intelligence helps enterprises to collect and store data in a central database that can be accessed by all departments. This ensures that everyone gets the latest information and can collaborate with other departments to increase productivity and sales. The following are the functionalities of BI. Role of Business Intelligence in Enterprises Business intelligence is used in various organizations to- Big names in the global market like Netflix, Adobe, Amazon, Siemens, Cisco, Philips, etc., grabbed prominent places on the list of Transformation 20 for making the best business decisions of 2019. Though the companies have been working on bringing new changes over the years, the major results were seen in 2019. For example, Netflix became the leading online live-streaming platform and the top provider of original content on OTT. Amazon’s Web Services helped reduce the cost of building and maintaining the IT infrastructure for many startups. AWS became a profitable venture in no time. These developments have been possible because the brands changed their business processes to data-driven models and invested heavily in business intelligence. Advantages of Using Business Intelligence Tools Without using the right software and technology, enterprises cannot expect to get the desired results. Adopting and investing in BI tools can change the way businesses look at data. There are many benefits of using Decision Making Platforms in business- Types of BI Tools Business intelligence tools are of different types and serve various purposes in the enterprise. Digital Dashboards It is a real-time interactive dashboard that presents data in an easy-to-understand graphical format. Power BI, Tableau Desktop, Qilk Sense Desktop Personal Edition, etc., are some examples. Advanced Spreadsheets There are open-source and web-based spreadsheets with much more features than MS Excel offers. Reporting and Querying Tools These tools are used to report, sort, filter, and process data based on queries. Some of the Best Business Intelligence Tools for Effective Reporting are Power BI, Sisense FineReport, Oracle Business Intelligence, IBM Cognos Analytics, etc. OLAP Tools Online Analytical Processing tools make it easy to analyze data from multiple sources and present it in multiple ways to include various business perspectives. Data Visualization Software Helps create graphical reports for complex insights and makes it easy to understand and share the key details with other departments in the enterprise. A few popular data visualization tools are Tableau Public, DataWrangler, D3.js, Plotly, and many more. Mobile BI Software Using BI tools on mobile devices so that employees are always armed with the latest reports no matter where they are working. Example: Power BI Pro, Zoho Reports, Amazon QuickSight, etc. Data Warehousing Tools These tools help collect data from various sources to structure and store it in a single location known as the data warehouse. Data Mining Tools Identifying patterns and hidden trends in the large data sets using AI and ML algorithms. Decision Intelligence Using a framework of data analytics, reasoning, and machine learning techniques to make informed decisions. Unified Modeling Language Using a modeling language/ techniques to specify and develop software for the business. Cloud-based/ SaaS BI tools that can be accessed via the cloud and used on the cloud platforms without downloading the software to the on-premises services. Power BI Pro, Zoho Reports, Tableau Online, etc., are a few cloud-based BI tools. Business Process Management Using a set of processes to manage the performance of the business by carefully planning and aligning the goals. Example: MagicDraw, Adobe LifeCycle, Enterprise Architect, ProcessMaker, and many more. How Advanced BI Tools Help in Decision Making There are many ways in which BI tools play a role in helping enterprises make the right business decisions. Whether it is in the sales department, managing resources, hiring eligible candidates, or training the existing ones, BI tools help enterprises have a better and deeper understanding of the intricacies in the business. 1. Generate Reports with Valuable Insights Reports are an inherent part of every business. From inventory to accounts to sales, each department generates reports to present the current business situation in different formats. Business Intelligence Consulting helps enterprises produce detailed reports that share valuable information about the company’s revenue, sales, inventory, past performance, and more. 2. Data Visualization to Understand the Reports Data visualization tools can be used by employees with minimum or no training. The aim of these tools is to present data in a simple and easy manner that can be understood by most people. Employees can add and delete features on the dashboard using the drag-drop option. This lets them see how different factors impact the overall performance of the business, that too without much effort. 3. Increase Benchmarking Standards Setting up benchmarks is not an easy task. It requires large-scale research to study the processes and systems used by the industry leaders and then create an action plan to implement the same in the organization. But BI tools have simplified the process of benchmarking. 4. Unlocking the Power of Unstructured Data This helps in getting in-depth insights from data, which is not possible when enterprises work with only one type of data. Traditional tools are capable of working

Yashica Chopra
June 29, 2021
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Data Lake vs Data Warehouse: Which is Best For You?

Data is a salient factor for every business. While it has always been a necessity, nothing in the past compares to the need for big data we see today. No matter if it is a startup or a multinational enterprise, data from the past and present are collected, processed, analyzed, and presented to help make better decisions. Business intelligence and data analytics are an imperative part of many enterprises now. But where does all this data go? It sure needs to be stored somewhere secure, private, and easy to access, right? Many of you might have heard of the terms data lake and data warehouse. These are data storage architectures that allow you to store a huge amount of data in one place. While their main purpose is the same, the two have nothing much in common. Do you know that 95% of businesses face a problem due to unstructured data? However, several SMEs and organizations tend to get confused between a data lake vs data warehouse. And without knowing what they are, there’s no way an enterprise can choose the right one for their requirements. What is a Data Warehouse? A data warehouse is a depository that stores data in one place before it is analyzed and presented using various BI tools. It is one of the first things you need to work on when revamping the business processes. All business intelligence applications require a data warehouse to deliver meaningful insights. The data warehouse combines components and technologies where raw data is structured and processed to derive information. A data warehouse is more of a traditional data storage system tried and tested by many businesses. Does that mean it’s the best, or does it mean it’s an older version and not as useful? It’s neither. The data warehouse has its advantages and disadvantages. Advantages: Faster Data Retrieval The role of data warehouse in business intelligence is a lot more intricate than you would expect. Whether you want to retrieve data in less time or find a crucial piece of information without searching all over the enterprise, a data warehouse offers a quick and effective solution. Easy Integration The data warehouse can be integrated with numerous other systems so that it becomes easy to translate data and present it in an understandable format. If you want to know more about your customers, all you need to do is connect the data warehouse to your CRM system. Great Performance DWs usually have schema-on-write, SQL servers understand how the system works. That makes it simpler for the data warehouse to deliver good performance whenever its need arises. Identification and Correction of Errors DWs ensure that the data stored in them is not incorrect. It shows the errors that need to be fixed, the duplicates that have to be removed, etc., before proceeding to the next step. However, there is a difference between data warehousing and business intelligence. A data warehouse is not a business intelligence tool. DW deals with data acquisition, data cleansing, management, metadata, data transformation, backup, and more. Proven Storage Solution The data warehouse has been here long enough to easily find resources and tools to use with it. While it can be a little challenging to work with the latest functionalities, DW is a reliable and proven storage option for enterprises. Flexibility Third-party consulting companies offer Data warehousing services to help you build, manage, and upgrade the data warehouse in your enterprise. The advantage of DW is that it can be housed on-premises or can be stored and accessed from the cloud platforms. That said, DW has its share of disadvantages that makes enterprises consider data lakes. Let’s check the cons of data warehousing before reading about data lakes. Disadvantages: Time Taking Process Even though DWs are used to simplify the business processes, it might take a little more time to manually feed raw data to the data warehouse. That is something many enterprises are wary of. Limited Use of Data The confidential nature of data might result in restricted access to the data warehouse. And that can directly translate to limited use of data. Data warehousing might be a little less effective if only certain employees can access data. High Costs of Maintenance Data warehouse delivers its best when it’s upgraded to the latest version. While the process isn’t hard, the cost can be slightly on the higher end. Unless you can invest money to maintain and upgrade the DW, it won’t be as effective. What is a Data Lake? A data lake is a relatively new concept that has gained a lot of attention in recent times. A data lake is different from traditional storage systems as it stores data in its raw format. Of course, it can also hold structured and semi-structured data, including binary data. It is pretty much a single storage location for raw data and transformed data. The data lake architecture is flat, where every element has a label and a corresponding metadata tag for easy identification. The data collected from numerous sources are added in real-time to the DL in its original format. No changes are made to the data at this stage. Advantages: Variety and Volume Data lakes make it an easy job of handling big data, whether it is structured or unstructured. A data lake is schema-on-read, and this lets us read the format only when we read it back out. Fast Processing DLs are easy to update. You don’t require to spend too much time transferring data to the data lakes. It all happens in real-time. Accessibility Any user group can easily find the data they want by looking at the open data copies. Of course, you can control and restrict access to certain groups, but it’s still easy to get hold of what one wants without compromising data security. Cost-Effective Storage While data lake is not cheap, it is a cost-effective option when compared to data warehouses. That allows us to store

Damian Alderson
June 17, 2021
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4 Ways Data Governance Can Improve Business Intelligence

Successfully running a modern business has become inconceivable without an effective Business Intelligence strategy. Developing and implementing proper BI, on the other hand, is hardly done without optimized Data Governance. Data is the very fulcrum of a streamlined workflow, regardless of the industry your company belongs to. Many business leaders have known that Data Governance can improve Business Intelligence. It means that applying an efficient data management solution is a prerequisite to achieving advanced BI levels. According to a Forbes study conducted across numerous global organizations, businesses that deploy robust DG strategies and use them to enhance their BI efforts report breakthrough ROIs when investing in business intelligence. However, developing a powerful Data Governance plan based on consistency, reliability, and flexibility is not exactly a walk in the park. It requires a multi-faceted approach to data storage, tracking, and management, and needs to be tackled with proper attention to detail. It is true for both small businesses and enterprise-level environments. Otherwise, your DG is likely to have certain pitfalls that would render all other aspects ineffective. Before we jump to the ways Data Governance can improve Business Intelligence, let’s take a look at what it actually is. What is Data Governance? Data Governance involves various processes, policies, roles, standards, metrics, and infrastructures that help businesses achieve optimized and effective use of data. Proper information management enables companies to reach their goals and objectives on time while being cost-effective. DG encompasses operational processes and employee responsibilities that allow businesses to achieve high quality and security levels across an entire ecosystem of data and data flow an organization is handling. Effective and tactical Data Governance strategy should ensure that all data management roles are clearly defined across all departments and should provide a clear overview of who takes what action and when, and for what purposes. If your business has dynamic data flows daily, and your communication channels convey pertinent information regarding business analytics and security, creating a functional, consistent, and well-crafted DG strategy should be among your top priorities. Data comes in various forms. For example, it can convey personal information that belongs to your clients. At the same time, it can also include raw performance metrics that your business can use to analyze and glean valuable insight into: – how it can improve its operational processes, – shorten time to market, – recognize business drivers and boost ROI. For instance, if you run a SaaS-based business. Your user base is enormous and diverse; one of your main drivers for having a powerful data governance strategy should be ensuring that client-based information is stored and processed securely. It flows through your company and communication channels. Tracking user behavior and analyzing user data on a granular level will also help you improve your product and drive more revenue. Data retention and proper regulatory compliance handling should also be among your priorities since a single data breach can cause fatal damage to your project. Now let’s go over some of the essential best practice tips suggested by top business intelligence consultancy firms on how to use Data Governance to enhance your Business Intelligence methods. 1. Streamlined Data Governance Enables Timely Business Analytics Every business owner knows that timing is everything. One can have a killer product and service, but if the strategy isn’t impeccably data-driven and timely, likely, their business won’t reach its full potential. Having an effective DG tactic in place can help you improve all aspects of your business workflow and all other operational processes, allowing you to improve data gathering, storing, and processing. This way, you can perform deep data analysis, and optimized ROI will undoubtedly ensue, followed by the enhanced discovery of new business opportunities. This type of advanced business flow can hardly be achieved if each of your teams and departments isn’t getting the right data. The right raw data leads to smart insight, and intelligent insight then leads to improved Business Intelligence. However, suppose your raw data isn’t governed and managed the right way. In that case, the process of timely data analytics is severely hindered, which results in a ripple effect across all your processes and workflows. 2. Proper DG Improves Operational Processes Data is the lifeblood of all operational processes. Data is an asset that needs to be managed so that it is highly accessible, easily usable and reusable, and highly secure. Developing effective data governance can help business owners streamline all operational processes and improve decision-making, so any potential efficiency gaps are easily mitigated. When properly implemented, it can reduce data inconsistencies to a minimum and remove the risk of human error from the equation. 3. Secure Email-based Information to Prevent the Loss of Sensitive Data According to Statista, the US alone saw over 1000 data breach cases with over 150 million records exposed to cybercriminals. Granted, this is lower than back in 2018 when 471 million records got exposed, and these attacks seem to be decreasing lately, but the overarching trend since 2005 is alarming. Minimizing the risk of cyberattacks should start with handling the data management and protection of your emails, as email platforms are the most widely used communication channels that permeate all modern companies. If utilized and implemented correctly, this type of mindset should trigger businesses to deploy top-tier email archiving methods and retention policies so any malware infiltration or data loss is obviated and network breaches are prevented. Additionally, having a tight email retention policy in place allows you to determine DG for email-based data in terms of security, cost-effective retention, and retrieval of data, as well as ensure regulatory compliance. 4. High Data Quality Fuels Business Intelligence In order to make proactive and revenue-driving decisions, businesses need to make sure that their data is of high quality. Subpar data quality results in false insights and can cause huge damages to your cost-effective resource usage. In order to always have quality data at your disposal, your DG plan needs to tackle a continual process of tracking, collecting,

Yashica Chopra
June 03, 2021
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How Advanced Data Analytics help to Achieve Organization’s Goals?

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