Page 37 – DataToBiz

Yashica Chopra
June 29, 2021
No Comments

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
No Comments

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
No Comments

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

Kavika Roy
June 01, 2021
3 Comments

Role of Machine Learning in the Medical Industry

Technology in healthcare helps provide better treatment and save lives. But do you think it is as simple as it sounds? Quality of services, valuing human life, and delivering better outcomes are the primary purposes of using advanced technology like Machine Learning or ML in medicine. Businesses and SMEs are extensively adopting artificial intelligence and machine learning (a subset of AI) in various industries. The medical sector is no different. Many research centers and healthcare organizations have recognized the potential of machine learning and are actively improving their patient care services and administrative processes and systems. Machine learning algorithms become more accurate as they gather and process data over time. As a result, it increases accuracy and efficiency. From screening a patient to prescribing the right medicines, AL can empower the healthcare providers to understand the patients’ condition down to the minute details. Machine Learning and Healthcare ML services help in automating recurring tasks. It saves time for the nurses and allows them to focus on the patient rather than spend their energy on filing records, processing claims. Patient management is one of the most challenging things to handle for healthcare centers. Artificial intelligence and machine learning can help streamline the processes to provide high-quality treatment and services to patients. Chatbots are not the only way to use machine learning. Machine learning is fast becoming a part of the pathology, oncology, and other such departments. ANN (artificial neural networks) helps with image modeling, disease diagnosis, identifying harmful cells (cancerous cells) in the early stages, and so on. Of course, this is only limited to direct patient care. Machine learning algorithms are just as valuable for medical research, where labs run clinical trials, discover and develop new drug combinations, and process a vast amount of historical and real-time data to study and control the spread of an outbreak or an epidemic. It is one of the main reasons why the demand for artificial intelligence in the medical industry has doubled during the last year. The Covid-19 pandemic resulted in various researchers and healthcare centers relying on technology to understand the spread of the virus and look for ways to stop it. Role of ML in Medicine Several organizations hire offshore machine learning consulting companies to implement AI technology in their processes. ML has various roles to play in the medical industry, and here are some of the most important ones. 1. Improving Health Records Data entry might have become more accessible during the last few years, but maintaining the health records up to date is still a labor-intensive job. Nurses and the non-medical staff spend a lot of time updating the records. If this were to be handled by machine learning, wouldn’t it save time, money, and other resources? AI can help build and maintain smart health records for every patient: – Whether it is about storing the records on the cloud and making them easily accessible to the medical staff or – Using ML-based handwriting technology to understand and convert written files into other formats. When the necessary patient details are already available on the file, the doctors will have more data to understand the patient’s medical condition. Hence, it contributes to a higher quality of treatment. 2. Diagnosing Diseases It would seem quite natural that machine learning is very good at diagnosing diseases. This is one of the prominent areas where machine learning is highly effective. Machine learning algorithms can quickly identify diseases like cancer that are hard to diagnose early (especially skin cancer). IBM Watson Genomics by IBM Watson Health in partnership with Quest Diagnostics is a prime example. Using genome-based tumor sequencing with cognitive computing, harmful cells were detected faster and with greater accuracy. Artificial Intelligence consulting services are used by healthcare centers for predictive analytics to diagnose brain diseases like depression. Furthermore, it helps plan a proper treatment chart for the patient before getting too late. 3. Manufacturing Drugs Research and development (R&D) is an inherent part of the medical industry. By using machine learning during the early stages of discovering and developing drugs, researchers can know the possible outcomes and the success of using the medicine to cure a disease. Researchers can identify the potential side-effects of using the drug and find alternate components to reduce the side-effects while increasing efficiency. Next-generation sequencing and precision medicine are two AI technologies used in discovering and manufacturing drugs. Precision medicine helps in identifying multifactorial diseases and finding alternative therapies. Project Hanover (by Microsoft) used ML technology to treat cancer and helps in personalizing the drug combination for patients suffering from Acute Myeloid Leukemia. 4. Personalized Treatment and Medicine Personalization is seen everywhere, and the medical industry is no exception. It helps to provide better and accurate treatment to patients based on their health conditions rather than based solely on the disease troubling them. In a way, this point is an extension of the previous one. Though doctors can only choose from a limited set of data, ML makes giant leaps in this region. IBM Watson Oncology is the first thing that comes to mind when you talk of using the patients’ history to predict their health and make a list of treatment methods that are best suited for their conditions. 5. Diabetes Prediction Diabetes is both common and dangerous. It leads to more health complications by damaging other vital organs in the body such as the heart, kidneys, and even the nervous system. While type-2 diabetes is something many people are familiar with, type-1 diabetes (also called juvenile diabetes) is still not known by many. Do you know that WHO estimated around 1.5 million deaths in 2019 due to diabetes? Using Artificial Intelligence in Healthcare to predict diabetes can help physicians detect the disease in its early stages and help patients find the correct method to control their blood sugar levels. Predicting diabetes can save lives and allow the patients to lead a quality life. 6. Liver Disease Prediction Fatty liver or liver cirrhosis is a liver disease caused by alcohol abuse. The simplest

Kavika Roy
May 25, 2021
3 Comments

How is AI Contributing to the Education Sector?

Artificial intelligence has entered every industry, and the educational sector is no exception. The administrative staff, management, teachers, and students are all using AI in different ways to achieve similar goals. During the last few years, AI has spread its roots much wider and deeper in this sector. Though we haven’t seen humanoids replacing teachers, we sure are seeing AI systems taking over teaching and guiding students during their online courses. Whether it is automating tasks or personalized training courses based on students’ capabilities, AI is changing the way people look at education. Before we say the various ways in which artificial intelligence is contributing to the education sector, let us look at how an educational institution should implement AI in its establishment. Implementation of AI in Education Sector Do you want to implement artificial intelligence solutions in your educational institution? You will first need to define the long-term goals and understand the limitations of the existing system. Without knowing what you want to achieve, investing in AI will not be beneficial. Then, you will either have to hire AI experts to bridge the talent gap or outsource the responsibility to a machine learning consulting company. If you want to hire a third-party company, it is recommended to do so in the beginning. This will ensure that your institutional goals and the company’s services are aligned and in sync with each other. Well-known institutions such as Nuance (Massachusetts), Querium (Texas), Kidsense (California), and many others have successfully adopted AI models in their educational systems. Identify Needs: What do you expect AI to do in your establishment? Which processes do you want to automate? Which training areas do you want to focus on? Determine Objectives: Are you aware of how AI works? Do you know the drawbacks of using AI systems? How do you plan to overcome the challenges in adopting artificial intelligence? Align Processes: The AI solutions you choose should create the right environment to align your establishment’s talent, technology, and work culture. You need to convert your institution into an analytical-based model that focuses on actionable insights to make better decisions. Control the Environment: The new work environment should accommodate and allow humans and AI systems to work in tandem. The processes should be transparent, efficient, effective, and secure. Role of Artificial Intelligence in Education Automating Administrative Tasks Is there an establishment that can function without backend work by the administrative department and the teaching faculty? Whether it is filing paperwork, sending emails and messages, contacting students and their parents, or making periodic reports, the administrative staff constantly works from morning to evening. Similarly, the teachers and professors have to mark the homework, test papers, create a teaching schedule, prepare for the next classes, etc. Do you know that a teacher spends less than half their time actually teaching the students? According to an article posted by The Telegraph, teachers spend only 43% of their time on teaching. The remaining time is allotted to marking test sheets, planning future lessons, and completing the administrative work. The artificial intelligence consultant will help you create an AI system that will allow the staff and the teachers to be more productive at work. Teachers can use smart question generators like PrepAI that help them set better question papers for the tests and save time in the process. Personalizing Learning for Students Personalized learning is pretty much similar to personalized recommendations offered by online marketplaces or Netflix. The courses and subjects are recommended based on the interests and preferences of the students. AI for online learning doesn’t replace teachers. Instead, it helps teachers understand the abilities and limitations of each student. It makes things easier for the students and teachers to create an exclusive course plan for each student. Smart tutoring systems also offer instant personalized feedback to help students understand how they can do better. Teachers can convert their lessons into interactive sessions and flashcards so that students will take more interest in learning the subjects. Creating Smart Content Smart content is a popular topic in the education sector. It is nothing but creating virtual content by digitizing textbooks, notes, creating video lessons and lectures, developing interactive training sessions, and more. AI in education helps create customizable interfaces for the students to learn easily and understand better. Be it the elementary level or the postgraduate level, smart content can be developed for any subject and any class. Microlearning and skills mapping are two concepts that belong to this category. Skills mapping is the process of creating a visual representation of the skills needed by students to perform well in their studies. Microlearning is a skill-based learning module that deals with specific areas to achieve expertise in the field. Breaching the Geographical Barriers Online courses have made it possible for students from any region to join a course from any other country. The physical barriers between countries have been eliminated in terms of online education. Technology made it possible to acquire specialized skills from the best teachers, no matter where they are located. Now, students have even better chances of updating their skills through AI-powered training sessions. Information Technology is one area where numerous courses are using AI. That shouldn’t be surprising since artificial intelligence is an inherent part of the industry. Students and professionals who wish to become AI and ML experts are enrolling for these courses. Education Made Easy for People with Special Needs It has always been a challenging task to create a learning program for people with special needs. Though there are exclusive schools for students with hearing, speech, and visual impairment, the teaching process could have been more effective. Also, students with ADHD, dyslexia, autism, etc., have to be enrolled in special schools to help them deal better. But why can’t these students continue to be a part of regular schools? Is there a way teachers can understand and pay more attention to these kids? Artificial intelligence is making it possible for kids with special needs to study most comfortably. SpeakIt!, Widex’s Evoke, and Empower Me are a few examples of AI in education that

Beatrice Johnson
May 18, 2021
1 Comment

4 New Implementations of Computer Vision in Marketing

For many in the business and marketing world, computer vision is still a new and somewhat obscure concept. However, it is also one that is rapidly becoming more relevant, particularly with regard to the acquisition, service, and retention of customers. Leaders and professionals implement Computer Vision in Marketing, Operations, Sales, Retail, Security, and many other areas. To recap the core concept quickly, we’ll turn to a simple definition from Towards Data Science, which characterizes computer vision as a field of computer science that enables computers to identify and process objects in images and videos the same way that humans do. We would also add that the improvement of augmented reality technology is in some respects extending computer vision into the world — such that computer systems can also recognize real-world objects and images through their own cameras. It’s all extraordinarily impressive technology, and it can be used for a wide range of purposes. In this piece, though, we’re going to look specifically at some of the ways in which computer vision can help businesses in marketing. Visual Search Assistance Monitoring Store Traffic Customer Personalization Searchable Images Visual Search Assistance Nowadays, marketers are assisted by certain automated features that help to make recommendations and narrow down selections for online shoppers. The process can work in different ways, but typically a customer’s search activity produces unseen tags that reflect apparent interest. Those tags can then be used to filter through additional store offerings so that customers can be presented with suggestions they’ll be likely to appreciate. It is a simple, automated means of improving direct customer engagement. Now, however, computer vision in marketing is refining this same general concept. Through this technology, a company’s system can actually recognize — look at, in a sense — what customers are observing. Rather than relying on tags, which can be somewhat vague, a computer can identify customers’ selected items and actually look for similar items or appropriate accompaniments. The potential is there to improve customer engagement with even greater accuracy. Monitoring Store Traffic Some time ago, The Atlantic posted a thorough, interesting article on what stores do to “follow every step you take.” The idea is to track customers within stores in order to gather data that can effectively shape in-store marketing strategies. By tracking customers — through Bluetooth and Wi-Fi signals, the customers’ own smartphones, etc. — companies can gain insight into which products are being favored, how the store layout might be made more effective, and so on. And now, computer vision is essentially simplifying this process for marketers and shows how computer vision in marketing is beneficial. Customer Personalization Customer personalization is something we typically think of as having to do with content marketing and data analytics. In a broader sense, today’s businesses go to great lengths to make sure that their written and shared content is tailored to specific audiences. Ayima Kickstart examines this as an aspect of content SEO — explaining that companies employ “expert writers” to research target audiences and construct content according to that research. Beyond this, on more of a customer-to-customer basis, a lot of modern businesses are also using various analytical methods to track activity and tailor follow-up recommendations as needed. Through those practices, consumers are effectively guided toward conversions: They’re found and spoken to strategically within broader audiences and then tracked and catered to via tracking as they browse or otherwise engage with the business. It’s an effective process, but we’re now beginning to see computer vision in marketing simplifying it. Searchable Images The last benefit of computer vision in marketing that we’ll discuss here — and perhaps the simplest — is its impact on consumer searches. With computer systems better able to recognize and interpret images, consumers now have the option of plugging images into search mechanisms. This means that if a consumer should come across a photo of an intriguing product — or even take that photo personally — it can be used to search for further information. As this practice becomes more common, it will naturally produce benefits for businesses. However, it also gives marketing departments a whole new way to think about image-driven product marketing and social media outreach. Conclusion All of these represent significant changes and advancements. And yet they’re also only the beginning! In our article ‘How is Vision Analytics Retransforming Modern Industries?’ We pointed out that the global computer vision market anticipates a 7.6% CAGR between 2020 and 2027. Due to sharing of visuals among the customers, online marketing using visual datasets has become crucial for marketers. With the help of computer vision, they can gain customer insights, improve the campaigns and ultimately improve their buying behavior. As Computer Vision is maturing with each passing year, it holds many new opportunities for marketers. That amounts to a prediction of significant growth, which means that computer vision in marketing is going to become even more sophisticated — and produce even more beneficial concepts — over time.

Yashica Chopra
May 12, 2021
No Comments

11 Advantages of Having Predictive Analytics in Healthcare Industry

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

Yashica Chopra
May 05, 2021
1 Comment

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

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

Yashica Chopra
April 27, 2021
1 Comment

10 Things to Consider While Building your Right Data Science Team

Enterprises these days no longer follow outdated business processes. The focus is more on adopting the latest technology and relying on new software and tools to increase productivity and ROI. But working with advanced systems means hiring experts who have experience in the said field. Data is a major part of every business. Be it artificial intelligence, machine learning, natural language processing, or business intelligence, these technologies work with vast amounts of data. Organizations need employees who can work with data and the latest software to derive insights and generate reports. Ensuring data quality helps an organization make better decisions. Many of you would have already heard of data scientists. A core data scientist is supposed to know everything and manage it all. But that is hardly possible. Even though the same person should have a varied skill set, a data scientist alone will not be enough. What enterprises need is a fully equipped data science team to work on analysis. What is Data Science? Before we talk in detail about why and how you should build a successful data science team, let us first see what it actually is. The simplest definition is- In computer science, data analytics is the process of analyzing raw data to make conclusions about it. A data science company offers multiple services related to data analytics, data infrastructure, data strategy, big data, deep learning, machine learning, artificial intelligence, and much more. The technologies and services are interlinked with the teams working on different aspects of data science and advanced analytics. Not every business has the suitable infrastructure to build a data science team. While some find it easy, others have to make a lot of effort. A global digital framework can introduce governance, the social environment, business, and technology. In such instances, hiring the services of a data science company is a better option. How Data Scientists are different from Data Architects? A data architect has an evolving role, so there is no industry-standard certification program. As data engineers, data scientist experts, or solutions architects, individuals typically gain experience in data design, data management, and data storage work as they work their way up to the role of a data architect. The Importance of Building the Right Data Science Team What if you want to build a team for data science projects? At what stage should you introduce data science into your enterprise? Mostly, the decision has the highest impact at early stages only. How important is it to build the right team? Introducing data science roles into your business processes requires a lot of planning. You will need to be sure that you have enough budget to invest in systems, people, and processes. You also need to be assured that your existing employees will welcome the changes and embrace them. If your employees do not value the insights offered by data scientists, the purpose will be lost. Many companies expect data analysts to be able to convert alienating numbers in order to provide tangible insights. The following are some reasons you should invest in building data science for your business. Empower the employees and management Recommending future actions based on insights derived from past and real-time data Identifying growth opportunities in the market Helping in better decision making based on data-driven reports Assisting the employees to adopt industry best practices Analyzing and evaluating the decisions made by the management Identifying the target audiences Identifying customers’ issues and finding solutions Helping recruit the right talent for the business Data Science Team Roles A data science team has multiple experts, each dealing with different aspects of the field. The roles and responsibilities of the team members depend on their experience in domain expertise, technical knowledge, and quantitative skills. Team Leader- Chief Analytics Officer or Chief Data Officer Data Strategist Data Scientist Data Engineers and Architects Data Analysts Machine Learning Engineer Business Analyst Data Journalists Data Visualization Engineer The actual team positions might differ, depending on the types of data science teams an enterprise wants to build and how much it can invest into it. How to Structure the Data Science Team? The data science team structure can further be classified. You will first need to decide the type of team you want to build in your organization and then hire the right kind of experts who require data analysts with market-tested skills. Decentralized: Works the best for short-term, initial data science integration activities and SMEs that don’t want to become a full-fledged data-driven business. It totally depends on the business objectives to opt for a decentralized model. Centralized: These are data science teams that work on multiple projects scattered in different departments throughout the enterprise. This structure works well for enterprises focusing on long-term growth and development. Functional: One team works with one department like the marketing or the logistics. The focus area is limited to that department. This structure is best suited for startups where there is no need to analyze every single piece of information. Center of Excellence (CoE): This is similar to the centralized structure but with a separate unit for data scientists. It is known as one of the most balanced structures since there is a higher level of coordination between the teams. Consulting: This is similar to having a data analytics company within the enterprise. The data engineering team can be hired by different departments to work on specific projects. This structure works from SMEs where the management cannot allocate many external resources to the teams. Democratic: This allows you to combine and integrate the simple or specialized data science model with other systems in the enterprise. Employees have access to data science systems and can make changes to them. This works when businesses focus on building data science infrastructure for the enterprise. Federated: This is similar to employing a SWOT team in the organization. The federated structure is a combination of decentralized and CoE types. So does this make you wonder who should the data science team report

Clutch
April 21, 2021
No Comments

DataToBiz Becomes Clutch’s Top Artificial Intelligence Company in India

Taking data-driven decisions instead of intuition is what we do here at DataToBiz. We are an AI and data science service company that connects businesses to well-studied data. Our company consists of experts with the goal of helping clients in managing their data assets and looking for the best ways to implement insights from their data. DataToBiz becomes Clutch’s Top Artificial Intelligence Company in India. The DataToBiz team is open to working with all kinds of businesses regardless of their size. We focus on making them understand the potential of data and implement the perfect structure to process it and make decisions. Recently, Clutch, a B2B reviews platform, crowned DataToBiz as one of the 2021 leading agencies in artificial intelligence. This award not only acknowledges our hard work but also inspires us to strive higher. “Great to see ourselves amongst the contributors of Data Science, AI, Power BI, Tableau and Big Data Solutions.” – CEO, DataToBiz We also would like to take this opportunity to extend our gratitude to our clients who willingly shared their experiences with us. Here are some of the feedback they left on Clutch’s website: “The team at DataToBiz was patient, and they listened to all of our needs. We would occasionally make last-minute requests at odd hours, but they were always available to help us resolve the issue quickly.” – Senior Analyst, Absolutdata Analytics “We’re happy with the outcomes of DataToBiz’s work.” – Amritpal Singh, CTO, Spice Labs Pvt Ltd We are overwhelmed by the award we received from Clutch! If you are interested in our services, you can contact us anytime. Let’s make your business AI-driven! DataToBiz has not only made its mark in the Indian subcontinent but also provides data strategy consulting solutions in countries such as the United Kingdom, United States, Canada, Australia, and the middle-east.