Data Engineering & Analytics Archives - Page 6 of 9

Arya Bharti
January 06, 2022
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Effective Big Data Analytics Use Cases in 20+ Industries

If we have to talk about the modern technologies and industry disruptions that can benefit every industry and every business organization, then Big Data Analytics fits the bill perfectly. The big data analytics market is slated to hit 103 bn USD by 2023 and 70% of the large enterprise business setups are using big data. Organizations continue to generate heaps of data every year, and the global amount of data created, stored, and consumed by 2025 is slated to surpass 180 zettabytes. However, they are unable to put this huge amount of data to the right use because they are clueless about putting their big data to work. Here, we are discussing the top big data analytics use cases for a wide range of industries. So, take a thorough read and get started with your big data journey. Let us begin with understanding the term Big Data Analytics. What is Big Data Analytics? Big data analytics is the process of using advanced analytical techniques against extremely large and diverse data sets, with huge blocks of unstructured or semi-structured, or structured data. It is a complex process where the data is processed and parsed to discover hidden patterns, market trends, and correlations and draw actionable insights from them. The following image shows some benefits of big data analytics: Big data analytics enables business organizations to make sense of the data they are accumulating and leverage the insights drawn from it for various business activities. The following visual shows some of the direct benefits of using big data analytics: Before we move on to discuss the use cases of big data analytics, it is important to address one more thing – What makes big data analytics so versatile? Core Strengths of Big Data Analytics Big data analytics is a combination of multiple advanced technologies that work together to help business organizations use the best set of technologies to get the best value out of their data. Some of these technologies are machine learning, data mining, data management, Hadoop, etc. Below, we discuss the core strengths of big data. 1. Cost Reduction Big data analytics offers data-driven insights for the business stakeholders and they can take better strategic decisions, streamline and optimize the operational processes and understand their customers better. All this helps in cost-cutting and adds efficiency to the business model. Big data analytics also streamline the supply chains to reduce time, effort, and resource consumption. Studies also reveal that big data analytics solutions can help companies reduce the cost of failure by 35% via: 2. Reliable and Continuous Data As big data analytics allows business enterprises to make use of organizational data, they don’t have to rely upon third-party market research or tools for the same. Further, as the organizational data expands continually, having a reliable and robust big data analytics platform ensures reliable and continuous data streams. 3. New Products and Services Because of the availability of a set of diverse and advanced technologies in the form of big data analytics, you can take better decisions related to developing new products and services. Also, you always have the best market and customer or end-user insights to steer the development processes in the right direction. Hence, big data analytics also facilitates faster decision-making stemming from data-driven actionable insights. 4. Improved Efficiency Big data analytics improves accuracy, efficiency, and overall decision-making in business organizations. You can analyze the customer behavior via the shopping data and leverage the power of predictive analytics to make certain calculations, such as checkout wait times, etc. Stats reveal that 38% of companies use big data for organizational efficiency. Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. 5. Better Monitoring and Tracking Big data analytics also empowers organizations with real-time monitoring and tracking functionalities and amplifies the results by suggesting the appropriate actions or strategizing nudges stemming from predictive data analytics. These tracking and monitoring capabilities are of extreme importance in: 6. Better Remote Resource Management Be it hiring or remote team management and monitoring, big data analytics offers a wide range of capabilities to enterprises. Big data analytics can empower business owners with core insights to make better decisions regarding employee tracking, employee hiring, performance management, etc. This remote resource management capability works well for IT infrastructure management as well. 7. Taking Right Organizational Decisions Take a look at the following visual that shows how big data analytics can help companies take better and data-driven organizational decisions. Now, we discuss the top big data analytics use cases in various industries. Big Data Analytics Use Cases in Various Industries 1. Banking and Finance (Fraud Detection, Risk & Insurance, and Asset Management) Futuristic banks and financial institutions are capitalizing on big data in various ways, ranging from capturing new markets and market opportunities to fraud reduction and investment risk management. These organizations are able to leverage big data analytics as a powerful solution to gain a competitive advantage as well. Take a look at the following image that shows various use cases of big data analytics in the finance and banking sector: Recent studies suggest that big data analytics is going to register a CAGR of 22.97% over the period of 2021 to 2026. As the amount of data generated and government regulations increase, they are fueling the demand for big data analytics in the sector. 2. Accounting Data is Accounting’s heart and using big data analytics in accounting will certainly deliver more value to the accounting businesses. The accounting sector has various activities, such as different types of audits, checking and maintaining ledger, transaction management, taxation, financial planning, etc. The auditors have to deal with numerous sorts of data that might be structured or unstructured, and big data analytics can help them in: Using big data analytics will also improve regulatory efficiency, and minimize the redundancy in accounting. 3. Aviation Studies reveal that the

Arya Bharti
December 13, 2021
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10+ Disruptive Big Data Trends That Made Headlines

The global big data market revenue is projected to hit the 103 billion US dollar mark by 2027. And, the current BI and analytics software market are valued at 16 billion USD globally. Apart from the stats that speak a thousand words, big data, in association with AI, ML, and other technologies, is fueling what we call the Fourth Industrial Revolution. Big data analytics is one of the most powerful technology trends and is reshaping numerous business processes and operations across the world. Rapidly expanding IoT networks, Data as a product, quantum computing, and data use for hyper-personalization – there are many emerging trends in the big data segment. Big data is also being used with AI, ML, and other innovative processing technologies to analyze, process, and parse the massive datasets in multiple sectors, such as Healthcare, eCommerce, Government Data, Public Infrastructure, Banking & FinTech, Security, Manufacturing, Natural Resources Management & Harnessing, etc. The latest studies reveal that in just 2 years, Big Data has spurred a change in the business perspective across the entire globe. It forced business organizations to rethink their: The following visual shows the major potential applications of Big Data, AI, and other technology landscapes: With more than 2.5 quintillion bytes of data being generated daily. it is more than safe to assume that Big Data is gearing up for changing the way we think! Here, we are sharing the top 10 Big Data trends that are going to be the major change drivers. Top 10 Big Data Trends : Revolutionizing the Core of Modern Business Landscape Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. 1. AI Will Continue to Improve, but Humans Will Remain Crucial The rise of artificial intelligence is empowering businesses and industries across the globe and empowering people with unprecedented capabilities. While the general perception is of “massive overlays and mass firings“, the experts say that humans are still going to be “crucial“. A recent Forbes feature outlined how robots are not going to wipe out the human task force. Further, an MIT study suggests that AI will continue to fuel massive innovation across the globe, and will create many sectors for growth and employment. Hence, it will improve the human race and the way we live, work and grow. However, in order to keep the social and economic divide contained, the stakeholders have to have some common goals to adhere to, such as: Also, humans have a distinct way of learning, perceiving, and responding to things, incidents, and people around them. Hence, while the future might have smart AI-powered customer staff, the overall management of operations will still require humans. 2. Business Intelligence in Every Walk of Life Business intelligence or BI is already bringing changes in multiple sectors, namely marketing, consumer services, customer experiences, and the entire eCommerce segment. The value of the global BI and analytics software market is expected to be 17.6 bn USD by 2024. The flawless and efficient data processing capabilities of BI software help companies around the world to accomplish their corporate and data goals without any hassle. Apart from the current sectors employing BI, such as supply chain management, resource management, and customer interaction management, it is expected to revolutionize the technology and manufacturing sectors as well. BI software draws its strength from: Take a look at some of the future applications of BI: 3. Predictive Analytics Big data is empowering business organizations and data analytics stakeholders with its fundamental approach for quite a time now. It helps them to gain a competitive edge and accomplish their goals, such as better services, more sales, more customers, happier customers, and so on. Business organizations use multiple tools to achieve these goals and predictive analysis is a common feature of these tools. Predictive methods use historical events and modern data to uncover hidden trends and present actionable insights, such as: All in all, predictive analysis offers you a realistic and data-driven future prediction for various things. With advancements in Big Data, AI, ML, and other technologies, predictive analytics is all set to gain more power and offer more crucial insights. So, you will be able to predict customer response, churn, purchase behavior and optimize your brand offerings, services, and business agendas accordingly. The visual shows how predictive analytics can identify the meaningful patterns of big data and generate future predictions to identify the value proposition of various options. 4. Cloud-Native Analytics Will Become Necessary Gartner says that, public cloud services will assume a mandatory stance for 90% of data analytics innovation and processes. As data analytics will move to the cloud, cloud-native analytics will become a necessity for all the leaders and industry stakeholders. Cloud-native analytics will empower the data analysts to align the right services with the right use cases, which might give birth to governance and integration overheads. Apart from an in-depth analysis of the cost and pricing models, the data and analytics leaders will also be required to prioritize workloads to exploit cloud capabilities. Some other additional responsibilities will include: Some of the sectors that are in for a huge wave of change in the light of cloud-native analytics are: Because of the inherent advantages of cloud-native platforms and analytics, such as elasticity, self-service, monitoring, etc, industry leaders consider cloud-native platforms mandatory in order to succeed with Big Data. 5. Digital Transformation Digital transformation stems from the ability of an organization to combine both automation and digitization. As the global business landscape becomes more competitive, more sophisticated, and extremely data-centric, Big Data emerges as one of the key drivers of digital transformation. Businesses across the globe utilize huge chunks of unstructured data to discover the hidden patterns in relation to their business models and Big Data becomes all the more important. Take a look at some ways Big Data triggers digital transformation: 6. Climate Change Research Climate change research

Kavika Roy
November 24, 2021
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15 Best Big Data Tools That You Need to Check Out Today!

Big Data is a large amount of data collected in real-time in various formats and structures. Latest technologies have simplified data gathering from multiple sources. Data warehouses and data lakes can store this data on-premises or on the cloud. However, the collected data is of no use to the business until it is analyzed. Basic data analytics tools like MS Excel cannot process Big Data due to the excess volume and complex nature of data. Big Data needs tools designed explicitly for the purpose. Big Data Analytics is a type of advanced analytics where statistical algorithms, what-if models, and predictive analysis are used to identify the patterns, trends, and correlations between different elements. What Are Big Data Tools? Big Data tool is a software used to clean, format, and process vast data in real-time. It is an analytical system capable of understanding complicated information and deriving actionable insights from it. Big Data tools help enterprises make data-driven decisions and increase returns. Why Do We Need Big Data Tools? The US economy faces around $3.1 trillion yearly losses due to poor data quality. The losses can be minimized by adopting a data-driven model and investing in the right Big Data tools. Organizations have begun understanding the importance of Big Data Analytics tools and technology. An Executive Survey report by New Vantage says that 97.2% of enterprises are investing in Big Data and artificial intelligence. Big Data tools can help businesses with the following: Picking the right Big Data tools for the business is crucial. The accuracy of Big Data analytics and derived insights depends on the tools used for the process. In this blog, our expert talks about the best Big Data analytics tools preferred by numerous enterprises from around the globe. There are numerous tools available in the market. However, our list has been compiled based on the data and usage details collected from enterprises. The Best Tools for Big Data Analytics 1. Apache Hadoop Apache Hadoop is one of the best open-source Big Data analytics tools in the market. It’s written in Java and is used to handle clustered file systems through the MapReduce programming model. Hadoop is cross-platform software used by more than half of the Fortune 50 companies. 2. Apache Storm Apache Storm is another open-source Big Data tool that offers the best real-time processing capabilities. The Storm has cross-platform abilities and provides distributed stream processing. It’s written in Java and Clojure and is fault-tolerant. 3. Atlas.ti Atlas.ti is known as a comprehensive all-in-one software for research. It is used to research markets, understand user experience, and help with academic research and qualitative analytics. The software is available in two versions- desktop for on-premises use and web version for cloud applications. 4. Tableau Tableau falls in the category of leading tools for Big Data visualization and is available in three versions- Tableau Desktop, Tableau Server, and Tableau Online for cloud solutions. The open-source version of the software is known as Tableau Public. The data visualization tool works with data of all sizes and formats and provides real-time reports through the interactive dashboard. 5. Apache Cassandra Apache Cassandra is a free, open-source software that deals with vast volumes of data on several servers connected to one another. The NoSQL DBMS uses CQL (Cassandra Structure Language) to share information with the databases in the enterprise. Low latency is one of the significant advantages of using Cassandra. 6. Rapidminer Rapidminer is an open-source Big Data analytics tool that SMEs and large enterprises alike can use. It’s a perfect choice to use with data science models, predictive analytics, and new data mining models in the business. Rapidminer helps with data preparation, implementing machine learning, and deploying models. 7. Knime Knime is Konstanz Information Miner, open source Big Data software used for analytics, reporting, and data integration. The tool helps integrate machine learning and data mining models. Knime is the best choice for research, BI, CRM, etc. It has a rich algorithm set and is still easy to use in the enterprise. It is a free tool that comes with GNU General Public License. 8. MongoDB MongoDB is written in C, C++, and JavaScript. It is a NoSQL and document-oriented database that works with multiple operating systems. It is a free open-source Big Data tool that processes massive amounts of data and develops file systems for storage. 9. Cloudera If you’re looking for quick and secure data platforms, Cloudera is the answer. Cloudera is free and open-source software that works with any data environment and encompasses Apache Hadoop, Spark, Impala, etc. Data collection, processing, managing, modeling, and distribution are easily performed using Cloudera. 10. Oracle Data Miner Oracle Data Miner is used by data scientists for business and data analytics. It provides the easy drag and drop feature to make changes to the editor interface and customize the reports. The Big Data tool is an extension of the Oracle SQL Developer and deals with graphical workflows. 11. Apache Samoa Apache Samoa stands for Scalable Advanced Massive Online Analysis and is an open-source software tool used for data mining and machine learning. It is a well-known platform that allows data stream mining of Big Data. Data classification, clustering, regression, and development of new ML algorithms can be performed using Apache Samoa. 12. Apache Spark Apache Spark is an open-source Big Data analytics tool that deals with machine learning and cluster computing. Spark has gained fame for being a lightning-fast analytics engine that can process massive amounts of Big Data with the utmost ease. 13. Apache Kafka Apache Kafka is a publish-subscribe messaging system that sends messages from one endpoint to another. It works online and offline and prevents data loss by replicating the messages on disk storage and within the cluster. Apache works seamlessly with Spark and Storm to process and distribute Big Data analytics within the enterprise. 14. Apache CouchDB Apache CouchDB is an open-source, document-oriented NoSQL database with cross-platform abilities. It stores data in JSON documents and responds to JavaScript queries. Fault tolerance and the ability to run a single logical database on numerous servers are the two advantages of using Apache CouchDB. 15. Apache

Kavika Roy
November 18, 2021
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7 Ways to Get Ahead of the Competition Using Big Data for Supply Chain

Supply chain management and logistics have always been complex. Be it sourcing raw materials or delivering finished goods and managing returns, everything comes under the supply chain. Vendors, manufacturers, warehouses, logistics service providers, etc., are all a part of the supply chain. Supply chain management (SCM) relied on ERP software and outdated data storage systems. With the traditional methods taking much time and not delivering enough results, enterprises are looking at data analytics and big data to streamline the supply chain, automate the recurring processes, and increase overall efficiency. Data generated in the supply chain belongs to more than one enterprise (especially if the manufacturer uses third-party logistics services). This has made it rather hard to analyze a vast amount of data without using the latest technology. Big data for supply chain solves the problem. What is Big Data Analytics? Data sets that are larger and complex than what a traditional data processing system can handle are called big data. To collect, store, and analyze such vast amounts of unstructured, semi-structured, and structured data, enterprises need to invest in advanced analytics. This use of advanced analytics on big data is called big data analytics. In Big Data Analytics, data is collected in real-time from numerous sources in multiple formats. It has high volume, high velocity, more variety, etc., and is processed using artificial intelligence, predictive analytics, and other subsets of AI (like machine learning and natural language processing). You can also convert big data to smart data using data analytics. Many companies offer big data consulting services to help SMEs and large-scale enterprises process data and gather in-depth insights. Importance of Big Data Analytics in Supply Chain So how does big data analytics help in supply chain management? What is the importance of investing in data analytics for SCM and logistics? Compare and Match Data As we mentioned earlier, data in a supply chain is produced within the enterprise and outside the enterprise. This data has to be collected, cleaned (remove duplicates, formatting, etc.), structured, and analyzed to derive insights. Both historical and real-time data need to be used to make decisions for improving the supply chain. Decisions about demand and supply, weather conditions, seasonal changes and their impact, etc., are made based on the insights gathered from big data. Big Data and IoT IoT (Internet of Things) makes it easy to share information among different devices connected to the network. It helps leverage data within the supply chain. And when this is combined with big data analytics, enterprises can create a network to facilitate continuous and live data exchange. Speed Up the Planning Process By integrating data across the supply chain, enterprises can use statistical models and predictive analytics to understand the coming trends in the market. This will help in planning the production, warehousing, and delivery of the finished goods. Data management services are used to process historical and real-time data and derive insights. Sourcing Raw Materials According to the Global CPO Survey 2016 by Deloitte, there’s no clear digital strategy for 60% of the procurement. Many SMEs can save costs by sourcing raw materials based on comprehensive data. Real-time data analytics will help enterprises in the following ways- Executing the Plans Planning individual elements in the supply chain will not be enough. The success of a business lies in executing these plans with no errors or glitches. Big data analytics makes it possible to optimize the use of resources while increasing productivity and returns. Delivering the Final Products How does a manufacturer get back the return on investment and earn profits? It’s possible by delivering the finished goods to the end customer on time and keeping the customer happy. To avoid delays and uncertainties in deliveries caused by traffic, weather conditions, etc., enterprises can make the necessary changes to speed up deliveries. Handling Returns Regardless of how good a product is, every manufacturer will have to deal with returned goods. Reverse logistics is costly as it involves additional warehousing and transportation costs while returning the amount to the customers. Data analytics can help enterprises reduce returns by increasing data visibility and aligning the inventory and sales systems. Customized Simulation Models By using big data in the manufacturing industry, custom models can be designed to replicate real-life scenarios in a simulated environment. This helps enterprises try out various strategies, make changes to the model, and come up with the best solution to streamline the supply chain in the business. Ways to Get Ahead of the Competition Using Big Data for Supply Chain Big data analytics can tremendously help manufacturers, logistics providers, and enterprises to streamline their business operations on multiple levels. This empowers them to move ahead of their competitors and make a name for themselves in the market. In our highly competitive scenario, being equipped with the right kind of advanced technology can place a business on the top of the charts. Improving Inventory Management Managing inventory is one of the toughest jobs for any manufacturer or supplier. Even 3pl logistics and warehouse service providers need to be careful about how they manage the inventory in the warehouses. Big data analytics helps choose the nearest warehouse, the distribution of inventory, calculation of distribution and transportation costs, and helps track every move. Also, by automating the process, enterprises do not have to manually check the stock each time to ensure if there is sufficient inventory to cater to customer demands. It is handled by the software, and the employees are alerted when the stock has to be moved. Streamlining Online Retail and eCommerce With more customers relying on online purchases, it has become important for businesses to streamline eCommerce operations and make the entire process as smooth as possible. Cloud data analytics services help retailers align the BI processes and tools to improve decision-making. Offshore companies offer cloud computing services to collect, store, clean, and analyze data to derive accurate insights. Today’s customers want their orders to be delivered in a single day. For this to work, enterprises need to have complete control over their supply chain. Understanding Customer Behavior The

Kavika Roy
October 18, 2021
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How Embedded Analytics Provides Value for Your Business

Embedded analytics adds real-time data analytics reports to websites and software applications. It empowers enterprises to adopt the data-driven decision-making model and add value to the business. Data analytics is the process of acquiring, transforming, and using AI-based business intelligence tools to convert real-time raw data into structured data to derive actionable insights. Data analytics are performed in separate environments and are then shared with employees and management to make decisions. Employees have to work on different systems and move back and forth to use these analytics at work. Data visualization tools have simplified the process through embedded analytics and third-party integrations. This enables you to use the insights in the same system on which you are working. It provides a seamless workflow with little or no disruptions so that business intelligence analytics can be used to their maximum potential. The issue with the underuse of insights in the traditional method is solved through embedded analytics. When the analytical insights are a separate entity, some of it may be lost or not considered for decision-making. By embedded analytics to other software and platforms, this loss can be minimized and even nullified. Every important piece of information is effectively used by businesses. What is Embedded Analytics? Embedded analytics is the integration of data analytics reports to websites, third-party software, and applications. The dashboard of the BI tool is embedded (included and presented) on another software or website so that the insights can be directly shared and used from the main source. You can see changes in real-time when using embedded analytics. The report will change as data is processed and insights are generated. These changes are visible in real-time when you look at the website or the app. News portals, public health reporting websites, research platforms, etc., are a few embedded analytics examples where the dashboard of a BI tool is embedded into the website to share information with the public. Inserting the dashboard is a great way to present information to the management and the end-user. Be it the sales team, the marketing team, the accounts department, or the production team, every department in the enterprise can be provided with embedded analytics to use the latest insights to make the right decisions at work. It is an effective way to adopt the data-driven model in the business. Relationship Between Embedded Analytics and Business Intelligence Embedded analytics solutions effectively use business intelligence tools and insights by fully integrating them into the system. Instead of restricting BI to its isolated framework and accessing the insights when needed, embedded analytics helps you understand data with more clarity. Once you take data insights outside the traditional BI tools, they can be presented anywhere. In fact, the insights generated by your business can be shared with or sold to another organization and embedded into their systems. They can either use it for internal decision-making or share it with their end-users. Using embedded analytics can increase your business value and give you a definite edge over your competitors. It also helps improve your B2B connections and expand your business. How to Use Embedded Analytics There are three ways to use embedded analytics within and outside the business and take data insights to the users. Internal Tools Within an SME or a large-scale enterprise, embedded analytics software is used to streamline workflows in various departments and align them to achieve organizational goals. Even though software like CRM has in-built data visualization capabilities, the features are limited and do not provide all the necessary information to the teams. By integrating embedded analytics with these systems, various teams across the enterprise can access the data required for their projects and use the insights directly to create better strategies. What happens here is that if a team is working on a CRM system, they don’t have to move to another software or application to access data analytics. Whatever they require can be found in the same system. Web Embedded analytics on the website is used to take the information to the B2B audience and end-users. It helps engage the audiences and share complex data in an easy-to-understand manner. You can plug tools like Chartio with most HTML frames without much fuss. Instead of detailing a report on the blog or website, you can rely on graphical presentation and illustrations to make the post interactive and fun. Buy-In Now, what if you want to monetize embedded analytics? Simply make it a premium feature and sell it. Interested parties can opt for the premium feature (either by paying a one-time fee or monthly subscriptions) to access embedded analytics and use it in their systems. This will enable data analytics to happen at the same place where the decisions are being made. In case of any error, you can fix it within the same system instead of going to a reporting tool or visualization tool. Steps to Start Using Embedded Analytics Employing business intelligence consulting services in an enterprise will make embedded analytics easy. Embedded analytics needs people, processes, and technology to be aligned with each other and with the organizational goals. Let’s look at how to start using embedded analytics in brief. Use Cases of Embedded Analytics There are many ways in which embedded analytics can help your organization. The following are some embedded analytics use cases- Helps Monetize Data Analytics Embedded analytics allows you to monetize data by providing customers access to the insights. You can do this by embedding the data visualization tools to the front end or the user interface and allow only paid or subscribed customers to view the details. Can Optimize Mobile Workforce If your employees work using mobile devices, it can be frustrating to move between different apps and tabs. Embedded analytics makes it easy for them to work on the same app. It helps employees finish work faster and increases the quality of work. Promote and Increase Use Adoption Since analytics are integrated into the existing workflow, employees will be more open to adopting the latest technology. This will increase productivity and add value to the business. The money you spend on adopting the data-driven model will be well-spent when employees embrace technology. Reduce the Risk of Errors There is always a risk of misrepresenting data when

Yashica Chopra
September 03, 2021
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How to Get Started with the Modern Data Management Approach

Data is priceless in today’s world. Never before was it considered as essential to a business as it is in recent times. Whether it is easy to access real-time data or the availability of advanced technology to process and analyze this data, enterprises are trying to make the most of this precious raw material. Data management in today’s world needs a modern approach to effectively handle the complex problems of dealing with multiple data formats. Data in its original form is raw, unstructured, and comes in multiple formats. Data management is the process of collecting, sorting, arranging, and managing vast amounts of data to help businesses derive valuable insights from it. From data governance to data architecture and security, modern data management is a multifaceted discipline. But following the modern data management approach is not easy, especially for enterprises that are used to traditional business methods. That’s where consulting companies take care of data management services and help SMEs unlock the full potential of data on their own. In this blog, we’ll see how to adopt the modern data management approach in an enterprise. But before that, let’s read a little about data management, why it is necessary for every business, and what benefits an enterprise can get by investing in modern data management. What is Data Management? Data management is a combination of various functionalities that help enterprises use accurate and reliable data to derive insights that help in decision-making. An enterprise needs a comprehensive data management strategy if it wants to adopt the data-driven approach and rely on business intelligence tools to generate valuable reports. The enterprise needs to establish policies and best practices to ensure that the data management strategy is aligned with the goals. The data management system includes a wide range of processes like- Types of Data Management Data management can be classified into the following types- Master Data Management (MDM) This is a process where all data sources lead to the same data center. A master database is updated and used to make all business-related decisions. A single, latest version of the database is used as a reliable source of data. Data Security Security filters and applications are used at every level to ensure that the data in the database cannot be easily accessed or hacked by people without permission. It also safeguards the data from being deleted or modified by mistake. Data Quality Management Identifying and removing duplicates, eliminating inconsistencies, etc., are a part of data quality management. This is done when data is collected from all sources and stored in the database. Data Stewardship A data steward is a tool that is used to supervise and implement data policies across the enterprise. A data steward is also a person responsible for the same. Data Governance These are the set of laws framed by the top management to ensure proper use of data. It is a framework of rules and regulations which the data stewards implement in the business. A data governor is a person who supervises the data steward. Data Warehousing The process of storing vast amounts of data and using it for analytics is known as data warehousing. It can either be managed on-premises or on the cloud. Choosing the right data warehouse is crucial for a business to successfully utilize its data. Big Data Management Collecting, cleaning, and processing huge amounts of data is known as big data. Managing raw, semi-structured, and structured data collected from multiple sources is called big data management. Traditional systems are not enough for big data management. What is Modern Data Management? So how is modern data management different? It is nothing but an advanced approach where data is connected with the latest technology like artificial intelligence to get predictive insights, identify challenges and opportunities in the market, and help SMEs make faster and better decisions. Why Do You Need Data Management? There are quite a few reasons for a business to invest in data management and ensure there are no glitches in the system. Something as basic as feeding wrong information can lead to disastrous results for the business. Enterprises hire big data consulting services to streamline their business processes and migrate from the traditional approach to the modern one. It makes the transitions easier and amplifies the results. In short, the success of a modern enterprise depends on how effectively it can manage the huge amounts of data in this system. How to Build a Modern Data Architecture? Following a comprehensive step-by-step is essential if you want to build a modern data architecture in your business. Benefits of Modern Data Management Whether it is cloud data analytics or on-premises services, the following are some of the benefits of following the modern data management approach in an enterprise. Data Security and Data Privacy Compliance The increasing cyberattacks have resulted in new and stringent data security policies. Unless businesses adhere to these regulations, they cannot be eligible to bid for government projects. Modern data management services include high-level data security and enable enterprises to use automated tools for data protection. Speeding up Modernization Digital transformation is a continuous process where the systems, tools, employees, and work environment has to be aligned to achieve the common goals. Modern data management makes it simpler for the management and employees to accept and adapt to the changes. This increases business efficiency and productivity. Optimization of Resources There is no point if data scientists spend more than half of their time collecting and cleaning data. When will this data be analyzed? What about real-time data? Data strategy consulting services are the answer. Automating recurring tasks, using AI-based technology, and investing in advanced software for data analytics will help enterprises optimize resources and increase returns. Enhancing Decision-Making Abilities Isn’t this what every enterprise wants? Imagine getting in-depth reports that are easy to understand. It will help employees and management choose the best way forward for the business. Being armed with accurate insights can make an enterprise a leader in the industry. Modern Data Management Approach: Things to

Prasansa Singh
August 27, 2021
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How Can Big Data Help in Augmenting Cybersecurity?

With the ever-evolving cybercrimes, data handling, and security challenges, users must remain informed with ways to tackle them. The good news is that various enterprises are coming up with multiple solutions to combat the nuanced tools and mechanisms being used for data intrusions. The most prominent of them being Big Data Services. Big Data analytics in cybersecurity industry has various applications if enforced properly. If we’re to believe the reports, the incidents of data breaches continue to rise every single year which is why we must make cybersecurity an absolute priority. The world faces more than 10,000 malicious files and 100,000 malicious websites on a daily basis. Phishing attacks account for over 80% of the reported security incidents. Since the pandemic outbreak, remote workers have also been the target of alarming cyberattacks. Despite knowing about the risks related to cyberthreats, most of the companies were still not cautious and ended up being a victim to hackers and cybercriminals. What is Cybersecurity? Cybersecurity can basically be defined as a mechanism meant to protect your devices and the data contained in them. Its application also expands to networks and software. It’s not just a department that’s responsible for protecting your data, rather every step you take towards creating a safer environment for your system contributes to an overall secure user experience. A cyber threat comprises any malicious attempt at unauthorized access to a network for stealing private data or making changes to it. Phishing attacks, ransomware, malware, MITM, SQL Injection, Emotet, etc are just a few types of cyber threats. Why is Cybersecurity Important? Due to various vulnerabilities in the system, a company may end up becoming a victim of a cyberattack. There have been multiple instances of really dangerous cyberattacks in the past, for e.g the SamSam ransomware attack that took place in Atlanta, the Stuxnet worm that attacked the nuclear facilities in Iran in 2010, and one of the most recent and global data theft account- Pegasus, the spyware. Overall cyberattacks cost the world economy more than $1 trillion according to McAfee- a leading cybersecurity firm. Apart from the financial point of view, there are many other benefits to cybersecurity like- managing and protecting big data thereby improving confidence in a company’s reputation and hence customer loyalty with a company. There are various global laws like HIPAA, GDPR, DCC, etc that are enforced globally to protect citizen data and to regulate security standards. Application of Cybersecurity Some steps that can be taken for securing your browser and system experience: Apart from these small steps, there are dedicated cybersecurity professionals and companies like Intruder, Cipher CIS, Symantec, Cisco, etc who are responsible for defending and even predicting data breaches on a much larger scale. Their services include vulnerability assessment, external network security audit, cloud security, network security, endpoint security, etc. There are a few added benefits to cybersecurity like productivity enhancement, gaining client confidence in terms of data security, and improved system performance. What is Big Data? Big Data comprises a huge amount of data, structured or semi-structured. Data is generated in a high volume, at a great velocity, and in a huge variety. Did you know that Social Media is the highest contributor towards the generation of data? Important tools like Apache Hadoop, HPCC, Statwing, Qubole, etc can be used to analyze and interpret a huge amount of data accurately and efficiently. The analysis and storage involve Artificial Intelligence and Machine learning as well. Big Data can in fact be converted into Actionable Intelligence to get an added advantage over one’s competitor. Data Analytics can also act as a catalyst to achieve an organization’s goals if applied properly. Big Data is creating a revolution due to its multifold usage throughout the world. The retail industry benefits from an accurate representation of customer behaviors and patterns thereby contributing towards achieving higher sales. And the healthcare industry benefits by harnessing data pertaining to past records and thereby providing more personalized medications and even developing more effective treatments. Cybersecurity companies have been applying big data technologies for cybersecurity in recent times and the results have been promising. Big Data’s role in mitigating cyber-threats Considering the rate of both data production and cyberattacks, it’s not surprising that Big Data seems like the perfect solution to fight and prevent cybercrimes. Cyber professionals can comb through the huge amount of data in a time-efficient manner and thus detect and resolve the issues, enabling them to even prevent and predict any future possibilities of data invasion and data theft. Big Data analytics insights are crucial for the detection and prevention of cyber threats like ransomware, malware, phishing attacks, etc. Using Big Data Analytics in cybersecurity comprises analytical tools, data management, and examination techniques that integrate data from various hosts, devices, and networks and then providing the information to sensors installed within, to detect and prevent any upcoming attacks. A Few Big Data Technologies for Cybersecurity Predictive Nature: Big Data analytics make use of past records, algorithms, and statistics to look out for certain patterns which might’ve caused an issue in the past. Thereby, predicting future attacks while also providing information about previous attacks that might have occurred in a system. Constant Monitoring: Data validates access to the systems while tracking logged-in activities thereby preventing a breach as well as managing passwords efficiently. Complete Automation: Professional Big Data analysts can actually automate the entire detection and prevention of threats process, saving time and human efforts. Actionable Intelligence-Driven Risk-Management: With constant monitoring and processing of datasets, a foundation for actionable intelligence is formed which further plays a crucial role in detecting and predicting any cyberthreats Now, Big Data may apply any past or current insights pertaining to cybersecurity, collected from various sources to solve any issue. It, therefore, becomes rather convenient for the analysts to address and resolve any threats really fast. Threat Forecast: With the help of AI algorithms, companies can even predict any upcoming threats based on past and present records and identification of any patterns after

Yashica Chopra
August 26, 2021
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How Demand Forecasting Is Helping the Retail Industry?

Forecasting is the process of analyzing existing data to determine future events. In the retail sector, forecasting is used to understand the customers’ purchase behavior. Actual data that is in existence and its related market research might differ by the kinds of products a retailer usually intends to trade for, but the basic denotation of forecasting in retail accompanies similar patterns, even across different product lines. Demand forecasting is one of the major aspects of running a successful retail business. A retailer cannot accurately predict and prepare for the changing market conditions and customers’ requirements without demand forecasting. And one such indispensable component is demand forecasting. Put separately, demand describes the eagerness of consumers to purchase a specific product, whereas, forecasting predicts future performances by utilizing statistics of the pre-existing data. Hence, demand forecasting in retail is the insightful prediction of consumers’ purchase actions regarding a specific product, at a specific price and in a specific time area. Simply, it is the demand forecasting of future bids that lead the businesses to create a win-win situation for consumers and themselves. Many small and big companies implement demand forecasting for supply chain control, financial planning, and customer success management. This strategy allows businesses to prevent any sort of loss and ensures client retention while making any necessary and sudden changes. Without proper implementation of demand forecasting techniques, businesses might find it hard to possess an adequate amount of stocks in hand. Especially, in the COVID times where physical interaction has come to a halt and companies are making rapid changes to their working models. It plays a significant role in the growth and survival of the retail market. Importance of Demand Forecasting in Retail Here are the two major points that define the importance of demand forecasting: 1) Cost-efficient One of the simplest ways to maximize profit in the retail business is to cut down costs. Initially, you can diminish the amount of money you have invested in unnecessary inventory. That will result in lower carrying costs since you have lesser stock in hand. Plus, this also ensures that you don’t go out-of-stock and capitalize on every sale you make. While the above ways do work, a systematic approach towards the need for demand forecasting ensures to beef up the profits and business structure. Once you forecast the demand, you can easily have a look over the time period and check if you are close to hitting the anticipated sales. If you were left behind on your goals, you can always use marketing and advertising techniques. And, in case you underestimated, you can reorder the stuff and cross-promote a similar product. 2) Customer Experience Suppose you visited an e-commerce site to purchase groceries and essentials for your home. Unfortunately, the brand you prefer is currently out of stock. What will be your next move? Will you wait for the brand to restock them and then proceed to make a purchase? Or, will you look for another brand that offers similar items? The latter, right? Enhancing the customer experience is the quickest way to improve profits. Neglecting the “out-of-stock” from your business can retain your consumers and will stop them to entertain your competitors. Plus, aiming towards the end-users might lead to more referrals and loyalty, rather than focusing on raising the prices, altogether. What Are the Advantages of Demand Forecasting in Retail? It is necessary to perform demand forecasting in a proper way to stay in the retail industry game. Forecasting may not be easier for both brick and mortar and giant companies, however, the results-driven by it can make a huge impact. Here are the advantages of demand forecasting: 1) Better Financial Planning The demand forecasting formula is used to help you achieve better and smarter financial planning for your retail business. Detailed and accurate information obtained from the forecasting lets retail companies build better financial plans. Accurate predictions can easily highlight the peak intervals, demand trends followed by months, dates, or even a specific time of the day, and seasonality. Hence, making it easier for companies to look upon the cash flow and safeguard themselves from peaks and unexpected expenses. 2) Eliminates Staffing Problems A well-structured demand forecasting process can result in preventing staffing problems in companies. These issues generally arise during the peak season. It can be easily avoided by anticipating the hike and fall in the demand during different time intervals or days. This way, companies can eliminate the guessing and come up with a more structured schedule list. Moreover, managers can also plan the shifts accordingly for the workers. 3) Enriches the Marketing Plans Forecasting also plays a vital role in developing effective marketing campaigns. For instance, if a company experiences a loss in sales, it is advisable to put effort into its marketing strategies. With proper demand forecasting solutions, marketing executives can develop properly tailored marketing promotions that can take on the loss. Moreover, making modifications to the pricing index according to the customers can also make an impression. 4) Advanced Inventory Management Not only the inventory but forecasting also helps the companies willing to enhance their production management. Companies strive for inventory overflow whenever their product goes out of stock or sales fall down in peak times. Precise forecasting allows them to anticipate and adapt the situation to meet the required demands accordingly. In addition, it also ensures that the company holds the required material, shipping, and labor. What Are the Reasons to Use Demand Forecasting in Retail? The biggest benefit reason to use demand forecasting is to reduce uncertainty in retail operations. Demand forecasting kills uncertainty, substantially, with its predicted calculations and thus, allows retailers to order, allocate, and refill accordingly. Additionally, it is also beneficial in terms of workforce scheduling and optimizing capacity management systems. Benefits of Demand Forecasting Here is the list of benefits driven by demand forecasting: How Is Demand Forecasting Performed? Demand forecasting relies on 3 main models that are used in the retail industry. However, each model

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 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