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 Supply chain management (SCM) relied on ERP software and outdated data storage systems.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

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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

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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

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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

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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

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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

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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

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How Advanced Data Analytics help to Achieve Organization’s Goals?

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

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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

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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

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