Leveraging Big Data for Competitive Advantage: Benefits

Business Intelligence, Big Data Management, Big Data

Article | July 4, 2023

Real estate firms can enhance their decision-making with the help of Big Data Analytics. The real estate industry was using the data of past events which were not that effective. But now Real estate businesses can use Big Data Analytics for accurate real-time data. The real estate business holds very big risks, not for only developers, it affects the businessmen and investors too. Big Data Analytics help them to get out of this situation and help them to identify their prime opportunities in real estate. With the help of Big Data Analytics real estate professionals will be able to use geographic as well as structured data and that too for targeted marketing. Big Data Analytics analysis the insights of the needs of investment trends and customer desires and the personalized interactions too. So what are those fields in which Big Data Analytics can contribute to the real estate firms? Accuracy in property appraisals Price predictions Risk managing Healthy selling and buying habits Now Bow Big Data Analytics Is Going To Transform The Real Estate Business? 1) Management of Risk With the help of Big Data Analytics Real estate businesses can precisely predict the age of the property and also they can redesign and renovate their property according to their needs. Well, with the help of this the potential risk factors for the buyers and the investors will be reduced. The buyer can make fair cash offers regardless of the condition of the property and also BIg data analytics will take care that their customers never be at a loss. 2) Prospective And Interested Buyers Let me tell you one use of big data analytics. With the help of this technology, the agents no longer need to project blindly they can actually predict the behavior of their customers. The needs of potential buyers can also be analyzed by Big Data and as agents find their customers the customers will be able to find relevant agents to buy their expected real estate property. 3) Higher Property Valuation Property valuation is one of the most important things in the Real Estate market. It decides to make or break the real estate firm. Big Data analytics give insights to the customers about the market conditions, buyer profiles, and other data precisely. Data analytics work on prediction bases and they provide the demographic changes with the help of these changes the estate marketing will be able to forecast the behavior of the customer. Based on the location with the help of Big Data Analytics real estate managers can design and develop their projects likewise. Also, there are apps that use big data analytics. Also Read | Banks in the Metaverse: Why to Get In Early and 3 Ways to Start 4) Improvement In Marketing Strategies The private and public data sources, business surveys, and social media gives them insights that enhance your ability to determine the right market for your project. For instance, take an example, Big Data analytics can give you the data by sorting gender, age, preference, interests, and region. Which will eventually enhance the market interaction of specific firms. 5) Customer’s Experience On Top Big Data insights collected from several platforms like CRM Systems, and social media can help customers to enhance their experience. The agents in the real estate industry can really use and utilize the data to target their potential customers. Well, the customers should rely on agents in the terms of Big Data analytics because Agents understand the needs of customers, and make suggestions on properties depending on buyer preferences. 6) Perfect Predictions With the help of Big Data analytics, the buyers and the sellers can avoid the risks which come in way of the project. Gone are those days when we guessed the real estate trends. Now we can actually analyze them in a proper manner and work on it like it’s left-hand’s play. Also with the great reduction in time. Perfect Predictions of Big Data can be done with the help of computer algorithms. Big data Analytics help buyers and sellers forecast the market fluctuations and that too real-time. There are two different perspectives. Let me discuss them in detail. Low risks properties can be appreciated well with the help of great predictions. Meanwhile, agents and investors think that high risks can give great results. 7) Personalization Of Property Data Big data companies focus on the things that go unnoticed. For example, the amount of sunlight that comes into a room. If you follow your steps to the real estate possession, it will be time-consuming, but with the Big Data analytics, it will be easier for you to get real-time and accurate information about the property. Conclusion Big data analytics is now in use in several industries. Meanwhile, the Real estate industry is also trying to step up and get the help of big data analytics. Well, you can not say that they are not using this technology, They are using it for their greater good. Big Data analytics have become a decision-making factor for all sectors. If you want to transform your business, real estate business particularly, this article will help you from the start to the end.

Business Intelligence, Big Data Management, Big Data

Article | May 15, 2023

The acronym DMaaS can refer to two related but separate things: data center management-as-a-service referred to here by its other acronym, DCMaaS and data management-as-a-service. The former looks at infrastructure-level questions such as optimization of data flows in a cloud service, the latter refers to master data management and data preparation as applied to federated cloud services.DCMaaS has been under development for some years; DMaaS is slightly younger and is a product of the growing interest in machine learning and big data analytics, along with increasing concern over privacy, security, and compliance in a cloud environment.DMaaS responds to a developing concern over data quality in machine learning due to the large amount of data that must be used for training and the inherent dangers posed by divergence in data structure from multiple sources. To use the rapidly growing array of cloud data, including public cloud information and corporate internal information from hybrid clouds, you must aggregate data in a normalized way so it can be available for model training and processing with ML algorithms. As data volumes and data diversity increase, this becomes increasingly difficult.

Business Intelligence, Big Data Management, Big Data

Article | April 27, 2023

Contents: 1 Introduction 2 Top Data Storage Trends That Simplify Data Management 2.1 AI Storage Continues to be The Chief 2.2 Price Markdown in Flash Storage 2.3 Hybrid Multi Cloud for The Win 2.4 Increased Significance of Software-Defined Storage 2.5 Non-Volatile Memory Express (NVMe) Beats Data Center Fabrics 2.6 Acceleration of Storage Class Memory 2.7 Hyperconverged Storage – A Push to Edge Computing 3 The Future of Data Storage 1. Introduction There’s more to data than just to store it. Organizations not only have the responsibility of dealing with a plethora of data, but are also anticipated of safeguarding it. One of the primary alternatives that enterprises are indulging in to keep up with the continuous data expansion is data storage entities and applications. A recent study conducted by Statista revealed that worldwide spending on data storage units is expected to exceed 78 billion U.S. dollars by 2021. Going by these storage stats, it can be certainly said that data is going to be amplified at a much faster rate, and companies do not have a choice but to be geared up for a data boom and still be relevant. When it comes to data management/storage, information technology has risen to all its glory with concepts like machine learning. While the idea of such profound approaches is thrilling, the real question boils down to whether organizations are ready as well as equipped enough to handle them. The answer to this might be NO. But, can companies make changes and still thrive? Most definitely, YES! To make this concept more understandable, here is a list of changes/trends that companies should adopt to make data storage a lot more easy and secure. 2. Top data storage trends that simplify data management Data corruption is one big issue that most companies face. The complications that unfold further because of the corruption of data are even more complicated to resolve. To fix this and other such data storage problems, companies have come up with trends that are resilient and flexible. These trends have the capability of making history in the world of technology, so, you better gear up to learn and later adapt to them. 2.1 AI storage continues to be the chief The speed with which AI hit the IT world just doesn’t seem to slow down even after all these years. We say this because, amongst all other concepts that were and are constantly being introduced, artificial intelligence is one applied science that has made the most amount of innovations. To further add to this, AI is now making enterprise data storage process easier with its various subsets like machine learning and deep learning. This technology is helping companies in accumulating multiple layers of data in a more assorted format. It is automating IT storages including data migrating, archiving, protecting, etc. With AI, companies will be able to control data storage across multiple locations and storage platforms. 2.2 Price markdown in Flash storage As per a report by Markets and Markets, the overall All-Flash Array Market was valued at USD 5.9 billion in 2018 and is expected to reach USD 17.8 billion by 2023, at a CAGR of 24.53% during this period. This growth only states that the need for all-flash storage is only going to broaden. Flash storage has always been a choice that most companies stayed away from mainly because of the price. But with this new trend of adopting flexible data storage ways coming in, flash storage has been offered at a much-depreciated price. The drop in the cost of this storage technology will finally enable businesses of all sizes to invest in this high-performance solution. READ MORE: HOW BUSINESS ANALYTICS ACCELERATES YOUR BUSINESS GROWTH 2.3 Hybrid multi cloud for the win With data growing every minute, just a “cloud” strategy will not be enough. In this wave of data storage services, hybrid multi-cloud is one concept that is helping manage off-premises data. With this growing concept, IT authorities will be able to collect, segregate and store, on-premises, and off-premises data in a much-sophisticated manner. This will enable in centrally managing while reducing the effort of data storage by automating policy-based data placement across a hybrid of multi-cloud and storage types. 2.4 Increased significance of software-defined storage More the data, less reliability on hardware devices – this is the growing attitude of most companies. This fear certainly has the possibility of becoming a reality. Hence, an addition to the cybersecurity strategy that companies can make is adopting software-defined storage. This approach of data storage disconnects the underlying physical storage hardware. It is programmed in a way that can function on policy-based management of resources, automated provision, and computerized storage capacity reassignment. Due to the automated function, scaling up and down of data is also faster. Some of the biggest advantages of this trend will be the governance, data protection, and security it will provide to the entire loop. 2.5 Non-Volatile Memory Express (NVMe) beats data center fabrics NVMe – as ornate as the name sounds, is a concept that is freshly introduced with the aim of making data storage simpler. Non-Volatile Memory Express is a concept that enables accessibility of high-speed storage media. It is a protocol that is showing great results in a short amount of time of its inception. NVMe not only increases the performance value of existing applications, but also enables new applications to real-time workload processing. This feature of high performance and low latency is surely a highlight of the concept. All in all, this entire trend seems to have a lot of potential that are yet to be explored. READ MORE: HOW TO MAXIMIZE VALUE FROM DATA COLLECTED FOR BUSINESSES SUCCESS 2.6 Acceleration of storage class memory Storage class memory is a perfect combination of flash storage and NVMe. This is because it perfectly fills in the gap between server storage and external storage. As data protection is one of the major concerns of enterprises, this upcoming trend, does not only protect data but also continually stores and improves it for easier segregation. A clear advantage that storage class memory has over flash and NVMe storages is that it provides memory-like byte-addressable access to data thus reducing piling up of irrelevant data. Another benefit of this trend is that it indulges in deeper integration of data for ensuring high performance and top-level data security. 2.7 Hyperconverged storage – a push to edge computing The increased demand for hyper converged storage is a result of the growth of hybrid cloud and software-defined infrastructure. Besides these technologies, its suitability for retail settings and remote offices is add on to its already existing set of features. It’s the capability of capturing data from a distance also enables cost-effectiveness and scales down the need to store everything on a public cloud. Hyper converged storage if used in its true essence can simplify IT operations and data storage for enterprises of all sizes. 3. The future of data storage According to the Internet World Stats, more than 4.5 billion internet users around the world relentlessly create an astronomical amount of data. This translates to propel companies into discovering methods or applications that help them store this data safe from harmful ransomware attacks and still use it productively for their advantage. One of the prime changes that can be estimated about the future of data storage is that companies will have to adapt to the rapid changes, and mould their process to enable quick and seamless storage of data. Another enhancement would be that IT managers and responsible authorities would have to be updated and proactive at all times to know what data storage has been newly introduced, and how it can be used for the company’s advantage. Here’s a thing, amongst all the research that enterprises are conducting, not all data storage technologies will end up becoming a hit, and will fulfil the specification of high-speed storage. But, looking at all the efforts that researchers are taking, we don’t think they are going to stop any sooner and neither is the augmentation of data!

Article | August 9, 2021

The financial industry has been going through digital transformation for years. Digital technologies have helped to automate manual and tedious tasks like processing and reporting of historical data to forecasting and financial predictive analytics. The financial services industry owes its success to data. Data is constantly evolving in the form of market trends, client investment, customer service, campaigns. Data gives a boost to banking strategies. As reported by Accenture in a recent survey, 78 percent of banks have made the shift to using data for operations; however, only seven percent of them have extended to using predictive analytics in finance. Predictive analytics in finance has had a slow but steady start. It is an area of growing interest for banks and other institutions as new newer technologies launch in the market. To complete your company’s digital transformation, data analytics in finance will make a difference in that process. To be successful, organizations must have the ability to adapt to changes. Having predictive analytics on your side, your organization can deal with ever-changing circumstances with less to no difficulty. Understanding Predictive Analytics: What is it? Predictive analytics is a process of interpreting data to measure any possible future outcomes. It is carried out with the help of statistical modeling, historical data sets, and machine learning. The collected historical data is fed into an algorithm that recognizes patterns and forecast trends and possible future behavior from days to years in advance. Analyzing historical data and predicting the future has been an old practice in the finance sector. Banks and financial institutions have been evaluating past events or historical data for a long time now. Making precise forecasts in trends and analyzing data becomes easier due to predictive analytics. There is a wider scope to predictive efforts with more speed and accuracy and apply them throughout strategic and tactical business practice areas. Predictive Analytics in the Financial Sector: What are the Benefits? Many organizations are ready to accept the positive applications of predictive analytics but remain skeptical about the return on investment. It is worth understanding the potential of predictive analytics to any business big or small. It doesn’t matter if you are not in the banking sector to benefit from taking a peek into the future of financial performance. Any finance and accounting department can take advantage of advanced predictive analytics for the following reasons: Precise Monitoring The technology keeps a regular track of the consistency between expectations and reality to warn you about possible gaps. Risk Alleviating Analytics accurately helps you identify any possible threats to your business and warns you. Enhanced User Experience Predictive analytics guides you to recognize the strengths of your business and lets you know how to maximize customer satisfaction. Analyzed Decision Making You can understand your customers better with predictive analytics. With this information, you can correctly match your customers with the product in a better way. Importance of Predictive Analytics Most successful banking and financial institutions depend on predictive analytics because it simplifies and integrates data to increase profits for companies. Predictive analytics can improve different finance processes. But the importance of analytics goes beyond just banking services and actually goes into a better quality of customer service. Better customer service is only possible because of the advanced technology that shares customer feedback and preferences throughout the organization, in turn giving relevant information to every employee to make necessary product enhancements. To understand the importance of predictive analytics, below are some of its use cases: Customer first Predictive analytics in financial institutions and banking give you a complete profile of your customer base. It is impossible to contact every customer and interview them about their likes, needs and wants. This is where big data analytics in finance comes into play. It gives you the whole information about your customers regardless of the services they subscribe. Customers usually don’t have the same needs throughout their lives. As they grow older and have families, their financial needs change accordingly. For instance, a young person considering getting married will always try and save monetarily to buy a house, life insurance, college funds, whereas an older couple will save that money for their retirement. Apart from enabling different financial services, predictive analytics empowers you to serve individual customers with ease. Let’s take an example. When a customer applies for a loan, predictive financial services can help you analyze if the customer can repay the loan. Predictive analytics also helps offer alternative services like secured loans to customers who may not qualify for the originally applied services. Online Banking Made Better Consumer interest fluctuates in spikes. Predictive analytics informs managers enough in advance so they can set up online infrastructures in those areas. Predictive analytics has made it easier to identify a possible customer base. For example, it can provide metrics to the marketing teams. In turn, the marketing teams can target the customers with ads for probable mortgage loans or business loans in hopes of converting them into their customers. Data analytics in finance also helps in preventing and detecting fraud and abuse. Although detecting fraud doesn’t necessarily fall under predictive analytics, it can inform the IT department about potential scammers and which online services must be protected. Foreseeing Market Variations Predictive analytics can predict market variations and changes. By combining internal and external data, your organization can predict revenue growth in particular market sectors. For nascent or growing companies, predicting market changes is an important ability. Profitable companies should also be reviewed through predictive analytics to generate demand projections owing to the uncertainties caused by the Covid-19 pandemic. Your return on investment can grow or reduce even with the minutest changes to the growth plans that would seriously impact investor confidence in the future. Predictive analytics also help to establish which marketing campaigns are working and which strategies need to change. Predictive Analytics and the Future: What Next? Technological improvements have allowed predictive analytics in finance to improve and change constantly. Any organization can use customized data solutions to meet your customers’ needs and reach new ones efficiently. Your organization can use predictive analytics to move your business and products ahead and understand how the market will thrive, giving you the much needed heads up you would need to change your strategies and tactics. Frequently Asked Questions Is predictive analytics is the future of finance? Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness. How can analytics be used in finance? Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection. How do predictive analytics benefit financial institutions? 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