A Modern Application Must Have: Multi-cloud Database

Aashish Yadav | July 6, 2022 | 190 views | Read Time : 2 min

A Modern Application Must Have: Multi-cloud Database
To function well, modern apps require enormous amounts of diverse data from sensors, processes, interactions, etc. However, these apps cannot understand the unstructured big data and extract commercial value for effective operations unless this data is maintained properly.

In today's age of cloud computing, apps gather and analyze data from various sources, but the data isn't always kept in the same database or format. While increasing overall complexity, several formats make it more difficult for apps to retain and use various data.

Multi-model databases, a cutting-edge management system, provide a sophisticated approach to handling varied and unstructured data. A multi-model database allows various data models to natively utilize a single, integrated backend, as opposed to combining different database models.


Why Has Multi-Model Database Become a Necessity for Modern Applications?

Modern applications can store diverse data in a single repository owing to the flexible approach to database management, which improves agility and reduces data redundancy.


Improve Reliability

Each database might be a single point of failure for a larger system or application. Multi-model databases reduce failure points, enhancing data dependability and recovery time. Such recovery minimizes expenses and maintains customer engagement and application experience.


Simplify Data Management

Fragmented database systems benefit contemporary applications but complicate development and operations. Multi-model databases provide a single backend that maintains data integrity and fault tolerance, eliminating the need for different database systems, software licenses, developers, and administrators.

 

Improve Fault Tolerance

Modern apps must be fault-tolerant and respond promptly to failures promptly. Multi-model databases enable this by integrating several systems into a single backend. The integration provides system-wide failure tolerance.


Closing Lines

As applications get more complicated, so do their database requirements. However, connecting many databases and preserving consistency between data gathered from various sources is a time-consuming and expensive undertaking.

Fortunately, multi-model databases provide an excellent option for generating the data models you want on a single backend.

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Blockchain has been causing ripples across major industries and verticals in the recent couple of years. We are seeing the future potential of blockchain technology that is scaling beyond just cryptocurrencies and trading. It is only natural that Blockchain is going to have a huge impact on Data Analytics, another field that has been booming and seems to continue in the same trajectory for the foreseeable future. However, very little research has been done on the implications of blockchain on Data Science or the potential of Data Science in Blockchain. While Blockchain is about validating data and data science is about predictions and patterns, they are linked together by the fact that they both use algorithms to control interactions between various data points. Blockchain in Big Data Analytics Big Data has traditionally been a very centralized method where we had to collate data from various sources and bring it together in one place. Blockchain, considering its decentralized nature can potentially allow analysis of data to happen at the origin nodes of individual sources. Also, considering that all data parsed through blockchain is validated across networks in a fool proof manner, the data integrity is ensured. This can be a game changer for analytics. With the digital age creating so many new data points and making data more accessible than ever, the need for diving into depth with advanced analytics has been realized by businesses around the world. However, the data is still not organized and it takes a very long time to bring them together to make sense of it. The other key challenge in Big Data remains data security. Centralized systems historically have been known for their vulnerability for leaks and hacks. A decentralized infrastructure can address both of the above challenges enabling data scientists to build a robust infrastructure to build a predictive data model and also giving rise to new possibilities for more real time analysis. Can Blockchain Enhance Data Science? Blockchain can address some of the key aspects of Data Science and Analytics. Data Security & Encoding: The smart contracts ensure that no transaction can be reversed or hidden. The complex mathematical algorithms that form the base of Blockchain are built to encrypt every single transaction on the ledger. Origin Tracing & Integrity: Blockchain technology is known for enabling P2P relationships. With blockchain technology, the ledgers can be transparent channels where the data flowing through it is validated and every stakeholder involved in the process is made accountable and accessible. This also enables the data to be of higher quality than what was possible with traditional methods. Summing Up Data science itself is fairly new and advancing in recent years. Blockchain Technology, as advanced as it seems, is still at what is believed to be a very nascent stage. We have been seeing an increasing interest in data being moved to the cloud and it is only a matter of time when businesses will want it to be moved to decentralized networks. On the other hand, blockchain’s network and server requirements are still not addressed and data analytics can be very heavy on the network, considering the volume of data collected for analysis. With very small volumes of data stored in blocks, we need viable solutions to make sure data analysis in blockchain is possible at scale. At Pyramidion, we have been working with clients globally on some exciting blockchain projects. These projects are being led by visionaries, who are looking to change how the world functions, for good. Being at the forefront of innovation, where we see the best minds working on new technologies, ICOs and protocols, we strongly believe it is only a matter of time before the challenges are addressed and Blockchain starts being a great asset to another rapidly growing field like Data Science and Data Analytics.

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Every business tries to improve their return on investment (ROI) every year by deploying different marketing strategies and technologies. Businesses are constantly adding new technologies to their content stack in order to enhance their efficiency and boost their revenue and growth. Data is inevitable in today's digital era, and Dan Zarrella correctly describes its role in marketing. He stated, "Marketing without data is like driving with your eyes closed." Indeed, the development of better marketing analytics tools and methodologies in recent years has provided business leaders with tremendous added decision-making power. Marketing analytics enables businesses to harness data points about their prospects and their journey through the selling process to enhance the effectiveness of their go-to-market efforts while optimizing ROI. The benefits can be experienced across teams and business segments. 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For example, if you launched a content marketing campaign and have reliable data, it's easy to see that overall sales improved as a result of that marketing push. Campaign Analyses Only marketing analytics can provide a complete overview of how a marketing campaign or strategy actually performed. The data can be dug deeper to track individual messaging across a broad spectrum of outlets, making sure no approach is wasted. Plan for the Future Once you have an understanding of which marketing strategies are meeting expectations, you will be able to plan strategically for future marketing initiatives. Not only is this helpful for organizing marketing efforts, but it also makes it easier to allocate funds across boards. Maximize ROI with Marketing Analytics When marketers use marketing analytics tools, they can find patterns and signs that can be used to improve the performance of their company. This data can assist account managers to acquire new prospects, reallocate marketing expenditures to the most effective channels, and forecast future possibilities. Integration of marketing analytics software into the sales process can save time, boost revenue, and maximize ROI. Lead Prospecting Marketing analytics can enhance customer acquisition in multiple ways. Many marketers merely acquire data about website visitors and ad viewers via ad networks. They just receive basic demographic data, not tips about how to convert leads to sales. Marketing analytics tracks every prospect in your sales funnel or website in real-time. With a detailed picture of your potential customers, you can recognize qualified leads and target them with marketing. Using data insights, you can boost sales, get rid of bottlenecks, increase conversions, and find opportunities that were hidden in plain sight. Campaign Performance Monitoring Online advertising and marketing have the distinct advantage of allowing campaign managers to keep checks on ad performance in real-time. Businesses can use marketing ROI metrics like clicks, impressions, and conversions to figure out which ads work best. Real-time campaign monitoring is a valuable tool for today's marketers. Placements that are underperforming are paused or modified, while those with a great ROI could get extra ad revenue. These insights usually result in more efficient ad spending. Information from different media channels and data from online applications can be put together to learn about the prospect-to-customer journey. Demand Forecasting With suitable data at the right time, marketers gain more power. Tracking historical data is essential to identifying patterns and predicting demand. Seasonal patterns, for example, can have a significant impact on how well a campaign performs. Detailed research can indicate these factors and assist you in re-allocate or altering your marketing investment. Understanding the product or campaign performance helps to identify which items will be in high demand in the future quarter through the use of marketing analytics. Boost Sales Consumers are more knowledgeable than ever before. Reviews, social networks, blogs, etc., now influence most purchasing decisions. Marketing analytics provides valuable information. Focus on how marketing impacts sales to evaluate ROI. When to contact a potential customer, which product would have the most impact, and who is best suited to close the deal. Find sales-boosting marketing strategies. Marketing analytics can enhance revenue by: Understanding the decision-making process of a consumer. Tracking website user behavior and sales trends. Discussing your ROI strategy with the entire company, rather than just the sales or marketing teams. 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Article | May 18, 2022

Predictive analytics and business intelligence have become some of the most important tools for businesses because of their outstanding capabilities. Most people believe that predictive analytics is a part of business intelligence (BI), but that is not the case. If we look at the definition of business intelligence, we can argue that predictive analytics actually falls under the umbrella of BI, but that's not entirely true. While that definition is pretty much correct for both terms, if we dig down a little deeper, we will see that there are significant differences between business intelligence and predictive analytics in both practices as well as theories. Let’s drill down to understand the key differentiators between predictive analytics and business intelligence. Key Differentiators: Predictive Analytics VS BI BI seeks to answer queries like "what happens now" and "what is happening now," whereas predictive analytics tries to predict "what will happen" and provides a more practical method to assess information. Data Raw data is processed into insights for direct consumer use during the business intelligence process. With predictive analytics, unstructured data is turned into structured data that can be used to make predictions about the future. Decision Users can make decisions based on insights provided by business intelligence. Businesses can use predictive analytics to make decisions based on facts, data sets, and predictions. Purpose The objective of business intelligence tools is to equip users with information about their company's historical data performance. Predictive analytics utilizes forecasting techniques to help in the solving of complex business challenges. Methods Business intelligence uses data visualization, data mining, reporting, dashboards, OLAP, etc., with previous performance indicators. Predictive analytics predicts future occurrences and analyzes raw data patterns. Technologies Ad-hoc reporting technology, alerting technology, and other technologies are covered in business intelligence. Predictive analytics includes technologies such as predictive modeling, forecasting, etc. Use Predictive Analytics in Business Intelligence to Optimize Marketing Efforts Businesses now have a plethora of information about their customers’ and target audience's purchasing patterns and preferences, all thanks to business intelligence insights. With all of this information, predictive analytics can determine the possibility of a consumer purchasing a product, allowing businesses to target their marketing efforts on customers who are more likely to purchase their items. Businesses that employ predictive analytics and business intelligence solutions can constantly remain one step ahead of their competition. Summing Up At times, the sheer variety of tools available can be intimidating, and misinformation can sometimes hamper the selection process of technology. Business intelligence and predictive analytics are two of the most productive technologies in the market, but when combined, they can do wonders for businesses.

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