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Digital Transformation with Self Service analytics

The term digital transformation was coined decades ago to express the use of digital technologies as a way to replace and transform older methods to promote efficiency. Throughout the years, more systems have gone through digital transformations including buying through eCommerce, ordering food through delivery apps, or using wearable activity trackers to analyze exercise results through IoT. In a company, the same kinds of transformations can be seen, like advertising through social media instead of print ads, using a chatbot for customer service instead of a person, or presenting a customer with options based on their previous behavior. It should be noted that a digital transformation is not simply the technology put in place, but the entire process that gets set up around using it. An increasingly popular way for companies to expand their digital transformation efforts revolves around the use of self-service analytics – simplified interfaces and BI tools that can be used just as eas…

Traditional BI vs Modern BI

Slow. Inflexible. Time-consuming. Does that sound like any sensible way to get users the business insights they need to do their jobs? A few years ago, this might have been the only option for business intelligence, but now there’s a fork in the road. Users can go one way with centralized BI run by their IT department, or they can strike out with modern BI solutions they can use by themselves. As you might imagine, in the traditional vs self-service BI debate, there are pros and cons to be considered before making a choice. Is Traditional BI a solution?Let’s take the traditional approach first. There are reasons for this controlled BI environment to exist. When you control the data and the BI application, you have a chance of controlling the quality of the results. IT departments concerned about quality (meaning every conscientious IT department) can make sure that data is properly prepared, stored, and secured. They can build systems that offer standardized, scalable self-service repo…

Role of Analytics in Telecom Industry

The telecoms industry is one which not only sees a large customer base but a customer base whose needs and desires are constantly evolving and/or shifting. On top of this, telecom firms face cut-throat competition, making it a highly dynamic and challenging industry. In such a scenario, each decision taken becomes all the more crucial. It is therefore imperative for the firm to make decisions based on extensive data analytics to ensure the efficient and effective use of business resources. Although analytics can be instrumental in the telecom industry in many ways, some of the major applications include: ·Customer retention/improving customer loyalty:With fierce competition between the numerous players in this industry, customer retention is essential. Telecommunications are now much more than making calls and analytical tools can help firms to identify cross-selling opportunities and make impactful decisions to retain the customer. Analytics can also help in identifying trends in cust…

Applications of BI in Aviation Industry

It’s no secret that the airline industry faces many problems. Obviously, there are operational troubles like overbooking, high prices, pilot shortages, and baggage issues. But the list of the industry’s rough spots doesn’t end there. 
An ‘intelligence inertia’ towards machine learning outcomes will be the equivalent of the comet that wiped out the dinosaurs. Businesses must take time to understand its immense value to the business Outdated technologies plague aviation today, as well as cumbersome rules and complexities, infrastructure questionability and airport issues. Dirty, unhygienic planes with uncomfortable seats don’t help much, either. Further, over the past few decades, innovations in the industry have been mediocre, and customer service has been lousy. And perhaps more urgent than any other problem on aviation’s long laundry list are the well-known environmental issues that stem from air travel. Is there hope for the future of the aviation industry? Thanks to solutions and innov…

Life Sciences - In the era of Modern Data Analytics

Though data analytics have the potential to improve any company or industry, data analysis for life sciences is seldom discussed. We have solutions that predict benefits in the healthcare-payer system, evidence-based medicine, research and development, and public health — many life sciences areas that will improve via data analysis. Nowadays, in this modern data analysis era, the lag between collection and processing doesn’t have to exist any longer. Life science and healthcare organizations can put their data into instant, relevant use with artificial intelligence (AI) analytics platforms. Penetrate Through Billions of Rows of Data from Heterogeneous Data SourcesFew industries are as complex as the healthcare ecosystem. Myriad patient data and sources have the potential to present a robust picture, but historically it’s been difficult to access it all through one view. The scope of life sciences in business intelligence can be changed by integrating with various data sources and using ev…

Tabular Models Vs Multidimensional Data Models

Introducing the Tabular and Multidimensional model
The Tabular model, otherwise known as In-Memory Cubes are in-memory databases in SQL Server Analysis Services. Using state-of-the-art compression algorithms and multi-threaded query processing, the Xvelocity™ engine delivers fast access to the tabular model objects and data which boost the performance of data retrieval in reporting tools like Power BI.The Multidimensional model which is a traditional OLAP Cube organizes summary data into multidimensional structures, aggregations are stored in the multidimensional structure in cells at coordinates specified by the dimensions.
The ultimate goal of these two models is to provide a semantic layer on top of the data warehouse provided with high performance capabilities. Both data models have similar features and will give the users an impression that we can easily switch from one model to another, but both are two different entities, having different d…

Leveraging Power BI for better insights than Excel

Stepping up from ExcelNowadays, various domains deal with enormous amounts of data. Analyzing them is essential for better performance and obtain business insights. Here is where analytics came into play. Earlier, raw data were analyzed in Excel for useful insights. These insights are represented in customizable charts. Microsoft Power BI is a self-service business intelligence tool that helps visualize data from multiple sources and transform them into informative insights. Many people still use Excel for accounting and related works, but Power BI can help them to save time from manual work involved and focus on insights and adding value to the business. Power BI over ExcelData Volume and Simplicity-Power BI can handle huge volume and variety of data - subject to design and environmental constraints. Several tables can be loaded as well as correlated, if required, based upon communal fields. The Power Query Editor and the Data Modelling sections are simpler to use in terms of user int…