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Everything You Need To Know About Self-Service Analytics

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Everything You Need To Know About Self-Service Analytics

What is Self-Service Analytics? It’s time to explore each and Everything about datasets

Regardless of one’s position, self-service analytics is a reality in today’s data-driven business environment. In the age of Big Data, business users working outside the realm of the data specialist need to access and finesse data that is aggregated from disparate sources.

Not that long ago, almost all data analytics functions were performed by skilled data scientists and data engineers. Regular business users usually only accessed data via BI products and automated dashboards. but today businesses are creating and accumulating big data at an incredible pace. And making those datasets easily available to everyday users is one of the biggest obstacles to turning your datasets into real business value. With self-service data analytics, users can gain insights far more easily and quickly, helping out organizations understand the value of their datasets faster. Self-service analytics facilitates a data-driven culture by providing non-technical users insights at the exact moment they require them, and informed guidance in the form of recommendations.

 

Benefits of Self-Service Analytics

Organizations adopting self-service analytics technologies get a wide range of benefits that can assist them more easily build a data-driven culture. Below are some of the finest ways businesses can gain value from these tools.

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Less Reliance on IT: Empowering users to generate their insights will turn into fewer IT requests and more technical innovation. As a result, IT will have more time to work on high-value, long-term projects like data governance and application development.

Insights Without the Wait: With self-service analytics, business users can spend more time exploring data, weighing insights, and proceeding with decisive action; and pay less time going back and forth with IT on requirements and debating data science vs data analytics.

Limitless Data Discovery: Every business user has special needs, so they should be able to slice and dice their data in the way they see fit. But if they’re limited to linear, query-based data analysis, their insights are expected to come up short. With Qlik’s Associative Engine, users can explore data liberally and discover insights they never expected.

Higher Data Literacy: With a self-service analytics platform, you can increase data literacy across your business. It helps employees grow their analytical skills. That’s the definition of data literacy and the foundation of a data-driven culture.

Adopting a Self-Service Analytics culture is a marathon, not a sprint, and the value realization continues to grow high with the maturity and capability of users.

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The post Everything You Need to Know About Self-Service Analytics appeared first on Analytics Insight.

Big Data

Big Data Is The Future Of Metaverse, In A Sustainable Way

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Big Data Is The Future Of Metaverse, In A Sustainable Way

Big Data is the Future of Metaverse, in a Sustainable Way

Sustainability and Metaverse: How Metaverse Is Changing Big Data and How to Adapt in the Future?

While the Metaverse may offer certain people a vast array of options for business expansion and development, for others, the concept is still relatively novel. Businesses should in some manner use this new development as justification for making sure best practices are being applied to their processes and doing right.

Big data is the center of this new technology revolution. In the Web3 era, consumers will generate such massive amounts of data that intelligent insights won’t be far away. Big data analysis may also aid in resolving some of the major sustainability problems facing the planet. The Metaverse has the power to change how businesses operate, and with the amount of data that will be generated at an exponential rate expected to reach 160 zettabytes by 2025, businesses will have a lot more information that has to be handled along. The Metaverse market might be worth $800 billion by 2024.

Importance of the Metaverse

The metaverse, the subsequent technological advance, is a three-dimensional representation of the internet. Initially, text-based, the Internet and computing later shifted to image-based. Now, a virtual public place and a network will be developed so that we can live parallel digital and physical lives.

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Despite being perceived as a video game, it is the foundation of the global economy and the future of the digital economy. When we ponder, “What is the next great thing in climate adaptation technology?” it is because climate issues now influence everything. Metaverse and cloud computing are the solutions once more.

How will the metaverse contribute to sustainability?

One of the topics that are being raised more frequently in the technology industry, as well as by connectivity companies and others, is this. The metaverse is widely believed to be the upcoming big thing. The creation of the virtual metaverse has been compared by many experts to the birth of the internet itself as the next technical revolution of the century.

Forecasts are genuinely optimistic. A completely new reality, one that is entirely digital, would emerge from the metaverse and grow concurrently with the physical one. Through individual avatars, residents of this virtual metaverse would communicate with one another using a variety of gadgets, including smart clothes or interfaces that were physically affixed to the body. This would entail developing new connections and ways of interpreting them, possibly developing entirely new economies, the emergence of new markets and goods, the sharing of experiences and information, and much more.

The first corporation to discuss its metaverse was Meta. Meta was formerly known as Facebook. However, some powerful voices are keeping their foot on the brake. “This universe is not all that close,” asserted Raja Koduri, Senior Vice President and Group Head of Accelerated Computing Systems and Graphics at Intel, “since our computing, data storage, and connectivity capacities simply aren’t yet great enough to make this vision a reality.” Naturally, there is also the issue of sustainability.

Energy consumption, one of the great unknowns of the metaverse

Our most recent encounter with the metaverse, if we go back in time, was Second Life, which debuted in the video gaming industry in 2003. Technology was one of many factors that contributed to the gradual decline of this “grandfather” of the virtual metaverse. Simply said, the majority of computers at the time were incapable of supporting the information processing necessary for a seamless experience. As we’ve seen, something comparable to what’s going on right now with the technology of the metaverse.

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This scenario might be replicated but substantially enhanced in the metaverse. Before 2030, the energy consumption of the technology sector may rise by 14%, claims ETLA Economic Research.

However, these projections are taken into consideration not the energy requirements of emerging technologies or devices. The Sustainable Development Goals of Agenda 2030, in particular addresses accessible and environmentally friendly energy, if there are any ethical questions regarding the metaverse, sustainability will be the most vocal opponent.

The metaverse as a driver of sustainability

The virtual metaverse shouldn’t be a barrier to sustainability, notwithstanding any potential roadblocks. It might already be spurring modifications to support it. Like any virtual world, the metaverse requires data transfer with massive bandwidths and incredibly low latencies. We need to start taking the issue of expanding data centers seriously once we take into account the significance of the cloud for virtual settings like the metaverse.

And at the same time, the carbon footprints they leave behind, which, according to Lancaster University experts, might increase by up to 30% by 2030. Large technology companies are attempting to make data centers substantially more sustainable because of this. For instance, Microsoft has pledged to use only renewable energy as of 2025 for its Azure cloud platform, as well as to return more water than it uses and achieve certification for zero emissions by 2030.

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Datafication Is The Only Way To Live In The Data-Fied Society

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Datafication Is The Only Way To Live In The Data-Fied Society

It quantifies previously unknown phenomena into discrete numeric data to predict data for the present or the future

Imagine having a business idea and desperately wanting to implement it. You will need proof of feasibility and the go-to source, and it definitely cannot be The Internet if you need to skip the trial-and-error phase by all means. Earlier, aspiring entrepreneurs would depend on personal experience or experts’ opinions to get to the bottom of the problem to weigh all the pros and cons. Now it is numbers that people are finding a resort in. Data that is objective and real, unlike conceptual ideas that do not inspire hope or rather inspire false hope. When a clothing store sends you a notification for baby clothing just when you check for a pediatrician on your mobile, is not a trick of magic internet plays but a part of corporate online marketing strategy. If you ask how is that they could do it, welcome to the world of datafication. Nope, datafication is not about just the internet collecting data about what is being currently surfed on the internet but is also about converting previous events into data. Consider the example, of risk profile development by an insurance executive. How would he know the risk of a person getting hit by a car or having a heart attack, if not he could access the past traffic data captured by CCTV cameras or medical record data of his family members, which apparently cannot be accessed directly from the applicant.

 

Defining Datafication

Data and data-driven businesses have become vogue in the last few decades but datafication as a concept hasn’t gained so much popularity yet. It doesn’t even have a definition, not even a place in the dictionary. However, few data scientists have come up with a definition for sake of clarity. Summarizing datafication, MayerSchoenberger and Cukier say it is the process of transforming social action into data accessed online facilitating predictive analytics in real-time. It quantifies previously unknown phenomena into discrete numeric data to predict data for the present or the future– a trend that is fast catching up with business magnets.

 

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Netflix, the classic case of Datafication:

Netflix’s story has its beginnings in renting discs (DVD and Blu-ray) for orders received over e-mail. The business model simply depended on subscribers creating a queue for media content they want to watch. Shorter the queue, the more time they can keep the rented discs. And now it provides its digital services in more than 40 countries and has more than 33 million subscribers. Netflix could achieve this scale of traction simply by resorting to datafication. While an email-based reservation system has fewer data points available, an online streaming service can get hold of distinctive data points, for example, data from catalog access, data from search queries, time spent watching a show, discontinued shows, etc, with around 1000 facets attached to the title alone. Netflix’s datafication involves around 30 million daily games and 3 million random queries that direct its engines towards the recommendations a user receives. Nothing can explain the charm of datafication more than the transition of Netflix from a content curating platform to one that is creating and selling content with bombastic success, so much so that it could confidently remake a popular TV show like House of Cards, investing over $100 million. It is only big data that helped Netflix to see that viewers who liked the TV show also liked movies starring Kevin Spacey, who played the lead role in Netflix’s version. Although experts believed datafication would have problems with shared ontology and semantics for understanding the patterns in the data, the way it is being applied to data and the idea generation is par cutting-edge. When Tim O’Reilley, the Founder of O’Reilly Media, says, “We are entering a new world in which data may be more important than software”, perhaps he means datafication will turn out to be the ultimate destination for entrepreneurial innovation.

The post Datafication Is the Only Way to Live in the Data-Fied Society appeared first on Analytics Insight.

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Apache Iceberg

Is Apache Iceberg The Future Of Cloud Data Workload?

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Is Apache Iceberg The Future Of Cloud Data Workload?

Why Apache Iceberg will rule cloud data and is optimal for cloud data workloads?

Cloud data has allowed data teams to collect vast quantities of big data and store it at a reasonable cost, opening the door to new analytics use cases that leverage data lakes, data mesh, and other modern architectures. But for very large volumes of data sets, generic cloud storage also presents challenges and limitations. The importance of using Apache Iceberg arises in this scenario.

It has become challenging for cloud data to access, manage and use big data. This is where applying table formats to data becomes immensely useful. Identifying which table format to be used is a critical decision because it carries the potential to enable or limit the features available. Over the past two years, we have seen significant support emerging for Apache Iceberg, a table format originally developed by Netflix. Iceberg was built from the ground up to address some of the challenges in Apache Hive when working with very large data sets, including issues around scale, usability, and performance. As a Netflix engineer recorded at the time, table formats for very large-scale data sets should work as authentically and predictably as SQL, “without any unpleasant surprises.”

With several options available, we believe Apache Iceberg is superior to other open table formats available. Here are five reasons why.

Apache Iceberg makes a clean break from the past. Iceberg was built from the ground up to address shortcomings in Apache Hive, which means it has avoided some of the undesirable qualities that held back data set lakes in the past. How schema changes can be handled, such as renaming a column, is a good example.

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Apache Iceberg is agnostic to the processing engine and file format, by decoupling the processing engine from the table format, Iceberg provides greater flexibility and choice. Instead of being enforced to apply one processing engine, engineers can pick the best tool for the job.

Iceberg is a well-run open-source project. Apache Iceberg makes its project management public, so you are aware of who is running the project. Other table formats are not disclosing who has decision-making authority. A table format is an elementary choice in data architecture, so choosing a project that is truly open and collaborative can significantly reduce the risks of accidental lock-in.

Collaboration in Iceberg is spawning new ideas and help. There are numerous signs that the collaborative community around Apache Iceberg is helping users and setting the project up for long-term success. Iceberg includes features that are paid in other table formats. Unlike some other table projects, Iceberg has performance-oriented features built in from the start, which is helpful for users in multiple ways. There are some excellent resources within the Apache Iceberg community to learn more about the project and to get involved in the open-source effort.

The post Is Apache Iceberg the Future of Cloud Data Workload? appeared first on Analytics Insight.

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