Every aspect of modern-day society is driven by data; how often you see your doctor is not only monitored, except for some developing countries, your health history is likely stored in a physical location or a cloud. Meanwhile, travel is now planned on Google, your trip to the restaurant is not complete without a brief check on reviews and your travel destinations are decided by algorithms that show you the best price, location, and flights.
The world is about to be split into two, with Britain, France, and the United States banning Huawei from their communication networks to varying extents. The reason for this is the fact that the Chinese telecoms giant can share your data with the Chinese Communist party on demand. China can essentially use this data to spy on your habits and preferences and therefore monitor how you live, eat, travel, and trade.
While this might be true, claims on potential spying have been unsubstantiated and if the same charge were labeled against Apple or Microsoft, it is not clear that it will be readily believable. Meanwhile, data is the new oil and credit ratings, home viewings and education are increasingly sought online. The EU recently imposed the General Data Protection Regulation to give consumers better control over their data, but similar approaches are deemed “data populism” by countries such as the United States.
As a result, consumers must now decide if they are willing to outsource complete data sovereignty to institutions in exchange for access to their platforms and better recommendations for books, TV programs, and restaurants. Some people, of course, don’t mind such an exchange, but the implications of data monopolies on society are broad-based.
Think of the lady who empties your bin, she is unlikely to benefit from the data economy as much as a tech professional or trader whose value-added to the economy hinges on better access to consumers. If current tax incentives discourage redistribution, should you simply acquiesce to requests to share your data with companies that have different objectives for you? Now why don’t we flip this on its head; if you don’t have a problem with Facebook using your data to improve its revenues, is it logical to fear global companies — Chinese owned — potential misuse of your data.
#bigtechtrends #data #privacy #consumer #global-trade-data #data analysis
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.
Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.
Now, addressing the main topic of interest – how are data analysis and data science different from each other.
As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –
#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Data privacy has been all the talk in the tech sector as of late. With the emergence of smartphones over a decade ago, our entire lives have been put online. Our behaviors and thoughts have been monitored not just through Facebook status updates, but through applications and browser tracking page visits, link clicks, and google searches. Everything we do on our phones is recorded and collected as data used for a variety of purposes from personal safety to advertising. In recent months, data privacy, or rather a lack thereof, has come to the forefront of tech conversations. With Apple launching an increased effort to protect users’ privacy, the personal data world as we know is about to change.
Have you ever wondered how your ads on various web pages know exactly what you like? Or how Amazon knows exactly what purchase to suggest next? All of this is due to data collected on your phone that goes by the term cookies. A cookie is a small text file from a website you visit that attaches to your browser.
While cookies have been around for quite some time, users have begun to question just how much data they have access to. There has been a recent push in protecting user data and data privacy. Because of this, tech giants like Apple and Google have taken steps to reduce the amount of data applications and browsers have access to. Their smartphones now prompt users to choose which platforms are allowed to track their online behaviors.
This severely limits the access that businesses and advertisers can have to large sums of personal data. So you might be wondering, is increased data privacy all good? Like all things, it has its upsides and downsides and boils down to personal preference.
#data #privacy #data-privacy #data-protection #cookies #internet-data-privacy
Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.
If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?
Let’s take a look at the most important things you need to know.
#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company