Build the Future of Big Data Today

| May 20, 2016

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Big data is not a trend; it’s shorthand for business opportunity. Why is data bigger and why is it a business opportunity now more so than in the past? Three important advancements are underway. First, there is more humangenerated data being produced through interactions with social media, mobile and messaging applications. Second, keen interest in monetizing this data has driven innovation around data management and analysis techniques and technologies into high gear…

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SoftProdigy

SoftProdigy is an award-winning web design and development organization. With an expertise in outsourced product development and custom software development, the organization has been excelling on the web for over 10 years now. The experts at SoftProdigy have a proficient skillset in using the latest Open Source platforms, Microsoft Technologies, iOS, and Android operating systems, aiming to redefine the user experience for the web, mobile, and multi-touch environments.

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Evolution of capabilities of Data Platforms & data ecosystem

Article | October 27, 2020

Data Platforms and frameworks have been constantly evolving. At some point of time; we are excited by Hadoop (well for almost 10 years); followed by Snowflake or as I say Snowflake Blizzard (who managed to launch biggest IPO win historically) and the Google (Google solves problems and serves use cases in a way that few companies can match). The end of the data warehouse Once upon a time, life was simple; or at least, the basic approach to Business Intelligence was fairly easy to describe… A process of collecting information from systems, building a repository of consistent data, and bolting on one or more reporting and visualisation tools which presented information to users. Data used to be managed in expensive, slow, inaccessible SQL data warehouses. SQL systems were notorious for their lack of scalability. Their demise is coming from a few technological advances. One of these is the ubiquitous, and growing, Hadoop. On April 1, 2006, Apache Hadoop was unleashed upon Silicon Valley. Inspired by Google, Hadoop’s primary purpose was to improve the flexibility and scalability of data processing by splitting the process into smaller functions that run on commodity hardware. Hadoop’s intent was to replace enterprise data warehouses based on SQL. Unfortunately, a technology used by Google may not be the best solution for everyone else. It’s not that others are incompetent: Google solves problems and serves use cases in a way that few companies can match. Google has been running massive-scale applications such as its eponymous search engine, YouTube and the Ads platform. The technologies and infrastructure that make the geographically distributed offerings perform at scale are what make various components of Google Cloud Platform enterprise ready and well-featured. Google has shown leadership in developing innovations that have been made available to the open-source community and are being used extensively by other public cloud vendors and Gartner clients. Examples of these include the Kubernetes container management framework, TensorFlow machine learning platform and the Apache Beam data processing programming model. GCP also uses open-source offerings in its cloud while treating third-party data and analytics providers as first-class citizens on its cloud and providing unified billing for its customers. The examples of the latter include DataStax, Redis Labs, InfluxData, MongoDB, Elastic, Neo4j and Confluent. Silicon Valley tried to make Hadoop work. The technology was extremely complicated and nearly impossible to use efficiently. Hadoop’s lack of speed was compounded by its focus on unstructured data — you had to be a “flip-flop wearing” data scientist to truly make use of it. Unstructured datasets are very difficult to query and analyze without deep knowledge of computer science. At one point, Gartner estimated that 70% of Hadoop deployments would not achieve the goal of cost savings and revenue growth, mainly due to insufficient skills and technical integration difficulties. And seventy percent seems like an understatement. Data storage through the years: from GFS to Snowflake or Snowflake blizzard Developing in parallel with Hadoop’s journey was that of Marcin Zukowski — co-founder and CEO of Vectorwise. Marcin took the data warehouse in another direction, to the world of advanced vector processing. Despite being almost unheard of among the general public, Snowflake was actually founded back in 2012. Firstly, Snowflake is not a consumer tech firm like Netflix or Uber. It's business-to-business only, which may explain its high valuation – enterprise companies are often seen as a more "stable" investment. In short, Snowflake helps businesses manage data that's stored on the cloud. The firm's motto is "mobilising the world's data", because it allows big companies to make better use of their vast data stores. Marcin and his teammates rethought the data warehouse by leveraging the elasticity of the public cloud in an unexpected way: separating storage and compute. Their message was this: don’t pay for a data warehouse you don’t need. Only pay for the storage you need, and add capacity as you go. This is considered one of Snowflake’s key innovations: separating storage (where the data is held) from computing (the act of querying). By offering this service before Google, Amazon, and Microsoft had equivalent products of their own, Snowflake was able to attract customers, and build market share in the data warehousing space. Naming the company after a discredited database concept was very brave. For those of us not in the details of the Snowflake schema, it is a logical arrangement of tables in a multidimensional database such that the entity-relationship diagram resembles a snowflake shape. … When it is completely normalized along all the dimension tables, the resultant structure resembles a snowflake with the fact table in the middle. Needless to say, the “snowflake” schema is as far from Hadoop’s design philosophy as technically possible. While Silicon Valley was headed toward a dead end, Snowflake captured an entire cloud data market.

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CAN QUANTUM COMPUTING BE THE NEW BUZZWORD

Article | March 30, 2020

Quantum Mechanics created their chapter in the history of the early 20th Century. With its regular binary computing twin going out of style, quantum mechanics led quantum computing to be the new belle of the ball! While the memory used in a classical computer encodes binary ‘bits’ – one and zero, quantum computers use qubits (quantum bits). And Qubit is not confined to a two-state solution, but can also exist in superposition i.e., qubits can be employed at 0, 1 and both 1 and 0 at the same time.

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Taking a qualitative approach to a data-driven market

Article | February 18, 2021

While digital transformation is proving to have many benefits for businesses, what is perhaps the most significant, is the vast amount of data there is available. And now, with an increasing number of businesses turning their focus to online, there is even more to be collected on competitors and markets than ever before. Having all this information to hand may seem like any business owner’s dream, as they can now make insightful and informed commercial decisions based on what others are doing, what customers want and where markets are heading. But according to Nate Burke, CEO of Diginius, a propriety software and solutions provider for ecommerce businesses, data should not be all a company relies upon when making important decisions. Instead, there is a line to be drawn on where data is required and where human expertise and judgement can provide greater value. Undeniably, the power of data is unmatched. With an abundance of data collection opportunities available online, and with an increasing number of businesses taking them, the potential and value of such information is richer than ever before. And businesses are benefiting. Particularly where data concerns customer behaviour and market patterns. For instance, over the recent Christmas period, data was clearly suggesting a preference for ecommerce, with marketplaces such as Amazon leading the way due to greater convenience and price advantages. Businesses that recognised and understood the trend could better prepare for the digital shopping season, placing greater emphasis on their online marketing tactics to encourage purchases and allocating resources to ensure product availability and on-time delivery. While on the other hand, businesses who ignored, or simply did not utilise the information available to them, would have been left with overstocked shops and now, out of season items that would have to be heavily discounted or worse, disposed of. Similarly, search and sales data can be used to understand changing consumer needs, and consequently, what items businesses should be ordering, manufacturing, marketing and selling for the best returns. For instance, understandably, in 2020, DIY was at its peak, with increases in searches for “DIY facemasks”, “DIY decking” and “DIY garden ideas”. For those who had recognised the trend early on, they had the chance to shift their offerings and marketing in accordance, in turn really reaping the rewards. So, paying attention to data certainly does pay off. And thanks to smarter and more sophisticated ways of collecting data online, such as cookies, and through AI and machine learning technologies, the value and use of such information is only likely to increase. The future, therefore, looks bright. But even with all this potential at our fingertips, there are a number of issues businesses may face if their approach relies entirely on a data and insight-driven approach. Just like disregarding its power and potential can be damaging, so can using it as the sole basis upon which important decisions are based. Human error While the value of data for understanding the market and consumer patterns is undeniable, its value is only as rich as the quality of data being inputted. So, if businesses are collecting and analysing their data on their own activity, and then using this to draw meaningful insight, there should be strong focus on the data gathering phase, with attention given to what needs to be collected, why it should be collected, how it will be collected, and whether in fact this is an accurate representation of what it is you are trying to monitor or measure. Human error can become an issue when this is done by individuals or teams who do not completely understand the numbers and patterns they are seeing. There is also an obstacle presented when there are various channels and platforms which are generating leads or sales for the business. In this case, any omission can skew results and provide an inaccurate picture. So, when used in decision making, there is the possibility of ineffective and unsuccessful changes. But while data gathering becomes more and more autonomous, the possibility of human error is lessened. Although, this may add fuel to the next issue. Drawing a line The benefits of data and insights are clear, particularly as the tasks of collection and analysis become less of a burden for businesses and their people thanks to automation and AI advancements. But due to how effortless data collection and analysis is becoming, we can only expect more businesses to be doing it, meaning its ability to offer each individual company something unique is also being lessened. So, businesses need to look elsewhere for their edge. And interestingly, this is where a line should be drawn and human judgement should be used in order to set them apart from the competition and differentiate from what everyone else is doing. It makes perfect sense when you think about it. Your business is unique for a number of reasons, but mainly because of the brand, its values, reputation and perceptions of the services you are upheld by. And it’s usually these aspects that encourage consumers to choose your business rather than a competitor. But often, these intangible aspects are much more difficult to measure and monitor through data collection and analysis, especially in the autonomous, number-driven format that many platforms utilise. Here then, there is a great case for businesses to use their own judgements, expertise and experiences to determine what works well and what does not. For instance, you can begin to determine consumer perceptions towards a change in your product or services, which quantitative data may not be able to pick up until much later when sales figures begin to rise or fall. And while the data will eventually pick it up, it might not necessarily be able to help you decide on what an appropriate alternative solution may be, should the latter occur. Human judgement, however, can listen to and understand qualitative feedback and consumer sentiments which can often provide much more meaningful insights for businesses to base their decisions on. So, when it comes to competitor analysis, using insights generated from figure-based data sets and performance metrics is key to ensuring you are doing the same as the competition. But if you are looking to get ahead, you may want to consider taking a human approach too.

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COMBATING COVID-19 WITH THE HELP OF AI, ANALYTICS AND AUTOMATION

Article | April 9, 2020

Across the world, governments and health authorities are now exploring distinct ways to contain the spread of Covid-19 as the virus has already dispersed across 196 countries in a short time. According to a professor of epidemiology and biostatistics at George Washington University and SAS analytics manager for infectious diseases epidemiology and biostatistics, data, analytics, AI and other technology can play a significant role in helping identify, understand and assist in predicting disease spread and progression.In its response to the virus, China, where the first case of coronavirus reported in late December 2019, started utilizing its sturdy tech sector. The country has specifically deployed AI, data science, and automation technology to track, monitor and defeat the pandemic. Also, tech players in China, such as Alibaba, Baidu, Huawei, among others expedited their company’s healthcare initiatives in their contribution to combat Covid-19.

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Spotlight

SoftProdigy

SoftProdigy is an award-winning web design and development organization. With an expertise in outsourced product development and custom software development, the organization has been excelling on the web for over 10 years now. The experts at SoftProdigy have a proficient skillset in using the latest Open Source platforms, Microsoft Technologies, iOS, and Android operating systems, aiming to redefine the user experience for the web, mobile, and multi-touch environments.

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