Think about your experience when you book a hotel room, order a taxi, or purchase something online. You reach the best offer in a few clicks and you get additional guidance with ratings to so you can predict the quality of the goods or services you’re buying. It’s really helpful to have all that additional information. So why don’t we get a similar experience when consuming data?

A secret to adopting machine learning that has nothing to do with the actual technology. Machine learning has the potential to transform your business. To automate processes, uncover new insights, make your products and services better, and customers happier. Integrating the capability into your organization requires operational transformation and lots (and lots) of experimentation. But, you know this already.

As the COVID-19 outbreak progressively impacts the world, many companies are grappling with the strain. It’s a very uncertain time for business right now. Even with so many factors out of your control, but there are a few things you can do proactively to protect your business. Keep your customers happy. Do everything you can to provide the best customer experience and remedy leaks early.

Cloudera’s operational database (OpDB) in CDP delivers a real-time, always available, scalable OpDB that serves traditional structured data alongside new unstructured data within a unified Operational and Warehousing platform. Cloudera delivers an operational database that serves traditional structured data alongside new unstructured data within a unified open-source platform.

As you may have heard, Hitachi Vantara recently announced the intent to acquire the assets of Waterline Data. Today, that deal has become official and, as Waterline’s founder, let me say we’re super excited about our strategic role in furthering Hitachi Vantara’s vision to become the world’s preferred digital innovation partner.

As we navigate unprecedented times together, we inevitably find ourselves to be participants in a global data storytelling exercise; either as an author, contributor, or a reader. There are now hundreds, if not thousands, of data storytelling examples out there. It’s a riveting data story that’s been read over 40 million times, receiving over 230,000 claps, and translated into 30 languages. But is this data story ’safe’?

In hospitals around the world, ICU hospital beds and ventilators are reserved for patients—regardless of age—who are in critical condition and require mechanical support to keep their bodies functioning and to fight the onset of sepsis. This condition is a complication caused by the human body’s response to infection, and it can lead to organ failure and death, accounting for 1 in 5 deaths globally.

With this click-bait title and an answer as short as “more leads, better leads and cheaper leads”, I could wrap this article up in one sentence. Yet, even though that answer would not be very far from the truth for many startups, there is a lot more to it. Marketing is all about measurable results. The days when you could buy a billboard next to a football field without asking a few critical ROI questions are long gone.

Currently, many enterprises, including many Cloudera customers, are experimenting with machine learning (ML) and creating models to tackle a wide range of challenges. While today, many models are used for dashboards and internal BI purposes, a small and rapidly growing group of enterprise leaders have begun to realize the potential of ML for business automation, optimization and product innovation.

Last year, Cloudera released the Cloudera Data Platform, an integrated data platform that can be deployed in any environment, including multiple public clouds, bare metal, private cloud, and hybrid cloud. Customers are increasingly demanding maximum flexibility to adhere to multi-cloud, hybrid data management demands. Unravel has from the beginning has made it a core strategy to support the full modern data stack, on any cloud, hybrid as well as on-premises.

Streamlining patient workflows through predictive analytics can enable efficient, proactive treatment for patients at risk of sepsis. The real heroes in this pandemic are the frontliners—the medical professionals and staff risking their lives and giving their all to provide care for those who need it. They play the most crucial role of addressing pressing healthcare needs, but they’re also key to early intervention for any disease, not just COVID-19.

Apache Ozone is a distributed object store built on top of Hadoop Distributed Data Store service. It can manage billions of small and large files that are difficult to handle by other distributed file systems. Ozone supports rich APIs such as Amazon S3, Kubernetes CSI as well as native Hadoop File System APIs.

Analyze, identify, and treat patients at risk of sepsis-associated mortality and morbidity as a result of respiratory failure. Every few years, we are faced with strains of antibiotic resistant bacteria—SARs, Ebola, MERS, H1N1, and most recently COVID-19. Are we prepared to handle this kind of pandemic as COVID-19 will not be the last one to put a strain on the healthcare system.

The more time I spend working with data, and watching how our customers work with data, the more convinced I am of two things: 1) the power to do extraordinary things is embedded within data and 2) all of us working or dealing with data have a role to play in using our knowhow and technology to apply data to benefit humanity and tackle some of the biggest challenges of our lifetime – the environment, equality, education, health and safety.

In 2019, Google has rolled out lots of updates out of which March 2019 Core Update, June Core Update, September Search Reviews update, and BERT were the major ones. BERT update was intended to better understand long-tail and conversational search queries while June Core Update impacted the websites that failed to implement E-A-T (Expertise, Authority, and Trust) Guidelines. On September 16, 2019, Google released a new algorithmic update to review (crawl and index) review snippets/search results.

Cloudera Data Platform (CDP) is now available on Microsoft Azure Marketplace – so joint customers can easily deploy the world’s first enterprise data cloud on Microsoft Azure.

Right now, we are seeing the importance of trusted data in helping people navigate the situation we are currently facing. And by people, I mean everyone! A lot of people who would normally never look at a report or use a dashboard, are sharing reams of data on social media, discussing #flatteningthecure and infection/mortality rates. The list goes on.

Time Series as Fast Analytics on Fast Data Since the open-source introduction of Apache Kudu in 2015, it has billed itself as storage for fast analytics on fast data. This general mission encompasses many different workloads, but one of the fastest-growing use cases is that of time-series analytics. Time series has several key requirements: At first glance, it sounds like these requirements would demand a special-purpose database system built specifically for time series.

The telecommunications industry is in the midst of a fundamental reinvention and transformation. Faced with a range of emerging pressures – including consolidation, a changing competitive landscape, and commoditization of traditional services – communication service providers (CSPs) are seeking new revenue streams and novel business approaches.

The low code, no code paradigm is an evolution in development environments. Historically, if you wanted to build a product, you went to a developer and they started from scratch building the product from the first line of code. But now people with little or no coding experience can build simple applications themselves.

Hadoop ecosystem now serves as a comfortable home to Big Data now, and the Hadoop data stores now have a greater acceptance across the world by programmers, developers, data scientists, and database management experts. These ecosystems are as convenient as the data storages; however, the inherent reporting system of Hadoop poses a few challenges for the users to overcome.

The theoretical bases for Machine Learning have existed for decades yet it wasn’t until the early 2000’s that the last AI winter came to an end. Since then, interest in and use of machine learning has exploded and its development has been largely democratized. Perhaps not so coincidentally, the same period saw the rise of Big Data, carrying with it increased distributed data storage and distributed computing capabilities made popular by the Hadoop ecosystem.

The Shoemaker (often) goes barefoot. It is often the case, that while one is working hard on helping their customers get better, they neglect their own processes, taking the same shortcuts they warn their clients against. It was like that at Keboola a few years back, until we agreed that this is no longer acceptable, and created a job role (mine) to apply our teachings internally as well.

The introduction of AI into products and services – across all sectors – is creating new capabilities at a scale that software developers could never have dreamed of just a decade ago. But this development is not just about the tech.

Augmented analytics is when you take what was traditionally a very manual workflow and automate it. This gives you the ability to analyze data far more rapidly and to package up changes for humans to interpret. Essentially you’re augmenting a human experience, so rather than spending all your time looking for a needle in the haystack, the machine finds the needle and gives it to you.

Automation and robotics in retail is rapidly changing the retail landscape – so much so that there are clearly winners and losers. I’m not talking about the war between brick and mortar stores and digital marketplaces, but rather I’m talking about the retail digital revolution where the winners are delivering greater than 4.5% comparable store/ channel sales growth compared to their brothers that have not embraced automation and robotics.

For this blog post, I’m going to take a step back and not go into data visualization best practices. Rather, I’m going to explore what you can do with your data before arriving at a final visualization – what I like to call “re-expressing” your data. Accordingly, we are going to look at the topic of transforming your data.

Can you keep a secret? What will it take for me to trust you to keep and protect a secret that I share with you? If you are a friend or family member, I may not need more than you saying “Yes”, but if I don’t know you, I will likely want additional guarantees or proof that I can trust you. This is particularly true if you are an organization handling personal information about me.

With the technology today, electronic financial transactions offer a degree of convenience that simply cannot be provided by physical institutions. It’s a matter of being able to transfer money, make payments, and complete similar transactions—all without having to go to a bank or wait in line. While this brings immediacy to financial transactions, sometimes this convenience comes with a risk. The complicated nature of mobile money has the potential to compromise security.

Intel Optane DC persistent memory (Optane DCPMM) has higher bandwidth and lower latency than SSD and HDD storage drives. These characteristics of Optane DCPMM provide a significant performance boost to big data storage platforms that can utilize it for caching. One of such platforms is Apache Kudu that can utilize DCPMM for its internal block cache.

Platterz helps cultivate happy workplaces by creating delicious food experiences to offices across the country. To do this, the business intelligence team at Platterz is on a never-ending mission to derive important insights from the multitude of data at their fingertips. Platterz had the data and the world’s best visualization tool - Looker - but they needed a way to organize the data seamlessly in a way that was optimized for their teams.

Mall Group, the leading e-commerce group in Central Europe, recently employed Keboola to streamline operations and accelerate growth. In a matter of months, Keboola’s platform enabled Mall Group’s individual teams to become much more autonomous as well as accelerate feature development, testing, and deployment.

Platterz helps cultivate happy workplaces by creating delicious food experiences to offices across the country. To do this, the business intelligence team at Platterz is on a never-ending mission to derive important insights from the multitude of data at their fingertips. Platterz had the data and the world’s best visualization tool - Looker - but they needed a way to organize the data seamlessly in a way that was optimized for their teams.

Consultancy firms and system integrators are starting to productize analytics. They’re creating turnkey solutions for customers and adding value to them by offering managed services. If you’re thinking of creating an analytics solution for your customers, there are three things you need to think about when choosing a BI vendor to partner with.

One of my favorite Talend customer success stories is the International Consortium of Investigative Journalists (ICIJ). I love this story not only because they transformed investigation journalism with data, won the Pulitzer prize for the Panama papers, and helped the public to recover billions of dollars lost to illegal tax evasion.

The retail landscape is in the midst of a dramatic, data-driven renaissance. New tools help to build new connections — between consumers and retailers, and across supply chains. Data analytics and machine learning further these connections to better understand and predict customer behavior and improve demand forecasting. In this emerging era of smart retail, organizations have access to a range of powerful new capabilities and tools.

Raise your hand if you’ve ever heard that “data-driven companies make more money”. McKinsey started beating that drum half a decade ago. Financial Times wrote extensively about the topic. Google even commissioned a multi-year study from Boston Consulting Group (BCG), which showed that “best-in-class digital marketers benefit from 1.4 times greater cost benefits and up to 2.5 times revenue impact” after implementing analytics to drive their business growth.

Most of the day to day work for knowledge workers is spent helping the business make better decisions, like choosing whether it’s worth expending the effort (or actual money) to achieve the desired business goal. The example I often use when talking about ML is churn prediction (and I’m starting to think I’m overusing it now). It costs money to retain a customer who is thinking of moving, but this is less than the cost of getting new customers.

In February 2017, I was out on a routine training ride when I made a turn on loose gravel. Next thing I knew, I was laying on the pavement. Just like that, I broke my hip and collar bone, and I knew I woudn’t be able to race in the Ironman Lake Placid that year. So, I adjusted from racing to recovery. And, I’m happy to report, I returned to racing in 2019, completing the Ironman Lake Placid that year.

Do you still find yourself visually monitoring dashboards for anomalies? That leaves catching revenue-related issues to chance. It’s become humanly impossible to catch incidents on streaming data. This is why many eCommerce and data-driven companies have adopted automated anomaly detection.

In today’s business world, marketing segmentation is a must have for every organisation. It helps you process and aim different targets in a market into multiple customer or prospect segments to enhance your marketing actions. Through this discipline, you can hold a crucial competitive advantage over your competitors because you can adapt your offer and your communication according to the identified groups of personas you want to address.