• Exploring the journey from cloud to AI – with a few big data bumps along the way
    by James on June 19, 2019 at 9:03 am

    The potential of cloud computing and artificial intelligence (AI) is irresistible. Cloud represents the backbone for any data initiative, and then AI technologies can be used to derive key insights for both greater business intelligence and topline revenue. Yet AI is only as good as the data strategy upon which it sits. At the AI & Big Data Expo in Amsterdam today, delegates were able to see that the proof of the pudding was in the eating through NetApp’s cloud and data fabric initiatives, with Dreamworks Animation cited as a key client who was able to transform its operations. For the cloud and AI melting pot, however, there are other steps which need to be taken. Patrick Slavenburg, a member of the IoT Council, opened the session with an exploration of how edge computing was taking things further. As Moore’s Law finally begins to run out of steam, Slavenburg noted there are up to 70 startups working solely on new microprocessors today.  Noting how technology history tends to repeat itself, he added today is a heyday for microprocessing architecture for the first time since the 1970s. The key aspect for edge here is being able to perform deep learning at that architectural level, with the algorithms being more lightweight. Florian Feldhaus, enterprise solutions architect at NetApp, sounded out that data was the key to running AI. According to IDC, by 2020 90% of corporate strategies will explicitly mention data as a critical enterprise asset, and mention analytics as an essential competency. “Wherever you store your data, however you manage it, that’s the really important piece to get the benefits of AI,” he explained. The industry continues to insist that it is a multi-cloud, hybrid cloud world today. It is simply no longer a choice between Amazon Web Services (AWS), Microsoft Azure or Google Cloud Platform (GCP), but assessing which workloads fit which cloud. This is also the case in terms of what your company’s data scientists are doing, added Feldhaus. Data scientists need to use data wherever they want, he said – use it in every cloud and move the data around to make it available to them. “You have to fuel data-driven innovation on the world’s biggest clouds,” said Feldhaus. “There is no way around the cloud.” With AI services available in seconds, this was a key point in terms of getting to market. It is also the key metric for data scientists, he added. NetApp has been gradually moving away from its storage heritage to focus on its ‘data fabric’ offering – an architecture which offers access to data across multiple endpoints and cloud environments, as well as on-premises. The company announced yesterday an update to its data fabric, with greater integration across Google’s cloud as well as support for Kubernetes. Feldhaus noted the strategy was based on NetApp ‘wanting to move to the next step’. Dreamworks was one customer looking at this future, with various big data pipelines allied with the need to process data in a short amount of time. Ultimately, if organisations want to make the most of the AI opportunity – and time is running out for laggards – then they need their data strategy sorted out. Yes, not everything can be moved to the cloud and some legacy applications need a lot of care and attention, but a more streamlined process is possible. Feldhaus said NetApp’s data fabric had four key constituents; discovering the data, activating it, automating, and finally optimising. Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more. […]

  • What matters most in business intelligence 2019: Key enterprise use cases
    by louiscolumbus on June 17, 2019 at 11:03 am

    Improving revenues using BI is now the most popular objective enterprises are pursuing in 2019 Reporting, dashboards, data integration, advanced visualisation, and end-user self-service are the most strategic BI initiatives underway in enterprises today Operations, executive management, finance, and sales are primarily driving business intelligence (BI) adoption throughout enterprises today Tech companies’ operations and sales teams are the most effective at driving BI adoption across industries surveyed, with advertising driving BI adoption across marketing These and many other fascinating insights are from Dresner Advisory Associates’ 10th edition of its popular Wisdom of Crowds® Business Intelligence Market Study. The study is noteworthy in that it provides insights into how enterprises are expanding their adoption of Business Intelligence (BI) from centralized strategies to tactical ones that seek to improve daily operations. The Dresner research teams’ broad assessment of the BI market makes this report unique, including their use visualizations that provide a strategic view of market trends. The study is based on interviews with respondents from the firms’ research community of over 5,000 organizations as well as vendors’ customers and qualified crowdsourced respondents recruited over social media. Please see pages 13 – 16 for the methodology. Key insights from the study include the following: Operations, executive management, finance, and sales are primarily driving business intelligence (BI) adoption throughout their enterprises today More than half of the enterprises surveyed see these four departments as the primary initiators or drivers of BI initiatives. Over the last seven years, Operations departments have most increased their influence over BI adoption, more than any other department included in the current and previous survey. Marketing and Strategic Planning are also the most likely to be sponsoring BI pilots and looking for new ways to introduce BI applications and platforms into use daily. Tech companies’ operations and sales teams are the most effective at driving BI adoption across industries surveyed, with advertising driving BI adoption across marketing Retail/Wholesale and Tech companies’ sales leadership is primarily driving BI adoption in their respective industries. It’s not surprising to see the leading influencer among Healthcare respondents is resource-intensive HR. The study found that Executive Management is most likely to drive business intelligence in consulting practices most often. Reporting, dashboards, data integration, advanced visualisation, and end-user self-service are the most strategic BI initiatives underway in enterprises today Second-tier initiatives include data discovery, data warehousing, data discovery, data mining/advanced algorithms, and data storytelling. Comparing the last four years of survey data, Dresner’s research team found reporting retains all-time high scores as the top priority, and data storytelling, governance, and data catalog hold momentum. Please click on the graphic to expand for easier reading. BI software providers most commonly rely on executive-level personas to design their applications and add new features Dresner’s research team found all vertical industries except Business Services target business executives first in their product design and messaging. Given the customer-centric nature of advertising and consulting services business models, it is understandable why the primary focus BI vendors rely on in selling to them are customer personas. The following graphic compares targeted users for BI by industry. Improving revenues using BI is now the most popular objective in 2019, despite BI initially being positioned as a solution for compliance and risk management Executive Management, Marketing/Sales, and Operations are driving the focus on improving revenues this year. Nearly 50% of enterprises now expect BI to deliver better decision making, making the areas of reporting, and dashboards must-have features. Interestingly, enterprises aren’t looking to BI as much for improving operational efficiencies and cost reductions or competitive advantages. Over the last 12 to 18 months, more tech manufacturing companies have initiated new business models that require their operations teams to support a shift from products to services revenues. An example of this shift is the introduction of smart, connected products that provide real-time data that serves as the foundation for future services strategies. Please click on the graphic to expand for easier reading. In aggregate, BI is achieving its highest levels of adoption in R&D, executive management, and operations departments today The growing complexity of products and business models in tech companies, increasing reliance on analytics and BI in retail/wholesale to streamline supply chains and improve buying experiences are contributing factors to the increasing levels of BI adoption in these three departments. The following graphic compares BI’s level of adoption by function today. Enterprises with the largest BI budgets this year are investing more heavily into dashboards, reporting, and data integration Conversely, those with smaller budgets are placing a higher priority on open source-based big data projects, end-user data preparation, collaborative support for group-based decision-making, and enterprise planning. The following graphic provides insights into technologies and initiatives strategic to BI at an enterprise level by budget plans. Marketing/sales and operations are using the greatest variety of BI tools today The survey shows how conversant Operations professionals are with the BI tools in use throughout their departments. Every one of them knows how many and most likely which types of BI tools are deployed in their departments. Across all industries, Research & Development (R&D), Business Intelligence Competency Center (BICC), and IT respondents are most likely to report they have multiple tools in use. Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more. […]

  • How shared responsibility means CIOs and CFOs need to be close partners
    by wendypfeiffer on June 14, 2019 at 12:02 pm

    In today’s complex business ecosystem, the relationship between the CIO and the CFO has to be closely aligned, which goes beyond just an agreement on the budget. The CIO and the CFO have to be as one, working together as two strong pillars that ensure the organisation meets every demand the regulators, shareholders, customers, partners and employees place on it. The days of tension between the CIO and CFO are long gone. Whether your day to day responsibility is financial or technological, there is a binding commonality between you both: as CIO and CFO, you both share the responsibility for risk and compliance in the organisation. That shared responsibility extends to a complete understanding of each other’s roles and challenges. As CIO, you should be well versed on the financial processes and demands, and a transformational CFO is well briefed and often passionate about technology. With technology underpinning every aspect of an organisation and becoming increasingly important, the responsibilities of the CIO have evolved from just a set of assets and services that are a cost centre to the organisation. Technology is now a foundation of the business, no matter its vertical market. If we accept that technology underpins our organisations, it is important to realise there is an increased risk. With this risk comes greater demand for compliance. The last decade has seen a wealth of regulations, all of which bring a technology compliance element to your organisation. It is, therefore, vital for the CIO to be able to articulate risk in financial terms, whether it be data or cyber security threats to the CFO. This is critical because any problems that cause risk have a technical and operational impact and solution. For example, investing in new infrastructure or balancing the organisation’s concerns about intellectual property protection when using the public cloud, are technology questions. But, they are clearly business questions too. If the organisation opts to delay infrastructure investment, it could impact business continuity. That in turn impacts the customer experience. These may sound like decisions that are the sole domain of the CIO, but that is not the case. If technology is truly at the heart of the organisation, then the responsibility for these decisions has to be shared with your peers in the C-suite. The CIO has to understand, extremely well, the financial system of the organisation, and be able to calculate the risks in a way that the CFO finds accurate and helpful. Together, you need to be able to take these assessments to the audit, or risk, committee and on to the board. This entire discussion is not technical; it is a financial discussion. In fact, you should not have a C in your job title until you realise that part of your job entails being able to hold your own when having financial discussions. Honorific title There are a lot of people who have grown up through the technology ranks and eventually get a CIO title. And, in my experience, there are many who are CIO in name only; the title is somewhat honorific. Am I being disloyal to my CIO peers? No. You can tell the difference between CIOs that are fluent in financial terms and ideas and those that aren’t. IT leaders are new to the C-suite. Many CIOs have come up from an operational background, which has required and benefited from our detail orientation. Important as detail is at the C-suite, so too is a collaborative approach and this is where CIOs need to develop their skill set. Because, in truth, the C-suite is a lifeboat with just one packet of biscuits to survive on. The team that shares the biscuits of responsibility, is the one that will sail into a safe harbour. Any group of executives at the C-level are all working hard to figure out how to direct the company and they all share the authority. Lastly, a good relationship with the CFO – and everyone else at the C-level – in no way detracts from the relationship a CIO must have with the CEO. Buy-in and participation from the CEO is vital. Everyone in the C-level needs the CEO’s support and understanding. That CEO air cover is most effective when it is shared between the CFO and CIO.  Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more. […]

  • How to get your data scientist career up and running: A guide
    by louiscolumbus on June 14, 2019 at 9:35 am

    Note: The most common request from this blogs’ readers is how to further their careers in analytics, cloud computing, data science, and machine learning. I’ve invited Alyssa Columbus, a data scientist at Pacific Life, to share her insights and lessons learned on breaking into the field of data science and launching a career there. The following guest post is authored by her. Earning a job in data science, especially your first job in data science, isn’t easy, especially given the surplus of analytics job-seekers to analytics jobs. Many people looking to break into data science, from undergraduates to career changers, have asked me how I’ve attained my current data science position at Pacific Life. I’ve referred them to many different resources, including discussions I’ve had on the Dataquest.io blog and the Scatter Podcast. In the interest of providing job seekers with a comprehensive view of what I’ve learned that works, I’ve put together the five most valuable lessons learned. I’ve written this article to make your data science job hunt easier and as efficient as possible. Continuously build your statistical literacy and programming skills Currently, there are 24,697 open data scientist positions on LinkedIn in the United States alone. Using data mining techniques to analyse all open positions in the U.S., the following list of the top 10 data science skills was created today. As of April 14, the top 3 most common skills requested in LinkedIn data scientist job postings are Python, R, and SQL, closely followed by Jupyter Notebooks, Unix Shell/Awk, AWS, and Tensorflow. The following graphic provides a prioritised list of the most in-demand data science skills mentioned in LinkedIn job postings today. Please click on the graphic to expand for easier viewing. Hands-on training is the best way to develop and continually improve statistical and programming skills, especially with the languages and technologies LinkedIn’s job postings prioritise. Getting your hands dirty with a dataset is often much better than reading through abstract concepts and not applying what you’ve learned to real problems. Your applied experience is just as important as your academic experience, and taking statistics, and computer science classes help to translate theoretical concepts into practical results. The toughest thing to learn (and also to teach) about statistical analysis is the intuition for what the big questions to ask of your dataset are. Statistical literacy, or “how” to find the answers to your questions, come with education and practice. Strengthening your intellectual curiosity or insight into asking the right questions comes through experience. Continually be creating your own, unique portfolio of analytics and machine learning projects Having a good portfolio is essential to be hired as a data scientist, especially if you don’t come from a quantitative background or have experience in data science before. Think of your portfolio as proof to potential employers that you are capable of excelling in the role of a data scientist with both the passion and skills to do the job. When building your data science portfolio, select and complete projects that qualify you for the data science jobs, you’re the most interested in. Use your portfolio to promote your strengths and innate abilities by sharing projects you’ve completed on your own. Some skills I’d recommend you highlight in your portfolio include: Your programming language of choice (e.g., Python, R, Julia, etc.). The ability to interact with databases (e.g., your ability to use SQL). Visualisation of data (static or interactive). Storytelling with data. This is a critical skill. In essence, can someone with no background in whatever area your project is in look at your project and gain some new understandings from it? Deployment of an application or API. This can be done with small sample projects (e.g., a REST API for an ML model you trained or a nice Tableau or R Shiny dashboard). Julia Silge and Amber Thomas both have excellent examples of portfolios that you can be inspired by. Julia’s portfolio is shown below. Get (or git!) yourself a website If you want to stand out, along with a portfolio, create and continually build a strong online presence in the form of a website.  Be sure to create and continually add to your GitHub and Kaggle profiles to showcase your passion and proficiency in data science. Making your website with GitHub Pages creates a profile for you at the same time, and best of all it’s free to do. A strong online presence will not only help you in applying for jobs, but organisations may also reach out to you with freelance projects, interviews, and other opportunities. Be confident in your skills and apply for any job you’re interested in, starting with opportunities available in your network If you don’t meet all of a job’s requirements, apply anyway. You don’t have to know every skill (e.g., programming languages) on a job description, especially if there are more than ten listed. If you’re a great fit for the main requirements of the job’s description, you need to apply. A good general rule is that if you have at least half of the skills requested on a job posting, go for it. When you’re hunting for jobs, it may be tempting to look for work on company websites or tech-specific job boards. I’ve found, as have many others, that these are among the least helpful ways to find work. Instead, contact recruiters specialising in data science and build up your network to break into the field. I recommend looking for a data science job via the following sources, with the most time devoted to recruiters and your network: Bring the same level of intensity to improving your communication skills as you do to your quantitative skills as data scientists need to also excel at storytelling. One of the most important skills for data scientists to have is the ability to communicate results to different audiences and stakeholders so others can understand and act their insights. Since data projects are collaborative across many teams and results are often incorporated into larger projects, the true impact of a data scientist’s work depends on how well others can understand their insights to take further action and make informed decisions. Recruiters Friends, family, and colleagues Career fairs and recruiting events General job boards Company websites Tech job boards Bring the same level of intensity to improving your communication skills as you do to your quantitative skills – as data scientists need to also excel at storytelling One of the most important skills for data scientists to have is the ability to communicate results to different audiences and stakeholders so others can understand and act their insights. Since data projects are collaborative across many teams and results are often incorporated into larger projects, the true impact of a data scientist’s work depends on how well others can understand their insights to take further action and make informed decisions. Alyssa Columbus is a Data Scientist at Pacific Life and member of the Spring 2018 class of NASA Datanauts. Previously, she was a computational statistics and machine learning researcher at the UC Irvine Department of Epidemiology and has built robust predictive models and applications for a diverse set of industries spanning retail to biologics. Alyssa holds a degree in Applied and Computational Mathematics from the University of California, Irvine and is a member of Phi Beta Kappa. She is a strong proponent of reproducible methods, open source technologies, and diversity in analytics and is the founder of R-Ladies Irvine. You can reach her at her website: alyssacolumbus.com. Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more. […]

  • Dropbox revamps as enterprise collaboration space to help users conquer ‘work about work’
    by James on June 13, 2019 at 12:58 pm

    Dropbox has launched a significant redesign, repositioning itself as an enterprise collaboration workspace and moving away from its file storage heritage. The move will see Dropbox aim to be a one-stop-shop. The relaunched desktop app will enable users to create, access and share content across the Google and Microsoft portfolio, opening Google Docs and Microsoft Office files, offer synchronised search, alongside partnerships with Atlassian, Slack, and Zoom. The latter partnerships are of particular interest; users will be able to start Slack conversations and share content to Slack channels directly from Dropbox, while also being able to add and join Zoom meetings from Dropbox, as well as again share files. The new features with Atlassian weren’t announced, but Dropbox promises ‘enhanced integrations [to] help teams more effectively manage their projects and content.’ As a blog post from the Dropbox team put it, the primary motivator for the move was to address the ‘work about work’ which slowed many organisations down. “Getting work done requires constant switching between different tools, and coordinating work with your team usually means a mountain of email and meetings,” the company wrote. “It all adds up to a lot of time and energy spent on work that isn’t the actual work itself. But we’ve got a plan, and we’re excited to share how we’re going to help you get a handle on all this ‘work about work.’” From the company’s perspective, the move makes sense. As regular readers of this publication will be more than aware, the industry – and almost all organisations utilising cloud software – has moved on from simple storage. Dropbox has made concerted efforts in the past to help customers get more out of their data, rather than the data itself. In October the company upgraded its search engine, Nautilus, to include machine learning capabilities – primarily to help understand and predict users’ needs for documents they search for as and when, rather than being slaves to any one algorithm. Indeed, it can be argued the company has shifted away from cloud computing as both a marketing message and as an internal business process. Writing for Bloomberg at the time of Dropbox’s IPO filing last March, Shira Ovide noted that the company building out its own infrastructure – a two and a half year project to move away from Amazon Web Services (AWS) – helped make its IPO proposition more viable. You can read more about the redesign here. Picture credit: Dropbox Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more. […]