2 items tagged "amazon"

  • Big Data on the cloud makes economic sense

    With Big Data analytics solutions increasingly being made available to enterprises in the cloud, more and more companies will be able to afford and use them for agility, efficiency and competitiveness

    google
    For almost 10 years, only the biggest of technology firms such as Alphabet Inc.’s Google and Amazon.com Inc.
    used data analytics on a scale that justified the idea of ‘big’ in Big Data. Now more and more firms are
    warming up to the concept. Photo: Bloomberg

    On 27 September, enterprise software company SAP SE completed the acquisition of Altiscale Inc.—a provider of Big Data as-a-Service (BDaaS). The news came close on the heels of data management and analytics company Cloudera Inc. and data and communication services provider CenturyLink Inc. jointly announcing BDaaS services. Another BDaaS vendor, Qubole Inc., said it would offer a big data service solution for the Oracle Cloud Platform.

    These are cases in point of the growing trend to offer big data analytics using a cloud model. Cloud computing allows enterprises to pay for software modules or services used over a network, typically the Internet, on a monthly or periodical basis. It helps firms save relatively larger upfront costs for licences and infrastructure. Big Data analytics solutions enable companies to analyse multiple data sources, especially large data sets, to take more informed decisions.

    According to research firm International Data Corporation (IDC), the global big data technology and services market is expected to grow at a compound annual growth rate (CAGR) of 23.1% over 2014-2019, and annual spending is estimated to reach $48.6 billion in 2019.

    With Big Data analytics solutions increasingly being made available to enterprises in the cloud, more and more companies will be able to afford and use them for agility, efficiency and competitiveness.

    MarketsandMarkets, a research firm, estimates the BDaaS segment will grow from $1.8 billion in 2015 to $7 billion in 2020. There are other, even more optimistic estimates: research firm Technavio, for instance, forecasts this segment to grow at a CAGR of 60% from 2016 to 2020.

    Where does this optimism stem from?

    For almost 10 years, it was only the biggest of technology firms such as Alphabet Inc.’s Google and Amazon.com Inc., that used data analytics on a scale that justified the idea of ‘big’ in Big Data. In industry parlance, three key attributes are often used to understand the concept of Big Data. These are volume, velocity and variety of data—collectively called the 3Vs.

    Increasingly, not just Google and its rivals, but a much wider swathe of enterprises are storing, accessing and analysing a mountain of structured and unstructured data. The trend is necessitated by growing connectivity, falling cost of storage, proliferation of smartphones and huge popularity of social media platforms—enabling data-intensive interactions not only among ‘social friends’ but also among employers and employees, manufacturers and suppliers, retailers and consumers—virtually all sorts of connected communities of people.

    g tech web
     
    A November 2015 IDC report predicts that by 2020, organisations that are able to analyse all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers.

    The nascent nature of BDaaS, however, is causing some confusion in the market. In a 6 September article onNextplatform.com, Prat Moghe, founder and chief executive of Cazena—a services vendor—wrote that there is confusion regarding the availability of “canned analytics or reports”. According to him, vendors (solutions providers) should be carefully evaluated and aspects such as moving data sets between different cloud and on-premises systems, ease of configuration of the platform, etc., need to be kept in mind before making a purchase decision.

    “Some BDaaS providers make it easy to move datasets between different engines; others require building your own integrations. Some BDaaS vendors have their own analytics interfaces; others support industry-standard visualization tools (Tableau, Spotfire, etc.) or programming languages like R and Python. BDaaS vendors have different approaches, which should be carefully evaluated,” he wrote.

    Nevertheless, the teething troubles are likely to be far outweighed by the benefits that BDaaS brings to the table. The key drivers, according to the IDC report cited above, include digital transformation initiatives being undertaken by a lot of enterprises; the merging of real life with digital identity as all forms of personal data becomes available in the cloud; availability of multiple payment and usage options for BDaaS; and the ability of BDaaS to put more analytics power in the hands of business users.

    Another factor that will ensure growth of BDaaS is the scarcity of skills in cloud as well as analytics technologies. Compared to individual enterprises, cloud service providers such as Google, Microsoft Corp., Amazon Web Services and International Businsess Machines Corp. (IBM) can attract and retain talent more easily and for longer durations.

    Manish Mittal, managing principal and head of global delivery at Axtria, a medium-sized Big Data analytics solutions provider, says the adoption of BDaaS in India is often driven by business users. While the need is felt by both chief information officers and business leaders, he believes that the latter often drive adoption as they feel more empowered in the organisation.

    The potential for BDaaS in India can be gauged from Axtria’s year-on-year business growth of 60% for the past few years—and there are several niche big data analytics vendors currently operating in the country (besides large software companies).

    Mittal says that the growth of BDaaS adoption will depend on how quickly companies tackle the issue of improving data quality.

    Source: livemint.com, October 10, 2016
     

     

  • Insights from Dresner Advisory Services’ 2016 The Internet of Things and Business Intelligence Market Study

    • Sales and strategic planning teams see IoT as the most valuable.
    • IoT advocates are 3X as likely to consider big data critical to the success of their initiatives & programs.
    • Amazon and Cloudera are the highest ranked big data distributions followed by Hortonworks and Map/R.
    • Apache Spark MLib is the most known technology on the nascent machine learning landscape today.

    These and many other excellent insights are from Dresner Advisory Services’ 2016 The Internet of Things and Business Intelligence Market Study published last month. What makes this study noteworthy is the depth of analysis and insights the Dresner analyst team delivers regarding the intersection of big data and the Internet of Things (IoT), big data adoption, analytics, and big data distributions. The report also provides an analysis of Cloud Business Intelligence (BI) feature requirements, architecture, and security insights. IoT adoption is thoroughly covered in the study, with a key finding being that large organizations or enterprises are the strongest catalyst of IoT adoption and use. Mature BI programs are also strong advocates or adopters of IoT and as a result experience greater BI success. IoT advocates are defined as those respondents that rated IoT as either critical or very important to their initiatives and strategies.

    Key takeaways of the study include the following:

    • Sales and strategic planning see IoT as the most valuable today.The combined rankings of IoT as critical and very important are highest for sales, strategic planning and the Business Intelligence (BI) Competency Centers. Sales ranking IoT so highly is indicative of how a wide spectrum of companies, from start-ups to large-scale enterprises, is attempting to launch business models and derive revenue from IoT. Strategic planning’s prioritization of IoT is also driven by a long-term focus on how to capitalize on the technology’s inherent strengths in providing greater contextual intelligence, insight, and potential data-as-a-service business models.

    IoT-Importance-by-Function-cp

    • Biotechnology, consulting, and advertising are the industries that believe IoT is the most important to their industries.Adoption of IoT across a wide variety of industries is happening today, with significant results being delivered in manufacturing, distribution including asset management, logistics, supply chain management, and marketing. The study found that the majority of industries see IoT as not important today, with the exception of biotechnology.

    IOT-Importance-by-Industry-cp

    • Location intelligence, mobile device support, in-memory analysis, and integration with operational systems are the four areas that most differentiate IoT advocates’ interests and focus.Compared to the overall sample of respondents, IoT advocates have significantly more in-depth areas of focus than the broader respondent base. The four areas of location intelligence, mobile device support, in-memory analysis, and integration with operational systems show they have a practical, pragmatic mindset regarding how IoT can contribute greater process efficiency, revenue and integrate with existing systems effectively.

    IoT-Advocates-Circle-cp1

    • An organization’s ability to manage big data analytics is critically important to their success or failure with IoT. IoT advocates are 3X as likely to consider big data critical, and 2X as likely to consider big data very important. The study also found that IoT advocates see IoT as a core justification for investing in and implementing big data analytics and architectures.

    importance-of-big-data-cp

    • Data warehouse optimization, customer/social analysis, and IoT are the top three big data uses cases organizations are pursuing today according to the study. Data warehouse optimization is considered critical or very important to 50% of respondents, making this use case the most dominant in the study. Large-scale organizations are adopting big data to better aggregate, analyze and take action on the massive amount of data they generate daily to drive better decisions. One of the foundational findings of the study is that large-scale enterprises are driving the adoption of IoT, which is consistent with the use case analysis provided in the graphic below.

    big-data-use-cases-with-cp

    • IoT advocates are significantly above average in their use of advanced and predictive analytics today. The group of IoT advocates identified in the survey is 50% more likely to be current users of advanced and predictive analytics apps as well. The study also found that advanced analytics users tend to be the most sophisticated and confident BI audience in an organization and see IoT data as ideal for interpretation using advanced analytics apps and techniques.

    advanced-and-predictive-analytics-cp

    • Business intelligence experts, business analysts and statisticians/data scientists are the greatest early adopters of advanced and predictive analytics. More than 60% of each of these three groups of professionals is using analytics often, which could be interpreted as more than 50% of their working time.

    users-of-advanced-and-predictive-analytics-cp

    • Relational database support, open client connectors (ODBC, JDBC) and automatic upgrades are the three most important architectural features for cloud BI apps today. Connectors and integration options for on-premises applications and data (ERP, CRM, and SCM) are considered more important than cloud application and database connection options. Multitenancy is considered unimportant to the majority of respondents. One factor contributing to the unimportance of multi-tenancy is the assumption that this is managed as part of the enterprise cloud platform.

    Cloud-BI-Architectural-Requirements-cp

    • MapReduce and Spark are the two most known and important big data infrastructure technologies according to respondents today. 48% believe that MapReduce is important and 42% believe Spark is. The study also found that all other categories of big data infrastructure are considered less important as the graphic below illustrates.

    big-data-infrastructure-cp

     Forbes, 4 oktober 2016

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