4 items tagged "IBM"

  • IBM verdient beter aan lagere omzet


    Valutaire tegenwind, het afstoten van bedrijfsonderdelen en lagere marges in groeisegment de cloud hebben de omzet van IBM op bijna alle fronten doen dalen. Maar IBM hield wel meer over aan zijn bedrijfsactiviteiten

    IBM rapporteerde over het vierde kwartaal een omzet van 24,1 miljard dollar. Dat was bijna 12 procent minder dan in het vierde kwartaal van 2013. Ook over het hele jaar opgeteld daalde de omzet. Met 92,8 miljard dollar kwam de omzet in 2014 5,7 procent lager uit dan in 2013.

    De omzetdaling lijkt spectaculairder dan die in feite is. In 2013 had IBM in verschillende divisies nog inkomsten van de System X-divisie - die het afgelopen jaar werd verkocht aan Lenovo. Daarnaast treden er verschillen op door de uitbesteding van klantenservice en door de koersstijging van de dollar. Gecorrigeerd voor die factoren bleef de daling in het vierde kwartaal beperkt tot 2 procent. Wat de invloed ervan was op de jaaromzet, specificeert IBM niet.

    Cloud eist zijn tol
    Dat IBM ook kampt met structurele veranderingen in de markt, blijkt echter uit het wel en wee van de divisie software. Omzetdaling was in die divisie jarenlang ondenkbaar. Maar in het vierde kwartaal daalde de omzet daar met een kleine 7 procent. Gecorrigeerd voor valutaschommelingen resteert nog altijd een min van 3 procent. Over het gehele jaar gerekend nam de omzet in software met 2 procent af. Die negatieve ontwikkeling reflecteert de opkomst van cloud. Met clouddiensten haalde IBM in 2014 7 miljard dollar binnen, 60 procent meer dan in 2013, maar de schaal van zijn clouddiensten is naar eigen zeggen nog onvoldoende om de lagere marges te compenseren.

    Hogere brutomarge, lagere nettowinst
    Desalniettemin wist IBM het bedrijfsresultaat als percentage van de omzet nog iets op te schroeven. De bruto winstmarge over 2014 was 50 procent, die over 2013 was 49,5 procent. In het vierde kwartaal was het gat met 2013 nog iets groter: 53,3 om 52,4 procent.

    Die toegenomen efficiëntie vertaalde zich niet in een hogere netto winst. Na belastingen, afschrijvingen en bijzondere lasten boekte IBM in het vierde kwartaal een netto winst van 5,5 miljard dollar, 11 procent minder dan in het vierde kwartaal van 2014. Over het hele jaar gerekend nam de netto winst zelfs met 27 procent af tot 12 miljard dollar. Behalve de valutaschommelingen spelen daarbij ook enkele bijzondere kostenposten een rol, zoals de kosten van inkrimp van de divisie micro-electronica en een voorziening van 580 miljoen dollar voor inkrimping van het personeelsbestand.

     

    Automatiseringsgids, 21 janauri 2015

  • IBM vestigt 'Watson Internet of Things' hoofdkantoor in München

    IBM-Watson-IoT-HQ-300x179IBM investeert meer dan 3 miljard dollar in IoT-campus en innovatiecentrum.

    De campus zal naar verwachting duizend IBM ontwikkelaars, adviseurs, wetenschappers en ontwerpers gaan huisvesten. Met de opening gaat een investering gepaard van meer dan 3 miljard dollar: de grootste investering van IBM in Europa in 20 jaar.

    'Het Internet of Things vormt op korte termijn de grootste databron ter wereld, terwijl we momenteel met 90 procent van die data helemaal niets doen', zegt Harriet Green, general manager van IBM's nieuwe Watson IoT divisie. “Door deze data te koppelen aan IBM’s Watson technologie met haar unieke vaardigheden van op het gebied van waarneming, argumentatie en zelflerend vermogen, opent dit deuren voor bedrijven, overheden en individuen om verbanden uit hun data te halen die tot nieuwe inzichten leiden.'

    Naast het nieuwe hoofdkantoor lanceert IBM tevens vier nieuwe Watson API’s op de Watson IoT cloud:

    •Natural language processing (NLP) API. Stelt gebruikers in staat om te communiceren met toestellen en systemen via menselijke taal. Watson linkt taal aan andere databronnen die voor de juiste context zorgen. Zo kan een onderhoudsmonteur aan Watson vragen wat een bepaalde trilling in een toestel veroorzaakt. Watson zal automatisch de bedoeling achter de vraag herkennen en linken aan databronnen over bijvoorbeeld het onderhoud van het toestel en een aanbeveling doen om de trilling weg te werken.

    •Machine Learning Watson API. Automatiseert dataprocessen en monitort datasets op continue basis. Daardoor kan het systeem trends waarnemen en aanbevelingen doen wanneer een probleem zich voordoet.

    •Video and Image Analytics API. Doorzoekt ongestructureerde datasets van afbeeldingen en video’s en kan hieruit trends en patronen identificeren.

    •Text analytics API. Biedt de mogelijkheid om grote hoeveelheden tekst door te ploegen en patronen te herkennen. Denk bijvoorbeeld aan de transcripten van call centers, tweets en blogs.

     

    Source: Adformatie

  • Research details developments in the business intelligence (BI) market that is estimated to grow at 10% CAGR to 2020

    HOIThe global business intelligence market report, an analyst says In the past few years, social media has played critical roles in SMEs and mid-sized organizations. Many SMEs are increasingly embracing this trend and integrating their BI software with social media platforms.

    Market outlook of business intelligence market - market research analyst predicts the global business intelligence market to grow at a CAGR of around 10% during the forecast period. The growing adoption of data analytics by organizations worldwide is a key driver for the growth of this market.

    The majority of corporate data sources include data generated from enterprise applications along with newly generated cloud-based and social network data. business intelligence tools are useful in the retrieval and analysis of this vast and growing volume of discrete data.

    They also help optimize business decisions, discover significant weak signals, and develop indicator patterns to identify opportunities and threats for businesses.

    The increased acceptance of cloud BI solutions by SMEs is also boosting the growth of this market. The adoption of cloud services allows end-users to concentrate on core activities rather than managing their IT environment.

    Cloud BI solutions enable applications to be scaled quickly, can be easily integrated with easy integration with third-party applications, and provide security at all levels of the enterprise IT architecture so that these applications can be accessed remotely.

    Market segmentation by technology of the business intelligence market:

    • Traditional BI
    • Mobile BI
    • Cloud BI
    • Social BI

    The mobile BI segment accounts for approximately 20% of the global BI market. It enables the mobile workforce to get business insights by data analysis, using applications optimized for mobile and smart devices.

    The growing smartphone adoption is likely to emerge as a key growth driver for this segment during the forecast period.

    Market segmentation by deployment of the business intelligence market

    • Cloud BI
    • On-premises BI

    The on-premise segment accounted for 86% of the market share during 2015. However, the report anticipates this segment to witness a decline in its shares by the end of the forecast period.

    In this segment, the software is purchased and installed on the server of an enterprise. It requires more maintenance but is highly secure and easy to manage.

    Geographical segmentation of the BI market

    • Americas
    • APAC
    • EMEA

    The Americas dominated the market during 2015, with a market share of around 56%. The high adoption of cloud BI solutions in this region is the major growth contributor for this market.

    The US is the market leader in this region as most of the key vendors are based out of here.

    Competitive landscape and key vendors

    Microsoft is one of the largest BI vendors and offers Power BI, which helps to deliver business-user-oriented, self-service data preparation and analysis needs through Excel 2013 and Office 365. The competitive environment in this market is expected to intensify during the forecast period due to an increase in R&D innovations and mergers.

    The market is also expected to witness a growing trend of acquisitions by the leading players. The key players in the market are expected to diversify their geographical presence during the forecast period.

    The key vendors of the market are -

    • IBM
    • Microsoft
    • Oracle
    • SAP
    • SAS Institute

    Other prominent vendors in the market include Actuate, Alteryx, Board International, Brist, Datawatch, GoodData, Infor, Information Builders, Logi Analytics, MicroStrategy, Panorama Software, Pentaho, Prognoz, Pyramid Analytics, Qlik, Salient Management Company, Tableau, Targit, Tibco Software, and Yellowfin.

    Key questions answered in the report

    • What will the market size and the growth rate be in 2020?
    • What are the key factors driving the BI market?
    • What are the key market trends impacting the growth of the BI market?
    • What are the challenges to market growth?
    • Who are the key vendors in the global BI market?
    • What are the market opportunities and threats faced by the vendors in the BI market?
    • Trending factors influencing the market shares of the Americas, APAC, and EMEA?
    • What are the key outcomes of the five forces analysis of the BI market?

    Source: WhaTech

  • The big data race reaches the City

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    Vast amounts of information are being sifted for the good of commercial interests as never before

    IBM’s Watson supercomputer, once known for winning the television quiz show Jeopardy! in 2011, is now sold to wealth management companies as an affordable way to dispense investment advice. Twitter has introduced “cashtags” to its stream of social chatter so that investors can track what is said about stocks. Hedge funds are sending up satellites to monitor crop yields before even the farmers know how they’re doing.

    The world is awash with information as never before. According to IBM, 90pc of all existing data was created in the past two years. Once the preserve of academics and the geekiest hedge fund managers, the ability to harness huge amounts of noise and turn it into trading signals is now reaching the core of the financial industry.

    Last year was one of the toughest since the financial crisis for asset managers, according to BCG partner Ben Sheridan, yet they have continued to spend on data management in the hope of finding an edge in subdued markets.

     
    “It’s to bring new data assets to bear on some of the questions that asset managers have always asked, like macroeconomic movements,” he said.

    “Historically, these quantitative data aspects have been the domain of a small sector of hedge funds. Now it’s going to a much more mainstream side of asset managers.”

     
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    Banks are among the biggest investors in big data

    Even Goldman Sachs has entered the race for data, leading a $15m investment round in Kensho, which stockpiles data around major world events and lets clients apply the lessons it learns to new situations. Say there’s a hurricane striking the Gulf of Mexico: Kensho might have ideas on what this means for US jobs data six months afterwards, and how that affects the S&P stock index.

    Many businesses are using computing firepower to supercharge old techniques. Hedge funds such as Winton Capital already collate obscure data sets such as wheat prices going back nearly 1,000 years, in the hope of finding patterns that will inform the future value of commodities.

    Others are paying companies such as Planet Labs to monitor crops via satellite almost in real time, offering a hint of the yields to come. Spotting traffic jams outside Wal-Marts can help traders looking to bet on the success of Black Friday sales each year – and it’s easier to do this from space than sending analysts to car parks.

    Some funds, including Eagle Alpha, have been feeding transcripts of calls with company executives into a natural language processor – an area of artificial intelligence that the Turing test foresaw – to figure out if they have gained or lost confidence in their business. Trades might have had gut feelings about this before, but now they can get graphs.

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    There is inevitably a lot of noise among these potential trading signals, which experts are trying to weed out.

    “Most of the breakthroughs in machine-learning aren’t in finance. The signal-to-noise ratio is a problem compared to something like recognising dogs in a photograph,” said Dr Anthony Ledford, chief scientist for the computer-driven hedge fund Man AHL.

    “There is no golden indicator of what’s going to happen tomorrow. What we’re doing is trying to harness a very small edge and doing it over a long period in a large number of markets.”

    The statistics expert said the plunging cost of computer power and data storage, crossed with a “quite extraordinary” proliferation of recorded data, have helped breathe life into concepts like artificial intelligence for big investors.

    “The trading phase at the moment is making better use of the signals we already know about. But the next research stage is, can we use machine learning to identify new features?”

    AHL’s systematic funds comb through 2bn price updates on their busiest days, up from 800m during last year’s peak.

    Developments in disciplines such as engineering and computer science have contributed to the field, according to the former academic based in Oxford, where Man Group this week jointly sponsored a new research professorship in machine learning at the university.

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    The artificial intelligence used in driverless cars could have applications in finance

    Dr Ledford said the technology has applications in driverless cars, which must learn how to drive in novel conditions, and identifying stars from telescope images. Indeed, he has adapted the methods used in the Zooniverse project, which asked thousands of volunteers to help teach a computer to spot supernovae, to build a new way of spotting useful trends in the City’s daily avalanche of analyst research.

    “The core use is being able to extract patterns from data without specifically telling the algorithms what patterns we are looking for. Previously, you would define the shape of the model and apply it to the data,” he said.

    These technologies are not just been put to work in the financial markets. Several law firms are using natural language processing to carry out some of the drudgery, including poring over repetitive contracts.

    Slaughter & May has recently adopted Luminance, a due diligence programme that is backed by Mike Lynch, former boss of the computing group Autonomy.

    Freshfields has spent a year teaching a customised system known as Kira to understand the nuances of contract terms that often occur in its business.

    Its lawyers have fed the computer documents they are reading, highlighting the parts they think are crucial. Kira can now parse a contract and find the relevant paragraphs between 40pc and 70pc faster than a human lawyer reviewing it by hand.

    “It kicks out strange things sometimes, irrelevancies that lawyers then need to clean up. We’re used to seeing perfect results, so we’ve had to teach people that you can’t just set the machine running and leave it alone,” said Isabel Parker, head of innovations at the firm.

    “I don’t think it will ever be a standalone product. It’s a tool to be used to enhance our productivity, rather than replace individuals.”

    The system is built to learn any Latin script, and Freshfields’ lawyers are now teaching it to work on other languages. “I think our lawyers are becoming more and more used to it as they understand its possibilities,” she added.

    Insurers are also spending heavily on big data fed by new products such as telematics, which track a customer’s driving style in minute detail, to help give a fair price to each customer. “The main driver of this is the customer experience,” said Darren Price, group chief information officer at RSA.

    The insurer is keeping its technology work largely in-house, unlike rival Aviva, which has made much of its partnerships with start-up companies in its “digital garage”. Allianz recently acquired the robo-adviser Moneyfarm, and Axa’s venture fund has invested in a chat-robot named Gasolead.

    EY, the professional services firm, is also investing in analytics tools that can flag red flags for its clients in particular countries or businesses, enabling managers to react before an accounting problem spreads.

    Even the Financial Conduct Authority is getting in on the act. Having given its blessing to the insurance sector’s use of big data, it is also experimenting with a “sandbox”, or a digital safe space where their tech experts and outside start-ups can use real-life data to play with new ideas.

    The advances that catch on throughout the financial world could create a more efficient industry – and with that tends to come job cuts. The Bank of England warned a year ago that as many as 15m UK jobs were at risk from smart machines, with sales staff and accountants especially vulnerable.

    “Financial services are playing catch-up compared to some of the retail-focused businesses. They are having to do so rapidly, partly due to client demand but also because there are new challengers and disruptors in the industry,” said Amanda Foster, head of financial services at the recruiter Russell Reynolds Associates.

    But City firms, for all their cost pressures, are not ready to replace their fund managers with robots, she said. “There’s still the art of making an investment decision, but it’s about using analytics and data to inform those decisions.”

    Source: Telegraph.co.uk, October 8, 2016

     

     

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