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Killexams : IBM Cognos approach - BingNews https://killexams.com/pass4sure/exam-detail/COG-612 Search results Killexams : IBM Cognos approach - BingNews https://killexams.com/pass4sure/exam-detail/COG-612 https://killexams.com/exam_list/IBM Killexams : 8 Big Data Solutions for Small Businesses
  • Big data is information too complex, large or fast for many traditional data processing methods.
  • Big data can help your company resolve key issues, bolster its cybersecurity, and plan a meaningful data and analytics strategy.
  • SAS, Qualtrics and Google Analytics are some of today’s most prominent big data solutions.
  • This article is for business owners interested in using big data to bolster their business practices.

It’s hard to escape all the talk about big data. Armed with actionable information, companies can more effectively and efficiently market to customers, design and manufacture products that meet specific needs, increase revenue, streamline operations, forecast more accurately, and even better manage inventory to hold the line on related costs.

But can your business afford to take advantage of it?

To successfully compete in today’s marketplace, small businesses need the tools larger companies use. Of course, small businesses don’t have all the resources of an enterprise-level corporation, like data scientists, analysts and researchers. However, there are many ways your small business can gather, analyze and make sense of the data you already have, as well as gain additional insights to help level the playing field. To that end, we’ve rounded up eight big data solutions for small businesses – but first, take the time to learn the basics and importance of big data.

What is big data?

Big data is information too large or complex for traditional data processing methods to analyze. To better understand this definition, consider the three V’s of big data:

  • Unstructured data received in large amounts, such as Twitter data feeds, can sometimes comprise terabytes or petabytes of storage space. (For comparison, a Word document often takes up just a few dozen kilobytes.)
  • As internet use grows, businesses receive more data at once, thus requiring more processing capacity.
  • Think of the diversity of extensions among the files in your database – MP4, DOC, HTML and more. The more extensions you see, the more varied your data.

Key takeaway: Big data is so high in volume, velocity or variety that traditional processing methods may fail to keep up.

The value of big data for business

Big data benefits businesses because it helps to:

  • Quickly uncover the causes behind issues, defects and failures.
  • Instantly create coupons at the point of sale based on a customer’s buying habits.
  • Rapidly recalculate an entire risk portfolio.
  • Identify fraudulent or malicious cyberactivity before its worst consequences can occur.
  • Inform your full data and analytics strategy.

Below, we’ll explain how today’s big data solutions can help you achieve these goals.

Key takeaway: Big data is useful for business purposes such as resolving issues, enhancing cybersecurity, and determining your data and analytics strategy from top to bottom.

8 big data solutions and how they work

These are some of today’s most prominent big data solutions:

1. SAS

Being a small business is no longer an obstacle to obtaining market and business intelligence, according to SAS, a leader in business analytics software and services since 1976. SAS transforms your data into insights that help inform decision-making and supply a fresh perspective on your business, whether it’s a small, midsize or large organization.

Small and midsize businesses (SMBs) face many of the same challenges as large enterprises. SAS’ easy-to-use analytics, automated forecasting and data mining enable businesses without a lot of resources to accomplish more with less. These analytics help companies overcome challenges to grow and compete. SAS’ message to SMBs is simple: “Identify what’s working. Fix what isn’t. And discover new opportunities.” Contact SAS for more details and pricing and to learn about its free software trials.

2. Alteryx

Analyzing complex business intelligence doesn’t have to be rocket science. Alteryx offers advanced data mining and analytics tools that also present information in a simple, understandable way.

Alteryx combines your business’s internal data with publicly available information to help you make better business decisions. These insights allow you to create graphs, storylines and interactive visuals from the dashboard. It also offers collaboration features that enable team discussion.

In addition to business data, Alteryx can provide department-specific data, including marketing, sales, operations and customer analytics. The platform also covers a wide variety of industries, such as retail, food and beverage, media and entertainment, financial services, manufacturing, consumer packaged goods, healthcare, and pharmaceuticals. Contact the company for pricing information.

3. Kissmetrics

Looking to increase your marketing ROI? Kissmetrics enables you to understand, segment and engage your customers based on their behavior.

With Kissmetrics, you can create, manage, and automate the delivery of single-shot emails and ongoing email campaigns based on customer behavior. The platform measures campaign impact beyond opens and clicks. The company has also launched Kissmetrics for E-Commerce, which is designed to increase your Facebook and Instagram ROI, reduce cart abandonment rates, and drive more repeat purchases.

As a Kissmetrics user, you can access web-based training and educational resources to Strengthen your marketing campaigns, including marketing webinars, how-to guides, articles and infographics. As part of your onboarding, you get a dedicated customer success representative for the first 60 days and strategic guidance to help you get the most out of the platform. Plans start at $300 per month.

4. InsightSquared

With InsightSquared, you don’t have to waste time mining your own data and arduously analyzing it with one spreadsheet after another. Instead, InsightSquared connects to popular business solutions you probably already use – such as Salesforce, QuickBooks, Google Analytics and Zendesk – to automatically gather data and extract actionable information.

For instance, using data from CRM software, InsightSquared can provide a wealth of sales intelligence, such as sales and pipeline forecasting, lead generation and tracking, profitability analysis, and activity monitoring. It can also help you discover trends, strengths and weaknesses, and sales team wins and losses.

InsightSquared’s suite of products also includes marketing, financial, staff and support analytics tools, as well as custom reporting to let you slice and report data from any source in any way you choose. InsightSquared offers a free trial, and its service plans are modular and scalable. Contact InsightSquared for pricing.

Editor’s note: Looking for CRM software for your business? If you’re looking for information to help you choose the one that’s right for you, use the questionnaire below to receive information from vendors for free:

5. Google Analytics

You don’t need fancy, expensive software to begin gathering data. It can start with an asset you already have – your website. Google Analytics, Google’s free digital analytics platform, gives small businesses the tools to analyze website data from all touchpoints in one place. 

With Google Analytics, you can extract long-term data to reveal trends and other valuable information so you can make wise, data-driven decisions. For instance, by tracking and analyzing visitor behavior – such as where traffic is coming from, how audiences engage, and how long visitors stay on your website (known as your bounce rate) – you can make better decisions to meet your website’s or online store’s goals.

You can also analyze social media traffic, enabling you to make changes to your social media marketing campaigns based on what is and isn’t working. Studying mobile visitors can help you extract information about customers browsing your site on their mobile devices so you can provide a better mobile experience. Here’s how to sign up for Google Analytics for your website.

6. IBM Cognos Analytics

While many big data solutions are built for extremely knowledgeable data scientists and analysts, IBM’s Cognos Analytics makes advanced and predictive business analytics easily accessible to small businesses. The platform doesn’t require any skills in using complex data mining and analysis systems; it automates the process for you instead. This self-service analytics solution includes a suite of data access, refinement, and warehousing services, giving you the tools to prepare and present data yourself in a simple and actionable way to guide your decisions.

Unlike the many analytics solutions that focus on one area of business, IBM Cognos Analytics unifies all your data analysis projects into a single platform. You can use it for all types of data analysis, including marketing, sales, finance, human resources and other parts of your operations. Its “natural language” technology helps you identify problems, recognize patterns, and gain meaningful insights to answer key questions, like what ultimately drives sales, which deals are likely to close, and how to make employees happy. Contact IBM for pricing information.

7. Tranzlogic

It’s no secret that credit card transactions are chock-full of invaluable data. Although access was once limited to companies with significant resources, customer intelligence company Tranzlogic makes this information available to small businesses that lack the big business budget.

Tranzlogic works with merchants and payment systems to extract and analyze proprietary data from credit card purchases. You can use this information to measure your sales performance, evaluate your customers and customer segments, Strengthen promotions and loyalty programs, launch more effective marketing campaigns, write better business plans, and perform other tasks that lead to smart business decisions.

Tranzlogic requires no tech smarts to get started. It’s a turnkey program, meaning no installation or programming is necessary. You simply log in to access your merchant portal. Contact Tranzlogic for pricing information.

8. Qualtrics

If you don’t currently have any rich sources for data, research may be the answer. Qualtrics lets you conduct a wide variety of studies and surveys to gain quality insights for data-driven decisions. Further than that, the company recently announced Qualtrics Experience Management (Qualtrics XM), four applications that allow you to Strengthen and manage the experiences your business provides to every stakeholder – customers, employees, prospects, users, partners, suppliers, citizens, students and investors.

Qualtrics XM helps you measure, prioritize and optimize the experiences you provide across the four foundational experiences of business: customer, employee, brand and product experience. Additionally, Qualtrics offers real-time insights, survey software, advertising testing, concept testing and market research programs. The company can also help you conduct employee surveys, exit interviews and reviews. Contact Qualtrics to discuss pricing.

Key takeaway: SAS, Qualtrics, Google Analytics and many other software platforms offer big data solutions to small businesses.

Max Freedman contributed to the writing and research in this article.

Tue, 28 Jun 2022 12:00:00 -0500 en text/html https://www.businessnewsdaily.com/6358-big-data-solutions.html
Killexams : How MSU Turns Data into Donations

When raising money, good timing is essential. That’s why Michigan State University uses Big Data tools from IBM — SPSS, Cognos and Watson — to identify not only who is more likely to support its mission, but also the best way to approach them.

MSU gathers 177 touchpoints for each stakeholder, combining university records with data from potential donors’ tweets, Facebook comments and other social media activity. It then runs sentiment analysis to determine if potential donors feel positive about the university and might be likely to make a financial investment.

“IBM’s predictive analytics tools enable us to focus our gift officers on the right relationships at the right time,” says Monique Dozier, MSU’s assistant vice president of advancement information systems and donor strategy.

In some cases, this type of data analysis can increase the probability of donations up to 85 percent. MSU can also use data to determine whether athletic events are better fundraising vehicles than academic ones, or if regional events are more effective than those on MSU’s East Lansing campus.

So far MSU’s seven-year Empower Extraordinary campaign has raised more than $1.1 billion and will likely exceed its $1.5 billion goal by the time it concludes in 2018, says Dozier. The funds will enable MSU to create 3,000 new scholarships, double the number of endowed faculty positions and pump more money into research programs.

Data alone isn’t enough. Donors must have the ability and the desire to give. But predictive analytics helps MSU focus efforts where they will be most effective. “The tools aren’t raising the money,” says Dozier. “But they’re making us more efficient in our ability to transform data into information we should be paying attention to.”

Sun, 26 Jun 2022 12:00:00 -0500 Dan Tynan en text/html https://edtechmagazine.com/higher/article/2017/01/how-msu-turns-data-donations
Killexams : Predictive or Prescriptive Analytics? Your Business Needs Both
  • Predictive and prescriptive analytics are essential data strategies for small business management.
  • Predictive analytics helps find potential outcomes, while prescriptive analytics looks at those outcomes and finds more options.
  • Both analytics types can help any small business get ahead of the curve.
  • This article is for small business owners who want to understand and apply predictive and prescriptive analytics practices.

Throughout the business world, big data solutions attract a great deal of attention. Data analytics can provide valuable insights about your business and its customers. However, to fully benefit from those insights, you need to know how to interpret source data before applying it to your business strategy.

Business analytics has three primary components: descriptive, predictive and prescriptive. Descriptive analytics is a basic statistical analysis that summarizes raw data. It includes social engagement counts, sales numbers, customer statistics and other metrics that show what’s happening in your business in an easy-to-understand way. 

Predictive and prescriptive analytics aren’t as straightforward. They take descriptive data and transform it into actionable information. We’ll dive deeper into predictive and prescriptive analytics, explain how they compare to each other, and show you how to put analytics to work to make better decisions. 

Predictive vs. prescriptive analytics

Predictive and prescriptive analytics inform your business strategies based on collected data. Predictive analytics forecasts potential future outcomes, while prescriptive analytics helps you draw specific recommendations.

Predictive and prescriptive analytics are tools for turning descriptive metrics into insights and decisions. But you shouldn’t rely on one or the other; when used together, both analytics types can help you shift your business strategy to create the best possible outcomes. 

“Predictive by itself is not enough to keep up with the increasingly competitive landscape,” said Mick Hollison, president of enterprise data management company Cloudera. “Prescriptive analytics provides intelligent recommendations for the optimal next steps for almost any application or business process to drive desired outcomes or accelerate results.”

What is predictive analytics?

Predictive analytics is an advanced analytics category that helps companies make sense of potential outcomes or a decision’s repercussions. By leveraging mined data, historical figures and statistics, predictive analytics uses raw, up-to-date data to peer into a future scenario.

Until a few years ago, predictive analytics was the province of enterprise-level businesses – the only ones able to afford to parse and interpret reams of data from multiple sources. However, the growth in software as a service (SaaS) providers and CRM analytics means even small companies can access valuable data analytics. 

A key aspect of predictive analytics involves segregating superfluous or misleading data that could distort the insights. For example, a travel company with a sales rep in every state shouldn’t emphasize data provided by an employee in Alaska. 

Key TakeawayFYI: Sentiment analysis is a type of predictive analytics that helps you discover what your target customers want and how they think about your products or services.

What is prescriptive analytics?

Prescriptive analytics also looks at future scenarios, but it employs a more technological approach. It uses complicated mathematical algorithms, artificial intelligence and machine learning to take a deeper look into the “what” and “why” of a potential future outcome. 

Prescriptive analytics can also help a company see multiple options and potential outcomes. As more data comes in, prescriptive analytics can alter its predictions and suggestions accordingly.

“Prescriptive analytics can help companies alter the future,” said data-driven digital strategist Immanuel Lee. Predictive and prescriptive analytics are “both necessary to Strengthen decision-making and business outcomes,” he added.

Examples of predictive and prescriptive analytics in action

We use predictive and prescriptive analytics in our everyday lives. Here are three examples of predictive and prescriptive analytics working together.

Navigation apps

Motorists rely on GPS-enabled navigation apps to get from point A to point B. GPS navigation is equally essential for small businesses that rely on delivery services. Predictive analytics can take existing GPS-sourced travel data and map a potentially faster route. 

Thomas Mathew, chief product officer at influencer engagement platform Zoomph, said that’s where the effort starts. “Prescriptive analytics builds on [predictive analytics] by informing decision-makers about different decision choices with their anticipated impact on specific key performance indicators.” 

For example, consider the traffic navigation app Waze, which blends multiple factors to respond to users’ origin and destination input. The app advises you on different route choices, each with a predicted ETA. “This is everyday prescriptive analytics at work,” Mathew said.

Did you know?Did you know?: Industries like construction, transportation and distribution use the best GPS fleet tracking systems to gather data that can help them Strengthen driver safety, optimize vehicle performance and health, and comply with regulations.

Inventory planning

Retailers need to know how much stock to order to fill their shelves. While many retailers rely on educated guesses, analytics can help them plan a more precise inventory management strategy.

Guy Yehiav, president of SmartSense by Digi, said that as the retail landscape changes, businesses can use prescriptive analytics to clarify predictive data and Strengthen their sales plan

Yehiav gave the example of a retailer offering free expedited shipping to loyal customers. Based on past customer behavior, a predictive model would assume that customers will keep most of what they purchase with this promotion. However, imagine a scenario where one customer purchases eight items of clothing before returning all but one.

“The retailer paid for expedited shipping with the assumption that there’s this great consumer out there who bought eight items, so they’re willing to invest and lose a little margin” on shipping, Yehiav said. “The algorithm didn’t take [return] behavior into account.”

For this retailer, reducing losses on outlier customers who don’t follow what predictive analytics forecasted means having policies in place to cover itself. Using prescriptive analytics, Yehiav said the retailer might decide to supply an in-store-only coupon to customers who make returns (to encourage another purchase in which shipping isn’t a factor) or notify customers that they must pay for return shipping.

Weather forecasts

Predicting the weather can be a dicey proposition, but with the change of seasons comes the shift from indoor activities to fun in the sun. Sporting goods stores comprise one small business sector that benefits from nicer weather and increased physical activity.

If the store’s forecasts indicate that sales of running shoes will increase as warmer weather approaches in the spring, it might seem logical to ramp up the running-shoe inventory at every store. However, in reality, the sales spike likely won’t happen at every store across the country at once. Instead, it will creep gradually from south to north based on weather patterns.

Arijit Sengupta, former CEO of automated business analytics company BeyondCore and founder of Aible, said predictive and prescriptive analytics could help you plan for this scenario.

“To flip the switch on massive running-shoe distribution nationwide would be a huge mistake, even though the predictive analytics indicate sales will be up,” he added. “But with prescriptive analytics, you can pull in third-party sources, like weather and climate data, to get a better recommendation of the best course of action.”

Did you know?Did you know?: Weather apps like Carrot Weather, which collate weather data from several sources, are location-based services that use real-time geodata from a smartphone.

Putting analytics to work

Here are a few tips to help you get the most out of your analytics programs.

1. Start small with data analytics.

Data analytics is a complex subject that can be overwhelming, and you don’t want your best insights to get lost. Lee advised thinking big with your overarching analytics strategy but starting small tactically.

“With the complexity of big data and the systems that manage and process data, we can easily overlook the fact that sometimes there’s a solution in the simplest thing,” he said. “Small wins will help earn support for long-term analytics projects.”

2. Create rich data sets.

There are many what-if scenarios when you run and market a business, and predictive analytics doesn’t always account for alternate possibilities. Mathew said looking at your predictive analytics more closely to create richer information sets (by accounting for demographics like gender and age) will yield better results from your prescriptive recommendations.

“Social media marketers care about maximizing engagement and reach on their social posts,” he said. “Prescriptive analytics can make data-driven recommendations, such as use of a specific hashtag or emoji, to maximize social traction with a specific audience segment.”

3. Understand the reasons behind prescriptive recommendations.

Sengupta emphasized the importance of fully understanding the logic, nuances and circumstances behind the results of prescriptive analysis before taking action. Be prepared to prove that your results are statistically sound.

“Pretty graphs can be very compelling, but this is only software, and its analytical power is only as accurate as the human who designed it and the data we feed it,” Sengupta said. “It’s critical that business users understand the ‘story’ behind the results and the prescriptive action suggested.”

4. Keep your systems up to date.

As your business grows and evolves, so should your algorithms. Hollison noted that both predictive and prescriptive analytics should be updated continuously with the latest data to Strengthen predicted and prescribed actions based on real-time successes and failures.

“Predictive and prescriptive analytics depend on a solid data foundation,” Mathew added. “The analytics are only as good as the data that feed them.”

There’s a common misconception that the analytics industry is dominated by tech giants like Microsoft and IBM, which offer analytics software through their Power BI and Cognos with Watson platforms. Almost inevitably, web services titan Amazon also has a presence in the market with its cloud-based QuickSight BI service.

Alongside these tech giants, there are numerous more specialized entrants into this congested marketplace, including SAP, Zoho and Sigma. Many offer free trials, though some will only reveal costs when you register for a quote. That’s not a step companies tentatively considering analytics are always happy to take.

Some analytics platforms are code-specific (for example, Dash focuses on Python), while others are geared around no-code for simplicity, such as GoodData. Tableau works on Windows or Linux, whereas Domo is cloud-native.

Factors to consider when looking for small business analytics tools include how many data streams you have and what format they come in, how you want to visualize parsed data, and what objectives you’re looking to achieve. For example, InsightSquared studies every revenue activity throughout a business before calculating successful deal profiles and determining where revenue-raising improvements can be made.

Neil Cumins and Andrew Martins contributed to the writing and reporting in this article. Source interviews were conducted for a previous version of this article.

Thu, 07 Jul 2022 12:00:00 -0500 en text/html https://www.businessnewsdaily.com/8655-predictive-vs-prescriptive-analytics.html
Killexams : Microsoft Vs. IBM: Long/Short Strategy On The Old And New IT Giants
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Microsoft, IBM

Both International Business Machines Corporation (NYSE:IBM) and Microsoft Corporation (NASDAQ:MSFT) are among the top IT companies. IBM was the largest IT company by revenue ($7.2 bln) from the 1970s until 2007 and was not even in the top 10 in 2021. In contrast, Microsoft, which was founded in 1975, continued to grow to become the second-largest IT company in 2021 by revenue ($168 bln) only behind Apple ($365 bln).

We analyzed Microsoft and IBM’s businesses and contrasted their strategies based on product innovation, diversification, acquisition, integration, and cloud strategies to determine which company has better executed their strategies and ranked the companies against each other.

Moreover, we compared the companies in terms of their financials. In terms of diversification, we broke down its segments and analyzed its revenue growth. Also, we compared their margins in terms of gross, net and FCF margins. Additionally, we looked at both companies based on efficiency and credit analysis.

Finally, we looked at their valuation ratios and compared them against each other and their 5-year averages, as well as our in-house DCF analysis and compared it against analyst consensus. Considering their historical investment returns and dividend yields, we formulated a long/short strategy for the two stocks.

Strategies

microsoft vs ibm revenue

IBM, Microsoft, Khaveen Investments

Product Innovation Strategy

IBM was founded in 1911 but rose to the top of the tech sector in 1962 as the largest computer company with a 62% market share, as it created the first portable computer (IBM 5100). However, with the rise of IBM clones created by Compaq, competition for the company increased while its market share eroded to 32% in 1980. According to All About Circuits, IBM had tried to make clone computers redundant by developing the new Micro Channel Architecture computer architecture but, it was not even compatible with its software. Overall, the company was unable to develop a competitive advantage in software and hardware to remain competitive in the computer market due to increased competition from competitors’ compatible and cheaper products as prices of mainframe computers fell by 90% along with product innovations such as PCs and workstations from Compaq and Sun Microsystems. It held its market leadership in the PC market until 1994 before falling behind Compaq, Apple (AAPL) and Packard Bell.

In comparison, since Microsoft’s launch in 1975 and securing the contract to provide OS to IBM’s computers, Microsoft had dominated the OS market since the late 1980s with a market share of over 80%. In 2021, Microsoft continues to be the market leader with a market share of 74% in OS but is seeing increasing competition from Apple’s macOS with the strong sales of its PCs. Microsoft focused on OS with continuous innovation with subsequent product development and launches of new OS products including 15 Windows product launches (including Windows 11) while IBM only had 8 main PC product series. Also, it made deals with PC makers to supply OS to be preinstalled at discounted prices. Although it tried to run crack down on clone software and free alternatives such as Linux which continue to exist, it instead tolerated piracy of Windows OS as explained by the following quote of the potential opportunities to monetize it.

As long as they're going to steal it, we want them to steal ours. They'll get sort of addicted, and then we'll somehow figure out how to collect sometime in the next decade. - Bill Gates, Founder & Former CEO of Microsoft

Overall, we believe Microsoft edges out IBM in terms of its product innovation strategy as it maintained its market share in the OS market with its continuous product development and more product launches than IBM, which lost its market share leadership to stronger competitors. Microsoft also established various partnerships with PC makers, which IBM failed to do.

Diversification Strategy

In 1980, IBM’s revenue from its largest (Hardware) segment was 84%. In 1993, the company decided to focus on higher-margin software businesses such as middleware and consulting, targeting enterprises along with its mainframe business. This was successful for the company as its revenues grew again from 1983. However, with the rise and shift towards public clouds, the company’s growth started to decline in 2013 as IBM began losing server hardware market share rapidly to ODM competitors for public cloud customers. Though, its Hardware segment still represented 25% of its revenue in 2021, its third-largest segment behind IT consulting (31%) and Software (27%).

On the other hand, Microsoft diversified away from its largest segment, system software (53% of revenue in 1986) into application software such as productivity and HR software. In 2021, Windows only accounted for 13% of the company’s revenues and had a 5-year average growth of 5.8% below its total average of 13.1%.

  • Diversified within the software industry with productivity software in 1983 with Microsoft Word and Microsoft Excel in 1985
  • Diversified into the Media Industry with its advertising segment with Internet Explorer web browsers launched in 1995
  • Expanded within the software industry with HR software Microsoft Dynamics in 2006
  • Diversified into the entertainment industry with its Xbox business in 2001

Company

Microsoft (1986 – Non-System Software)

IBM (1980 – Non-Hardware)

Revenue Breakdown (Past)

47%

16%

Revenue Breakdown (2021)

87%

75%

Revenue (Past) ($ mln)

93

4,192

Revenue (2021) ($ mln)

146,237

43,013

Annualized Growth %

23.4%

5.8%

Source: Microsoft, IBM. Khaveen Investments

Based on the table above, we calculated the annualized growth rate of the revenue contribution of the revenue streams of Microsoft’s non-System Software revenue in 1986 and IBM’s non-Hardware revenue from 1980 to 2021. Thus, we believe that Microsoft has a superior diversification strategy with its higher annualized growth of 23.4% compared to 5.8% for IBM.

Acquisition Strategy

IBM Acquisitions

Acquisition Cost ($ mln)

Revenue ($ mln)

Microsoft Acquisitions

Acquisition Cost ($ mln)

Revenue ($ mln)

Red Hat

34,000

3,400

Activision Blizzard

68,700

8,800

Cognos

5,000

1,000

LinkedIn

26,200

2,970

PwC Consulting Business

3,500

4,900

Nuance Communications

19,700

1,931

Lotus Development Corporation

3,500

1,150 (excluded)

Skype Technologies

8,500

622

Truven Health Analytics

2,600

544

ZeniMax Media

8,100

1750

Rational Software Corp

2,100

592

GitHub

7,500

200

SoftLayer Technologies

2,000

280

Nokia (Mobile Phones unit)

7,200

1990

Netezza

1,700

190.6

aQuantive

6,333

700

FileNet Corp

1,600

422

Mojang

2,500

412

Sterling Commerce

1,400

479

Visio Corp

1,375

166

Total

57,400

11,808

Total

156,108

19,541

Revenue Contribution

20.6%

Revenue Contribution

11.6%

Source: Company Data, Khaveen Investments

The table above shows the top 10 acquisitions by IBM and Microsoft in terms of their acquisition costs and revenue. IBM made a series of acquisitions such as Lotus Development Corp for middleware software which was later divested off to HCL in 2018 for just $1.8 bln, translating to a loss of $1.7 bln. Also, it acquired PwC’s management consulting arm in 2002. Besides that, it also acquired software companies such as Cognos, Truven Health Analytics, Rational Software, SoftLayer, Netezza, FileNet and Sterling Commerce as well as hybrid cloud company Red Hat which was its largest acquisition ever at $34 bln.

On the other hand, Microsoft’s largest deal is the planned acquisition of Activision Blizzard (ATVI) for $68.7 bln to expand its footprint in the gaming market, followed by LinkedIn, Nuance Communications, Skype, ZeniMax Media, GitHub, Nokia (Mobile phones), aQuantive, Mojang, and Visio Corp.

The total revenue contribution of Microsoft’s top 10 acquisitions ($19.5 bln) is higher than IBM at $11.8 bln (excludes Lotus) but its revenue contribution is lower at 11.6% compared to 20.6%. However, Microsoft ($156.1 bln) spent more than IBM ($57.4 bln). This translates to a revenue per cost of $0.21 for IBM which is higher compared to Microsoft at only $0.13.

Therefore, while IBM divested Lotus, its revenue contribution from its largest acquisitions is still higher than Microsoft. Additionally, with a higher revenue per acquisition cost, we believe that IBM has a superior acquisition strategy to Microsoft.

Integration Strategy

IBM provides mainframe software such as containerized software, DevOps and operations management which are integrated with its server systems. Despite its expansion into software, consulting, and cloud, we believe this is its only product integration and it has not built up an ecosystem of integrated products and services. Moreover, its other segments such as software had stagnant growth of 1% and hardware’s revenue declined which we believe indicates its inability to successfully synergize the segments.

As covered in our previous analysis, Microsoft had established solid market leadership in office productivity with over 80% market share based on Gartner and we expect it to continue defending its lead with its comprehensive features and integrations with its other Microsoft products and competitive cost against competitors. Besides that, other examples of Microsoft’s integrations are:

  • Integration between Windows and Microsoft through Office Hub for easier access to documents
  • It developed the cloud-based Dynamics 365 in 2016 which is integrated with Power Platforms, Teams and SharePoint.
  • In Gaming, Microsoft focused on its gaming segment with its gaming subscription in 2017 integrated with Windows.
  • It also acquired companies such as LinkedIn in 2016 and integrated them with its Dynamics 365 and the Office suite.

Thus, we believe Microsoft, with its ecosystem with integrated productivity & HR software, gaming and LinkedIn is stronger compared to IBM (only hardware and software) and we believe it supports its growth outlook. In our previous analysis, we saw that Microsoft was the only company out of the top 3 software companies (IBM and Oracle) which had above industry 7-year average revenue growth (7.9%).

Cloud Strategy

Microsoft entered the cloud market with the launch of Azure in 2010, followed by IBM in 2011. Although both entered roughly similar periods, we compared both companies’ cloud strategies based on a comparison of their market share & revenue growth, number of availability zones, features and number of users.

cloud market share

Synergy Research, Company Data, Khaveen Investments

Cloud Service Providers

Average 5-year Revenue Growth

IBM Cloud

3.1%

AWS (AMZN)

37.5%

Azure

57.2%

Google Cloud (GOOG)

47.5%

Alibaba Cloud (BABA)

51.9%

Source: Synergy Research, Company Data, Khaveen Investments

Based on the chart and table above, Microsoft had strengthened its cloud market share with the highest average revenue growth (57.2%) in the past 5 years to catch up with market leader AWS which had a lower growth (37.5%). In comparison, IBM had the lowest growth among its competitors at only 3.1%. As highlighted in our previous analysis, IBM had divested its public cloud business (Kyndryl) and focused on its hybrid cloud business following its RedHat acquisition in 2019. That said, based on the data, we believe Microsoft had strengthened its positioning in the cloud market and could provide it with an advantage over IBM.

Company

Microsoft

IBM

Cloud Users

550 mln

0.0038 mln

Growth % YoY

10%

35%

Type of Users

Companies, Individuals

Companies

Source: IBM, Microsoft

To understand the rise of Microsoft’s cloud business vs IBM, we looked into the user bases of each company. Compared to Microsoft, IBM specifically targets companies while Microsoft targets both companies and individuals with its cloud. Microsoft had 500 mln active monthly users, on Azure Directory compared to 3,800 clients for IBM. However, IBM had a higher growth rate of 30% YoY than Microsoft at 10%.

Company

Availability Zones

Number of Features

Pricing

IBM

19

43

$71.27

AWS

84

60

$154.50

Microsoft

66

70

$152.60

Google

88

90

$116.10

Source: Source: IBM, AWS, Microsoft, Google, GetApp, Khaveen Investments

To compare the reach of their cloud services globally, we examined the number of availability zones by each company in the table above from our previous analysis of Oracle (ORCL). As seen in the table, Microsoft is third with 66 availability zones behind AWS and Google. However, Microsoft was still more than 3 times higher than IBM Cloud. Microsoft had announced its planned data center expansions across the US in Georgia and Texas and across the globe in Israel and Malaysia.

To determine the competitiveness of their cloud services, we examined the features and compared them with each company. Based on the table, IBM has the least number of features (43) according to GetApp while Microsoft Azure has 70 features, the second-highest behind Google Cloud Platform with the most features (90).

Furthermore, we also compared their cloud pricing to determine the competitiveness of their cloud services. As seen in the table, Azure has the second most expensive pricing behind AWS based on its online website cost estimator (2 virtual CPUs and 8GB of RAM running on Windows OS). In comparison, IBM’s pricing ($71.29) is the lowest among competitors and has pricing below the average of $123.62 which we believe indicates its pricing advantage.

To sum it up, we believe Microsoft’s cloud strategy had been superior to IBM's due to its superior market share, growth, availability zones and a wider user base despite IBM’s higher user growth and competitive pricing.

Overall

We believe Microsoft edges out IBM in the Strategy factor by being the superior company in Product Innovation, Diversification, Integration and Cloud strategies. However, we believe IBM is better in terms of the Acquisition strategy.

Strategy

Advantage

Product Innovation

Microsoft

Diversification

Microsoft

Acquisition

IBM

Integration

Microsoft

Cloud

Microsoft

Overall

Microsoft

Source: Khaveen Investments

Financials

To compare the differences between Microsoft and IBM based on their financials, we looked at their diversification, revenue growth, profitability, efficiency and credit position.

Diversification

Industries

(Growth Rate)

Microsoft

Average Revenue Growth

Revenue Breakdown %

IBM

Average Revenue Growth

Revenue Breakdown %

IT Services (12.4% CAGR)

Server products

22.6%

31.3%

Internet Services

& Infrastructure

26.0%**

15.2%

Enterprise Services

4.2%

4.1%

IT Consulting &

Other Services

1.4%

31.1%

Software (5.78% CAGR)

Office products

10.8%

23.7%

Software

-0.1%

26.9%

Windows

5.8%

13.8%

Dynamics

14.8%

2.3%

Entertainment (8.3% CAGR)

Gaming

11.4%

9.1%

Interactive Media & Services (8.4% CAGR)

LinkedIn

25.1%*

6.1%

Media (15% CAGR)

Search advertising

9.6%

5.1%

Technology Hardware, Storage & Peripherals (8.76% CAGR)

Devices

-0.9%

4.0%

Technology Hardware, Storage & Peripherals

-2.3%**

24.7%

Others

Other

8.7%

0.4%

Others

-0.9%

2.1%

Total

13.1%

100.0%

-5.7%

100.0%

*3-year average

**1-year average

Source: Microsoft, IBM, The Business Research Company, Statista, Khaveen Investments

Firstly, we compiled and compared their revenue streams based on GICs classification. Based on the table, Microsoft’s revenues are split across 6 industries namely IT Services, software, entertainment, interactive media & services, media, technology hardware, storage & peripherals. Whereas IBM’s revenue streams are more concentrated as it is only split across 3 industries (IT Services, software, technology hardware, storage & peripherals). Moreover, Microsoft has more segments (9) compared to IBM (4).

The largest segment for Microsoft is Server products (31.3% of revenue) followed by Office products (23.7%) and Windows (13.8%). In comparison, IBM’s largest segment revenue contribution is similar to IT consulting (31% of revenue) and followed by software (27%). In terms of revenue growth, IBM’s Internet Services & Infrastructure segment was its fastest-growing segment (26%) and was higher than Microsoft’s Server products (22.6%). Though, IBM’s Internet Services & Infrastructure segment only accounted for 15% of the company’s revenue compared to Microsoft’s Server products segment (31% of revenue).

Also, 3 of Microsoft’s segments (Server products, LinkedIn, Dynamics) totaling 40% of revenue have above-average revenue growth (13.1%) compared to IBM which only has 2 segments (Internet Services & Infrastructure and IT Consulting) totaling 46% of revenue which grew above its total company average (-5.7%).

Thus, we believe that Microsoft has better diversification compared to IBM with a wider revenue exposure across more GICS industries than IBM and more segments. Microsoft’s fastest-growing segment’s revenue contribution is also twice that of IBM’s.

microsoft revenue

Microsoft, Khaveen Investments

ibm revenue breakdown

IBM, Khaveen Investments

Revenue Growth

growth

SeekingAlpha

Furthermore, as seen in the tables above, Microsoft’s YoY revenue growth (20.37%) is higher than IBM's (14.9%). Both companies’ revenue growth had been higher than their 5-year averages, but Microsoft’s 5-year CAGR (16.02%) had been superior to that of IBM (-5.97%). Based on our previous analyses, we forecasted Microsoft to have a higher 5-year forward average revenue growth of 21.5% compared to just 5% for IBM. Also, the company has higher current and 3-year average EBITDA growth than IBM. Therefore, we believe all the factors above point to Microsoft’s superior growth compared to IBM.

Profitability Analysis

For the profitability comparison, we compared the companies in terms of their TTM gross, operating, EBITDA, net and FCF margins.

profitability

Seeking Alpha

Based on the table, IBM has remained profitable with positive average margins despite its revenue decline. Notwithstanding, Microsoft has superior profit margins than IBM with higher gross, operating, EBITDA, net margins and FCF margins based on both current and a 5-year average.

More importantly, Microsoft’s margins have been improving with higher current margins than its 5-year averages for its gross, operating, EBITDA, net margins and FCF margins. Whereas for IBM, except for its gross margins, the rest of its margins have declined compared to its 5-year average. Therefore, we believe Microsoft clearly beats IBM with superior profitability.

Efficiency Analysis

efficiency analysis

SeekingAlpha

In terms of its efficiency, Microsoft has superior ROE, ROTC, and ROA to IBM. Though, Microsoft had higher capex/sales compared to IBM in both current and a 5-year average. Compared to IBM, Microsoft's ROE, ROTC and ROA ratios have improved in the past 5 years while IBM had deteriorated due to the decline of its profitability margins. Additionally, Microsoft had a higher asset turnover ratio as well as cash from operations which highlights its superior efficiency.

Thus, we believe Microsoft has an advantage over IBM based on efficiency analysis with superior ratios to IBM and improving ratios compared to its 5-year average.

Credit Analysis

For credit analysis of both companies, we compared their net debt, interest coverage ratios, EBITDA/Net debt and Debt to Equity ratios.

Credit Ratios

Microsoft

IBM

Net Debt ($ mln)

46,372

101,800

Net Debt as % of Market Cap

2.3%

79.7%

Debt/Equity (5-year Average)

1.64x

5.94x

EBITDA interest coverage (5-year Average)

406.1x

16.5x

FCF interest coverage (5-year Average)

246.8x

7.8x

EBITDA/Net Debt (5-year Average)

1.60x

0.14x

Source: Company Data, Khaveen Investments

From the table, Microsoft has a lower net debt than IBM and lower net debt as a % of the market cap (2.3%) compared to IBM (79.7%). Also, Microsoft has a much lower debt/equity ratio of 1.64x than IBM (5.94x). In the charts below of their cash to debt ratios, both companies saw their ratios declining over the past 10 years. Notwithstanding, Microsoft continues to maintain a higher cash-to-debt ratio than IBM.

Moreover, Microsoft has superior coverage ratios compared to IBM which indicates its superior ability to service its debt obligations.

msft net debt

Microsoft, Khaveen Investments

ibm net debt

IBM, Khaveen Investments

Source: IBM, Khaveen Investments

In the charts above of their cash to debt ratios, both companies saw their ratios declining over the past 10 years. Notwithstanding, Microsoft continues to maintain a higher cash-to-debt ratio than IBM. Therefore, we believe Microsoft has an advantage over IBM based on credit analysis, beating IBM on all 7 metrics we looked at.

Overall

We believe Microsoft has the overall advantage over IBM based on its financials as we determined it to have better diversification, revenue growth, profitability, efficiency and credit than IBM.

Financials

Advantage

Diversification

Microsoft

Revenue Growth

Microsoft

Profitability analysis

Microsoft

Efficiency analysis

Microsoft

Credit analysis

Microsoft

Overall

Microsoft

Source: Khaveen Investments

Valuation

Multiples

Ratios

Microsoft Current

Microsoft 5-year Average

Upside

IBM Current

IBM 5-year Average

Upside

P/E

26.5x

34.4x

16.0%

25.14x

18.48x

-22.0%

P/S

9.9x

9.4x

-15.8%

2.12x

1.64x

-41.0%

P/FCF

30.7x

29.5x

25.6%

13.09x

7.91x

-54.6%

Average

9.5%

-39.2%

Source: Seeking Alpha, Company Data, Khaveen Investments

Based on the table above of the valuation multiples, all of Microsoft’s ratios (current) including P/E, P/S and P/FCF are higher than IBM. Moreover, based on a 5-year average, all of Microsoft’s valuation ratios are also higher than IBM indicating IBM’s better value compared to Microsoft.

However, when comparing each company’s current and 5-year average ratio, Microsoft’s PE (26.5x) ratio is below its 5-year average (34.4x) while the other ratios are higher than its 5-year average. Whereas all of IBM’s ratios are above its 5-year average. Based on their ratios, we obtained an upside of 9.5% for Microsoft and -39.2% for IBM.

Therefore, we believe that despite IBM’s better value compared to Microsoft with its lower current and 5-year average ratios, Microsoft edges out IBM with its higher upside than IBM as its P/E and P/FCF ratios (current) are lower than its 5-year averages while IBM’s ratios are above its 5-year average

Ratings

Ratings

Microsoft

IBM

DCF Rating

Strong Buy

Sell

DCF Upside

114.4%

-13.3%

DCF Price Target

$550.55

$122.37

Analyst Consensus Rating

Strong Buy

Hold

Analyst Consensus Upside

39.3%

1.6%

Analyst Consensus Price Target

$357.85

$143.50

Source: Seeking Alpha, Company Data, Khaveen Investments

Based on the table above, we derived a DCF upside of 114% for Microsoft and rated it as a Strong Buy whereas for IBM we obtained a 13.3% downside and rated it as a Sell. With reference to analyst consensus estimates, Microsoft was rated as a Strong Buy with a 39.3% upside compared to IBM at only a Hold with a 1.6% upside. Thus, we believe Microsoft is clearly superior to IBM with a higher rating from DCF and analyst consensus.

microsoft valuation

Khaveen Investments

ibm valuation

Khaveen Investments

Investment Returns

Company

5-year Stock Return

Annualized Return

Dividend Yield

Total Annual Return

Microsoft

275.54%

30.2%

0.96%

31.16%

IBM

-5.78%

-0.89%

4.77%

3.88%

Source: Seeking Alpha, Company Data, Khaveen Investments

Finally, we compared the investment returns based on the past 5 years between Microsoft and IBM and computed its annualized return and calculated the total return with its dividend yield. Overall, Microsoft (31.16%) has a higher annualized total return than IBM (3.88%) which highlights its advantage

Overall

To sum it up, based on the multiples, ratings and investment returns, we believe Microsoft edges out IBM with its Strong Buy ratings based on DCF and analyst consensus and higher investment return compared to IBM.

Valuation

Advantage

Multiples

Microsoft

Ratings

Microsoft

Investment Returns

Microsoft

Overall

Microsoft

Source: Khaveen Investments

Risk: Dividend Growth

IBM

ibm dividend

SeekingAlpha

We believe the risk for the long/short strategy between Microsoft and IBM is its high dividend yield. IBM had a dividend yield of 4.77%, which is one of the considerations for shorting the stock. IBM’s dividend growth was 0.77% as seen above. However, IBM’s dividend growth had continuously slowed in the past 10 years from a 10-year CAGR of 8.17% to 3.6% (5-year) and 2.42% (3-year), which reduces the probability of much higher dividend payments going forward.

Verdict

Factor

Advantage

Product Innovation

Microsoft

Diversification

Microsoft

Acquisition

IBM

Integration

Microsoft

Cloud

Microsoft

Strategy Factor

Microsoft

Diversification

Microsoft

Revenue Growth

Microsoft

Profitability analysis

Microsoft

Efficiency analysis

Microsoft

Credit analysis

Microsoft

Financials Factor

Microsoft

Multiples

Microsoft

Ratings

Microsoft

Investment Returns

Microsoft

Valuation Factor

Microsoft

Overall

Microsoft

Source: Khaveen Investments

To summarize, we determined that Microsoft is superior to IBM for the Strategy factor based on their strategies as we believe its product innovation, diversification, integration and cloud strategy to be superior to IBM. Moreover, we also determined its superior financials based on diversification, revenue growth, profitability, efficiency and credit. Finally, we determined Microsoft comes up on top for the Valuation factor considering its upside based on multiples, DCF and higher investment returns. We determined our long/short strategy by taking into account its dividend yield, borrow fee (between 0.3% to 3%) and short sale proceeds interest of 0%.

Case

Stock

Upside

Div Yield

Gross Return

Avg Borrow Fee

Int

Net Return

Weight

Net Return

Our Case

MSFT

114.4%

1.0%

115.3%

115.3%

81.3%

94.99%

IBM

-13.3%

4.8%

-8.6%

1.7%

0%

6.9%

18.8%

Base Case

MSFT

16.10%

0.96%

17.06%

17.06%

81.25%

34.25%

IBM

-16.42%

4.77%

-11.65%

1.65%

0%

10.00%

18.75%

Bear Case

MSFT

16.10%

0.96%

17.06%

17.06%

81.25%

15.74%

IBM

-16.42%

4.77%

-11.65%

1.65%

0%

10.00%

18.75%

Bull Case

MSFT

60.0%

1.0%

61.0%

61.0%

81.3%

54.1%

IBM

17.6%

4.8%

22.3%

1.7%

0%

24.0%

18.8%

Source: SA, IBKR, Khaveen Investments

To determine the appropriate weights for the trade, we computed a sensitivity analysis based on the different scenarios and long-short weights. As seen in the chart below, long Microsoft (81% weight) and short IBM (19% weight) would generate the highest net return in all scenarios.

sensitivity analysis

Khaveen Investments

Overall, we believe both Microsoft and IBM have contrasting fundamentals which provides a clear opportunity for a Long-Short trade. While IBM was the old leader of the IT sector, Microsoft has emerged as the new dominant player with a clear-cut advantage owing to its superior product innovation, diversification, integration, cloud strategies as well as solid financials and attractive valuation. Thus, we believe a long/short strategy can be implemented on this pair of stocks, and carry a Strong Buy rating on Microsoft and a Sell rating on IBM.

Sun, 03 Jul 2022 20:27:00 -0500 en text/html https://seekingalpha.com/article/4521603-microsoft-vs-ibm-long-short-strategy
Killexams : IBM To Acquire Databand.ai

 

IBM (NYSE: IBM) is to acquire Databand.ai, a Tel Aviv, Israel-based provider of data observability software that helps organizations fix issues with their data.

The amount of the deal was not disclosed.

The acquisition further strengthens IBM’s software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that data is being put into the right hands of the right users at the right time. Employees will join IBM Data and AI, further building on IBM’s portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data.

Led by Josh Benamram, Co-Founder and CEO, Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable data. Its data observability software helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Its proactive approach ties into all stages of data pipelines, beginning with source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world’s largest companies in entertainment, technology, and communications. Databand.ai is backed by Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. 

Databand.ai is IBM’s fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

FinSMEs

11/07/2022

Sun, 10 Jul 2022 20:17:00 -0500 FinSMEs en-US text/html https://www.finsmes.com/2022/07/ibm-to-acquire-databand-ai.html
Killexams : Downer and IBM Consulting ink 10-year partnership
Adam Williams (Downer) and Ric Lewis (IBM)

Adam Williams (Downer) and Ric Lewis (IBM)

Credit: IBM

Integrated services company Downer has entered into a 10-year collaboration deal with IBM Consulting to explore possibilities working with artificial intelligence (AI) and other technologies in reducing its carbon footprint across its rail and transit systems. 

Downer first began working with IBM in 2017 to modernise its technology platform, embedding digital and intelligent capabilities into its civil infrastructure operations.

The platform now uses IBM Maximo along with IBM Cognos Analytics with Watson to Strengthen availability, reliability and safety of its services and the fleets it maintains for customers.

This next phase of Downer’s digital transformation journey will involve a range of IBM technologies and services that work together to supply Downer a single view of the life, health and carbon footprint of all assets within the Rail and Transit Systems division, while working to keep it secure from cyber security threats.

“Sustainability is a critical focus for Downer and our customers. We have a clear roadmap to get to Net Zero by 2050,” Downer head of growth for rail and transit systems Adam Williams said.