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Across an organization, business data holds a special place in driving value and enhancing the efficiency of operations. The process of harnessing or in-depth analysis of business data is termed as business analytics. According to MicroStrategy, being a subset of business intelligence, business analytics serves the goal of determining which datasets are useful and how they can be leveraged to solve problems and increase efficiency, productivity, and revenue. Also, as compared to business intelligence as a whole, it is more prescriptive, devoted to the methodology by which the data can be analyzed, patterns recognized, and models developed to clarify past events, create predictions for future events, and recommend actions to maximize ideal outcomes. With the continuous growth in business data, the demand for business analysts is also at rise. The widespread applicability of such data is paving way for future job opportunities and career prospects for aspiring data professionals. To prosper in the field of business analytics (BA), one must have a technical background with a computer science or equivalent bachelor’s degree and further pursue master’s for better opportunities.Qualifications Required for Pursuing a Master’s
Below are the relevant fields for the Bachelor’s degree that are considered for this course. However, the eligibility criteria may differ in the case of some universities. • Mathematics • Statistics • Economics • Physics • Accounting • Engineering, • Data science • Computer science • Business educationWhat does a Master’s in Business Analytics hold?
According to MIM-Essay, Master’s in BA is a perfect blend of Data Science, Information Theory, Business Intelligence, and Computer Science. Its major aim is to change heavy data into actionable intelligence by using different quantitative and statistical methods. To put it simply, Master’s in BA is concerned with mining data in order to get particular business objectives; instead of focusing on measuring past performance, it’s more concerned with predictive and prescriptive techniques.Reasons to opt for Master’s in Business Analytics
• Owing to its high demand in the IT sector, various Business Schools have started to offer a Master’s in BA. • The full-time course is of 12 to 14 months and it is also considered as one of the shortest Master’s Programs for an in-demand job. • The pay scale for such candidates is quite high. For Example, in the USA, the average salary of a Business Analyst is US$80,000. • Not only business analysts, but candidates also can embark on their careers differently. Candidate with a Master’s in BA can serve the job profiles including Financial Analyst, Market Research analysts, Statistician, etc.Specialization through Master’s in Business Administration (MBA)
If the candidate wants to specialize through the MBA route then, he can opt for primary specializations including Marketing, Finance, Supply Chain and Operations Management, and Information Systems. According to Pulse Headlines, out of the four, the highest-earning professionals are generally found to work in marketing or supply chain and operations management. At the same time, due to the wide applicability and use for business analysis in nearly every sector, employability and pay are above average for data analysts with a relevant MBA or MSc.Difference between Master’s in Business Administration and MSc
The course for MBA tends to focus more on the business aspect while the other one is best to hone the professional skills of a working analyst. It can be said that an MBA in Business Analytics deals more with equipping an executive/analyst with a broader range of skills. However, an MSc in BA will be more technical, specialized and detailed in their knowledge.
1MIT Sloan School of ManagementUS
2UCLA Anderson School of ManagementUSMaster of Science in Business Analytics
3France & SingaporeMSc in Data Sciences & BA
4Austin McCombs School of BusinessUSMaster of Science in Business Analytics
5Imperial College Business SchoolUKMSc Business Analytics
6USC Marshall School of BusinessUSMaster of Science in Business Analytics
7ESADE Business SchoolSpainMSc in Business Analytics
8Alliance Manchester Business SchoolUKMSc Business Analytics
9Warwick Business SchoolUKMSc Business Analytics
10University of Edinburgh Business SchoolUKMSc in Business Analytics
Across an organization, business data holds a special place in driving value and enhancing the efficiency of operations. The process of harnessing or in-depth analysis of business data is termed as business analytics. According to MicroStrategy, being a subset of business intelligence, business analytics serves the goal of determining which datasets are useful and how they can be leveraged to solve problems and increase efficiency, productivity, and revenue. Also, as compared to business intelligence as a whole, it is more prescriptive, devoted to the methodology by which the data can be analyzed, patterns recognized, and models developed to clarify past events, create predictions for future events, and recommend actions to maximize ideal outcomes. With the continuous growth in business data, the demand for business analysts is also at rise. The widespread applicability of such data is paving way for future job opportunities and career prospects for aspiring data professionals. To prosper in the field of business analytics (BA), one must have a technical background with a computer science or equivalent bachelor’s degree and further pursue master’s for better opportunities.Below are the relevant fields for the Bachelor’s degree that are considered for this course. However, the eligibility criteria may differ in the case of some universities. • Mathematics • Statistics • Economics • Physics • Accounting • Engineering, • Data science • Computer science • Business educationAccording to MIM-Essay, Master’s in BA is a perfect blend of Data Science, Information Theory, Business Intelligence, and Computer Science. Its major aim is to change heavy data into actionable intelligence by using different quantitative and statistical methods. To put it simply, Master’s in BA is concerned with mining data in order to get particular business objectives; instead of focusing on measuring past performance, it’s more concerned with predictive and prescriptive techniques.• Owing to its high demand in the IT sector, various Business Schools have started to offer a Master’s in BA. • The full-time course is of 12 to 14 months and it is also considered as one of the shortest Master’s Programs for an in-demand job. • The pay scale for such candidates is quite high. For Example, in the USA, the average salary of a Business Analyst is US$80,000. • Not only business analysts, but candidates also can embark on their careers differently. Candidate with a Master’s in BA can serve the job profiles including Financial Analyst, Market Research analysts, Statistician, chúng tôi the candidate wants to specialize through the MBA route then, he can opt for primary specializations including Marketing, Finance, Supply Chain and Operations Management, and Information Systems. According to Pulse Headlines, out of the four, the highest-earning professionals are generally found to work in marketing or supply chain and operations management. At the same time, due to the wide applicability and use for business analysis in nearly every sector, employability and pay are above average for data analysts with a relevant MBA or chúng tôi course for MBA tends to focus more on the business aspect while the other one is best to hone the professional skills of a working analyst. It can be said that an MBA in Business Analytics deals more with equipping an executive/analyst with a broader range of skills. However, an MSc in BA will be more technical, specialized and detailed in their knowledge.
You're reading Master’s In Business Analytics: Universities, Jobs And Current Industry Scenario
Analytics Insight has selected the top Data Analytics jobs available in India for 2023.
The pandemic forcing everyone to work from home has not been able to hamper the data analyst’s job as it is highly connected with technology. Moreover, the gigantic rise of death and number of people affected by coronavirus has demandedData Analyst at BARC INDIA
Broadcast Audience Research Council (BARC) India is a joint-industry body founded by bodies that represent Broadcasters (IBF), Advertisers (ISA), and Advertising & Media Agencies (AAAI). It is also the world’s largest television measurement science industry-body. It uses Audio Watermarking Roles and responsibilities The person assigned with this job will have to prepare insights about upcoming trends and share them with management to help decision-making. He will drive automation to improve internal efficiencies. Self-enablement of end-user is required through compact and visualization of data and collaboration with the cross-functional teams has to done to drive quality projects. The applicator should have at least 2 to 4 Years of Experience. Skills He should have R /Python skills and be an expert in the MS office. He should also have a better understanding of Business Processes. ApplyData Engineer Job at Impetus
Impetus is focused on creating big business impact through big data solutions for Fortune 1000 enterprises. The company offers a unique mix of software products, consulting services, data science capabilities, and technology expertise. Required skills The applicator should have strong programming experience with Python OR Pyspark. One must have experience working with Relevant Experience The applicator should have 2-8 years of experience. ApplyData Scientist Job at Jio
Reliance Jio Infocomm Limited, doing business as Jio, is an Indian telecommunications company and a subsidiary of Role The company is looking for a self-driven Lead Data Scientist with a “Can-Do” and “Can-Share” attitude to accelerate solving the complex problems at the scale of India. Jio Big Data Lake is the central data hub inside Jio. This role will give a person the opportunity to work across a wide variety of technology domains. Responsibilities Skills required The applicator should have experience in NLP, Machine Learning, Deep learning, AI, Python, SQL/NoSQL Databases, Distributed Systems, and Cloud Native Micro Services. Qualifications The applicator must have 6 to 9 years of experience required in the Data Science field with a very good academic background. Work location Navi Mumbai, Bangalore, Hyderabad ApplyBusiness Intelligence Analyst at Cognizant
Cognizant (Nasdaq-100: CTSH) is one of the world’s leading professional services companies, transforming clients’ business, operating, and technology models for the digital era. Its unique industry-based, consultative approach helps clients envision, build and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant is ranked 194 on the Fortune 500 and is consistently listed among the most admired companies in the world. Business Skills The business intelligence analyst should have an understanding of trade life cycle events and front office to back-office system flows and should know about other financial data such as Balance Sheet, Risk-Weighted Assets, P&L, etc. One should possess working knowledge of data analysis, management, and governance concepts and tools. One must understand the contemporary requirement’s elicitation, analysis, specifications, verification, and management practices and have the ability to apply them in practice. One should have a familiarity with requirements engineering books and resources. Technical Skills The applicator should have proficiency with Excel, MS Word, MS Visio, and PowerPoint. He should possess basic SQL skills with the ability to extract and analyze large volumes of data using Oracle or Sybase databases. One should have experience in creating interface specification documents, attribute mapping documents, functional specifications. He must have experience of using JIRA, ALM Quality Centre (or equivalent tools). One should be familiar with creating Gliffy Diagram and Balsamiq Wireframes. ApplyQuantitative Analyst at Barclays
Barclays is a British universal bank. It is diversified by business, by different types of customers and clients, and by geography. With over 325 years of history and expertise in banking, Barclays operates in over 40 countries and employs approximately 83,500 people. Barclays moves, lends, invests, and protects money for customers and clients worldwide. Location Noida Responsibilities The applicator would have to provide development and implementation support to key treasury models, including driver-based models for projecting Barclay’s balance sheet. One would have to implement and design statistical projection model code within an IT infrastructure related to CCAR, IRRBB, and IFRS9 regulatory requirements and Treasury functions. One must handle the maintenance and design of the QA Asset & Liability Management quantitative library, ensuring an efficiently organized library and adherence to coding standards, regression testing, and continuous integration. This library supports quantification of Barclays funding and capital plans, forward-looking impairments, and pricing of liquidity and funding risk associated with the bank’s asset/liability profile. One should develop a statistical modeling library to support the quick and efficient determination of statistical models used in Barclays Treasury. Relevant Skills The applicator should have experience in delivering Python-based quantitative finance models. One must know about the statistical model development and implementation. One should have knowledge of relevant regulatory guidelines for CCAR, IFRS9, and IRRBB.
Concentration in the Banking Industry
So if the concentration in any market or industry is bad for consumers, does the same hold true for the banking sector? Most developed and industrialized countries have a high concentration within the financial sector, especially the banking industry. To put things in perspective,
Here are some figures from non-US markets:
In Finland, the top three banks control over 85% of the market.
In Norway, the top three banks have control of 84% of the market.
The top 3 banks in New Zealand and South Africa control 77% of the market.The Banking Industry in the United States of America
Things are very different in the financial sector in the United States. In fact, the U.S. banking sector has a relatively low level of concentration as compared to the markets above. In the U.S. the top 3 banks control only 19% of the market.
So what does this mean for the industry? Is low concentration the American market a good or bad thing? Let’s examine this in more detail.Concentration in the U.S. Banking Industry: A Good Thing
With the breakdown of the financial system in 2008 still fresh in our minds, concentration within the banking industry may not be a bad thing for American consumers. According to a study by the NBER (National Bureau of Economic Research), a high level of concentration can lead to higher levels of stability within the banking industry. In fact, countries with a concentration level of 72% or above had fewer instances of banking failure.How Can Concentration be Beneficial?
Strictly within the banking industry, there are 3 key benefits of a higher level of concentration. They are as follows:
Higher concentration levels mean more profits for the banks that dominate the industry. On the flip side, the interest rates and service fees will go up. However, banks, and by extension, depositors remain safe from economic shocks. If banks have higher franchise values, they will be less willing to take financial risks that may be damaging.
Its easier to monitor a few large banks in the industry than numerous small ones. Regulatory bodies and financial watchdogs have an easier task keeping an eye on dominant banks. Larger banks will also have similar operations and systems, adding to the uniformity.So What Does This Mean for America?
As we mentioned above, the U.S. has a relatively low concentration level within the banking industry. This is one of the reasons the United States sees more economic fluctuation than more concentrated markets, according to experts. According to the NBER, banking failure and bank size have a negative correlation. As the banks’ sizes increase, the number of banking failures comes down. But that’s not to say there aren’t any negative effects.How Can Concentration be Negative?
Higher levels of concentration within any industry usually lead to lower levels of competition. Prices and fees will also rise with increasingly concentrated industries. This is part of the reason we see higher HughesNet bundle prices every year. And banking is no exception. In the banking industry, more concentration means higher interest rates. Investors start balking at risky investments. There’s also the fact that less profitable niches may be ignored by more popular banks in favor of the more profitable ones. You can see this in many industries, like the aviation industry which only flies popular routes.
SEO and Web Analytics : Using Your Analytics Properly
Analytics are very important to your web marketing campaign. If you do not use analytics properly you may not understand how effective your search engine marketing is.
In this article I look at some practical examples of when to use analytics and some things you need to identify in order to get the most out of your analytics.
I came across a situation today that I thought I’d share. It has to do with a client’s analytics.
Many times, as a search engine marketer, it is up to us to tell the client what they should be looking for in their analytics. Right away this seems odd to me. It’s like me telling my client what their business model is, or how they should be selling their product online.
But this does seem to be a common thread among some site owners. They had an idea for a product or service and they wanted to promote it online. So they had a website built, and may have initially had it optimized. But that is as far as their experience goes.
They have no idea on how to track progress or improvements. All to often the numbers they do look at are not the best results to view.
Two perspectives on analytics – SEO and client
With my client today, we were trying to nail down what should have been important numbers. And it was a very similar case – they had invested in this super-duper analytics package that was collecting and displaying data upteen different ways, yet they had no idea how to interpret the numbers.
And, as sometimes happens, we fell into the trap of telling them what they should be looking for.
“You want to see search engine referrals going up. That means it’s working” or “increased page views is a good thing.”
But this really isn’t solving their problem is it?
Sometimes as search marketers, we need to step back and say “I know what I need for numbers, but what does my client need to see.”
So this was the approach we took today – let’s have a discussion with the client and focus on what they want to see, not what we need to show them to prove our value as search engine marketers.
When we were done, we had not only shortened their monthly analytics report to a few key metrics (down from pages and pages of statistical analysis) but we had also decreased the time required to complete this analysis.
Sure we still will do some of the analysis for our own purposes, but does the client really care how many backlinks or pages indexed they have? Not likely.
Nope, more often than not, the client wants to know that they are making money. Pure and simple.
So, if you can show them that they are making money, that’s all they really care about. You can add value as a search engine marketer by showing areas of improvement (“did you know that your Google referrals went up by 15% this month? That proves the value of our services, yada yada yada…”)
As long as you can illustrate the bottom line to the client in terms they understand, at that it is improving, then you as a search marketer have done your job.
Keep the pages indexed, backlinks, referrals by keyword and other non-client related data to yourself and present a concise simple report that even the CEO (who has 25 hours per day of work) can look at and understand that the SEO program is paying for itself.
Now let’s look at analytics from the client’s perspective.
If you are a client of an SEO firm, or just want to get a better idea of just how your site is doing online, first you must decide what it is you want to see. Do you want to see sales figures? Or would you rather just look at the aggregate numbers like total visitors and search engine referrals?
What has more value to you – reams and reams of data, or a simple, one page summary of overall performance?
As a recommendation, I’d say you only need enough data to make your business decisions.
In other words, if your website is e-commerce based, all you really need initially are the sales numbers over time. You should also understand how the sales cycle works, and perhaps look at your conversion funnel to see where people are dropping off. Most good analytics packages offer some sort of funnel analysis.
Understanding your sales funnel can also help you improve your sales. Sometimes an analysis of the sales funnel can help you determine where the drop offs occur. By modifying the funnel you can improve your drop off rate, increasing your sales. And really, this has less to do with SEO and more to do with traditional business marketing.
For example, let’s say your site gets 2000 visitors per month. Let’s also assume your site has a 3 step sales process, and your average sale is $11 per item.
If half of your site’s visitors start down the sales path, that means 1000 start (a 50% drop off rate at the first step – this could be due by a requirement to sign up to browse your site). If 40% of that total drop off at the second step, and 30% of that group complete the sale, that equates to $495 in sales, about a 2.25% conversion rate as only 45 of the original 2000 people purchased.
Now let’s experiment with the sales funnel:
If you can improve the final step of the sale by just 10% – that equates to an additional $165 in sales, a 3% conversion rate. However if you can improve the first step of the conversion, reducing that 50% bounce rate to 25%, you can increase your sales by $247.50 – a 3.38% conversion rate.
Further, if you shorten the conversion funnel by 1 step – making a 2 step sale, rather than a 3 step sale, you can increase your sales by over $330 – a 3.75% conversion rate. That’s still assuming the same number of monthly visitors start down the conversion path.
However, if you don’t or can’t find this data in your analytics package you wouldn’t be able to perform such analysis.
And this is where, if you are dealing with an SEO firm, you must get the data you need.
Simply knowing how many referrals you got from Google or Yahoo! won’t help you make the business decisions you need to make.
So whether you are an SEO firm or professional, or employ one, be sure that the metrics you see are the ones you need to make your decisions.
As a client, don’t be afraid to ask – what does this do for me? Because unless you’ve discussed your needs with your SEO, they will likely provide you with the numbers they deem as the best. That is, the ones that illustrate their value to you.
That’s not to say that those numbers are invalid, its just that they don’t do you as much good as those you need to make your business decisions.
Similarly, as an SEO, if you don’t know what your client needs to see, in terms of numbers, how can you justify your income from them. If search engine referrals have gone up, but conversion haven’t then there is no immediate value to the client.
Sure you can say “but we got you all these top rankings” but unless they are turning into sales, your contract with that client won’t last that long.
So be sure as you work with your SEO firm or client that you nail those metrics early, so there is no misunderstanding, and everyone knows what successes are measured by.
Rob Sullivan of Text Link Brokers is an SEO Specialist and Internet Marketing Consultant.
Dave Chaffey’s summary of the most significant postings and developments giving guidance on Google Analytics Setup and configuration which I think all digital marketers need to be aware of. I have given a star rating of the importance [rating=5] of the updates based on their importance to the average small-business business installation of Google analytics. Analytics Ninjas or power users may well rate more highly.
As well as my take on the major updates, you will also want to check out the Google Analytics Update page if you don’t know about – it’s tough to find. I also track the Google Analytics Blog which is great for analytics folk, but not most general marketers.
Major developments in Google Analytics Setup and new features
Google Analytics Annotations now available in all Accounts (Importance: [rating=2]) Updated January 29th 2010. This featured was announced in December – see below, but frustratingly only available in some accounts.
Google Analytics Annotations Launched (Importance: [rating=2]) Add text annotations for campaign launches, landing page redesigns, etc on your trendlines – a handy tool for businesses of all sizes, but especially where several people are involved. Update added 7th December chúng tôi announcement also includes:
Facility to use custom variables as Advanced segments
Launch of setup Wizard for multidomain tracking
Note, a side effect of this new tracking method is that site engagement will appear to decrease since more shorter visits to the site will be included, i.e. bounce rates will increase.Detailed technical instructions on setting up Asynchronous Tracking – it still use chúng tôi tracking but requires new code in a new position in the HTML page.
Setting up Google Analytics Accounts and Profiles for a marketing team Update November 2009 – This is one of the best explanations I have seen for how to give different levels of access for different members of a sales & marketing team concentrating on different products and markets
Setup for Multiple domains By eNor for Google Analytics Blog Posted 30th September 2009
12 step guide to Google Analytics setup Last update August 2009 I have developed this as a summary and gradually update / add the additional links below
New custom variables (Importance: [rating=3]) October 2009 feature – useful for applying content groups idea through Page Level Custom Variables” – here they give the example of a newspaper site sectionConversion Goal and Event Tracking
Significant GA feature update (Importance: [rating=5]) October 2009 – – my summary on new Google conversion goals and Intelligence features
Google Analytics announce final release of Event tracking (Importance: [rating=4]) June 2009 – Event tracking often uses the example of Flash and video events, but can used for other events shown below
10 Must Track Google Analytics Goals March 2009 – Ran Nir’s posting has great ideas for tracking customer participation and engagement on a site
Using Google Analytics Intelligence
Review of Google Intelligence feature November 2009Custom variables in Google Analytics – example of applications (New Feb 2010)
These are probably the least used feature, but offer the ability to segment according to customer / non customer and even by profile using cookies.
Google Analytics Custom variables – Google Conversion Room Blog explanation and examples of using custom analytics – Updated 18th February 2010
Using custom variables in Google Analytics – Examples of applications of custom variableTracking search engines in different countries with Google Analytics
I started my career as a management consultant. Excel was our temple for analytics. For a recent graduate, macros and connected models perform miracles but albeit at great effort. However, our reach was extremely limited compared to the possibilities of today. We could not process anything with images, text, audio or video easily as non-technical users. Fast forward to today, and citizen data scientists are unleashing machine learning on all of companies data to run diagnostic, predictive, and prescriptive analytics.
In that regard, in this article, we plan to explain how exactly AI is transforming how analytics is done.How is AI contributing to analytics capabilities?
More efficient thanks to automation,
More accessible thanks to improved UI, with Natural Language Processing enabling analytics tools to understand natural language queries,
And more powerful, since previously-difficult-to-analyze data, such as text and videos, are now easily analyzable.Analytics is getting automated
Analytics is time consuming and analytics talent is expensive. The war for AI talent is well documented and impacts the cost and availability of analytics talent. Data scientist who compromise a significant share of modern analytics work force are also part of the AI workforce.
Therefore automating analytics tasks has significant potential value for firms.Analysis (i.e. discovering insights and acting on them) is getting automated
AI systems are able to analyze data autonomously. Based on the results of analysis, they can take automated actions or highlight insights to employees who can decide the best course of action.
Setting up auto notifications based on simple rules has been possible since the early days of computing. Now companies can set up complex, machine learning based triggers to identify insights or automate actions. For example, a machine learning system that detects intruders to physical secure locations based on video feeds can take actions (e.g. by informing the intruder) or highlight the event to the human personnel.Report preparation is getting automated, making analytics more accessible
Analytics are only as useful as long as it is accessible. Natural language generation (NLG) enables automated report preparation.Analytics is becoming more accessible
We mentioned the increasing cost of analytics talent due to the increased demand for data science talent. Data scientists are expensive as they are PhDs or graduates of computer science and other quantitative fields who have an understanding of statistics and computation. This enables them to build complex models including machine learning models which can extract insights from data.
What if you did not need data scientists to extract insights from data? Users can use natural language to easily and intuitively find answers. This is supported through natural language (NL) query (also called natural language interaction – NLI or natural language user interaction – NLUI). For example, Thoughtspot, which enables companies to run complex analytics queries via natural language, raised ~250m on a ~2 billion valuation in 2023.Scope of analytics is increasing thanks to AI Unstructured data is becoming analyzable thanks to AI
Excel and other legacy analytics tools are not effective at dealing with unstructured data such as text, audio and images. Advances in AI greatly expand the scope of analytics when compared to the days when excel was the primary analytics tool. Some ways that AI is becoming integrated in analytics includes these areas:
Natural language processing (NLP) enables analysis of text
Transcription enables speech analytics
Computer vision enables image and video analyticsSemi-structured data is becoming analyzable thanks to AI
A significant share of company data is locked up in semi-structured documents such as invoices, receipts, order forms etc. Deep learning based data extraction solutions enable companies to extract entities from their semi-structured data and use them to understand their business in more detail.New techniques enable analysis of anonymized personally identifiable data, expanding scope of analytics
Anonymization via synthetic data is a rather old technology. However with the increasing demand for analytics and increasing protection of personal data, demand for anonymized data has increased. Numerous synthetic data vendors are enabling companies to create synthetic (machine-generated, anonymized but following the same distributions as the underlying personally identifiable data) copies of their customers so they can run detailed simulations and improve their offering.Analysis is becoming more powerful thanks to AI
While simple regressions guided business decision making for hundreds of years, businesses now rely on machine learning. Machine learning is the use of statistical techniques to enable computers to identify and learn the patterns in the given data, rather than being programmed explicitly for a certain function.
Some analytics techniques that can be enhanced with AI and machine learning include:
Prediction: Using short and long term variability in data to enhance forecasting efforts.
Pattern recognition: Understanding normal trends in order to spot anomalies, as is often the case in fraud detection.
Classification algorithms: Grouping and organizing of data, includes clustering.Which industries rely on AI in analytics?
All industries can benefit from AI-powered analytics. We had a deep-dive into:
Some tools that can make the analytics process easier include:
NameStatusNumber of Employees Apache PredictionIOPrivate1,001-5,000 AyasdiPrivate51-200 Einstein Analytics by SalesforcePublic10,000+ IBM CognosPublic10,000+ Infosys niaPublic10,001+ IperceptionsPrivate51-100 Microsoft Power BIPublic10,000+ MicroStrategy IncPublic1,001-5,000 Oracle Analytics ClousPublic10,000+ PurePredictivePrivate21-50 Qlik SensePrivate1,001-5,000 SAP Analytics CloudPublic10,000+ Sisense IncPrivate501-1,000 StatisticaPrivate10,001+ TableauPublic1,001-5,000 TelliusPrivate11-50 ThoughtSpotPrivate201-500 TIBCO Software IncPrivate1,001-5,000For more on analytics
If you are interested in learning more on analytics, read:
Finally, if you believe your business would benefit from leveraging an analytics solution, we have data-driven lists of vendors on our analytics hub.
We will help you choose the best one suited to your needs:
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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