Finance Analytics using Microsoft PowerBI

Finance Analytics using
Microsoft PowerBI

Oversee and keep track of your business’
finances with interactive analytics and key statistics.

Finance Analytics using
Microsoft PowerBI

Oversee and keep track of your business’
finances with interactive analytics and key statistics

Every day, businesses all over the world generate profound amounts of financial data. As we approach Industry 4.0 where data is coined as the new oil, what companies ultimately do with these huge volumes of data sets them apart from the rest. Having the fundamental financial awareness and proper tools to uncover the right information is paramount for a firm to function and expand.

The Finance department is essential for any business out there. Being financially aware and constantly improving money management strategies are pivotal for an enterprise to prosper. However, keeping track of the revenues and expenditures of every single department of a large firm may come off as a daunting task due to the sheer amount of workload and data that the Finance department may shoulder.

 

What are some Current Trends of Finance departments and companies?

The role of the Finance team is ever-changing and dynamic. In these times, most companies rely on Financial Data to plan for the future. Coerced by the pressure to constantly evolve, Finance departments and companies have been continually investing in fresh Artificial Intelligence technologies, as well as modern Data and Analytics tools in hopes of saving time and boosting productivity. However, a large majority of these tools and technologies are unable to process and generate larger volumes of data, causing Finance departments to run into a wall in the long run.

So what can we do with all this data? 

With Microsoft PowerBI, we can generate informative reports and interactive dashboards from large amounts of data to provide businesses with an all-in-one, concise overview of their financial status. Data of revenues, budgets, balance by departments, and much more can be presented neatly on these dashboards, and actionable insights can be acquired from them conveniently. By aiding businesses in understanding the top and bottom-line performances, Financial Analytics offers multifarious perspectives into organizational financial statuses and promotes profitability. 

On top of replacing physical labor with automation and improving the overall efficiency of the Financial department, PowerBI possesses the ability to take in and exhibit large amounts of data in the form of interactive dashboards and graphs without overloading the system. Information and actionable insights gained from the dashboards only increase in value over time, and Finance departments need not worry about plotting out graphs repeatedly in the future. 

With a wealth of financial data from the numerous departments throughout the organization, Finance teams are able to leverage the data collected and identify patterns to make relevant business predictions. The application of the freshly extracted knowledge and insights from the data sets can be translated into tangible business value, allowing businesses to make informed decisions regarding their expenditure and investment control. Essentially, Finance Analytics shapes business strategies through reliable, factual insight rather than intuition.

Finance analytical dashboards and charts – How do I go about doing it?

Below is a short video demo on how PowerBI, a growing Microsoft Business intelligence tool, has been used on a sample financial data, giving insights to the firm about their actual budget, expenditures, revenue gained, and much more. The demo walks through numerous reports and drills down to uncover deeper information, showcasing the flexibility and ease of usage of a tool like PowerBI as well.

Enhancing the reports

In our next case study, we will talk about how such reports can be extended to have elements of predictive analytics and machine learning which will further help businesses control costs and maximize revenue. Systems such as recommendation, financial forecasting, and others can enhance the usability and effectiveness of such reports.

Want to know more?

If you think this solution would be useful for your organization or you have a relevant use case or pain point you would like to tackle, get in touch with us today and we can help you and work together towards a solution!

7 Innovative Digital Transformation Trends for 2021

Change is one constant in life. For the world of technology, change only means one thing—pushing the boundaries in search of more innovative developments.  Digital transformation (DX) is the unification of digital technology and a business, company, or organization. Digitizing various processes can boost efficiency and allow services and products to improve by leaps and bounds.  In 2021, there’s no way for any organization to stay relevant without keeping up with these new DX trends:
  • Cybersecurity

Whatever the industry, security is one of the most important considerations, especially since so many people are now working remotely. Consequently, so many processes have migrated online. These days, financial fraud or theft happens online more often than not, and data breaches cost companies an average of $3.86 million in 2020. Thus, implementing new and improved cybersecurity measures is crucial in fighting back.  The 2021 cybersecurity trend is a movement from reaction to proactive prevention. The focus now is on thwarting hackers’ attacks, rather than trying to mop up the mess. One approach is the zero-trust security model (ZTA). This is a system that works off the assumption that all network devices are untrustworthy and rely on authorization policies, authentication steps, and strict access control to mitigate risks. An example of this is Microsoft’s implementation of a ZTA security model a few years ago. The increasing use of mobile computing, Bring Your Own Device (BOYD) policies, cloud-based services, and the internet of things (IoT) prompted the move. Initially, the scope included common corporate services on Windows, MacOS, Android, and iOS, which are then used by the corporation’s employees, partners, and vendors. The corporation then expanded the focus to include all applications used on Microsoft. The corporation introduced the use of smart-card multi-factor authorization (MFA) to control administrative server access. It later extended this to include users who access resources beyond the corporate network. Eventually, the smart cards were exchanged for a phone-based challenge and the Azure Authenticator application. Microsoft also implemented device verification, access verification, and service verification. 

  • Data Analytics

One universal concern for businesses is the generation of revenue, and to grow that, they need to know what works and what doesn’t. The world is moving towards automation of processes and an increased use of AI to perform tasks that were previously manually performed.  Since the COVID-19 pandemic, many businesses have by necessity moved online; apart from this, the traditional AI techniques that relied on previous statistics and information are potentially becoming ineffective because of the vast changes of the past year. AI systems are heading towards working with “small data” instead of historical data. Data science developments and the use of cloud technology are closely linked, especially when it comes to using data as a service (DaaS), granting on-demand access to information without relying on proximity. Phoenix-based mining company Freeport-McMoRan is a fine example of how data-driven decisions can transform a business. Chief operating officer Harry ‘Red’ Conger told McKinsey Digital that real-time data, AI, and the company’s veteran metallurgists’ and engineers’ institutional knowledge combined to lower operating costs, bolster economic resilience, and speed up decision-making. In 2018, the company unveiled a $200 million plan to expand the capacity of its Bagdad, Arizona, copper mine. When copper prices plunged a few months later, however, they scrapped the plan. Not long after, the company started building an AI model that could boost productivity. Data scientists analyzed and challenged existing operations, and AI showed how they could better use equipment. Working together, the data scientists, engineers, and metallurgists made changes that led to the mine’s processing rate to increase by 10%. The company has since implemented the AI model in eight of its other mines. 

  • Democratization of Innovation

Democratization in the digital transformation field refers to the shift away from a provider-focused model towards a user-focused one.  Companies have a tendency to create fairly uniform products that are quickly being pushed aside by individuals’ and small businesses’ desires for innovations that are specific to their field. Developments are no longer going to be withheld by a smaller clique of establishments but made available to a much wider group of users.  According to the WorldBlu list of freedom-centered organizations, New Belgium Brewing in Fort Collins, Colorado, is a business that has democratized innovation. Founder Kim Jordan said the brewery involves all its workers in the business’ strategic planning every year. Jordan also encourages open communication, trust, engagement, and inclusivity, and has an open-book approach to management.

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  • The Cloud

Many people use the cloud for personal activities, and businesses already know the importance of using its capabilities to their full potential. In the world of COVID, where so many employees have transitioned to remote work, the worldwide ease of access to the cloud is imperative. In 2021, technology is moving towards the use of multi-tenant clouds and hybrid cloud models.  Organizations are letting go of the belief that it’s best to use a single cloud vendor and are venturing into the area of hybrid clouds. It’s predicted that by 2022, over 90% of companies will have transitioned to hybrid cloud technology consisting of private clouds, various public clouds, and legacy platforms. Toyota offers an example of how cloud technology can enhance its offering. The vehicle manufacturer used the cloud to transform its cars from regular vehicles into connected platforms. Apps connect the cars to Microsoft Azure-hosted social media sites to track electric vehicle reward points and to offer other elements that improve the customer experience.  

  • Real-Time Data Processing

Financial establishments that required immediate data processing and updates to provide a service primarily made use of this technology. There’s an ever-increasing need for the instant delivery of answers—within a second or two.  Change in the business and technology sectors is constant and accelerating, and that requires real-time processing; processing that is not only fast but automated and reliable too. Development in this field gears itself towards flexibility, cost-effectiveness, and scalability.  Nissan uses real-time data analytics to make decisions that are appropriate for local markets. The automobile manufacturer uses Google analytics e-commerce tracking to obtain product preference data, such as the colors, models, and categories of the vehicles that are in demand in each market. The company uses Hortonworks Data Platform for its data lake, which includes driving, quality, and other data from across the company’s operations.

  • Contactless Solutions

Another effect of COVID has been the drastic shift away from face-to-face interactions towards virtual connections—from family get-togethers to business meetings. Virtual meeting platforms such as Zoom have taken off because of social distancing.  Contactless solutions also include contactless payments, which are becoming more and more popular for businesses providing a public service. Tap to pay debit or credit cards has almost become the norm, and they do away with the need for multiple people to handle the same payment terminal.  Apart from contactless payments, there’s also been a surge in contactless fulfillment. This digital development has seen the rise of online shopping. This isn’t only about purchasing items on sites like Amazon, but also ordering groceries and other smaller products from local businesses and receiving them by door-to-door delivery or arranging curbside pickup. Many small, medium and micro enterprises (SMMEs) are among the South African businesses that have seen how contactless options can transform companies’ operations. Nedbank launched tap-on-phone functionality for SMMEs in October 2020, and customers have responded well to the option of tapping their cards rather than swiping them and entering their PIN number. A MasterCard study indicated that 75% of South Africans used contactless payment methods when given the opportunity. 

  • Development and Wider Launch of 5G

Contrary to what conspiracy theorists believe, 5G is a massively positive development that’s set to revolutionize internet use by upgrading response time, improving speed drastically, and granting greater ease of access for multiple connected devices.  5G promises to deliver to both telecom operators and users. For individuals, the step up from 4G will bring vast improvements in internet access through a low latency rate and larger access areas. No more endlessly searching for Wi-Fi or having to move your laptop around in the hopes of a stronger signal. For operators, 5G offers the ability to shift their value proposition, allowing them to change from network capacity providers to full-scale, innovative digital partners. The spectrum for growth is huge, as are the opportunities for increasing revenue.  Samsung is one of the biggest brands globally already capitalizing on 5G. The brand’s range of Galaxy 5G devices takes connectivity to new heights and has set the benchmark for other mobile manufacturers to follow. 

Looking to the Future

The past 18 months have brought about a multitude of changes, challenges, and crises for individuals and businesses alike. Some industries have suffered more than others, but it seems unlikely that things will go back to “normal,” at least in the next few years.  There are speculations that the world is about to enter an age of pandemics, which will cement the current trend of remote working, online shopping, and reduction in face-to-face interactions across all industries. These changes have forced industries to innovate at a rate that has previously been difficult to sustain. Change has become the order of the day, and to survive, organizations have had to dedicate every effort to stay ahead of the curve.

Evolve to Thrive

Every species undergoes evolution over centuries and millennia, and technology is no different. The speed of change may differ, but the purpose is the same: to adapt to an unstable environment and prepare for the future.  These digital transformation trends will positively drive change and pave the way for new developments that will build on existing structures.

Digital Transformation Roadmap for Businesses

The following map is one approach to digital transformation for an organization. By following these steps, your organization can update practices and gain a competitive edge over those who’ve yet to embark on the transformation process. Step 1 – Develop innovative business models and experiences. Step 2 – Encourage a digital DNA culture within the organization. Step 3 – Update existing infrastructure with new technologies. Step 4 – Use data, not gut reactions, to drive the decision-making process. Step 5 – Find and collaborate with innovative and creative partners.

Ready to start your digital transformation journey?  

Digital transformation is at the core of pushing the business forward. When done the right way, it can help businesses achieve sustainable growth and stay ahead of their competition. Building a successful digital transformation strategy doesn’t have to be a challenge. With the right support and expertise, you can adapt to change and outpace evolving demands. Xaltius is a trusted partner for these projects.  Our experience has proven that a successful digital transformation strategy needs to focus on two things. First, your strategy must include ways to manage evolving business goals. These strategies also need to account for the cultural change that comes with those advancements. Our Data Scientists provide the kind of external perspective, agility, and understanding required for real innovation.   When you partner with Xaltius, you’ll have access to highly skilled professionals, Business Analysts, Data Engineers, Corporate Trainers, and all the ancillary roles for the delivery of your strategy. Each of us at Xaltius is directly accessible to your project managers. We are a Singapore-based IT consultancy providing customized, cost-effective IT solutions to enterprises.  Let us know if we can do anything for you.  To learn more about what we can do for your organization, talk to an industry expert – Book a consultation.

About the Author: This article is written by Kristie Wright. Kristie Wright is an experienced freelance writer who covers topics on logistics, finance, and management, mostly catering to small businesses and sole proprietors. When she’s not typing away at her keyboard, Kristie enjoys roasting her own coffee and is an avid tabletop gamer.

HR Analytics using Microsoft PowerBI

HR Analytics using
Microsoft PowerBI

Analyze employees, attrition, diversity and
help your business take effective data-driven decisions

HR Analytics using
Microsoft PowerBI

Analyze employees, attrition, diversity and
help your business take effective data-driven decisions

What are the pain points faced by HR Professionals?

Even if you love your work as a human resource professional, every job has its “pain points”.

HR deals with many issues like:

  • Recruitment and Retention – finding and retaining talent is an ongoing challenge
  • Strategic Decisions (Time-Consuming) – work with unhappy employees
  • Monitor employee performance – especially critical for large companies with thousands of employees and multiple locations
  • Technology adoption – Workers who are used to old ways of doing things may resist change

How can Business Intelligence and Analytics help the HRs?

Business intelligence helps you take your business decisions more effectively with data and analysis. This creates the basis for success. It is an aid in all business areas, from growth to human resources to marketing, and helps transform several key processes.

Human resource departments fulfill several key functions within a company; such as hiring, training, organizing corporate events, and the not-so-pleasant business of firing. The HR manager’s role itself is to handle MANY things and demands unique solutions.

Business intelligence and analytics meet those needs in a variety of ways:

  • Business intelligence for…hiring
  • Business intelligence for… measuring success/performance
  • Business intelligence for… optimizing processes
  • Business intelligence for…cultural changes
  • Business intelligence for…high turnover rate

The main purpose of Human Resource management is to measure the work achievement of employees, their role in the services or business, and to analyze employee retention and attrition in the company.

All human resource reports and dashboards are persona-based, in the current context it explains why there is an increasing emphasis on finding and attracting the best talent. Information is spread by Powerful Views like Headcount summary, Actives & Separation Summary, Recruitment source, and many others.

Some common reports that HRs like to gain insights from

Headcount summary:

Headcount summary explains the present headcount of employees by location, gender, department, company, total employees by recruitment source.

Actives & Separation Summary:

Actives & Separation summary explains separation by region, active & separation by gender, active & separation by race, separation by performance score.

Recruitment source:

Recruitment source summary explains performance score by recruitment source, active & separation by recruitment source, recruitment source by gender, and so on.

Below is a short demo on how PowerBI, a growing Microsoft Business intelligence tool, has been used on a sample HR data, to give insights to the business about headcount, recruitment source, employee performance, and other metrics and indicators. The demo walks through multiple reports and drills down to give the organization detailed information. It also shows the ease of use of a tool like PowerBI with huge data, both for simple and complex reports.

Conclusion

HR analytics help HR teams set goals, measure success, and optimize processes so the company can focus on employee satisfaction. When used responsibly and effectively, HR analytics provide the insights companies need to tackle difficult challenges like lack of diversity or a high turnover rate.

Want to know more?

If you think this solution would be useful for your organization or you have a relevant use case or pain point you would like to tackle, get in touch with us today and we can help you and work together towards a solution!

Renowned Data Science Personalities

With the advancement of big data and artificial intelligence, the need for its efficient and ethical usage also grew. Prior to the AI boom, the main focus of companies was to find solutions for data storage and management. With the advancement of various frameworks, the focus has shifted to data processing and analytics which require knowledge of programming, mathematics, and statistics. In more popular terms, this process today is known as Data Science. Few names stand out and have a separate base of importance when the name data science comes into the picture, largely due to their contributions to this field and who have devoted their life and study to reinvent the wheel. Let’s talk about some of the best data scientists in the world.


Andrew Ng

Andrew Ng is one of the most prominent names among leaders in the fields of AI and Data Science. He is counted among the best machine learning and artificial intelligence experts in the world.  He is an adjunct professor at Stanford University and also the co-founder of Coursera. Formerly, he was the head of the AI unit in Baidu. He is also an enthusiast researcher, having authored and co-authored around 100 research papers on machine learning, AI, deep learning, robotics, and many more relevant fields. He is highly appreciated in the group of new practitioners and researchers in the field of data science. He has also worked in close collaboration with Google on their Google Brain project. He is the most popular data scientist with a vast number of followers on social media and other channels.

DJ Patil

The Data Science Man, DJ Patil, needs no introduction. He is one of the most famous data scientists in the world. He is one of the influencing personalities, not just in Data Science but around the world in general. He was the co-coiner of the term Data Science. He was the former Chief Data Scientist at the White House. He was also honored by being the former Head of Data Products, Chief Scientist, and Chief Security Officer at LinkedIn. He was the former Director of Strategy, Analytics, and Product / Distinguished Research Scientist at eBay Inc. The list just goes on.

DJ Patil is inarguably one of the top data scientists around the world. He received his PhD in Applied Mathematics from the ‘University of Maryland College Park’.

Kirk Borne

Kirk Borne has been the chief data scientist and the leading executive advisor at Booz Allen Hamilton since 2015. Working as a former NASA astrophysicist, he was part of many major projects. At the time of crisis, he was also called upon by the former President of the US to analyze data post the 9/11 attack on the WTC in an attempt to prevent further attacks. He is one of the top data scientists to follow with over 250K followers on Twitter.

Geoffrey Hinton

He is known for his astonishing work on Artificial Neural Networks. Geoffrey was the brain behind the ‘Backpropagation’ algorithm which is used to train deep neural networks. Currently, he leads the AI team at Google and simultaneously finds time for the ‘Computer Science’ department at the ‘University of Toronto’. His research group has done some overwhelming work for the resurgence of neural networks and deep learning.

Geoff coined the term ‘Dark Knowledge’.

Yoshua Bengio

Having worked with AT&T & MIT as a machine learning expert, Yoshua holds a Ph.D. in Computer Science from McGill University, Montreal. He is currently the Head of the Montreal Institute for Learning Algorithms (MILA) and also has been a professor at Université de Montréal for the past 24yrs.

Yann LeCun

Director of AI Research at Facebook, Yann has 14 registered US patents. He is also the founding director of NYU Center for Data Science. Yann has a PhD in Computer Science from Pierre and Marie Curie University. He’s also a professor of Computer Science, Neural Science and the Founding Director of the Data Science Center at New York University.

Peter Norvig

Peter Norvig is a co-Author of ‘Artificial Intelligence: A Modern Approach’ and ‘Paradigms of AI Programming: Case Studies in Common Lisp’, some insightful books for programming and artificial intelligence. Peter has close to 45 publications under his name. Currently the ‘Engineering Director’ at ‘Google’, he has worked on various roles in Computational Sciences at NASA for three years. Peter received his Ph.D. from the ‘University of California’ in ‘Computer Science.’

Alex “Sandy” Pentland

Named the ‘World’s Most Powerful Data Scientist’ by Forbes, Alex has been a professor at MIT for the past 31 years. He has also been a chief advisor at Nissan and Telefonica. Alex has co-founded many companies over the years some of which include Home, Sense Networks, Cogito Corp, and many more. Currently, he is on the board of Directors of the UN Global Partnership for Sustainable Data Development.

These are some of the few leaders from a vast community of leaders. There are many unnamed leaders whose work is the reason why you have recommender systems, advanced neural networks, fraud detection algorithms, and many other intelligent systems that we seek help to fulfill our daily needs.

Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence, Machine Learning, and Deep Learning are one of the most prominent topics in the domain of technology at the present. Although the three terminologies Artificial Intelligence, Machine Learning, and Deep Learning are used interchangeably, are they really the same? Every technophile is stuck at least once in the beginning whenever there is a mention of artificial learning vs machine learning vs deep learning. Let us try to find out how actually these three terms differ.

The easiest way to think of the relationship between the above terms is to visualize them as concentric circles using the concept of sets with AI — the idea that came first — the largest, then machine learning — which blossomed later, and the most recent being deep learning — which is driving today’s AI explosion — fitting inside both.

Graphically this relation can be explained as in the picture below.

As you can see in the above image consisting of three concentric circles, Deep Learning is a subset of ML, which is also a subset of AI. This gives an idea that AI is the all-encompassing concept that initially erupted, which was then followed by ML that thrived later, and lastly, Deep Learning that is promising to escalate the advances of AI to another level.

Starting with AI, let us have a more in-depth insight into the following terms.

Artificial Intelligence

Intelligence, as defined by Wikipedia, is “Perceiving the information through various sources, followed by retaining them as knowledge and applying them with real-life challenges.” Artificial intelligence is the science that deals with machines that are programmed to think and act like humans. By Wikipedia, it is defined as the simulation of human intelligence in machines using programs and algorithms.

Machines built on AI are of two types – General AI and Narrow AI

General AI refers to the machines capable of using all our senses. We’ve seen these General AI in Sci-Fi movies like The Terminator. In real life, a lot of work has been done on the development of these machines; however, more research is yet to be done to bring them into existence.

What we CAN do falls in the hands of “Narrow AI”. These refer to the technologies that can perform specific tasks as well as, or better than, we humans can. Some examples are – classifying emails as spam and not spam and facial recognition on Facebook. These technologies exhibit some facets of human intelligence.

Where does that intelligence come from? That brings us to our next term -> Machine Learning.

Machine Learning

Learning, as defined by Wikipedia, is referred to as “acquiring information and finding a pattern between the outcome and the inputs from the set of examples given.” ML intends to enable artificial machines to learn by themselves using the provided data and make accurate predictions. Machine Learning is a subset of AI. More importantly, it is a method of training algorithms such that they can learn to make decisions. (ReadAI and ML. Are they one and the same?)

Machine learning algorithms can be classified as supervised and unsupervised depending on the type of problem being solved. In Supervised learning the machine is trained using data which is well labelled that is, some data is already tagged with the correct answer while in unsupervised learning the machine is trained using the information that is neither classified nor labelled and the algorithm is supposed to find a solution to it without guidance. Also, a term called semi-supervised learning exists in which the algorithm learns from a dataset that includes both supervised and unsupervised data.

Training in machine learning requires a lot of data to be fed to the machine which then allows the machine (models) to learn more about the processed information.

Deep Learning 

Deep Learning is an algorithmic approach for the early machine-learning crowd. Neural Networks from the base for Deep Neural Learning and is inspired by our understanding of the biology of the human brain. However, unlike a biological brain where any neuron unit can connect to any other neuron unit within a certain physical distance, these artificial neural networks (ANN) have discrete layers, connections, and directions of data propagation.

For a system designed to recognize a STOP sign, a neural Network model can come up with a “probability score”, which is a highly educated guess, based on the algorithm. In this example, the system might be 86% confident the image is a stop sign, 7% convinced it’s a speed limit sign, and 5% it’s a kite stuck in a tree, and so on.

A trained Neural Networks is one that has been analyzed on millions of samples until it is sampled so that it gets the answer right practically every time.

Deep Learning can automatically discover new features to be used for classification. Machine Learning, on the other hand, requires to be provided these features manually. Also, in contrast to Machine Learning, Deep Learning requires high-end machines and considerably significant amounts of training data to deliver accurate results.

Wrapping up, AI has a bright future, considering the development of deep learning. At the current pace, we can expect driverless vehicles, better recommender systems, and more in the forthcoming time. AI, ML, and Deep Learning (DL) are not very different from each other; but are not the same.

Tableau vs PowerBI: 10 Big Differences

The concept of using pictures to understand patterns in data has been around for centuries. From existing in the form of graphs and maps in the 17th century to the invention of the pie chart in the mid-1800s, the idea has been exquisite. The 19th century witnessed one of the most cited examples of data visualization when Charles Minard mapped Napoleon’s invasion of Russia. The map depicted the size of Napoleon’s army along with the path of Napoleon’s retreat from the city of Moscow – and tied that information to temperature and time scales for a more in-depth understanding of the event.

Read more about data Visualisation in our previous blog – Practices on Data Visualisation.

In the modern world, when it comes to the search for a Business Intelligence (BI) or Data Visualisation tool, we come across two front runners. They are PowerBI and Tableau. These are the top data visualization tools. Both of these products are equipped with a set of handy features like drag-and-drop, data preparation amongst many others. Although similar, each comes with its particular set of strengths and weaknesses, and hence very often articles titled Tableau vs PowerBI are encountered. The following comparisons provide insights into which data visualization tool is best for different purposes.

The tools will be compared on the following grounds:

  • Cost
  • Licensing
  • Visualization
  • Integrations
  • Implementation
  • Data Analysis
  • Functionality

Cost
Cost remains a significant parameter when these products are compared. This is because at one end PowerBI is priced around 100$ a year while Tableau can be rather expensive up to 1000$ a year. PowerBI is more affordable and economical than Tableau and is suitable for small businesses. Tableau, on the other hand, is built for data analysts and offers in-depth insight features. So, when it comes to Tableau vs PowerBI cost comparison, Tableau is a better alternative to PowerBI.

Licensing
Tableau should be the first choice in this case. To explain why Tableau over PowerBI, the final choice is considered that is, whether one wants to pay upfront cost for the software or not. If yes, then Tableau should be chosen else one should opt for PowerBI.

Visualization
When it comes to visualization features, both the products have their strengths. PowerBI can prove to be better if the desired outcome is data with better visuals. PowerBI lets you easily upload datasets. It gives a clear and elegant visualization. However, if the prime focus is visualization, Tableau leads by a fair margin. Tableau performs better with more massive datasets and gives users efficient drill-down features.

Integrations
PowerBI has API access and pre-built dashboards for speedy insights for some of the most widely used technologies and tools like Salesforce, Google Analytics, and Microsoft Products. On the contrary, Tableau has invested heavily in integrations and widely-used connections. A user can view all of the connections included right when he/she logs into the tool.

Implementation
This parameter along with maintenance is primarily dependent on factors like the size of the company, the number of users, and others. Power BI comes out to be fairly more straightforward on the grounds of implementation and requires a low level of expertise. However, Tableau, although is a little more complex, offers more variety. Tableau incorporates the use of quick-start applications for deploying small scale applications.

Data Analysis
Power BI with Excel offers speed and efficiency and establishes relationships between data sources. On the other hand, Tableau provides more extensive features and helps the user in hypothesizing data better.

Functionality
For the foreseeable future, any organization which has users spending more than an hour or two per day using their Business Intelligence tool might want to go with Tableau. Tableau offers a lot of features and minor details that are unmatched.

Feature Power BI Tableau
Date Established 2013 2003
Best Use Case Dashboards & Ad-hoc Analysis Dashboards & Ad-hoc Analysis
Best Users Average Joe/Jane Analysts
Licensing Subscription Subscription
Desktop Version Free $70/user/month
Investment Required Low High
Overall Functionality Very Good Very Good
Visualisations Good Very Good
Performance With Large Datasets Good Very Good
Support Level Low (Or through partner) High

It all depends upon who will be using these tools. Microsoft powered Power BI is built for the joint stakeholder, not necessarily for data analyticsThe interface relies on drag and drop and intuitive features to help teams develop their visualizations. It’s a great addition to any organization that needs data analysis without getting a degree in data analysis or any organization having smaller funds.

Tableau is more powerful, but the interface isn’t quite as intuitive, which makes it more challenging to use and learn. It requires some experience and practice to have control over the product. Once this is achieved, Tableau is better than PowerBI and can prove to be much more powerful for data analytics in the long run.

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