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!

Book a Consultation

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?

Book a Consultation

Credit Card Fraud Detection

Analyze Credit Card Transactions in real-time and detect
potential fraudulent cases.

Credit Card Fraud Detection

Analyze Credit Card Transactions in real-time and
detect potential fraudulent cases.

Credit card fraud is a major issue. Last year, the Nilson Group reported that credit card fraud losses climbed to as much as $28.65 billion across the globe. EMV Chip Cards were supposed to fix this problem, and while we have seen a decrease in CP (Card Present) fraud, there has been a rapid increase in CNP (Card Not Present) fraud. By 2025, total payment card volume worldwide is projected to be $56.182 trillion, with gross card fraud globally expected to be $35.31 billion.

Some of the typical challenges which are faced while solving the credit card fraud problem are:

Large number/High volume of transactions on a daily basis
The requirement of high processing power to handle real-time transactions
Accuracy of the decision
Creating a robust machine learning/AI model

How can we help organizations like you looking for machine learning solutions to counter credit card fraud?

We build custom machine learning pipelines for you to help you make more effective decisions when countering fraud. As part of these machine learning pipelines, the following stages are incorporated to make the entire process seamless and effective:

Ingesting Data From various sources
Data Cleaning and Data Preparation
Custom Machine Learning
Feedback and Logging
Reporting

We use various tools to help architect the solution either on-premise or on the cloud. We work with cloud tools such as Databricks, Amazon Web Services, and others depending on your requirements.

How will incorporating machine learning enhance the current process?

Higher Accuracy

Working with Machine Learning models provides a much higher accuracy over time than manually trying to identify fraud based on a set of rules. Models have the capacity to learn and provide assistance to make decisions.

Avoid Manual Work

Creating Machine Learning Model pipelines will greatly reduce the amount of manual work to be done by the team. Having an automated system in place will greatly help in reducing the amount of time and effort.

Fewer False Positives and Negatives

One of the main benefits is to avoid re-iterating between correctly identified and incorrectly identified cases. We want to minimize identifying frauds erroneously. Machine learning models can focus on reducing these and hence greatly improve the accuracy of being able to detect frauds over time.

Want to know more?

Book a Consultation

Healthcare Analytics using Microsoft PowerBI

Healthcare Analytics using
Microsoft PowerBI

Monitor health, happiness and resources
to help your business make effective data-driven decisions

Healthcare Analytics using
Microsoft PowerBI

Monitor health, happiness and resources
to help your business make effective data-driven decisions

Introduction, Planning, and Establishing a Reason for the Project:

During a medical crisis like the global pandemic of COVID-19, healthcare is at the forefront of the news. Healthcare is crucial and looks different in each country. Looking at the global healthcare spending system while also being able to look into individual countries’ details is vital to many stakeholders. Accessibility to healthcare is linked to happiness, and economic determinants of whether an individual receives healthcare are, therefore, of great importance.

Some questions that could be asked on this topic are:

Does happiness increase in a country where healthcare is affordable?
What impact does government subsidies have on the happiness of its citizens?
Are countries with development assistance for healthcare systems happier?
What are the economics of healthcare?
How do the economics of healthcare relate to happiness within a country?

Data Collection

Data sources for this sample analytics report are from the United Nation’s World Happiness Report and the Institute for Health Metrics and Evaluation (IHME). Both sources are online, from the years 2017-2019.

Data from the United Nations are centered around ladder score (a happiness metric) by country and look at GDP per capita, social support, healthy life expectancies, freedom to make life choices, and other relevant contributors to happiness.

Data from the IHME looks at healthcare spending by country, looking into categories like government spending, out of pocket spending (spending by citizens), development assistance, and total health spending as a percentage of GDP.

The data from the United Nations and IHME give an overview of both happiness and healthcare expenses by country for the years 2017-2019. With these two data sets, we can begin exploring and creating dashboards within Power BI to aid stakeholders such as a non-profit looking to utilize donated money in a country with a low ladder score and little developmental assistance towards its healthcare system.

Data Analysis

Exploring and analyzing the data on a global scale, we find that there are some common-sense correlations. Citizens in nations with a higher GDP per capita, those with more freedom to make life choices, and those whose governments spend more towards subsidizing healthcare are happier.

If a country has a higher GDP per capita, it’s citizens will have more income to spend on healthcare, better diets, and other determinants of a healthy life like social relationships (going out with friends). An increase in GDP per capita will increase a healthy life expectancy.
If a country gives its citizens more freedom to make life choices, it will have a ladder score. This makes sense, as those within a country with more freedom to make life choices are freer to pursue their passions, interests, and more that lead to a happier life.
If a country’s government spends more on subsidizing healthcare, its citizens have more money to spend on experiences, services, and goods that increase their happiness rather than healthcare. This frees up money, compounding the freedom to make life choices and utilization of GDP per capita towards direct happiness and not costs of healthcare.

Dashboard Building and Evaluation

On the first page of this dashboard, we will focus on the global scale, and look at relevant metrics to it. We have the scatter plots with a trend line talked about earlier, as well as line and clustered column charts. The line and column chart on the left displays how total health spending is a high percentage of GDP (globally), and that out of pocket (personal) spending on health care is increasing.

The line and column chart on the right shows that there is a slight downtrend on developmental assistance for health care spending (per person), and an increase in government spending on healthcare (per person).

These visuals together start to build a narrative and give insight into the problem of increased healthcare spending by citizens globally. There is a decrease in development assistance, while healthcare costs rise; this increase in cost is eaten up by individuals’ out-of-pocket spending. An increase in healthcare costs is unavoidable, as some healthcare costs are necessary to continue living a healthy, long life. Therefore, the cost for individuals is important, as it directly affects disposable income, and income is correlated to happiness. Additionally, with a higher disposable income, individuals are freer to spend their money, and as we see on the freedom and ladder score scatter plot, the higher the freedom, the more happy a country is.

Healthcare, in conclusion, is rising in cost, and out of pocket costs are rising with that, while income is a significant contributor to happiness. Disposable income can be associated with freedom, and freedom to make life choices is another significant contributor to happiness. Government spending and development assistance are two ways to lower healthcare costs for an individual and increase a country’s ladder score.

Now, we begin to look at our data by country in this second dashboard. We are looking at metrics specific to a country, but relevant to the bigger picture of happiness and healthcare economics. From this interactive visual, we can look at a country’s relative ladder score standing, as well as the economics of its healthcare system. From this dashboard, we can get an overview of how a country is doing, and compare it to similar countries, getting insight into what changes can be made to increase ladder score.

For example, you may see that Australia has no developmental assistance, but a relatively large amount of government spending on healthcare per person. This results in a low difference in total health spending and out of pocket spending by GDP, meaning that those living in Australia spend less out of pocket (just over 1% of GDP) for healthcare. All of this contributes to their excellent standing of 10th in the world for happiness, according to the United Nations.

This third dashboard shows the key influencers to happiness and out of pocket healthcare spending. These two dependent variables are connected, as established earlier, the less out of pocket spending on health, the happier an individual will be. We can see how many social factors contribute to happiness, like perceptions of corruption or social support, as well as the variables established earlier. We also see that development assistance is a massive influencer to out of pocket healthcare spending, further supporting our narrative that third party money towards healthcare is a good thing, freeing up money for individuals to pursue happiness.

Forecasting

In this last dashboard, we can forecast some concluding variables. We see that development assistance is declining for Nicaragua, while healthcare spending is increasing along with out of pocket spending on healthcare. This will likely, based on this project’s data, result in a lower ladder score for Nicaragua.

This forecasting, along with the other dashboards, will give decision-makers with the ability to create change, aid in the restructuring of healthcare costs for countries that need it!

Conclusion

In conclusion, this project starts off with a narrative and outlook of the world by happiness in relation to healthcare costs with our first dashboard. This introduction to the data aids a decision-maker such as a non-profit with resources looking to invest in development assistance. That first group of visuals will help them understand the impacts of the economics in a healthcare system has on the happiness of a country.

The second dashboard and its visuals allow a decision-maker to understand deeper into the economics of healthcare within a country, with the potential for identifying those in need of more developmental aid. For those with little or no development assistance and little or no government spending on healthcare, additional developmental assistance would contribute most to an increase in ladder score.

The third dashboard, allows decision-makers to understand how much of an impact each variable has on happiness and out of pocket spending on healthcare. Both of these are connected.

And finally, the fourth dashboard gives a forecast of what a country’s healthcare system will look like economically, allowing for those who can intervene to do so and change the country’s path.

Want to know more?

Book a Consultation

Procurement Analytics using Microsoft PowerBI

Procurement Analytics using
Microsoft PowerBI

Build Vendor, Spend and Item reports and dashboards
to help your business make effective data-driven decisions

Procurement Analytics using
Microsoft PowerBI

Build Vendor, Spend and Item reports and dashboards
to help your business make effective data-driven decisions

The Procurement department is a key unit of any company, irrespective of the industry. Procurement is not only limited to product based companies in terms of raw materials, but it also takes into account the services that a company outsources to other vendors. For example, all the laptops that you use at your work has been procured from some vendor. The paradigm of Procurement is vast. The purchasing department should have optimized strategies that makes the procurement process smooth and efficient!

But how do we optimize these processes?

Procurement Analytics, to the rescue! Procurement Analytics takes into account procurement data from various sources and drills deep into the data to extract valuable and actionable insights. Organizations can leverage procurement analytics to make data driven purchasing decisions and manage effective vendor relationships.

Procurement Analysis can be divided into three broad categories:

Vendor Analysis: Vendor Analysis gives a better solution on selecting the best suppliers from a range of other competitive suppliers. Is your organization choosing top tier suppliers? Is your vendor providing the best price and discount than others?

Spend Analysis: One of the key factor in business is cost saving and it becomes important to optimize the spend. Analysis around spends and discounts across months are helpful to understand when your organisation can plan for purchases.

Item Analysis: You can closely look into the commodities being purchased and the various suppliers supplying the same commodity. This gives an organisation a better understanding of the cost incurred and discount offered by the suppliers.

If you haven’t optimised your Procurement strategies yet, below is a glimpse on how you can go about it!

Procurement analytics reports and dashboards – 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 procurement data, to give insights to the business about vendor, supplier and products. The demo walks through multiple reports and drill downs to give the organisation deeper information. It also shows the ease of use of a tool like PowerBI with huge data, both for simple and complex reports.

The above report gives an overview about the total spend of an organisation on procurement. It gives an understanding of the various vendors that this organisation outsources its products and services and the amount each vendor charges for the same products and services. This report also gives an essence of the savings attained due to discounts offered by various vendors and the months during which the vendors offer discounts. This will help the organisation to plan its procurement months in order to achieve maximum savings and lesser spend.

This report further gives an overview of the items that the company spends the most. Drilling down further gives the details of the items being purchased and the respective vendors related to the items. It gives a better understanding about the vendors that gives better price for the same item. This way the organisation is able to select the most profitable vendor.

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 the business use these insights to drive more productivity and reach out to customers better. Systems such as recommendations, price prediction, understanding and planning product procurement and others can enhance the usability and effectiveness of such reports.

Want to know more?

Book a Consultation