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In today’s world where the IT sector is booming, data is an aspect of research that is flourishing the most, yet is facing setbacks in terms of the value it offers to the people. The quantity of data that is produced every day, at every minute by people and machines makes it extremely difficult to save, analyze, manage and finally utilize it. Hence, the development and evolution of various kinds of tools for data analysis have helped immensely with the handling of customer data. According to research, almost 90% of  big data was produced in just the last couple of years. Apps are being developed and used to improve the service levels, utility and customer support, etc. The

Different Sources of Data

It is anyway a challenge to manage the large number of sources that produce data, let alone dealing with the amount and volume of data and speed at which it is being produced. The data originates from the organization’s internal sources like marketing, finance, etc. and the external sources like social media. This, in turn, makes the data extremely diverse and voluminous. It is anyway difficult to manage and optimize the use of this produced data irrespective of the expensive tools and varied methods and processes.  

Quality of Data Storage

With the fast-growing pace of various companies and organizations, the growth of the produced quantity of data is rapidly increasing too. It is hence becoming a huge challenge to store this data. Multiple options called data lakes or warehouses are being used to gather, store and process huge amounts of data that is unstructured, in its original format. The challenge nonetheless occurs when data lakes or warehouses try to merge this unstructured data from dissimilar sources. This is when the error occurs. Missing data, Inconsistent or unstructured data, logic conflicts, duplicates, etc. are all results of poor quality of data storage.  

Improved Quality of Data Analysis

The large sum of data produced by companies and organizations are used to come to the best probable solutions, hence obviously the data that they use must be correct and accurate in all probability, otherwise as a result, wrong decisions would be taken, which would ultimately snowball into being harmful to the future working and success of the company. This dependency on the data analysis makes it extremely important to maintain the quality of the analysis. It needs a lot of resources and people with the proper talent and proficiency in order to make sure that the information that is provided by the data produced is accurate. This process is, however, an expensive affair and is immensely time-consuming.  

People who Comprehend Big Data Analysis

It is extremely important to analyze the data that is being produced in huge amounts in order to make complete use of it. Hence, the need for data analysts and scientists arises, for the storage and optimum use of quality data. It is also important for a data scientist to have the required skills that are as varied as the job is. But, the number of people pursuing the job of a data scientist is very less as compared to the amount of data that is being produced every day. This is another major challenge that is faced by most organizations.  

Privacy and Security of the Big Data

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Top 5 Adsense Publisher Concerns

The Google AdSense team reached out via Twitter for feedback about the kinds of things AdSense publishers wanted help with.

Top AdSense Publisher Concerns

It’s understandable why AdSense would reach out. Google’s official AdSense forums and Facebook page are dominated by three questions:

Why was my AdSense account disabled?

Why hasn’t my account been approved yet?

How do I contact customer support? (presumably to ask one of the two above questions)

If you want to understand the mind of the typical AdSense publisher, one of the best sources is the WebmasterWorld AdSense community. I am a moderator of the WebmasterWorld AdSense community so perhaps my opinion may be biased. .

Top AdSense Publisher Concerns

The following is a list of top concerns that AdSense publishers have. They are listed in no particular order.

1. AdBlockers and AdSense Revenues 2. AdSense Monetization and AMP

One of the biggest fears of transitioning to AMP is of declining AdSense revenues. According to WebmasterWorld forum members, AdSense revenues have been in a freefall for years. Publishers are naturally wary of making a bad situation worse.

3. Better Communication Between AdWords and AdSense

There seems to be a disconnect between what happens between the AdWords and AdSense teams at Google. What happens on the AdWords side will affect publishers on the AdSense side. Publishers are asking for better communication from Google to Publishers about the impact of changes from AdWords on AdSense.

In a discussion in the AdSense forum from April 2023, many AdSense publishers had never heard of the Brand Safety update.

It may be helpful if the AdSense support team needs to communicate what’s coming down the line from the AdWords side and share with publishers discuss how it might impact them on the AdSense side.

4. Improved Coordination Between Google Play and AdSense

Another example is an apparent disconnect between the AdSense teams and the Google Play team. Software is available that will create apps that will frame a website, any website. That way, a website can be promoted as an app on the Google Play store.

What is happening is that people are using the software to display content that belongs to other publishers and monetizing that content with Ad Mob.

Does that violate AdSense policies? This is an example of a disconnect between the AdSense team and other teams at Google. Web publishers would appreciate seeing Google AdSense address this concern. 

5. Focus on Helping Publishers Make More Money

What publishers may find useful is content that is frank about how a new feature will help a publisher make more money. A majority of discussions in an AdSense community are about how to make more money or the fear of losing money (which is still about making more money).

It may sound crass to focus on making money. But making money (and lots of it) is what the AdSense program is all about for publishers. 

How does a new feature help an AdSense publisher make more money? how to make more money.

Was this new sensitive topic category created to placate publishers? How does it help publishers make more money? Is there an official AdSense post about it?

AdSense Publishers Responds via Twitter

Most of the responses to the AdSense tweet were fairly repetitive and of little use to the greater publisher community. However there were a few responses worth noting:

Google AdSense’s Official Request for Comment

Read the AdSense tweet and publisher responses here. Do you have a concern? Voice it, ask your question and with a little luck maybe Google will answer your question with an official video.

Top 10 Python Libraries For Data Visualization In 2023

In this article, we have discussed the top 10 python libraries for data visualization in 2023

Python is one of the most widely used programming languages. It serves to be a blessing in the field of data science. When one boasts of possessing good Python skills, it is expected out of that person that he/she is well acquainted with libraries in Python. Here are the top 10 Python libraries for data visualization in 2023 which make programming and developing models a lot easier.

1. SciPy

This stands for Scientific Python. This is yet another open-source library that comes in handy for all kinds of high-level computations. This plays a significant role to play in all those scientific and technical computations that you once thought weren’t easy to handle. This is user-friendly and no one denies this. One of its remarkable features is its ability to solve differential equations. This library has applications in linear algebra, solving differential equations, and optimizing algorithms to name a few.

2. Gradio

This library allows you to build and deploy applications web applications. The best feature of this library is that your task is done in as little as 3 lines of code. Yet another benefit of this library that’s worth a mention is how fast and easy the whole process gets. With Gradio, it is possible to test different inputs. Model validation is easier than ever with Gradio. Since there is a provision of public links, it becomes very easy to implement and distribute web applications.

3. Keras 4. Matplotlib 5. Orbit

This is yet another Python framework designed for Bayesian time series forecasting and inference. Its framework is built on probabilistic programming packages like PyStan and Uber’s own Pyro.

6. Seaborn

It is one of those data visualization libraries that helps in drawing attractive and informative statistical graphics. Seaborn provides a high-level interface. People consider it to be an extension of Matplotlib. While Matplotlib provides a range of basic plotting features, Seaborn lets users enjoy a range of visualization patterns. Yet another feature of this library that grabs attention is that the syntax is simple and not that complex.

7. Pandas

Pandas stands for ‘Python Data Analysis Library’. This is an open-source Python package that ensures delivering high performance. Here, you can find easy-to-use data structures and data analysis tools that serve to be extremely useful while programming in Python. Some of the best features of this library are:

One can plot data with a histogram or box plot.

It is very easy to add, delete and update columns.

Renaming, sorting, indexing, merging, and manipulating data frames.

8. Sktime

This is an open-source Python library exclusively designed for time series analysis. It provides an extension to the scikit-learn API for time-series solutions and contains all the required algorithms and tools that are needed for the effective resolution of time-series regression, prediction, and categorization issues.

9. Darts

Darts is yet another time series Python library that has made its way to the list of the top 10 Python libraries. Developed by Unit8, Darts is widely known for easy manipulation and forecasting of time series. It can handle large data quite well and supports both univariate and multivariate time series analysis and models.

10. Kats (Kits to Analyze Time Series)

5 Big Data Apps With Effective Use Cases

Even if your organization is compelled to become more data-driven, many don’t know how to transform themselves out of the use-your-gut mentality and into a data-first one.

The easiest way? Take shortcuts by refusing to reinvent the wheel and following the trails blazed by early adopters. Here are 5 cool Big Data apps, along with the use cases (and end users) that are helping to change the meaning of “business as usual.”

1. Big Data application: Roambi

How this Big Data app works: One thing often overlooked in the rush towards data-driven decision making is mobility. Increasingly mobile workforces need more ways to manipulate data from a smartphone that just basic business tools, which are so often stripped down for mobile. Mobile workers need the ability to access and analyze the same business data they use in the office in order to make smart, on-the-go decisions.

Roambi contends that it was founded to solve this very problem. Roambi’s goal is to reinvent the mobile business app to improve the productivity and decision-making of on-the-go employees. Roambi re-designs the way people interact with, share, and present data from a completely mobile perspective.

Use case of note: The Phoenix Suns.  In addition to their goal of consistently performing at an elite level on the court, the Phoenix Suns are making big strides off the court through the use of analytics, which they use to help drive strategy for both business and basketball decisions.

While considered by some in the NBA as a small business in terms of the infrastructure and processes in place, in the past three years, the Suns organization has invested significant resources in not only organizing the data they accumulate, but in also guaranteeing the accuracy of that data and ensuring that it is being used by all decision makers across the organization.

Whether it’s an off-site meeting or a long road trip, as is the nature with any professional sports team, a majority of their work is done away from the office. The organization’s ownership was looking for a way to make their critical business data available wherever their decision makers were located.

As the Suns began taking steps to become more mobile, there was a healthy amount of skepticism that a mobile solution could be found that was both valuable and, more importantly, easy enough for end users (most of whom don’t have a very technical background) in the organization to adopt.

That changed when the Suns adopted Roambi. The Suns started using Roambi Analytics with their front office, organizing and visualizing key player scouting information all in one place, as well as making this information available in real time.

After the success of the initial rollout, the Suns decided to expand their use of Roambi to their back office. On the business side, the Suns optimized their operations by providing KPIs across sales and marketing, reporting on everything from ticket sales to game summary reports to in-stadium promotions to customer buying behavior to inventory – all via mobile devices, so executives were all working off of same set of numbers and were able to make critical business decisions in a moment’s notice.

2. Big Data application: Esri ArcGIS

How this Big Data app works: Esri ArcGIS, as the name implies, is a Geographic Information System (GIS) that makes it easy to create data-driven maps and visualizations.

Use case of note (in this case it is more of a partnership): In mid-July at the Esri User Conference, the company radically updated its Urban Observatory project. Developed in partnership with Richard Saul Wurman and Radical Media and originally launched last year, the Urban Observatory helps cities use the common language of maps to understand patterns in diverse datasets.

I attended the Esri UC last week and spent plenty of time playing with (and before that standing in line to get access to) the Urban Observatory exhibit, an interactive exhibit that makes it easy to compare and contrast data from cities worldwide, all on a touch screen.

At least half of the world’s population is currently living in urbanized areas. The Global Health Observatory (GHO) projects that by 2050, 7 out of 10 people will live in a city. This year, nearly 60 cities are part of the Urban Observatory.

Participation in Urban Observatory is open to every city around the globe. Any city that has data its officials would like to share is eligible to be included. In February 2023, Urban Observatory will go on permanent display in the Smithsonian Institution.

3. Big Data Application: Cloudera Enterprise

Use case of note: Cloudera has a ton of customers, but Wells Fargo and home automation company Vivent are two to pay attention to.  Wells Fargo has used Cloudera Enterprise to build an enterprise data hub.

Vivent says that it has acquired more than 800,000 customers using a variety of third-party smart-enabled devices – roughly 20-30 sensors per home. Many of those devices come in the form of thermostats, smart appliances, video cameras, window and door sensors, and smoke and carbon monoxide detectors. Without a central internal repository to gather and analyze the data generated from each sensor, Vivent was previously limited in its ability to innovate and to add higher intelligence to its security offerings.

For example, knowing when a home is occupied or vacant is important to security – but when tied into the HVAC system (which tends to be the largest contributor to a home’s energy bill and carbon emissions), you can add a layer of energy cost savings by cooling or heating a home based on occupancy. Similarly, by adding geo-location into the equation, you can begin to adjust temperature changes to a home based on the proximity to an owner’s arrival, for instance, when the owner has a connected vehicle. Studies have shown that consumers could see 20 to 30 percent energy savings by turning off HVAC systems when residents are away or sleeping.

Why Is Java Important For Big Data?

Big data refers to extremely large and complex data sets that traditional data processing software and tools are not capable of handling. These data sets may come from a variety of sources, such as social media, sensors, and transactional systems, and can include structured, semi-structured, and unstructured data.

The three key characteristics of big data are volume, velocity, and variety. Volume refers to a large amount of data, velocity refers to the speed at which the data is generated and processed, and variety refers to the different types and formats of data. The goal of big data is to extract meaningful insights and knowledge from these data sets that can be used for a variety of purposes, such as business intelligence, scientific research, and fraud detection.

Why is Java needed for Big Data?

Java and Big Data have a fairly close relationship and data scientists along with programmers are investing in learning Java due to its high adeptness in Big Data.

Java is a widely-used programming language that has a large ecosystem of libraries and frameworks that can be used for big data processing. Additionally, Java is known for its performance and scalability, which makes it well-suited for handling large amounts of data. Furthermore, many big data tools such as Apache Hadoop, Apache Spark, and Apache Kafka are written in Java and have Java APIs, making it easy for developers to integrate these tools into their Java-based big data pipelines.

Here are some key points we should investigate where Java’s importance can be mentioned cut-shortly;

Performance and Scalability

Java is known for its performance and scalability, which makes it well-suited for handling large amounts of data.

Java APIs

Many big data tools such as Apache Hadoop, Apache Spark, and Apache Kafka are written in Java and have Java APIs, making it easy for developers to integrate these tools into their Java-based big data pipelines.


Java is platform-independent, meaning that the same Java code can run on different operating systems and hardware architectures without modification.

Support and Community

Java has a large and active community of developers, which means that there is a wealth of resources, documentation, and support available for working with the language.

Prime Reasons Why Data Scientists Should Know Java

Java is a popular language for big data scientists because it is highly scalable and can handle large amounts of data with ease. Data science has heavy requirements, and being the top 3 listed programming languages Java can meet the requirements easily. With active Java Virtual Machines around the globe and the capability to scale Machine Learning applications, Java offers scalability to Data science development.

Widely-used big Data Frameworks Large Developer Community

Java has a large developer community, which means that there is a wealth of resources available online for learning and troubleshooting. This makes it easy for big data scientists to find answers to questions and learn new skills, which can help them quickly and effectively solve problems that arise during data science development.


Java is platform-independent and can run on a variety of operating systems and architectures, which makes it a great choice for big data scientists who may need to develop applications that run on different platforms.


In short, Java is a powerful and versatile language that is well-suited for big data development, thanks to its scalability, wide use of big data frameworks, large developer community, portability, and familiarity in the industry. It is a language that big data scientists should consider learning to excel in the field.


In conclusion, Java is a powerful and versatile language that is well-suited for big data development. Its scalability, ability to handle multithreading and efficient memory management makes it an excellent choice for handling large amounts of data.

Additionally, Java is the primary language for many popular big data frameworks, such as Hadoop and Spark, which provide pre-built functionality for common big data tasks. The large developer community also means that there is a wealth of resources available online for learning and troubleshooting. Furthermore, Java is platform-independent, which makes it a great choice for big data scientists who may need to develop applications that run on different platforms.

Top 5 Expense Management Software In 2023

Expense management is the second hardest operational cost to control. And majority of businesses don’t have a robust expense management platform in place. Tallie’s survey of 585 finance professionals shows that:

46% don’t track cost-to-process expense reports

43% have a manual expense management process in place

If you want to scale and grow your business, you should use an expense management software to track employees expenses. But, Tallie’s survey shows that between 2013 to 2023, the percentage of respondents who used an expense management software stayed at 35%. Lack of vendor marketplace familiarity can be one reason most companies’ entire expense management process hasn’t yet been automated.

Today, we are exploring the top 5 expense management software so you could pick the one suitable one. We will explain the main features, benefits, negatives, and price of each expense management software.

How did we select the top 5 expense management software?

The original list had +300 expense management software vendors. We used 4 publicly-verifiable metrics to create a 5-vendor-shortlist. Those metrics are:

Tracking all employee expenses from the card

Allowing travel planning from the app

Setting spending limits to control expenses, and more.

1. SAP Concur Expense

Backed by large resources of SAP, SAP Concur Expense software is the largest company on our list in terms of employee count, references, and funding. In terms of reviews, they are ranked first on G2 and Gartner, and 2nd of TrustRadius.

SAP Expense management software allows users to submit expense reports, manage their payments through the expense management app, and monitor their travel related expenses.

1.1. Pricing

To get an exact price from Concur Expense, you need to contact them for a personalized quote depending on your industry, use case, expense report volume, etc. Unverified reports suggest that they charge $8 per report per ~250 transactions. That will be $994 per year, or $83 per month.

Concur Expense offers a free-trial.

1.2. Benefits of using SAP Concur Expense

Booking travel directly through Concur Expense

Easy to use interface

Travel accommodation by allowing employee expense submission through the app

ERP integration with SAP products, Grab and Tibco

1.3. Negatives of using SAP Concur Expense

Has an initial steep learning curve

Lack of integration with some travel and hospitality apps such as Uber

False positives in automated categorization of duplicate payments

2. TripActions

Targeted primarily at travel expense management, TripActions offers:

One-stop-shop for all bookings through 24/7 live agents

Smart, virtual credit cards with in-built controls fir expense policies that automate the entire approval process and streamline reimbursement management

Real-time notifications of employee expense reports to improve spend management

Instant expense record reconciliation and employee reimbursements after submitting expenses

2.1. Pricing

TripAction’s pricing model is quote-based, where customers have to get in touch with reps, discuss their needs, and would then be recommended the most suitable package. They do not offer a free trial, as the majority of their revenue comes with flight bookings and a free trial would mean lost commissions.

2.2. Benefits of using TripActions

Employees can book their hotel rooms and other accommodation, such as car rentals, airport pickups, etc. all through their app

TipActions has a specific focus on tracking loyalty points and suggesting upgrades and speciality deals automatically

Its Per Diem feature entices employees to book cheaper rooms and be rewarded for the savings2.3 Negatives of using TripAction

2.3. Negatives of using TripActions

Users claim it’s too much of a travel app and lacks the nuances of a hardened accounting software like SAP Expense

Bookings can sometime not go through until TripAction’s partners approve a reservation.

Because all reservations are done through them, in case of issues you can’t contact the airline agency directly

3. Brex

It’s more difficult to track the submitted expenses if transactions are done with different credit cards belonging to different banks. Brex has solved this issue by optimizing their virtual and physical credit card. One of their unique features is as an employee onboards/offboards, by having integrations with the ERP solutions, the software automatically creates a new credit card/cancels an old one.

3.1. Pricing

We found no specific pricing amount for Brex. But its product lines fall under 4 categories of:

Bill pay

Expense tracking

Corporate credit card

Business account

Where each have specific sets of features, and whose price should be directly enquired from Brex.

3.2. Benefits of using Brex

Their virtual and physical credit card transactions enable business expenses to be paid based on governance rules

The app is user-friendly and enables users to submit and manage expenses on the go, where purchased items automatically get submitted on the account

Foreign payments are processed with no additional costs3.3. Negatives of using Brex

3.3. Negatives of using Brex

Regionally limited to US and Canada

Limited to Mastercard which is less popular and accepted than other platforms

As the solution is an expense management platform and not an investment software, its forecasting features are lacking.

4. Zoho

Zoho’s expense management software’s APIs integrate with accounting tools, ride-sharing, banking, and other collaboration apps to streamline expense management. Some specific use cases of Zoho includes:

Receipt management

Expense management

Mileage tracking: This is a unique feature which other vendors do not mention. Especially when using car rentals, this feature helps with mileage breakdown.

4.1. Pricing

Zoho has four pricing plans:

If you have +500 users, you can get in touch with them for a customized quote.

4.2. Benefits of using Zoho

It has an easy to use interface, especially when it comes to receipt scanning

They have a host of difference applications within the Zoho Expense suit

They are optimized for small business owners, as most reviewers are from companies with

4.3. Negatives of using Zoho 5.1. Pricing

Emburse offers three pricing packages:

5.2. Negatives of using Emburse

Users claim the website or the application user interface isn’t friendly

Integration with ERPs and other accounting software is limited

Uploading receipt photos takes time and isn’t seamless

For more on fintech

To learn more about fintech, read:

And if you want to automate one of your financial processes, visit our financial services hub to find data-driven lists of vendors for different use cases.

We will help you through your vendor selection journey:

He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.





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