Trends We See

When the viability of your company is dependent on an ecosystem of smaller businesses…are you a helpful friend or a daunting foe?

With a better grasp on how to control its user-activity costs, ClassPass now faces a dilemma in growing and keeping its subscription base in tact. The company has redefined the gym business model, but still faces the nagging question of user revenue sustainability.

With so much talk about the overall dominance of Amazon to the retail industry, it’s easy to paint a picture of an imbalance of power and creeping influence. Yet, it would also be foolish to overlook the company’s ability to help retailers instead of monopolizing them.

If we were to ask you about a dashboard horror story, how many would you have? One, two or too many to count? Just like you and the rest of this world, our dashboards have not always been the greatest, with either too much information or not the right information, leading to a dashboard that gives no true insight.

Have you ever wondered whether your dashboards are communicating right? Are they actually telling relevant insights or are they just a collection of all your “aha moments”?

Throughout this article we will be discussing the difference between business analysts and data analysts in terms of job responsibilities and skill set.

For those of you looking to up your data analytics portfolio or if you’re taking a Data Science class, you’re probably here right now - trying to figure out where you can find a decent data set. And when I say decent, I mean a data set that has accurate, complete data and is very informative.

If there is one thing that the fourth Industrial Revolution is teaching us is that jobs are changing and with them job requirements.

Cleaning data is a crucial part of your data analysis. Bad quality data can impact your entire analysis. It can even make the best algorithm fail.

We here at Partners in Company believe that creativity and data analytics should be embraced in your daily work. Through this article we will provide you with examples where your business teams can use data for better performance.

We all understand the power of a well-designed dashboard. Whether you’re looking at staff performance KPIs or analyzing your company’s sales trends, an effective dashboard is what gets the data conversation going.

Most of us think that being a data scientist is a pretty left-brained role. A data scientist should be good at working with large datasets and performing complex analysis on them.

Ok, here it goes.. You have been assigned to present your company's sales data and you can’t decide whether you should use a report or develop a dashboard.

One of the first things they teach you in any data-related class is the four dimensions of Big Data or otherwise known as the four ”Vs”: volume, variety, velocity and veracity.

Our job, as data scientists, means that most of the time we are looking at KPIs and trends to help companies grow and optimize their processes.

Almost every organization in business and government realizes the value of data. As a result, many groups are seeking to build strategies to more effectively use the data at hand.

Nowadays, data science has grown to become a popular subject with many utilizing it across many businesses and industries. Some of the most prevalently stored data include information about consumers, sales, inventory, and even cash.

A data breach exposes confidential, sensitive, or protected information to an unauthorized person where the files in a data breach are viewed and/or shared without permission.

Many individuals are perplexed by the terms Data and Metadata. Though both are data types, they serve different purposes and have different requirements.

According to NewVantage Partners' 2018 study of senior executives, 73 percent of respondents indicated big data projects had provided demonstrable benefits. This value has shown itself in the form of advanced analytics/better judgments, enhanced customer service, cost savings, innovation and disruption, faster time to market, and data monetization. According to an Economist research, firms that employ DDDM outperform and are more lucrative than those that do not.

Data governance is a set of policies that helps to ensure data is usable, accessible and protected. Effective data governance leads to better data analytics, which in turn leads to better decision making and improved operations support. Data governance also helps to avoid data inconsistencies or errors in data, which lead to integrity issues, poor decision making, and a variety of organizational problems. It leads to improved data quality, decreased data management costs, and increased access to data for all stakeholders. The result is better decision making and better business outcomes.

Data mining is a combination of technological, processing, and analytical techniques that are used to highlight similarities and differences within large sets of data to illustrate key factors and decisions. It employs the use of statistical analysis, artificial intelligence, and machine learning to help process and simplify information. The reason behind the success of data mining is that these programs are often able to catch detailed information that human analysts may not be able to. They are also able to run non stop whereas humans are limited in many scopes and much more costly.

From your favorite streaming platform’s recommendation algorithm to self-driving cars, Machine Learning has grown its presence in our daily lives exponentially. In fact, the global Machine Learning market is projected to grow to $117.19 billion by 2027. Thus, it is without a doubt that businesses and industries should begin utilizing these technologies to their advantage.

In modern-day sports, organizations are constantly seeking new informational edges over their competitors. By utilizing analytics and data science, sports organizations are able to better make decisions and efficiently execute new ideas, enabling them to surpass the competition. In fact, machine learning is able to improve almost all aspects of their operations, making it an integral part of achieving success.

Machine learning (ML), a subset of artificial intelligence, is an increasingly important technology for big data analytics which enables organizations to detect patterns and emerging trends in their operations. In 2017, the Internal Revenue Service (IRS) adopted machine learning techniques to identify fraud and other types of noncompliance by individuals claiming tax refunds.

As an asset manager, the assessment of risk, needs, goals, and progress is crucial for success. Thus, many asset managers are turning to new technologies to build on the existing knowledge and systems that they have for their needs. Such types of technologies being utilized today include machine learning and artificial intelligence, tools that could help generate new insights to be at the disposal of asset managers.

There's a lot of disagreement about whether TikTok is safe to use. It's not surprising, given that many US leaders, including the Secretary of State and the President, have previously issued statements warning users about the app. So, what exactly do you need to know about TikTok? Here's some information to help answer your questions about TikTok and the data it collects.

From small business or a multinational enterprise, data is used everywhere as the backbone of businesses. However, due to its significance, the proper management of data is necessary in order to effectively utilize it. After all, improper data management may lead to detrimental problems to your business such as incorrect information, duplicate data, or wasted time and storage.

Data centers are concentrated locations hosting banks and banks of servers and networking gear that process mountains of information and on any given day, tens of millions of servers in the over 7.2 million data centers around the world are running applications for a range of customers around the globe. The energy data centers consume continues to accelerate as billions of devices continue to join the Internet of Things (IoT), artificial intelligence gains demand, and other applications like blockchain-powered cryptocurrency evolve.

A data center is a facility that centralizes an organization's IT operations and hardware for the purposes of storing, processing, and distributing data and applications. Data centers are essential to the stability of everyday operations because they store the most important and sensitive assets of a business. As a result, every organization's priority includes ensuring the security and dependability of data centers and the information they house.

Artificial intelligence refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.

All industries are regulated by the law. Whether it’s sales, partnerships, purchases, or merges, company actions are all under the provisions of our legal system.

Artificial intelligence is the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate the reasoning that people use to learn from new information and make decisions.

Today's world is data-driven, and technology is evolving at a rapid speed, so there is a significant need to handle and analyze data as soon as it enters an organization.

With so much information getting captured in the business world through data analysis, data visualization clarifies what information means by presenting visual context in the form of maps or graphs.

In modern times, fashion has evolved with the industry becoming more and more lucrative each year. In fact, the fashion industry is projected to grow by over $80 billion, a substantial 60% increase, from 2020 to 2024.

When considering R&D investment for new technologies, beauty firms should analyze major trends in tandem with data and analytics potential, especially as younger, independent brands become more creative and big brands make acquisition agreements. Diverse, inclusive goods may be built in the spirit of personalization—using algorithms to process client data and delivering a one-of-a-kind service component can increase engagement and attract new customers.

Data normalization is a way of organizing data within a database in which it manages the columns and tables in the database to ensure that their dependencies are correctly implemented using database constraints.

Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Advancements in machine learning have made our world and lives much better by impacting almost every aspect of our lives. We are now living in an era where technology is influencing the way medical facilities and medicine are administered to people but this requires a lot of research and development.

Marketers and analysts have spent a significant amount of effort in recent years researching changes in customer behaviour as customers transition from traditional to digital channels.

Data comes in various forms and formats, including numerical data, text, multimedia, research data, or a variety of other sorts of data. The data format is defined as a type of format used for coding data. The data is coded in many ways. It is being coded in order for it to be read, recognized, and used by many applications and systems.

The modern-day retail industry is driven by technology in terms of both business and consumer. In fact, you may even find yourself engrossed in comparing prices and deals and ensuring that you are receiving the best possible choice for your money.

Traditionally, old-fashioned banking included using checks for payments or making deposits through bank visits. In the present day, with the growing popularity of digital banking, each action could be made much more efficiently with the help of new technologies.

Data science technology has rapidly grown exponentially throughout recent years with many industries utilizing it lucratively. The Private Equity (PE) field is no exception as major investment funds and asset managers are already beginning to obtain and make use of every bit of predictive insight from the data they may collect.