In this era of big data, chances are that you have heard and used terms like machine learning and predictive modeling. For the average person, these terms may actually seem interchangeable. However, in the world of data analysis, there are big differences between these terms. In fact, these tools are used differently to provide different kinds of insights and add different strategic value. Today, we will take a closer look at predictive modeling, machine learning, and artificial intelligence, and discuss when to use them for the best results.
Once upon a time, spreadsheets were a holy grail and a force to be reckoned with. Spreadsheets revolutionized how business was done by offering a way to collect and organize data. Companies relied on them for almost everything in the business ranging from accounting to sales and even operations. They used the software to analyze data while generating reports.
However, spreadsheets are slowly becoming outdated. They can no longer keep up with the needs of our tech-forward world and changing business requirements. This article offers reasons businesses must upgrade their primary source of data consumption from spreadsheets to better alternatives.
2.5 quintillion bytes of data are produced everyday, transforming it into a visual format that is easy to understand is key to an effective presentation of critical information. Through visualization, text-based data sets that are otherwise hard to interpret are presented in easy to read fashion. But how do you choose the right type of graph that will help you achieve a compelling presentation? This five-minute read has all the tips you need to choose the right option for your data set.
Helios Company, a leading end to end analytics solutions provider for small and medium sized companies announces Accountant Analytics, a proprietary solution for accountants and bookkeepers to help their clients make better decisions with data.
The number 13 has often been associated with bad luck but it's no more unfortunate than any other figure. Since Halloween falls on the 31st, it's an unusual transposition of this number but that may change if some people get their way. Petitioners are proposing the date be changed to be the last Saturday in October, similar to Thanksgiving falling on the final Thursday of the month.
Halloween purists disagree and would rather the date remain the same. In this light, what are some other fun facts and stats about one of the most beloved holidays for children and adults of all ages? Here's a baker's dozen of these trivial tidbits to get our mouths watering in anticipation of the arrival of All Hallow's Eve along with all the sweets and treats associated with this date.
The decrease in storage costs and increase in computing power has led to the rise of big data and transformed the world into a data-driven space. Most businesses now rely on data to find out what their clients want, weigh customer satisfaction, and make decisions. The large volumes of data systems generate every day have, in turn, created the need for more efficient data processing channels. Even small companies deploy tech stacks with dozens of applications all capturing data. Mid-market and enterprise companies can find themselves with hundreds of applications and quickly become fragmented by division and location, making it hard to measure if the business is moving in the right direction. This is causing a massive paradigm shift; on-premise servers and one-size-fits most applications are being replaced with cloud-based applications unified into high-performance cloud based data warehouses. In order to effectively and repetitively get data out of multiple systems and into a central warehouse, companies must setup data pipeline tools to extract, transform and load data into their new home. The two most common solutions are ETL and ELT. If you're new to this scope, this might sound like a lot of technical jargon, which is why we went out of our way to break it down for you.
Nowadays, data is everywhere. It is a commodity of incalculable value. Almost everything you do results in the creation of new data. For example, when you withdraw money from a bank, data is created and stored. Similarly, when you visit a website, you create data that Google and other third-party companies can store and use.
As the era of big data kicks into high gear, businesses and organizations are now more focused on how they can leverage the data to gain a competitive advantage. This has, in turn, powered the popularity of data science tools - specifically the concepts of analytics and visualization. Despite hearing these words on every street corner in major cities, some business owners are still in the dark about what they actually are and how they can use them to grow their entities. If you're among them, don't worry, this definitive guide outlining the differences between data analytics and data visualization will help shed light on how to use them to improve your company.