Big Data is one of the most popular buzzwords of the 21st century. It is reshaping how companies look at their data and what they chose to do with it. According to the Global Opportunity Analysis and Industry Forecast, 2021-2030, the global Big Data and Business Analytics market size is projected to reach $684.12 billion by 2030, growing at a CAGR of 13.5% from 2021 to 2030. But what is Big Data? And how can it help your business?

While the term ‘predictive analytics’ was born in 2003, the process itself is a lot older than we realize. From our day-to-day life to business proceedings, we’ve been using predictive analytics for a long time.

According to Forbes, 27% of business leaders confess that they are unsure about the accuracy of their data. Data is the raw material of any enterprise and having access to prime-quality data is essential for future decision-making. Data alone does not suffice; it must be transformed into actionable knowledge to make use of it - which can only happen if you have easy access to your quality datasets. Fortunately, with the right tools and techniques, this puzzle can be easily solved.

As defined by Gartner, dark data are ‘information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes’. Those “other purposes” include, e.g., analytics, Machine Learning, Business Intelligence, or monetizing.

For another year, the most common frauds in the finance sector are fraudulent loan and credit products, and as many as 80% of leasing companies perceive an increase in the risk of their occurrence compared to the previous year.

Algolytics’ partners and Snowflake customers will gain access to the first Polish spatial data on Snowflake’s Data Marketplace accelerating the support of Location Intelligence and Business Intelligence development.

You already have a Machine Learning model, in addition, you created the running scenario all by yourself in just a few minutes. So… let's take it to the next level. What will you do to categorize customers based on performance?

Imagine that you want to predict, for example, the creditworthiness of customers - ideally in real time. Moreover, you already have a predictive model written in Python, R, or Groovy. However, the last question remains - how to deploy a Machine Learning model?

Microsoft Azure customers worldwide now gain access to Automatic Business Modeler (ABM) to take advantage of the scalability, reliability, and agility of Azure to drive application development and shape business strategies.

In previous articles, we discussed how to maximize the benefits of using high-quality databases. However, if you don't have your own address or location data, but you still want to develop your business by conducting spatial analyses and making accurate business decisions based on geographical criteria - nothing lost!

Business Intelligence technology (and in particular Location Intelligence) is key in the context of making the right business decisions. If in your company you determine, for example, the target group of marketing activities, study purchasing trends, or want to increase the predictive power of scoring models - consider enriching the already existing database with additional data.

Previous publications have covered the topics of geocoding and ‘golden record’ in databases. It is then high time for Location Intelligence in the context of customer experience – considering as the recipient of a package, as the user of a website or application.