It's an undeniable fact: the world now runs on data. The haves will succeed, and the have-nots are destined for failure. Specific to modern business, winners and losers are now being determined by the way organizations collect, analyze, and act on data – increasingly through automation. And much of the value of data today is being unleashed through models: predictive analytics, machine learning (ML) and the emerging world of artificial intelligence (AI). We've spent the last 20 years relentlessly pursuing digital transformation and building millions of applications based on programmed logic.

Providing excellent customer service needs to be a priority for every business. Think of your customer service department as the personality of your brand. It is also the reason why people will choose your products and services on a repeated basis. It is quite simple really, with no customers, there is no company. The path that consumers travel on to make a purchase, is called the "customer journey."

In this regular column, we'll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we're in close touch with vendors from this vast ecosystem, so we're in a unique position to inform you about all that's new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive. Matillion Advances Speed And Simplicity Of Data Integration With Release Of Matillion Data Loader – Matillion, a leading provider of data transformation software for cloud data warehouses (CDWs), announced Matillion Data Loader, a free Software-as-a-Service (SaaS) data integration solution that empowers data analytics professionals and business users to simply and easily load and migrate data with a powerful and scalable product.

The service being provided by MemSQL targets the massive wave of applications that are now being deployed in the cloud. MemSQL today unfurled a preview of a cloud service based on a database optimized for translytical applications. The MemSQL Helios service is based on the beta edition of a forthcoming 7.0 release of the MemSQL database, a distributed relational database optimized to run operational analytics applications in memory. The latest version of MemSQL also provides for the first time a "SingleStore" capability that eliminates the need to choose between a row store or a column store for different classes of workloads. That capability should reduce a lot of the performance tradeoffs organizations now make when building and deploying translytical applications, says Peter Guagenti, chief marketing officer for MemSQL.

For integrating ML and AI outside the database, two popular methods are integrating with Spark and TensorFlow. For Spark, MemSQL offers and open source MemSQL Spark Connector, which delivers high-throughput, bi-directional, and highly-parallel operations from partition to partition. This connector opens up unlimited ML and AI possibilities that can be combined with a scalable, durable datastore from MemSQL. One example of this integration is real-time machine learning scoring.

Machine learning (ML) is a method of analyzing data using an analytical model that is built automatically, or "learned," from training data. The idea is that the model gets better as you feed it more data points, enabling your algorithm to automatically get better over time. Machine learning has two distinct steps: training and operationalization. Training takes a dataset you know a lot about (known as a training set), then explores the dataset to find patterns and develop your model. Once you have developed your model you move on to operationalization.

It's time to dispel some myths surrounding SQL. That's the message from MemSQL, a scalable real-time Data Warehouse that is designed to ingest and transform millions of events of data per day, while simultaneously analyzing billions of rows of data using standard SQL. As that description makes clear, there's no reason to believe that there's no such thing as scalable SQL, according to Gary Orenstein, Senior VP for Products at MemSQL. One of the oft-cited reasons for moving from SQL to NoSQL is concern that SQL solutions can't scale, Orenstein says. But today, there's a renewed awareness that it is possible to scale SQL, partially thanks to Google's Cloud Spanner globally distributed, relational database service that counts among its features horizontal scaling.

Download the free report "Data Warehousing in the Age of Artificial Intelligence" from MemSQL for more on how to use data efficiently in a data warehouse. "Hot data" is the most recent snapshot of the real world. Hot data becomes big data when it comes to rest in a data warehouse, and that data warehouse is traditionally where data science happens. Machine learning models are typically trained on batches of big data at rest, but many operational use cases require hot data. If you are serving video ads to mobile gamers, supporting sales people walking into a meeting, or operating an oil drill, using the latest data is crucial for success.

Join Steven Camiña of MemSQL for "Building the Ideal Stack for Machine Learning," where he'll share how to use real-time data for machine learning. Machine learning has been a mainstream commercial field for some time now, but it's going through an important acceleration. In this podcast episode, I talk about that acceleration with two executives from MemSQL, a company that specializes in in-memory databases: Gary Orenstein, MemSQL chief marketing officer, and Drew Paroski, MemSQL vice president of engineering. Orenstein and Paroski identify a few crucial inflections in the machine learning landscape: machine learning models have become easier to write; computing capacity on the cloud has increased dramatically; and new sources of data--everything from drones to smart-home devices and industrial controllers--have added new richness to machine learning models. Computing capacity and software progress have made it possible to train some machine learning models in real time, says Orenstein: "given enough time in computing, you can do just about anything, but only recently have people been able to apply these machine learning models in real time to critical business processes."