In a world where organizations of all sizes gather vast quantities of data every single day, there is a real need to get insights from these huge data sets that drive better business decisions. After all, what is the point of collecting data from disparate sources if your business doesn’t get some benefit from all that information?

However, traditional database software cannot process and analyze such voluminous, complex data sets. This is, in essence, the problem that big data analytics solves, by providing powerful systems that can process and get insights from large data sets..

Marketing automation means using software platforms and tools to conduct marketing strategies more efficiently and effectively by automating repetitive tasks and processes. Typical tasks marketers might automate include segmenting customers based on interactions with a website, and managing email campaigns.

Marketing automation is a constantly evolving field, and as technology advances further and faster, marketing automation best practices today may not be relevant in five or ten years..

Both the volume and velocity of data growth in recent years are unprecedented. An IDC report found that digital data more than doubles every two years and will reach 44 zettabytes by 2020, 37 percent of which will be useful when analyzed.

To get data-driven insights which drive smarter business decisions, enterprises that collect data from many sources must have somewhere to store it for analysis. Businesses also need to store data for other uses, such as..

Modern enterprises gather data from many disparate sources, including social media, websites, customer databases (CRM systems, sales records etc), customer support systems, and HR software.

Merely collecting lots of data isn't useful in itself—it's the insights you get from such data that can drive more informed business decisions. This post aims to teach you about the bedrock processes required for obtaining insights from your data—ETL and ELT.

We'll begin by discussing data integration, before moving on to two vital forms of data integration—ETL and ELT. When you're finished reading, you'll fully understand what ETL and ELT are, and which process is better suited to your business in terms of getting actionable insights from different data sources..  

Agile test metrics and KPIs are measurements that enable Agile teams to assess how effective their software tests are and whether their testing efforts help achieve specific business objectives. An important aim of Agile software testing is to get testing up to speed with development—metrics and KPIs can assess the efficacy of the Agile testing team in this context.

In this article, you'll find out about the important difference between a metric and a KPI, and why you need to distinguish between the two. You'll also get some examples of KPIs and metrics relevant to Agile software testing efforts. Finally, you'll learn exactly how KPIs and metrics can lead to overall improvement in software testing within an Agile framework in addition to understanding some important measuring pitfalls..

Data warehouse security is vital for organizations that take the huge step of collating all their important data in one place. Unauthorized access to data warehouses can have devastating consequences, ranging from compromised customer information to exposing of high-level business intelligence that gives an enterprise its competitive advantage.

This article introduces warehouse security, discusses some of the challenges involved in securing a data warehouse, and offers some best practices for data warehouse security, including the use of FIPS 140-2 certified software and hardware..