Tiered storage is a way of managing data by assigning it to different types of storage devices/media depending on the current value that the underlying information provides. The efficient management of data recognizes that all information provides an intrinsic value from the time it’s created to the time it becomes obsolete and that this value changes over the information lifecycle.

The typical factor determining the value of information is how frequently you access it, however, policy-based rules can factor a number of other issues to determine information value. For example, old bank transactions, which might have a low value, could suddenly shift in value depending on special circumstances, such as a tax audit. This article discusses some pros, cons, and best practices for tiered storage..

Disaster recovery is a set of processes, techniques, and tools used to swiftly and smoothly recover vital IT infrastructure and data when an unforeseen event causes an outage.

The statistics tell the best story about the importance of disaster recovery—98 percent of organizations reported that a single hour of downtime costs over $100,000, while 81 percent indicated that an hour of downtime costs their business over $300,000...

Amazon Web Services (AWS) is a subsidiary of Amazon that provides cloud computing services, accessible to both individuals and companies.

While newer cloud providers like Microsoft Azure and Google grow at a faster rate, AWS still holds a commanding position at the top of the cloud provider market..

Big Data marketing refers to the use of high-velocity, voluminous, and variable data to improve a company’s marketing efforts. When people think of the term Big Data, they often make the erroneous assumption that it’s just about the size of the datasets.

However, Big Data refers to data expanding on two other fronts —velocity and variety..

Data visualization describes techniques and tools that help people understand the meaning of data by representing it in a visual context. It’s often easier to identify important trends, correlations, and patterns in data which is presented visually rather than in its native format. Data visualization is particularly helpful in a Big Data landscape, in which many enterprises try to derive insights from huge data sets, with information obtained from myriad sources, including social media, enterprise software, and web analytics.

Read on to find out how data visualization is linked to another extremely relevant aspect of technology—responsive web design and responsive images..

The process of taking data from different systems and putting it into a data warehouse for business analysis can be a complicated affair. In this article, we look at what is involved and how the cloud has made matters potentially trickier.

A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources..

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..