We go about our days oftentimes not realizing just how much information we’re creating. The reality here is that we’re creating data on a business as well as personal level. And, this information is so important in how we function and conduct business on a daily basis. Most of all, this data continues to grow in both importance and volume.
IDC forecasts that by 2025 the global datasphere will grow to 163 zettabytes (that is a trillion gigabytes). That’s ten times the 16.1ZB of data generated in 2016. All this data will unlock unique user experiences and a new world of business opportunities. Furthermore, IDC estimates that by 2025, nearly 20% of the data in the global datasphere will be critical to our daily lives and nearly 10% of that will be hypercritical.
Let’s pause there for a second. Let’s say you're an organization that’s trying to study how your product is impacting a certain market segment. You’ll need data to make this happen. But, if it’s disorganized, not properly handled, or missing parts—the results will hold far less value.
You’ll need better tools to help with data visualization, data analytics, and the way you ingest data in general. It’s exactly why this flood of new data has enabled a new set of technologies including AI, machine learning, and even data warehousing to turn data analysis from an uncommon and retrospective practice into a proactive driver of strategic decision and action.
This is a major reason why new and advanced data processing tools leveraging cognitive systems continues to grow in popularity. IDC estimates that the amount of the global datasphere subject to data analysis will grow by a factor of 50 to 5.2ZB in 2025; the amount of analyzed data that is “touched” by cognitive systems will grow by a factor of 100 to 1.4ZB in 2025.
As you take this all in, let’s go back to the very first step when working with data in a data-driven society. Before you can do any sort of data analytics or the application of cognitive systems, you need to actually ingest this data. There are several data ingestion points you can take. However, working with an intelligent data warehouse gives you the ability to then integrate with a variety of systems including AI, data lakes, machine learning engines, big data analysis, and so much more.
That said, data warehousing has come a long way. Data warehouses were traditionally based on a strict data model, and included only structured data. This is changing, and new data warehouses can ingest semi-structured and even unstructured data, and infer data models and schemas on the fly.
Now, with the cloud, we see the next generation of data ingestion points and where data warehouses come in. In a cloud-based data warehouse, the design process is much more lightweight. It is still necessary to identify data sources and user needs. But it is now possible to ingest data into the data warehouse at the click of a button, explore and transform it while already in the data warehouse.
This is a workflow known as Extract-Load-Transform (ELT).ELT provides tremendous flexibility for analysts and data engineers, because they don’t have to devise the entire process—data model, ETL, OLAP cube structure, etc.—in advance. They can define the process on an ad-hoc basis, as new data streams into the data warehouse and as new user needs emerge.
It’s really important to note that traditional data warehouse vendors are recognizing the competition from new data warehouse solutions in the cloud—from publicly traded companies such as Amazon and Google, to innovators like Panoply. Cloud-based data warehouses introduce better ways to extract information from data and analyze it. They make data warehousing accessible and effective not only for large and well-funded enterprises, but also small and medium businesses.Instead of spending months and millions of dollars to set up a monolithic Enterprise Data Warehouse, it is now possible to set up a cloud-based data warehouse in days, starting from a few hundred dollars per month in managed services and cloud-based storage.
Furthermore, new technology is helping users find better ways to derive real value out from data by making it easier to:
Beyond anything else, working with a data warehouse gives you data agility. This is the ability to pivot when the market shifts and changes. This is absolutely key to creating competitive advantages and staying ahead. So, if you’re working with various data sources and are struggling to find value in that data, think about how you ingest that information, and where a data warehousing solution can help.