Being Data Driven

Data Warehousing Concepts: Everything You Need to Know in 4 Minutes

Written by An Bui|May 16, 2018

Just ten years ago, even the most advanced analytics professionals only had to manage a handful of data sources. But data volume, velocity, and variety are now increasing exponentially. In 2016, IBM estimated that 90% of the world’s data had been created in the preceding two years, and the W.P. Carey School of Business found that the volume of business data doubles every 1.2 years. Social and mobile apps mean that information is streaming in real time, and from Instagram to Uber to Snapchat to mobile shopping to wearables and IoT devices, there’s more data sources than ever before. Data sources have seemed to grow faster than they can be integrated. Data analysts are spending so much time waiting for data and then managing it that they have almost no time left to analyze it. And the torrent just keeps on growing.

What Is a Data Warehouse?

A data warehouse can solve all these problems. What is a data warehouse? It’s a technology solution that allows a digital analyst to have all their data in one place. A good data warehouse will empower them to deliver analysis at lightning speed. The perfect data warehouse solution will store their data in its native format reorganized in ways that BI tools can use, allowing them to focus on the most important and rewarding part of their job: extracting needed business insights.

The Harvard Business Review recently wrote that, in the current business era, the most innovative companies are the ones with the most data. But a data stockpile isn’t enough. Those successful businesses thrive not just because they have the data but because they’re able to extract insights from that data to improve that decision making. And to select the right solution that will allow you to extract those insights, it helps to be familiar with certain data warehouse concepts.

Decrease Data Wrangling With the Right Data Warehouse

Every data scientist wants to develop groundbreaking insights, but 50 to 80 percent of their time is spent wrangling the data. Data streams are formatted in ways that don’t align. Every new data source increases the complexity on an exponential basis because it needs to be integrated with every other source. One human error may mean you have to tear it all down and start all over. It’s no wonder that Gartner Group calls data preparation “the most time-consuming task in analytics and BI.” Analysts may find that they’re spending so much time working on prepping the data for query and analysis that they have no time left to actually do the query and analysis.

To select a data warehousing solution that will reduce data wrangling time, it’s important to be familiar with some data warehouse basics to understand how solutions differ. Traditional data warehousing requires IT staff and DBAs to manage the servers, the integrations, the transformations and the data management. If they’re busy with something else, or even worse if there isn’t enough resourcing to have these people in-house full time, the data scientist’s analysis slows to the pace of IT and data engineering’s availability. A good cloud data warehousing service, however, includes IT and DBA expertise, meaning an analyst never has to wait for that availability.

With the best data warehousing solution, you’ll almost never have to do any data wrangling. For example, Panoply’s data warehouse uses AI technology to handle the transformation, integration, and management of big data. And you’re not limited to traditional data sources like customer relationship management systems or enterprise resource planning systems. Panoply can handle other crucial streams like social media activity, images, and web server logs, which means you have far more information to use in order to connect the dots. The best data warehousing solution gives you back the time you need to extract actionable insights that lead to business improvements and innovation.

With a Data Warehouse, You Can Access All Your Data

The companies that succeed do so because they have all the data available in one place to inform their decisions and actions. Incomplete data can lead to issues like serving the customer the wrong offer, such as when Pinterest sent single women a “Congratulations on your upcoming wedding” email with a discount on wedding invitations. It can lead companies to develop a product with little market potential. Incomplete data is literally a matter of life or death in hospitals; a bad medication interaction or unregistered allergy can kill a patient.

When you integrate massive amounts of data, you can transform an industry. Netflix and Amazon are both well known for using their customer data to improve recommendations. But each company has taken that data a step further. When Netflix went into original programming, the data indicated that House of Cards and Orange is the New Black would be successful. Amazon Prime Now succeeds because the company’s data allows it to have an unprecedented level of just-in-time inventory to match demand.

With a data warehouse, analysts have the space and the resources to ensure that all of your data is continually on tap. It ensures they have the facts on hand that support groundbreaking decisions that leave competitors in the dust. It’s one of the simplest data warehousing concepts to grasp, and also one of the most powerful.

The Right Data Warehouse Accelerates Your Access to Data

Once upon a time, data was kept on hard drives or even tape drives. The velocity and volume of data have increased exponentially since then. Data warehouses are crucial to managing both. For a company to succeed, the speed at which analysts can access and leverage the data needs to match the speed at which it’s created.

Again, it’s important to understand data warehousing concepts to see how traditional and cloud solutions deliver in this area. An on-prem data warehouse may not be able to scale fast enough to keep up with the volume of data. It can take 24 hours or more for data to flow in. Queries need to be queued to avoid crashing the server, which creates more delays. Possessing all the data doesn’t matter if analysts can’t extract insights from it in time to take action.

Cloud-based data warehousing solutions scale fast enough to keep up with the volume and velocity of data flow. They can reduce or eliminate the time it takes to extract, transform and load the data. Due to their sheer scale, they can store data in columns rather than in rows. This accelerates the speed at which warehouses can access the data because they only need to look at the one data element in the column rather than looking through an entire row of data in order to access that specific element.

With so much access to computing power, data warehouses can use Massively Parallel Processing (MPP) to analyze data at a speed unmatched by humans or on-prem data warehouses. For the ultimate in speed, however, AI is required. A smart data warehouse like Panoply uses AI to learn the kind of data analysts are most likely to access and queries they are likely to make. Data is then organized so that response time on frequent queries is accelerated. The faster an analyst can extract those key insights, the further the business can get ahead of its competition.

The Data Warehouse Built For Data Engineers and Analysts

We’re now in the era of the data tsunami. To master the wave, data analytics professionals need the best possible tools to get the insights that lead to groundbreaking innovation and market dominance. And no one understands this better than data engineers and analysts.

Panoply is built by data engineers and analysts, for data engineers and analysts. We understand your needs and your problems; just look at our demo to see how you can surf the big data wave and extract insights more quickly and easily than ever before.

 

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