In order to break down barriers in creating and storing data, understanding the modern data architecture is key. That was the focus of Mike Lamble, CEO at Clarity Solution Group, and Ron Huizenga’s, product manager at Embarcadero Technologies, presentation at Data Summit 2015.
“There is a wildly increasing pace of data accumulation and growth in the number of users and at the same time we have strained budgets to deal with that,” Lamble said.
10 Cornerstones of a Modern Data Warehouse Environment
Lamble outlined 10 cornerstones of a modern data warehouse environment which includes a radical assault on the EDW model, enterprise data lakes, self service centric BI, data governance, pooled infrastructure, “best fit” SQL DBMS, coexistence of waterfall and agile technologies, a focus on decommissioning, corporate IT as an enabler, and data scientists in IT.
Instead of offering a three layer solution radically assaulting the EDW model allows enterprises to offer one, reducing the time it takes to move data and giving people answers in days instead of months, Lamble explained.
“I hope you can see why I called it the radical assault on the EDW model because it’s basically saying data to dashboards in days not months,” Lamble said.
By implementing enterprise data lakes, it’s a cheaper and faster method to storing a variety of data, allowing IT to keep up with the growing amount of information.
A focus on self-service-centric BI is important because most business users and analysts in organizations will have access to self-service tools to prepare data for analysis. Most enterprises have more power users that will want to analyze data themselves, Lamble said.
Understanding data governance is the path to making sure enterprises have the best security and as data increasing at a rapid rate, the role of the chief data officer is increasing.
Pooled infrastructure reduces hardware costs by creating more opportunities to share hardware, increasing utilization and centralizing procured data.
“Best fit” SQL DBMS means finding the most appropriate efficient and economic database management system for enterprises which brings in the co-existence of waterfall and agile technologies, according to Lamble.
Enterprises should focus on eliminating unnecessary decommissioning, Lamble said, by realizing what a big change updating or consolidating technologies or jobs are.
A Shift from Centralized IT
Corporate IT is an enabler because there is a change in growth of IT professionals and redefining their roles within a company.
“We see a shift in the center of gravity from a centralized IT to a decentralized business unit,” Lamble said.
Which leads into data scientists moving up into IT, Lamble explained.
After Lamble broke down the top 10 cornerstones, Huizenga zeroed in on five data architecture challenges one might face including, the evolution of methods and cultures, adapting to changing architectures, complex data environments, data quality, and business focus.
He presented the evolution of methods such as traditional, present day agile, and present day hybrid techniques that will help companies adapt to changing architectures.
Addressing Complexity
Because big data so complex, enterprises need to address the complexities in a variety of ways such as using metadata attachments and more.
Companies must also look into data quality to make sure it’s accurate, timely, complete, consistent and relevant, Huizenga said.
Lastly, Huizenga said businesses need to focus on all these things and be a champion for data value and quality to establish a data culture and foster business collaboration.
“For a lot of companies it is difficult to implement this,” Huizenga said. “It does take a cultural change in leadership.”