The promise of advanced analytics capabilities is incredibly tantalizing, but actually reaching that level of sophistication can be an uphill battle. So much focus goes into acquiring analytics tools and data science expertise that it's easy for organizations to overlook a critical component in the equation: the actual architectural foundation.
Legacy data warehouse structures are simply not up to the task of processing data as fast and effectively as businesses require. The growing need for real-time data processing capabilities has driven the demand for modern data architecture. A 2015 TDWI survey of 662 data management professionals discovered that 89 percent of them viewed modernization as a pathway to further innovation. But such a massive and complex undertaking can be difficult to manage or even effectively track progress. How do organizations know when they've achieved a truly successful modern data architecture (MDA)?
Centralize data into a comprehensive repository
One of the major roadblocks analytics projects run into is data access—or lack thereof. When pursuing big data initiatives, organizations inevitably pull in large quantities of information from different sources and databases. These circumstances can easily lead to data silos that prevent users from getting their hands on the information they need.
"One of the major roadblocks analytics projects run into is data access."
When data is cut off in this fashion, analytics objectives can stall as stakeholders are unable to incorporate relevant and up-to-date information, resulting in project failure.
Analytics consultant and InfoWorld contributor Andrew C. Oliver explained that one of the first steps to take when modernizing your data architecture is to consolidate everything into a single data repository. This way, big data projects won't become stymied by siloed systems and every analyst and stakeholder will be able to leverage all relevant, available information.
There is one more caveat to keep in mind with database consolidation: to adhere to data security and compliance requirements, repositories should reflect data governance best practices. These include putting into place clearly defined access rules and establishing the proper oversight positions.
Use your user interface
Making data available to project stakeholders won't guarantee they'll actually get the most out of it. AtScale's Joshua Klahr stated that organizations need to go a step further and provide various team members with interfaces and dashboards that allow them to consume data as easily as possible. Depending on a person's role and level of experience, they may have drastically different preferences when it comes to data management dashboards.
Adequately meeting user demands here could require implementing a whole new UI, or simply providing training to help employees get acquainted with a particular platform. Either way you choose to proceed, be sure to do everything you can to make staff members as comfortable as possible with these tools.
Facilitate faster, real-time data processing
The ultimate goal of MDA is to drastically improve data processing times, allowing for real-time analytics capabilities. There are a couple of core ways to achieve this high level of sophistication:
Massively parallel processing
First, incorporate massively parallel processing. As EMC explained, traditional data warehouse configurations are incapable of keeping up with a major influx of real-time data because they have been designed to only handle one record at a time. While businesses attempt to work around that bottleneck, the fact remains that these setups are just not built for the kind of lightning-fast processing that today's advanced analytics projects call for.
That's where massively parallel processing comes in:
"Utilizing massively parallel processing for the data warehouse enables more granular data query, reporting, and dashboard drill-down and drill-across exploration," EMC stated. "It allows analysis to be performed on detailed data instead of just data aggregates."
The other element to consider is in-database analytics. Organizations have historically struggled with transferring large sets of data from one location to another in a timely fashion. This has led to bottlenecks and delays as teams work out how to move information as fast as possible.
As its name suggests, in-database analytics lets stakeholders run analytics within the existing databases. By eliminating data transfer needs, organizations can cover more ground and spend more time running analytics algorithms rather than waiting for records to be shifted around.
Successful modern data architecture transformation won't happen overnight, but if businesses follow the latest data architecture best practices, they can achieve a level of analytics sophistication previously seen only among the industry's leading innovators.