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New demands of data require balanced approach to migration and integration

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Data teams tasked with managing ever-increasing volumes of data are encountering challenges that traditional technologies often fail to meet. With data scattered across systems and accumulating rapidly––increasing information gaps within organizations––its value can only be fully realized through a comprehensive view. 

That “big picture” becomes more elusive, however, when migrating data––an investment that has quickly become commonplace, largely due to the pandemic escalating digital transformations across all industries. A 2021 IDC survey of database professionals found 63 percent of organizations are actively migrating data to the cloud, while another 29 percent plan to begin migration within the next three years. 

For the remaining 8 percent who are considering migrating to cloud databases, the writing seems to already be on the wall. But, with the compounding effects of demand for data migration and the rising expectations of what companies can get out of their data, haste to adopt these modern approaches comes with a variety of pitfalls. After migration to the cloud, performing data integration can make your data work together. Adopting architectures like a data lakehouse can increase data performance and decrease business information gaps, allowing data teams to get the most of a rapidly growing and increasingly complex data supply. But competing in the digital economy requires a balanced approach. 

Striking a balance

Data teams are accustomed to devoting the majority of their time to preparing data, leaving relatively little time for performing analysis, a cultural norm in the data world that is often referred to as the 80/20 rule. While spending 80 percent of their time on preparation and 20 percent on analysis can be viewed simply as an annoyance for data teams, these impacts of spending vastly more time on preparation should be viewed as an organizational issue, or even failure. 

Turning that raw data into valuable and actionable insights is complicated by the fact that it’s coming from an increasing variety of sources, often trapped in data silos and separated by barriers and conflicting applications. Data integration can help create a more efficient data infrastructure that can be leveraged for reliable results more easily and more quickly.

Efficiency in this process can be the competitive edge a company needs in the marketplace. Improving collaboration within the organization can be the first step toward shifting the balance of data analytics. Devoting time to training and appointing data stewards adds to the company’s data infrastructure, because integrating data is not a one-and-done process. It requires a regular commitment, which is why it’s all the more important to handle it efficiently. The overwhelming move to migrate data only accentuates the impact of any inconsistencies in the data down the line, as it’s being relied upon to inform critical decision-making.

The move to migration

When moving to a new home, we gather belongings from different rooms and consolidate them, ensuring they’re accounted for and organizing them in a fashion that will make them easy to put to use when they get where they’re going. 

Much in the same way, data integration supports the process of data migration. Simply migrating data without integration would be akin to preparing for a move by tossing each of your belongings individually into the back of a moving truck. Whether you can find and make use of those belongings once you arrive is anyone’s guess. 

Just moving data doesn’t make it any more useful without a detailed integration process, something more companies are starting to realize. There needs to be a plan and integrated approach to data migration where data teams are able to utilize processing––like that within a  data lakehouse––to ensure teams are operating on the same wavelength and able to access data in one centralized spot, leading to faster time to insights. The same IDC report found that two of the top three listed concerns among data team leaders were adapting applications in the cloud and converting data from on-premises locations to the cloud. However, there are better capabilities more suited to on-premise data architectures than the modern cloud data platform. So, if you’re looking to analyze data in the cloud, look for a cloud-native solution that supports a slightly different take on the data transformation stage––instead of some ETL solutions, which are lagging behind.

Furthermore, understanding how all of your raw data interacts with existing processes within the business. Utilizing those trained data stewards can help avoid breaking any existing business processes during the migration. Clean, clear, and transformed data is critical to shortening that preparation window, to give those data insights time to bloom.

Competing in the digital economy

Whether you’re simply moving data from on-premise to the cloud, or implementing a data warehouse, data integration is quickly becoming a standard part of managing your data. To ensure you’re turning that data into action as quickly and efficiently as possible, it needs to be reliable enough to stand on its own. 

Gone are the days where cloud migration simply meant running the same workloads in a new remote environment. Now, companies are finding newer and more efficient ways to bring that raw information into unique action. Knowing what suits your company’s particular needs, however, can be a challenge that may set you on the wrong path.

The most common concern cited in the IDC report, even above migrating applications and data to the cloud, was simply choosing the cloud database that best suits their needs. Organizations fear being locked into a particular system––especially in today’s digital-savvy environment––because they have seen how applications in their current set-up can conflict and cause problems. 

Finding the approach that works for your data is an ongoing process, so establishing systems to adapt and adjust are the difference between good insights and great insights. Preparing data to move to the cloud and for AI and machine learning is a critical step, but committing to a comprehensive system can ensure that not only is data found, organized, and cleaned up, it can be analyzed quicker.  Those data insights are the currency of the future and building an adaptive system today can pay significant dividends down the line.

Dave Langton, VP of product, Matillion

Dave Langton is the vice president of product at Matillion, a leading data integration platform. He is a seasoned software professional with over 20 years of experience creating award-winning technology and products. Prior to his role at Matillion, he worked as a data warehouse manager and contractor in the financial industry.