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Best Practices for Modernizing Your Data Architecture

Authors Photo Rachel Levy Sarfin | August 20, 2020

Can your current data architecture handle the massive influx of data that is coming into the enterprise every day? If not, it’s time to think about modernizing your data architecture to ensure you capture and manage one of the most valuable assets your organization has, its data.

6 Modern architecture best practices

You can achieve a positive ROI for your modernization project if you follow best practices and choose the right tools for the job. Here’s a look at how best to proceed. 

1. Eliminate internal data silos

Part of modernizing your data architecture is making your internal data accessible to those who need it when they need it. For many companies, information silos are the norm. That’s both inefficient and a show of poor data management practices.  When data is stored in disparate repositories, people unwittingly duplicate it. Then, no one knows which information is really correct. 

Modernizing data architecture includes breaking down those barriers, then cleansing and validating information to determine that it’s accurate and complete. Otherwise, it’s not useful to the enterprise.

2. Ensure all your data is trustworthy

Another part of modernizing your data architecture is making your data accessible to those who need it, when they need it. For many companies, Integrating, cleansing, and validating data from internal sources is a great start, but that’s just the beginning. Because the enterprise must now rely on data coming from external sources as well, modernizing your data architecture includes ensuring you have a way to ingest data from external sources, cleanse it, de-duplicate it when necessary, and validate it. 

3. Account for different data structures and formats

Gone are the days when your data consisted only of structured data that could be easily analyzed with standard tools. With the advent of big data and cloud computing, the sheer volume of both structured and unstructured data has risen exponentially, and there’s vital information for your enterprise lurking in all that data.

That means that your data architecture should be built to accommodate data from multiple sources in multiple formats, both structured and unstructured. Otherwise, you are missing out on vital information you need to make informed business decisions.

4. Implement solid data governance

Maintaining data quality is an ongoing process and your data architecture must support that process at every step. That means that a part of your modernization plan should be to implement a robust data governance policy for your organization. While many organizations may simply give lip service to the concept of true data governance, it is essential to modernize your data architecture to facilitate strong data governance. In this way, you can feel confident in your data, relying on it to help you make the type of strategic decisions that will give you a competitive edge.

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5. Build for the future

When modernizing your data architecture, you must keep a close eye on the future. You need a solution that scales quickly, that handles the volume of data you have now with no trouble and also has the capacity to handle much more data to come. Consider how the sheer volume of data has grown in the past five years for your organization and then extrapolate what the future will bring.

Build for that future with an architecture that is agile, flexible, and that enables real-time analysis and reporting.

6. Choose the right tools

If you’re embarking on a data architecture modernization initiative, putting the right tools in place is a best practice that enables you to implement the other best practices mentioned here. With years of experience in the helping clients make the most of their data opportunities, Precisely recommends Connect and Trillium DQ for Big Data as an excellent tools for maximizing the business value of big data at scale for the enterprise. 

Connect helps you gain strategic value from all your enterprise data by delivering information when, where, and how it’s needed. It integrates all data across an organization from mainframes, relational and NoSQL databases, the cloud, Hadoop data lakes, and more. You can easily move entire database schemas in a matter of minutes. 

Trillium DQ for Big Data, part of a suite of enterprise-grade data quality technologies that transform raw information into dependable insights, provides flexible, rapid deployment options on-premises or in the cloud. Moreover, it easily integrates with distributed data architecture environments including Hadoop and Spark, SAP, and Microsoft Dynamics. 

Trillium DQ scales to handle big data, so you don’t have to miss out on new business opportunities. Moreover, this solution puts an end to data silos by giving you the power to access data anywhere in your organization and assess its quality. In addition, Trillium DQ lets you put data governance best practices into place to ensure that your information is clean, valid, and reliable. 

To learn more about how to ensure data quality and integrity in the age of big data, download our eBook: Governing Volume: Ensuring Trust and Quality in Big Data