So, you’ve decided to upgrade your on-prem data warehouse to a cloud-based, modern data platform. Congratulations!
You recognized your old system had become slow and results from it were inconsistent. It was also contributing to the culture of siloed data throughout the organization, frustrating data users by forcing them to work from multiple versions of the truth when analyzing data. And you’re thrilled at the prospect of keeping and analyzing all your organization’s data – from sales and marketing data, to website stats, to information from next-generation IoT and edge devices – in one place.
But for all those new data types to be of significant use organization-wide, a great deal of behind-the-scenes data quality work has to take place including data cleansing, de-duplication, verification and the unification of disparate sources into a single version of the truth.
And that’s where data integration in a modern data platform comes in.
Data integration is the blending of different data from different sources, so users see a unified view of all relevant data (despite being different types, or residing in different places, or generated within various departments or business units) instead of just one source or type. Without a proactive data integration and governance strategy, it’s possible – probably even likely – your organization is making decisions based on bad or incomplete data.
But as in any project where considerable time and expense is involved, it’s always important to ask the big questions up front.
That’s why you should download our definitive data integration checklist. Print it out. Post it beside your whiteboard. Use it from the outset of your project to make sure you don’t forget anything important until it’s too late, including:
- Identifying key players and stakeholders
- Evaluating your current data, process and infrastructure landscape
- Auditing your current data
- Defining your data quality strategy
- Identifying the right platform and tools for your organization’s needs
These are just a few of the boxes you’ll need to tick to achieve true data integration using a modern, cloud-based data platform. Integration within cloud-native systems is more easily managed through the automation of data quality processes such as cleaning, de-duplicating and verification, while OpEx is minimized by performing transformations outside the data warehouse. This makes your data more valuable to every business unit in the organization, ensuring decisions are based on rock-solid foundations instead of siloed datasets.
You can rely on Pythian expertise
No matter what’s driving your data integration initiatives, Pythian experts can help you plan and architect the best solution for your specific needs and budget.
Take the first step on your journey toward useful and accessible data organization-wide by downloading our Definitive data integration checklist. And if you have further questions, please contact us today for a free assessment.
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