AI Agenda: Data Integration Checklist
Exploring the potential of big data and AI and revealing important facts.
Data Integration Checklist
Are you exploring the potential of big data and AI? A 2019 executive survey by data advisory firm NewVantage Partners revealed two important facts about big data and artificial intelligence (AI).
Why are success rates declining when the pace and amount of investment is on the rise?
Much of AI success depends on the data, and therein lies the problem. Data lives in different formats, structured and unstructured, video files, text, and images, kept in in different places with different security and privacy requirements, meaning that projects slow to a crawl right at the start, because the data needs to be collected and cleaned. As a result, organizations need a strategy in place that includes a well-defined process and critical technologies for maximizing the value of the data being ingested.
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5 Steps Supporting Successful Data Integrations
1. Identify stakeholders across the organization
- Establish responsibility for data governance
- Pinpoint departments to be involved at the start
- Choose a department to act as the ‘test pilot’
2. Evaluate readiness and potential pain points
- Review data solutions that are currently deployed in the organization
- Evaluate which ones still have value and which ones should be retired or replaced
- Determine which manual processes can be automated
3. Conduct a data audit
- Classify existing data by importance and type, i.e., structured or unstructured
- Ascertain how much time is spent on data wrangling
- Determine if there are gaps in the datasets and tap third-party sources to help fill those gaps
4. Define your Master Data Management (MDM) strategy
- Set rules for data use and access throughout the organization
- Build a flexible data management platform to handle changing MDM requirements
- Outline the processes and people needed to achieve measurable results
5. Identify the right platform and tools for continuous improvement
- Look for a platform that is scalable to match organization growth
- Determine if users require real-time data
- Ensure the platform is flexible enough to add new data sources and types as organization needs change
How can Nexis® Solutions help?
Search and Retrieve APIs
RESTful APIs
Bulk APIs
Constant Call APIs
With an unmatched global content collection enhanced through normalization and metadata enrichments, Nexis® Data as a Service (DaaS) delivers the highly relevant, archival and current data organizations require for a broad range of artificial intelligence applications.
Nexis Data as a Service offers access to current and archival data via a variety of application programming interfaces (APIs).
Frequently Asked Questions
Answers to some popular questions
What steps are important for successful Data Integrations?
- Identify stakeholders across the organization
- Evaluate readiness and potential pain points
- Conduct a data audit
- Define your Master Data Management (MDM) strategy
- Identify the right platform and tools for continuous improvement
What is MDM strategy?
- Set rules for data use and access throughout the organization
- Build a flexible data management platform to handle changing MDM requirements
- Outline the processes and people needed to achieve measurable results
Get in touch
E-Mail: information@lexisnexis.com
Telephone number: +31 (0)20 485 3456