Risks and Rewards of Emerging Data Technologies – Part 1
The Risks and Rewards of Emerging Data Technologies – Part 1
The big fuss over big data is justified: The technology is revolutionizing how businesses approach customer insights, architect data infrastructure, and think about data quality. Whether global powerhouses or local Mom & Pops, businesses have access to information on a scale unthinkable just a short time ago, and the big data trend shows no signs of slowing. In fact, a recent forecast by global market intelligence firm IDC predicts that the big data technology and services market “will grow at a 26.4% compound annual growth rate to $41.5 billion through 2018, or about six times the growth rate of the overall information technology market.”
Sill, while leading big data initiatives within their companies, data professionals must remain proactive about other emerging technologies. Specifically, there are four emerging technologies that are likely to impact data quality and data management practices:
- Cloud solutions (both software as a service and platform as a service)
- In-memory databases
- Crowdsourcing technologies
- Mobile applications
Data professionals should consider how each of these technologies can impact their overall data strategy and improve data quality management, while also being mindful of potential drawbacks.
The cloud is positively changing our view of the world and business, according to Andy Greig of SAP Services Marketing. “Without a doubt, cloud solutions provide greater consistency and standardization… From workload management to interaction, technology can be delivered at greater speed and with greater impact.”
From customer relationship management (CRM) to Human Resources (HR), countless cloud applications are now being used as building blocks across the spectrum of industries and departments.
When it comes to data management, though, the cloud can present a few challenges. Designing for simplicity, ease of use and high data quality standards requires additional functionality, yet most cloud packages offer limited data management customization. Adding more customization reduces some of the flexibility of a cloud offering and increases the total cost of ownership.
It’s critical, then, for businesses to involve data professionals in the selection of cloud solutions. In evaluating, consider the following questions:
- What are the data quality standards that are enforced in the cloud solution? Is there any flexibility in the data model?
- Are they sufficient to maintain the quality of the data?
- What customization is available in the cloud offering to add data quality checks, duplicate record analysis, and automated business rules for enrichment? Can you add modules to the application for this specific purpose?
- If there is limited customization, what data enrichment will be required, and by whom?
- What functionality does the cloud provider make available to ETL (extract, transform, load) the data?
Cloud applications can improve the responsiveness and flexibility of your IT organization while simultaneously slicing the costs typically devoted to maintaining on-premise hardware and software. Given the potential risks to data management, though, make sure that you consider the additional cost of rework or data remediation in the total cost of ownership.
Companies can no longer afford to wait days or weeks to format data and then retrieve answers. In-memory databases allow fast — real-time — evaluations, predictions and adjustments to business processes, and can also drive an improvement in the overall quality of data reporting for a range of businesses. Additionally, recent technology advances are making in-memory databases more affordable. Due to these factors, Forrester declared last winter that in-memory databases are “on fire.”
Businesses using in-memory data platforms to support their mission-critical apps are “more likely to respond quickly to business needs, deliver new products and services, and stay ahead of the competition,” the Forrester report stated.
Most business processes don’t require real-time or daily data quality reporting, but the benefits of in-memory databases can materially affect the outcome of processes like mergers and acquisition data integrations, business and financial forecasting, fraud management at call centers, and capital investment modeling. In the past, it often took weeks to move data from the collection stage to the reporting stage; that batch process would occur monthly or weekly, but certainly not daily or in real time.
This rapid response has a significant impact on governance. To use the fraud management example cited above, external data about individuals can be combined with internal fraud detection algorithms to detect credit risks or potentially fraudulent usage of an account. In-memory databases allow business to exploit this information much more quickly than in the past.
While the business benefits of cloud computing and in-memory data bases are clear, data professionals should be involved in the selection and implementation of these new emerging technologies to ensure the risks to data quality can be managed. In part 2 of this blog, we will cover 2 additional technologies, crowd sourcing and mobile technologies
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