How to Deliver Mission and Business Outcomes Using AI
Today’s organizations are creating enormous amounts of data every minute and need a way to not just organize it but to also understand where it’s going, how it is moving and what it all means to their business. Understanding and being able to process your data and glean insights into how to improve workflows and increase efficiencies can lead to better workforce utilization and a better bottom line.
Not surprisingly, there are many challenges when it comes to doing this. If you’re like most organizations, your most common challenges may include:
- Too many data sources across silos – interoperability is a common issue across governmental organizations, in particular
- Data at the edge, on-premises and multiple cloud environments – various homes for data can easily create silos and the inability to communicate across them or allow data to flow from one location to another
- Lack of trust in quality – if you’re not sure if the data is accurate, it’s much more difficult to make business driving decisions
- Enterprise-wide resistance to change – if your resources don’t trust the data they have, it’s a lot harder to adopt a system of change in a positive direction
- High cost of regulatory compliance – it can be costly for your organization to not meet the standards of regulatory entities or federally-issued mandates
With massive amounts of data sitting in multiple sources and the need to gain business-driving insights, organizations are looking for a way to extract the data and process it. It is becoming common for organizations to use Kubernetes as a way to gather, store and scale this information. Kubernetes allows space for these massive amounts of data so that organizations can then write algorithms to feed into AI that crunches through the data far faster and more accurately than humans can. It recognizes patterns as it goes, thus unveiling trustworthy insights for decision makers to make improvements to the business based on patterns shown from the AI processing.
Dell Technologies has created a standardized infrastructure made up of the combination of VMware’s Cloud Foundation, a Kubernetes control panel and bitfusion virtualized accelerators. Together, this technology can answer the questions many organizations have like what software an organization needs, what hardware they need and how they will organize and operationalize that data once it comes in. Using this standardization model can speed the adoption of all the necessary pieces and put AI to work more quickly. Intuitively, this is helpful for organizations because it means solutions are offered faster and more business goals are met.
So what outcomes can businesses and organizations expect when introducing AI into their data storage and processing? In short, it solves for the challenges mentioned above. But specifically:
- Security and governance – ensure data privacy and compliance to industry regulatory standards critical for business use cases
- Ease of use – democratization of data and the automation of AI lifecycles empowers all data users to be innovators
- Data quality – build trust of AI across the business, which will increase its adoption to enable more use cases and solutions
- Collaboration – allowing data to move between sources breaks down silos which leads to a boost in productivity, accelerating time to market
If you would like more information on Dell Technologies’ AI capabilities for your organization, click here. To learn more about how Iron Bow’s decades of subject matter expertise can help with project adoption, please contact us.
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