Business leaders require the ability to make fast, accurate predictions and exploit new insights on demand. Consider a financial institution. Timely, accurate predictions could identify a fraudulent transaction before it completes. What about an insurance company? The ability to predict based on customers’ driving records could help with setting competitive rates. In a support call center, rapidly predicting a customer’s needs and preferences can arm consultants with immediate access to recommended next steps for resolving problems.
How can you acquire the cognitive ability to make insightful predictions and stay ahead of your competition? Machine learning.
Simply put, machine learning enables a system to identify patterns in historical data, extract key correlating features of the data and apply statistical algorithms to build models that predict future business outcomes or customer behaviors. Machine learning is a process that can be applied across the lifecycle of a solution — such as cognitive analytics embedded in transactional processing — to continually enhance predictive accuracy.
This ability to learn is a hallmark of cognitive computing.
Meeting the need for on-demand insight in real time
We’re seeing an increasing need among our clients to derive insights in real time. A number of factors contribute to this urgency including the growing pervasiveness of analytics, the rising need for insights at the point of impact, and the expanding amount and variety of data.
Making real-time predictions is not just about how fast you can run an algorithm. It’s also about how current the data is that feeds your machine learning processes. Traditional extract, transform and load (ETL) mechanisms require moving significant amounts of data into a centralized repository — this approach often introduces latency comprising hours or days.
Organizations that apply analytics to ETL data only may miss out on a tremendous opportunity to accelerate delivery of insights that lead to creative business solutions. To make smarter, real-time decisions, organizations need to apply machine learning to their most valuable, current real-time data. This data often resides on a mainframe platform, such as IBM Z, where core business transactions take place.
Creating a data strategy for machine learning
How do you take the next step to apply analytics and machine learning to your analytics infrastructure? Achieving the desired results for your organization requires a data strategy specifically meant for machine learning processes that is tailored to the use case you want to address, such as:
Ensure efficient access to the data that is required for developing the use case. If the aim is to identify customer churn in a banking organization, for example, employees need to access relevant data in the core banking environment.
Take a federated approach by analyzing the most recent data to avoid using a serialized process such as ETL in which all the data needs to be moved and then analyzed.
Provide flexibility to add in new data sources dynamically to the machine learning process as the business needs evolve.
Ensure there is an end-to-end workflow management, particularly after model deployment so that if the model degrades over time, there is a continuous feedback to identify and address.
Use deployment structures that enable you to take advantage of open source machine learning techniques and skills that are prevalent today.
IBM helps clients apply their strategy and unleash their creativity by supporting natively running analytics where the data resides. IBM Z empowers clients with the ability to apply a federated technique to real-time analysis of current data, which can be combined with insights from data residing elsewhere. Another key area of commitment is the addition of runtimes for open source analytics. Our clients can apply open source skills to all of their environments, including IBM Z, and innovate more quickly.
Trusting data and the insight derived from the data
When implementing machine learning and cognitive analytics, trust in both the data and the insights derived from that data is essential. IBM z14 helps deliver on this goal by providing pervasive encryption for data at rest. Using that data at its source preserves the specialized encryption on disk, and the data is protected at the time of its analysis. In addition, using the most current data can yield optimal results with cognitive models, providing insights that can be trusted with a higher degree of assurance than traditional ETL data only.
Only businesses that embrace the most secure, connected and cognitive platforms and systems will win in the digital economy. Learn more about how your organization can implement a data strategy for machine learning that empowers creative business decision-making.
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