Intel Corporation describes how advanced analytics can help drive business transformations and empower workers across a diverse range of use cases.
How many decisions have you made at work today? How many of them were based on concrete information, and how many on your own expertise, experience, and gut instinct? Which ones were time-consuming administrative decisions and which was focused on developing or furthering your business strategy?
As evolving technologies like the Internet of Things (IoT), social media, and next-generation networks exert more influence on the economic landscape, the pace of business is speeding up. That means you need to make your decisions faster than ever before, but there’s also less room for error. The explosion in data volume offers an invaluable resource to help inform and improve decision making, but extracting the relevant insights in a timely and cost-effective way now becomes a challenge. At the same time, it’s essential to keep an eye on the bigger picture and ensure the decisions you’re making will not only help the business operate smarter today but that it will also stay ahead tomorrow.
Luckily, as data has grown in volume and complexity, the capabilities of analytics technologies to make sense of it have also been developing. Now is the time to consider how advanced analytics techniques such as artificial intelligence (AI) and machine learning can help drive transformation across your business and empower your people to focus on innovation and adding value.
Where you start is up to you, but make sure it’s in an area that will bring benefits to your organization relatively quickly. You can get some food for thought from this new eGuide, which outlines some of the successful AI initiatives that we have been running ourselves at Intel, including:
1. Product Design and Development
Being the first to the marketplace with a product innovation can bring huge advantages. The quicker you launch, the sooner you can start reaping the benefits. One of the most common bottlenecks in development can be the process of validating a prototype. It’s essential you get it right before you start mass production, so corners can’t be cut. But doing a thorough job often requires costly hours of tests, checks, and more tests. A combination of machine learning algorithms can help significantly speed up the process by imitating and supporting human validation capabilities. This can also have a positive impact on quality.
2. Manufacturing and Quality Control
Once a product line is in production, it’s essential to maintain a strict quality control (QC) process across the supply chain to ensure standards remain high and the customer experience consistent, even as customer bases grow, product portfolios diversify and geographic coverage expands. Each product may need its own QC process, with multiple steps and tests at each step. With AI and machine learning, you can build automated processes that constantly monitor how each test performs and the value of the results it generates. If a test turns out to be unnecessary at a certain step, it can be eliminated, helping to keep the production process as streamlined as possible.
3. Sales and Marketing
Whether they’re buying a t-shirt, a washing machine or a mortgage, customers today expect a more personalized level of service. They want to receive relevant, timely information and offers and they demand a consistent, integrated experience across mobile, online and in-store. Organizations that can effectively understand and communicate with their customers on an individual basis can boost customer loyalty and spend. Advanced analytics can help by pulling together disparate data sources – from internal CRM systems to social media and other public sources – to build a multi-layered, in-depth profile of the customer. Then a set of machine learning algorithms that imitates human perception and decision making helps to personalize and optimize the customer experience. For example, it may identify triggers to initiate a conversation with the customer or automatically support sales associates while they are speaking to a customer. This may include offering the most relevant products to meet their needs, personalizing benefits to promote the customer’s business and identifying relevant promotions and complementary products to suggest.
4. Healthcare and Pharmaceuticals
Developing new medications can be hugely expensive and time-consuming as the essential clinical trials often rely on manual efforts to gather and analyze data. Research teams must rely on face-to-face catch ups with each patient to assess how a trial drug is performing, which can create inconsistencies and limits the amount of data that can be captured. However, by providing a patient with a wearable device to track key metrics such as mobility or heart rate, researchers can create a constant stream of real-time data for every patient in the trial. Using advanced analytics they can also pull insights from this larger, more detailed body of data to generate more insights more quickly. As a result, decisions can be made faster and more confidently, helping speed time to market, improve expected outcomes and reduce the cost of a clinical trial.
5. Internet of Things
Processes and systems using IoT are ripe for advanced analytics due to the huge volume and velocity of data they can produce. They also provide a lot of opportunities to automate many tasks that today can take employees’ focus away from their core roles or from more value-adding initiatives. For example, by identifying pieces of equipment in a production line that are likely to fail soon, an IoT solution can help ensure maintenance is carried out to prevent a breakdown that could cause costly delays in production or even negatively impact the quality of a finished product.
Learn more about how advanced analytics can help you transform your business by reading this new eGuide from Intel.