Organizations are drowning in data right now.
This data is coming from all kinds of sources. It’s generated by consumer surveys, sensors on smart objects, your CRM, consumer behavior on websites — every business is generating more data than ever before.
In fact, according to IBM, every day, worldwide, 2.5 quintillion bytes of data is created and it’s only going to increase. For example, one big data generator, the Internet of Things (IOT), is still in a period of growth. The International Data Corporation (IDC) Worldwide Semiannual Internet of Things Spending Guide predicts that worldwide IoT spending will surpass the $1 trillion mark in 2020.
Having access to this data is wonderful, but there’s a problem: often organizations don’t know what to do with the data their technology is generating. They may have invested heavily in the IoT, for example, but get lost when they try to manage, analyze and understand the data.
The human brain isn’t designed for data management
People are great at things like creativity and decision-making. But when it comes to taking in huge amounts of data, making sense of it and making decisions based on that data, the human brain falls short.
That’s why technology like Machine Learning (ML) and Artificial Intelligence is so important — machines are good at following rules, can analyze data fast, and never get tired, so their decision-making skills don’t suffer.
ML, for example, makes it possible for machines to take in a lot of data, see patterns in that data and make suggestions to human workers, based on that data. Meanwhile, AI takes in massive amounts of data, analyzes it and actually makes decisions based on that data.
In other words, AI can help you maximize your investment in big data and the Internet of Things. In order to make sense of data and make good decisions and sound recommendations, most AI and ML requires data to be structured — in tables, for example — which is how a traditional database stores information.
But what if your business pulls data from a wide variety of sources?
That’s where Watson comes in.
IBM’s Watson and Your Business Intelligence
You may remember Watson from Jeopardy!, but IBM’s AI platform has evolved since then and has been developed to serve almost every industry.
Watson is an AI that analyzes and answers the sorts of information humans produce for each other — like books, social media posts, reports and articles. Unlike other AIs, Watson can understand natural language — including grammar and context. Because it understands language, Watson can analyze information from a wide variety of sources and make decisions and recommendations based on all of that information.
Watson doesn’t do all these things automatically — it is trained by experts, who tune the AI by creating a baseline of relevant questions and answers and by feeding it a body of relevant knowledge.
Case in point: H&R Block started using Watson to help prepare taxes in 2017. This version of Watson understands every nuance of the tax code and all relevant changes to tax law. Watson combines that knowledge with other data points —including the initial verbal conversation a customer has with the Tax Pro who is helping them prepare their return — to make suggestions about tax credits and deductions.
Watson is also being used to help insurance companies process claims more quickly — Watson is taught the complex rules that govern claims, then given the information collected by insurance employee about a claim that was filed. Using its knowledge of the specific claim and insurance policies, Watson quickly makes recommendations about whether the claim is eligible.
It’s not just fields with complex rules that are using Watson — one company is using Watson simply to retain institutional knowledge. Woodside, an energy company, was losing years worth of expertise when its long-time employees retired. Engineers were spending a lot of time trying to research problems — more time than they spent fixing those problems. Woodside trained Watson by having it analyze 600,000 pages of documents, including reports and correspondence and by enlisting the help of both employees and retirees, to teach the AI the language and jargon of the employees. Time spent on research has since been reduced by 75 percent — Watson has given Woodside easy access to its own institutional information.
Managing your data with Watson
You’ve invested in your people, your IoT and your software, all of which is generating a virtual firehose of data, as are your social media accounts, interactions with your customers and all your organization’s internal data.
You know some of that data will be valuable to you. What do you do with it and how you find a useful pattern in the chaos? We can help you answer that question.
By using Watson, you can find a signal in your unstructured data that will help you solve your business challenges intelligently and maximize your investments in technology and people.
If you’re interested in learning more about implementing Watson, contact us today.
About the Author
Veera Budhi is an analytical and highly adaptable management professional with 22+ years of extensive experience enhancing business outcomes across enterprise. Skilled in aligning end-user needs with long-term resolutions to complex business challenges. Track record of success in strategizing the plan, architecting the solutions, leading development and implementation teams aimed at improving quality and efficiency at organizations. Advanced expertise in occupying leadership roles in all facets of Sales, Software development, project and product life-cycle management. Accomplished communicator skilled in building and strengthening relationships across functions to drive cohesive, strategic operations.
Saggezza is a proven technology and consulting partner that delivers personalized, high-value solutions to accelerate business growth.