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Blog

Using AI to Enhance a Customer’s Experience

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GBM Executive

The world has changed! We as consumers are driving that change. The speed at which I can “self serve” myself with information from apps on my smartphone now directly affects, what I will do, how and when I will do it.

Organisations that have been in business for more than 15 years have to change their business models as the behaviour of consumers change. They are all faced with a deadlock of Legacy data, applications and products. Transforming into an “agile” “modern” business can seem incredibly difficult but with a considered step by step approach, it is manageable and we at GBM have a practice that directly addresses this transformation challenge.

Build a Data Practice

Companies moved to the digital economy to expand their scope and grow their businesses. We’ve always known the importance of networking at business lunches and parties. But in business, networking intelligence is vital to survival. All of this online activity generates a lot of data, and it’s important to control your data regardless of its location.

For a long time, I thought that data governance (DG) was a fancy term someone made up to sound important. The goal of DG is to manage the accessibility, discoverability, security, and integrity of enterprise data regardless of its location or form. Aha! That sounds like the answer to the data control problem!

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Image from Forrester on the benefits and disruption of data governance.

That’s what a lot of people think, but it’s only the first part of the answer. Being able to analyse your data and pull value from it is another part. This is why so many organizations are turning to artificial intelligence (AI) to help. For example, you can turn to solutions such as IBM Db2 Big SQL to help manage the quality of data within your entire organization. Now if only I could use DG and AI to help me remember where I parked my car at the airport!

David Chancellor-Maddison, AI and Cognitive Analytics Practice Leader, GBM, explains how GBM is leveraging the power of artificial intelligence and analytics to create “hyper-personalised” experiences for its customers.

Fortunately, companies like GBM are helping their customers build true data practices to control their data, understand where their data is (i.e. within legacy applications or across cloud providers) and then access that data securely—the true nature of data governance.

Bottom line: it’s vital to build an ethical data practice that looks at how you access and work with your data.

Build an Analytics Practice

Next, you need to perform deep analytics on your governed data to find the hidden value within it. The goal here is to understand the business trends leading up to this point, predict what will happen going forward, and determine the best course of action to meet your business needs and those of your customers. For example, solutions such asIBM SPSS Modeler helps to uncover data patterns, and gain predictive accuracy to help in decision making. Your data analytic practice should occur at the intersection of technology, people, and processes.

In a nutshell, your analytics practice tells you what to do with that data, how to act on it, and when to act on it. Sometimes when you look for a pattern in your data, you’ll find it simply because you’re looking for it, or want to find it. Being objective with your analytics to find the real truth in your data is much harder, and isn’t something you can do alone..

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Build an analytics practice and platform to support your data lifecycle across your business.

In a nutshell, your analytics practice tells you what to do with that data, how to act on it, and when to act on it. Sometimes when you look for a pattern in your data, you’ll find it simply because you’re looking for it, or want to find it. Being subjective with your analytics to find the real truth in your data is much harder, and isn’t something you can do alone.

Build an AI Practice

Next, lets put your data to work, which means you need to actually do something. We can spend hours looking at data subjectively to find hidden meaning, but let’s not kid ourselves; we’re not smart enough! AI promises to automate the otherwise manual procedures needed to analyse and act on data, and do it subjectively.

The goal is to combine the data and anyticals, hyper-personalize it for your customers, to determine your customers’ needs and predict when they have them.

At the later stages, it pays to “open up” your data to an ecosystem of collaborating partners via APIs across your organization to expose and consume data and business processes for an end-to-end infusion of AI in all your business practices. Be at the cutting edge of technology with an AI platform that leverages innovations such as image recognition, natural language interfaces and new customer experiences through the use of chatbots.

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The full data cycle combines your data, governance, analytics, and AI platform to uncover new business value and growth.

Your analytics practice will follow a maturity curve. Start small today, get your reporting right then grow your maturity level into predetice and ultimately prescriptive analytics, leveraging tools and expertise early on. As your analytics practice matures it automatically lends itself to using machine learning when enables you to build your own AI, defining new machine learning algorithms, and advancing your data science organization.

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At the later stages, it pays to define APIs across your organization to expose and consume data and business processes for an end-to-end infusion of AI in all your business practices. Be at the cutting edge of technology with an AI platform that leverages innovations such as image recognition, natural language interfaces and new customer experiences through the use of chatbots

Where to Go Next

Successfully integrating digital solutions and technology partners into your business can give you the edge by creating new ways of working, improving efficiency, and accelerating growth. Can you trust your AI?! Does it embody your own bias? That’s where the right technology partner will help. GBM is that partner, and AI infrastructure from IBM is an excellent platform to build upon for the future.

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GBM is a provider of complete solutions that help you advance your use of AI and analytics and machine learning. Integrate data into one trusted view of your business, manage risk and compliance, reveal data insights, and then apply customer-oriented processes. GBM is your experienced partner in planning, deploying and maintaining the right approach to data analytics.

Contact us now if you would like to talk about your AI practices and how we can help you build an AI practice.

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