The topic of Artificial Intelligence is everywhere in the business world. From the water cooler to the boardroom, terms like Chat GPT and machine learning are making their way into almost every business conversation on a daily basis. With inflation, labor cost increases, and new technology, efficiency goals have been pushed past growth objectives for many organizations and AI is being touted as a veritable cure-all.
According to Forbes, “64% of business owners believe AI has the potential to improve customer relationships.” So why is it so hard for companies to get it right? The answer might surprise you.
Companies today are investing huge amounts of money into AI. A CompTIA article reports that 91.5% of leading businesses invest in AI on an ongoing basis. They expect AI to function as a miracle tool, interacting with customers and providing insights and solutions where and when it’s most helpful. The problem is, if you don’t know where to focus your tools, you’ll end up building a bridge to nowhere. As with anything, it’s garbage in, garbage out.
The number one challenge we see is that organizations don’t know what problem they’re trying to solve. Take IBM’s Watson Health for example. Watson was a promising AI application with potential far beyond anything we’d previously seen. Watson won Jeopardy after all! Soon Watson was the centerpiece of IBM’s AI strategy, and the primary focus was healthcare.
Then came the big claim—IBM’s moonshot if you will: Watson was going to cure cancer. There was fanfare and sighs of relief that could be heard worldwide. Spoiler alert: Watson didn’t cure cancer. Why? Cancer was too big. The AI was pointed at too large a target. Cancer is incredibly complex, with each type of cancer having countless variations and underlying genetic causes.
Nirav R. Shah, the Chief Medical Officer at Sharecare indicates, “AI can work incredibly well when it’s applied to specific use cases … AI can work well when there is uniformity and large data sets around a simple correlation or association.” But the idea of cancer was just too broad and unfocused. Watson didn’t know what problem it needed to solve. Ultimately, IBM sold Watson Health and is working to refocus its AI efforts.
More often than not, the companies we see that are struggling with where to focus their AI efforts can map those struggles back to the lack of a strong data strategy. They’re solving the wrong problem by not asking the right questions. Businesses need a customer-first data solution. One that’s solving needs, not merely adding software and platforms. At G2O, we’ve successfully helped organizations focus their data strategies, so AI can have a meaningful impact on the key challenges they’re trying to solve.
There are four components of success when it comes to making data a strategic asset for your business.
Frame a Data Strategy From Business Needs
Data can only help move your business forward if it’s solving the right problem. Data without an aim is like a shot in the dark. Understanding what your business goals are is essential to framing a data strategy. Only then can you put your data and analytics to work for you.
A few questions to ask yourself are: What goals do we want to accomplish? Why are these areas a priority? What issues are you hearing from your customers that could be areas of focus? Until you have clarity on those questions, your data won’t be as efficient and you won’t be able to fulfill the potential of any data platform.
It’s also important to understand what you already have. Every organization has data, even if you don’t realize it. Assessing your current data environment allows you to find gaps between your goals and your current environment.
Starting with your business needs and your current data assessment you can create a roadmap for investment in data that will create real value for your organization. A Data Strategy Roadmap is what sets truly data-driven companies apart from those who are simply gathering inputs. Your strategy needs to include what you can do in-house, what you should outsource, and what you should buy.
Once your roadmap is set, develop ROI estimates based on your roadmap so you can see how much your data will return to your business and ensure it will grow your bottom line while growing your insights. This is also an opportunity to establish test scenarios within your roadmap for how and where AI can help you realize efficiencies and scale once you have the right data strategy in place.
Build a Data Foundation to Generate Insights
In a data-driven world, where everyone is invested in data in some way, the differentiation comes from extracting the insights from that data to make informed business decisions. A data platform built from a solid roadmap will support insight generation and is a must for companies that want to deliver the right data to the right team members to make the right improvements toward the goals set forward in your strategy.
Establish foundational governance practices and security models to ensure your modern data platform can be utilized in the most efficient manner. Include all stakeholders in your initial planning to ensure a strong partnership between all departments and divisions involved. Establish a clear project owner and roll out the plan in a way that ensures your team is prepared for the shift.
Create and Embed Insights to Fuel Efficiency and Growth
Capitalizing the robust insights you’re gathering in the right way will allow you to unlock the right data at the right time to drive analytics, encourage business decisions, and create personalized experiences.
Synthesizing your data into actionable insights allows you to cut through the clutter and set a plan of action for how to use your data to move your business forward. But synthesizing that data is no small task. This is where AI can come into play, automating routine tasks and saving significant costs by streamlining manual processes and reducing labor costs.
A Forbes article tells us “AI-powered tools can analyze vast amounts of data quickly and accurately, providing insights into customer behavior and preferences that can inform marketing strategies. This includes analyzing customer data from social media, search engines and customer reviews. By automating this process, businesses can reduce the need for human staff to analyze data manually, saving time and money.”
Enable Change Management Toward a Data-Driven Culture
To foster a data-driven culture, it is essential for individuals at all levels of an organization to participate. This includes top executives, managers, and employees across various departments, ranging from hourly production workers to sales personnel. Embracing a data-driven approach ensures that everyone is aligned in order to even begin a data strategy.
We recommend starting by working with business stakeholders to reframe your business problems and challenges in terms of explicit outcomes, with definitions of targeted business roles and decisions that will be required, and a clearly established, measurable metric for the impact on the bottom line you plan to have.
Next, it’s essential to have the right people who can mine, analyze, and leverage your data correctly. Create poly-skilled pods to deliver on your plans with target KPIs. Allow them to do multiple jobs together to increase efficiency. If those skills don’t exist yet within your organization, provide training to upskill teams and recruit against gaps.
Partner For Success
Finding the right partner can make all the difference. At G2O, we have four distinct solutions to make data a key strategic asset in your business.
1. DATA STRATEGY ASSESSMENT
A Data Strategy Assessment prioritizes data based on the customer challenge and business need you’re trying to solve to produce real ROI. At G2O, we assess your stakeholders’ needs as well as your current data environment to create a model and framework recommendation to make the most of your data.
2. PLATFORM MIGRATION
We lead and execute the migration of legacy data platforms to modern Data & Analytics Architectures. We’ll help you plan for the migration and guide you along the way, so you’re prepared to take the reins on day one.
3. END-TO-END ANALYTICS
We translate business initiatives into analytics needs, deliver complex data engineering, and develop analytics layers to put data into action for your business.
4. CX ANALYTICS
We leverage our deep data & analytics expertise and capabilities to enhance and complement your CX initiatives.
Accelerating Your Success
We offer accelerators in Data Management Framework, Automated Data Integration Pipeline, Reference Data Architecture, and our proprietary automation tool, Consensus. Our proven accelerators reduce implementation time and ensure best practices are followed.
Contact us to learn how we can help transform your data into a strategic asset.