Digital Transformation – Double Edge Sword
By Dr. Yingying Kang, 20+ yrs. Data Strategy Leader, Assurant Inc.
Digital transformation can be a double-sided sword for businesses. More businesses realize that converting their business operations into digital channels is the key to keeping their competitive advantage in the market. However, due to the lack of experience and the conflicts with legacy operational processes, shifting to a data value driven organization can bring more challenges than benefits, especially for the first several years. This requires visionary business executives and C suites to introduce state-of-the-art technological, management strategies, and foster a data driven culture while smoothly maintaining the business continuity to achieve successful digital transformation.
The most crucial technology for digital transformation is big data management technologies. Today businesses own terabytes or petabytes of daily operational and transactional data, which represent their business operations. This makes it crucial to possess a connected and resilient data platform to manage the large volume of data and consistently deliver the correct data to the right users at the right moments. A connected data platform connects data across business silos and enables business stakeholders to have a 360-degree view of customer needs, facilitates cross-silo analytics, and provides insights on business operations in real-time.
Implementing successful digital transformation requires the resolution from the entire C suites and executives to foster a customer focus, data driven, and innovative culture.
Good data management strategies are an organization’s roadmap for using data to achieve its goals. Today most businesses have multiple messaging channels like email, call, SMS, and web. Their IT infrastructures are commonly composed of multiple systems like mainframes, data warehouses, big data clusters, high performance computing centers, and cloud services. Businesses need a platform with prompt data sourcing, easy accessibility, and concise explain-ability to enable omni-channel and multi-messaging channels and make business processes extendable and manageable. At the same time, the enriched messaging channels introduce a large amount of unstructured data that requires Artificial Intelligence (AI) technologies to simplify managing and to analyze these unstructured data.
Based on a big data platform, Machine Learning (ML) empowers business process management by applying advanced computer algorithms to automatically improve prediction accuracy. Many business decision makers benefit from ML to perform time series forecasting, stream through underwriting, product segmentation, digital marketing, dynamic pricing, and channel management. More businesses use AI and ML to automate and prioritize their business processes, enhance cost avoidance, and release business experts from repeated tedious operations. Automation allows individuals and teams to focus on more intelligent decision making or highly skilled operations. For example, airports use biotechnologies to automate security checks and luggage management. Retailers utilize Augmented Reality (AR) and Robotic technologies along with ML and AI to merge online and in-store experiences, facilitate inventory and manufacturing processes, and product selection and check-out operations.
However, when the businesses adopt the new technologies, it requires them to have a solid plan to maintain the business continuity while achieving successful digital transformation. A common challenge for organizations is that their legacy applications, legacy practices, and legacy hierarchies create blockers for business transformations and make it difficult to measure the health of the entire business process. The business stakeholders have to rely on Subject Matter Experts (SMEs) to lead business decisions and be the liaisons with customers and between business units. Customers have no effective channel to share their experiences or track their service processes. Without an effective customer feedback mechanism, the consequences will typically not be detected until the hard lessons are learned, or severe financial losses occur. Businesses need data driven processes to gain firsthand knowledge of their customer needs and competitive advantages and use them as the success criteria to measure each business process instead of individuals’ decisions. It requires the entire executive team to be committed to shaping data-driven culture, setting up effective metrics systems, building healthy operations processes, and adopting new technologies and management strategies and approaches.
Another typical challenge for digital transformation is that there is no reference to whether a new operational strategy will bring benefits to businesses, or how it should be implemented. The classic ML algorithms need a large amount of data to learn the patterns and trends and identify anomalies. Since 2015, Deep Mind published a series of articles on Nature to introduce AlphaGo, a computer Go player, which won the world class champion Go players applying Deep Learning (DL) technologies. A major advantage of DL is that it owns reinforcement learning capabilities. The learning can be supervised, semi-supervised, or unsupervised. Recently transformers and transfer learning technologies made learning under data scarcity become even more feasible. The fast growth of supercomputers and computer clusters further empowers DL algorithms to solve complicated computing problems in milliseconds. According to Forbes, 76% of enterprises will prioritize AI and ML over other IT initiatives in 2021s.
Collaboration and knowledge sharing are the secret to businesses’ success in gaining fast growth and leading the market. Implementing successful digital transformation requires the resolution from the entire C suites and executives to foster a customer focus, data driven, and innovative culture. It is critical to transform the business operations into data driven processes that are transparent, measurable, analyzable, and responsive. This enables AI and ML to automate decision making processes and augment Business Process Management (BPM) initiatives. A recent Salesforce Research report found that 83% of IT leaders say AI & ML is transforming customer engagement and 69% say it is transforming their business. It is crucial to shape a data-driven and collaborative culture to achieve success. The lack of effective support from IT and businesses is a typical reason that AI solutions fail the complex and uncertainties of business operations. A successful AI solution requires a learning loop between AI and the business environment to allow new trends and scenarios to be continuously exposed and learned. It allows AI solutions to react fast and empowers business experts to be more knowledgeable and intelligent. This cannot do without the active collaboration between business, technology, and science teams, and the seamless collaboration between customers, employees, and partners.
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Author Bio: Dr. Yingying Kang serves as the Head of AI and Data Science at Assurant Inc. Before joining the InsurTech business, she had accumulated over 20 years of success in shaping data-value driven culture, strategies, and solution deliveries, with 10 years of AI/ML focus. She earned a Ph.D. in Mathematical Programming with 15 years of Optimization Modeling and Algorithm Design experiences, and additional 10 plus years in implementing large-scale Service Oriented Architecture based systems. As a visionary thought lead with a successful track record of cultivating Artificial Intelligence, Data Science, Data Engineering and Data Management teams, she and her teams have monetized over 50 AI and Machine Learning solutions which created over $50M cost avoidance and value increase. She has highly accomplished in strategic planning, leading across departmental collaboration, communicating with C Suite and executives to propose technological strategies, drive innovations, and lead successful deliveries of batch and real-time AI and Data Science platform services and persona driven solutions in InsurTech, TravelTech, Transportation, Retail, Software/IT, Social Media and Enterprise Management Solutions (ERP/PLM/SCM/CRM).