Using Data and Analytics to Win at Digital Transformation
By Ajay Khanna published in Database Trends and Applications at http://www.dbta.com/Editorial/Trends-and-Applications/Using-Data-and-Analytics-to-Win-at-Digital-Transformation-125507.aspx
Today’s business landscape is more dynamic than ever. Stringent regulations, new revenue models, and high customer expectations are forcing organizations to evolve or face being overrun by more nimble competitors. Chief data officers (CDOs) and chief information officers (CIOs) of established businesses are looking to digital transformation as a key initiative to drive organizational change.
Data drives capability in the information age. As people move toward a metrics-driven view of the world, access to reliable data must be democratized across all groups and divisions to provide business users with 360-degree views of customers, products, and organizations to make data-driven decisions.
Digital transformation begins with data because data is the foundation for understanding where the business is and for making informed decisions that achieve desired business outcomes. Every digital transformation initiative requires access to clean and consistent data, reconciled across systems and channels. Therefore, an enterprisewide data management foundation that ensures real-time access to reliable data of all types at scale is non-negotiable.
For example, take the case of the retail and consumer goods industries facing the retail apocalypse, exacerbated by the “Amazon effect.” With more than 300 U.S. retailers filing for bankruptcy in 2017, the retail apocalypse has only just begun, as both struggling and otherwise stable retailers carry billions in debt.
The Amazon effect magnifies the retail industry’s brick-and-mortar issues and accelerates the pace of digital transformation in retail. Amazon’s total U.S. apparel sales grew 25% in 2016, while the entire U.S. apparel market rose just 3%. Amazon’s 2017 acquisition of Whole Foods and its 2,000% growth and market value has become a model for what is possible with ecommerce.
Organizations in other industries as well are making investments in digitally savvy executives such as CDOs and CIOs to focus on the customer experience, improve organizational alignments, embrace and excel at omnichannel engagement, and transform the supply chain.
What are some of these digital transformation initiatives? Customers today seek connected experiences across various channels and departments with whom they interact. Customers are not asking for more choices but options that are very relevant to their needs. They are expecting companies to deliver timely information that they can access anytime, anywhere, using the channel of their choice—mobile, online, store, or even direct mail. Addressing these customer expectations requires more in-depth customer understanding and offering personalization at scale. Mass promotions with one-size-fits-all messages do not work.
Organizations strive for better alignment across all functional groups and higher productivity so they can provide connected and consistent customer experiences. That means sales, marketing, customer support, and customer success teams need access to relevant and consistent customer information to do their jobs. Operationally, digital transformation objectives may also entail ensuring correct order processing, with proper discounts, right taxations, and adherence to compliance requirements, such as the General Data Protection Regulation (GDPR).
Another critical transformation need is business agility. How fast an organization responds to changing customer needs, federal regulations, and competitive forces can determine its survival. It should not take months to align sales teams with geographies, customers, and products. Business leaders should be able to make data-driven decisions confidently based on accurate, watermarked data.
The challenge that most digital transformation initiatives face is that data remains dirty, with millions of duplicates and inaccuracies, such as wrong addresses and phone numbers. The problem is not just with the customer data—employee, product, and supplier data is equally unreliable. Moreover, data volumes are growing, and channels available to customers are increasing, which results in inconsistent experiences, different departments working with their own versions of the data, and disconnected applications that create more silos.
Debilitated by poor data quality, business decision makers are thus unable to get the information they need, while IT finds it harder to deliver to business promptly. Operationalizing digital transformation initiatives requires proper data management across the digital ecosystem. Companies need a reliable data foundation that provides relevant insights to the users and offers them intelligent recommended actions for informed decision management.
Companies must start with a sound data strategy that ensures a reliable data foundation through MDM that continuously manages and ensures high data quality. However, MDM is insufficient on its own. With data volume and variety continuing to increase, businesses must equip themselves to make fast, automated, data-driven decisions in order to remain competitive.
Organizations can begin by connecting to all required data sources from internal systems (CRM, marketing automation, order management), external systems, and social streams, if needed, and enriching it with third-party data subscriptions. The process of matching, merging, and cleaning the data creates a single, reliable source of truth. Modern data management enables companies to identify potential matches and overlaps of data profiles (customer, products, accounts, and suppliers); it helps to compare and contrast similar profiles and then automatically consolidates them to create operational values using survivorship rules.
The next important step is to uncover the relationships among data entities. This is where the graph technology can help. With graph technology (similar to LinkedIn or Facebook), you can relate customer profiles with products, accounts, family members, employees, and locations. You can uncover many-to-many relationships across these data entities to understand where your customer shops, which products she is interested in, and who can influence the buying decision.
Once a reliable data foundation with clean data and relationships is established, you can use advanced analytics to guide users and provide intelligent recommendations. Intelligent recommendations can tell you how to improve your data, suggest new relationships within your B2B customer accounts, identify influencers (such as in LinkedIn), and help with data-driven decision management. They can also advise next-best actions to sales and marketing for meaningful customer engagement.
To get rich and actionable insights with advanced analytics, organizations must correlate omnichannel interaction and transaction data from the back office and the front office to profile data for a complete 360-degree view of customers’ patterns of behavior. Once clean and consolidated data is available, machine learning can be used to analyze the information, predict and recommend actions, and enable desired customer experience through personalization. Rather than sending mastered profiles and dimensions to downstream data lakes or data warehouses, a proper data strategy and architecture can allow your data to be analyzed in-place in environments such as Apache Spark, eliminating the need to keep data models synchronized across master data and analytics environments.
Aggregated insights and attributes deduced from predictive analytics and machine learning (churn propensity, customer value, interests) can be written back to master customer profiles, enriching them with additional segmentation criteria that can be delivered to marketing and sales users within contextual data-driven applications. Similarly, you can get more profound insights into product performance, sales team productivity, or supplier compliance based on your transformation goals.
Digital transformation needs to happen now, and it doesn’t require a Big Bang approach. A focused data management strategy that closes the loop among your master data, operational data, and analytics will support continuous measurement of outcomes from generated insights and recommended actions. Your consumers will experience the benefits of getting the right products, through the right channels, at the right time. Your organization will benefit from better functional alignment, proper compliance, and realize cost savings from supply chains.