Data-Driven Enterprise? Why?

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The COVID pandemic, the war in Ukraine, global warming and other unparalleled events of the past years, have not only increased the need for digital transformation, but even more so for enterprises and businesses of all sizes to become, improve and protect their data-driven businesses. Data-driven enterprises thrive to leverage all available data and insights to create a unique customer experience, take better and more sustainable business decisions, and gain a competitive advantage through faster adoption to market changes and the development of added value services or new business models. In a nutshell, data-driven enterprises do not consider data a by-product of their business, but align their culture, organisation and technology to leverage data as a core product to achieve sustained and sustainable business growth. 

The history of technological and organizational approaches of the past decades tells that handling and understanding the ever-increasing amount of data, establishing and maintaining analytical and machine learning models and doing it in a sustainable, secure, cost-efficient, and compliant way has posed significant challenges to the quest towards becoming a data-driven enterprise. Challenges, that IBM has remedies and prescribed approaches to address them. 

In this series of articles, we want to share our thoughts on why becoming a data-driven enterprise is a desirable and a very achievable goal. The content will reflect what business stakeholders, executive roles such as CIOs/CTOs, Chief Data Officers, or Chief Analytics Officers, Data & AI Leaders, but also Enterprise Technology stakeholders, Data Stewards, Data Engineers, Business Analysts and Data Scientists are concerned with.

Figure 1 – Goals of a Data-Driven Enterprise

Goals of a Data-Driven Enterprise

1. Customer Experience and Hyper-Personalization

„Focusing on the customer makes a company more resilient„

Jeff Bezos

Unarguably, every business is centered around its customers. Providing a unique customer experience and delivering on that promise is the secret sauce to sustained business success. 

Providing the most differentiating customer experience requires a deep understanding of a customer, their needs, and their challenges. Hyper-personalization is aiming at addressing them at an individual level across all channels of interaction, rather than within artificially defined customer segments. The building blocks of such an optimal customer experience are a precise understanding of the customer; tailored messages and content to address them; highly individualized products and services; continuously improved customer services and more. 

Customers judge a brand, or a company based upon their experience across all customer touch points throughout the lifecycle. 78% of customers will back out of a purchase due to a poor customer experience. For data-driven enterprises, this means that the complete customer experience lifecycle from discovery and evaluation towards conversion and then from retention towards loyalty needs to be continuously improved across all functions. Marketing, sales, customer services and other customer facing functions are the obvious focus areas of data-driven customer experience optimization. Customer touch points can be handled by humans, machines or sometime both of them. Just providing them is not a viable solution though. Unreasonably long wait times to speak with a sales representative or service center employee are just as unacceptable as inept chat bots, which can not handle context or more than the simplest of questions. Customer experience needs to meet the customer where they are across different channels and provide the experience they need to solve the problem at hand or support them in their buying decision process. 

Establishing a deep understanding of a customer must fully comply with regulatory requirements. Capturing, processing and securing personally identifiable information (PII) is defined in regulations such as the EU General Data Protection Regulation (GDPR), Federal Trade Commission Act (FTC Act), and industry specific regulations for handling personally identifiable information (PII), such as Gramm-Leach-Bliley Act (GLBA), Health Insurance Portability and Accountability Act (HIPAA) to name just a few. Data-Driven Enterprises need to go beyond legal requirements though. Even within the boundaries of existing regulations, customer satisfaction and public sentiment can easily take damage if proper handling of personal information is not being respected in favor of potential business growth.

2. Data-Driven Business Process Optimization

„What get’s measured, get’s improved„

Peter Drucker

Creating a unique customer experience cannot and must not focus on immediate customer touchpoints only. Enterprises must live up to the customer experience and thus the promise they made. All core and extended processes of value generation in an enterprise need to be subject to continuous operational efficiency improvements based upon data and insights available internally and externally.

Despite a unique experience when identifying and customizing a product to individual needs, customers will still abandon a brand or company if the desired products or services cannot be delivered in time, in quality or at an appropriate price. Digital transformation is a prerequisite for data-driven enterprises to be able to continuously optimize processes.

If a customer enjoys the selection, even the configuration and ordering of a new car, but the OEM cannot supply the required parts in a reasonable time, a unique customer experience is easily ruined. Customers will understand the impact unforeseen challenges such as pandemics, wars, or other outsized impacts on the flow of goods, cost increases and product shortages can have on an enterprise’s supply chain management. Nevertheless, if competitors prove to be less impacted, loyalty is easily broken. 

Similarly, if such desired vehicle is offered at an inappropriately steep price or with inferior quality to competitive products, customer experience can hardly convince the customer to stay. Data-driven enterprises need to embrace data-driven product development just as much as HR, controlling and any other core process alike to improve operational efficiency and deliver upon the customer experience promise. 

3. Product or Business Model Innovation

„Too often we forget that genius, too, depends upon the data within its reach, that even Archimedes could not have devised Edison’s inventions„

Ernest Dimnet

Most enterprises have acknowledged the value of data insights and artificial intelligence for optimizing existing, internal, and external processes and their efficiency. But even today, the value of data for product innovation, and for the identification and definition of new business models is often neglected. 

Data-driven product or business model innovation leverages data and derived insights to support the end-to-end process of innovation. Analyzing the status quo, markets trends, customer needs, forecasted demand, key market players, technologies, and ecosystems, lays the foundation for ideation and definition of solution spaces, but also for proper risk management. High quality and trusted data greatly improve the ability to assess and test hypotheses identified along the way.

Data-driven prototyping allows for early validation of candidates and simplifies decision making for progressing into a potential implementation phase, identifying required refinements or for discontinuing a suboptimal or failed product/business model prototype. Identifying, acquiring and if necessary anonymizing relevant data is a challenge and needs to be core and center to improve the results through data-driven prototyping.

Just as much as identifying and validating new ideas can greatly benefit from corresponding data, risks can be identified much earlier and more reliably within the process. Technical, financial, legal, and other risks can be captured and more effectively managed in terms of their impact on the overall commercial success.

Finally, data-driven competitive analysis allows to identify and defend competitive advantages and differentiationmuch more effectively. 

4. Sustainability

„Sustainability is no longer about doing less harm. It’s about doing more good.„

Jochen Zeitz

Data-driven enterprises have a much better chance to align their business and purpose with the wider societal impacts. Deriving insights from data and optimizing businesses to address some of todays’ biggest challenges around climate, health, security and equality, will increasingly impact how customers, partners and employees feel about an enterprise. Regardless of specific focus, sustainability requires a commitment to ongoing transformation. 

Less travel through virtual work, lower levels of global physical trade, more efficient usage of production and compute resources can have an immediate impact on carbon in the atmosphere. A sustainable enterprise will go beyond immediate environmental impact though and weave sustainability into value propositions, business partnerships, customer, and employee engagement to influence how humans treat each other and the planet. Additionally, government imposed sustainability regulations are also on the rise, which requires enterprises to embrace an overall environmental, social and governance (ESG) approach to sustainability. Identification of relevant investment in ESG initiatives, performance measurement and improvement, all require data driven analytics and artificial intelligence to succeed. 

Summary

In this initial article “Data-Driven Enterprise? Why?“ we looked at the goals of a data-driven enterprise. Centered around building a unique customer experience, data-driven enterprises have the opportunity to develop a competitive edge by being more innovative, more efficient and more sustainable. Understanding and closely aligning with business goals in these domains is the most important first step. In order to become a Data-Driven Enterprise requirements need to be defined, cultural, organizational and technological approaches need to be laid out and implemented. The subsequent articles will provide further guidance based upon our experience helping clients to become data-driven enterprises. 

Part 2 “Data-Driven Enterprise? What’s so difficult?” will review requirements as well as inhibitors to achieve aforementioned goals. Part 3 “Approaches and Considerations becoming a Data-Driven Enterprise” will summarize and assess approaches to become data-driven from a strategy, organization and cultural perspective. Part 4 “Technological building blocks of a Data-Driven Enterprise“ will arm you with definitions of key technologies and concepts to consider when defining an enterprise architecture. In part 5 “IBM’s Modern Data Platform for Hybrid Data Governance, Data Integration and Data Science” we will specifically look at IBM’s technologies and their capabilities to establish and maintain a modern data platform addressing the requirements and inhibitors laid out in Part 1 and Part 2. 

Additional articles will deep dive on specific aspects such as Data and AI Governance, Data integration, Experiential Development and many more. 

So make sure to bookmark our series and to check for new content regularly. Most importantly, please leave your comments and feedback. Do you agree with our conclusions or can you share different perspectives on this important trend.