#24 | DCX - Perspectives and insights on digital customer experience
Inside The Data-Driven Enterprise Of The Future; DCX Top CX Books of 2022; Links to Industry news, ideas, insights, and the DCX Thought Leader Linkedin Profile of the week
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Inside The Data-Driven Enterprise Of The Future
Imagine an enterprise where every CX decision is driven by data, every risk is identified by data and mitigated by data. An enterprise which makes the best decisions possible with real time data at their fingertips. An enterprise driven by analytics and governed by a resilient architecture. Sounds like a dream, doesn’t it? Well, it’s not and it’s coming fast. Let’s take a look at where we are today and what to anticipate in just a few years.
𝗡𝗼𝘄: Data-driven approaches to business problems are slow, inconsistent, and take too long.
The problem with today’s data-driven approaches is that they're slow. First, they take time to collect and analyze the data, then more time to interpret the results and develop recommendations based on them. It's not unusual for this process to take weeks or months, which means that by the time an organization receives a recommendation, it may be too late to act on it — especially if it's a long-term recommendation.
Data-driven recommendations can also be inconsistent. Each department uses its data sources and tools, leading to different interpretations of the same data set. This makes it impossible for companies to have a uniform approach when making decisions about their customers or employees.
Finally, data-driven approaches are only sometimes effective at predicting future behavior because they're built on historical information rather than real-time information. For example, if you want to know whether someone will buy something in the future based on what they've purchased in the past, you have to know what they've bought in the past — but maybe they've already bought something similar recently that changed their buying habits?
Tomorrow: Data is embedded in every decision, interaction, and process—a solution that used to take weeks or months can now be implemented in hours.
The future of data isn't just about collecting more information; it's about using that information to deliver insights faster than ever before. This makes a data-driven enterprise so powerful: the ability to take action on real-time data rather than waiting until after the fact.
Data-driven enterprises are hyper-focused on the customer experience, which means they're constantly looking for ways to improve their interactions with customers and prospects. They've become so good at listening to customers and learning from them that they have the ability to anticipate customers' needs before they even know what those needs are. This enables them to deliver more personalized experiences that keep customers coming back for more.
We'll see this trend continue in the next few years as businesses leverage new technologies like artificial intelligence (AI) and machine learning (ML). These technologies can transform how we interact with data by uncovering patterns, predicting outcomes, and automating processes based on historical data sets.
𝗡𝗼𝘄: Limitations on data collection and analysis as a result of legacy technology structures.
Today's primary challenge is simply getting all the data into one place to analyze. Unfortunately, today's enterprise applications were built for a world where large amounts of data were unavailable or available only at a high cost. As such, most applications don't have suitable mechanisms for collecting all the relevant data in one place where the data can be analyzed together.
For example, you want to understand how different departments interact and how their work affects each other's productivity. In that case, there's no good way to get all that information into one place so you can analyze it together. Most companies need help with this problem today because they still rely on legacy systems that weren't built for today's needs.
Tomorrow: Data is processed and delivered to users almost instantaneously using sophisticated "in-memory" data tools.
Data is processed and delivered to users almost instantaneously using sophisticated "in-memory" data tools. As a result, data is real-time and predictive, based on the historical data that has been analyzed and processed.
Data is viewed across multiple dimensions, allowing for an understanding of how a single event can impact numerous business processes differently.
Data is shared across multiple systems and platforms, including mobile devices.
Analytics are built into every aspect of the business, including marketing, sales, and customer service, creating more effective ways of reaching customers and generating revenue.
𝗡𝗼𝘄: In relational databases, unstructured data is error-prone, time-consuming, and not scalable.
Data is stored in a relational database in tables, rows, and columns. This makes it easy to query using SQL queries or other programming languages. However, this also makes it difficult to scale as you need new hardware for each table that grows larger than a few GBs.
Unstructured data does not fit well into relational databases as there are no defined columns to store it in. This leads to storing unstructured data in separate silos, which makes querying difficult and slow.
Tomorrow: Flexible data stores can enable real-time access to integrated, ready-to-use information.
The data-driven enterprise of the future will be able to store and manage all data types in one place. Furthermore, it will leverage the power of artificial intelligence (AI) and machine learning to analyze the data collected from multiple sources, including cloud applications, Internet-of-Things (IoT) devices, and sensors.
The most successful businesses will recognize that the future of data lies in its integration, flexibility, and accessibility. A data-driven enterprise is where employees can access all the information they need to make an informed decision. It's an environment where data is available to everyone as soon as it's created. Everyone can use the same tools to find, analyze and act on all the information that flows through their organization.
Over the next few years, we'll see more companies embracing these systems by adopting flexible storage solutions that allow them to store all their files in one place without locking them into a specific platform or application ecosystem.
𝗡𝗼𝘄: Data silos result in fragmentation and duplication, making it difficult to access or integrate.
Data silos are among the most significant barriers to creating a data-driven enterprise. Data silos result in fragmentation and duplication, making it difficult to access or integrate. In addition, data silos prevent companies from using their data to gain insights into customers, products, processes, etc. This leads to a lack of visibility into what is happening across the business, resulting in poor decision making, loss of revenue, and wasted resources.
This can lead to inaccurate decision making, missed opportunities, and wasted money on unnecessary activities — not to mention the impact on customer satisfaction and loyalty. The business is also at risk of being blindsided by competitors who can use the same data but make better decisions with it.
Tomorrow: A data operating model treats data like products in a continuously improving cycle that provides businesses with solutions to their problems.
The future of big data looks like many services and solutions working together to provide businesses with the correct information at the right time.
To achieve this vision, companies must first embrace the idea of data as an asset and begin thinking about how it can be used to improve operations.
Data-driven enterprises view their data assets as products that are continuously improved by collecting real-time insights from customer interactions across channels and devices. The goal is to create a model that allows businesses to make more informed decisions based on customer feedback while improving operational efficiencies through automation and optimization.
Key Takeaways:
In the data-driven enterprise of tomorrow, data is treated like a product. It's in a continuously improving cycle that provides businesses with solutions to their problems.
Data is no longer siloed, disconnected, and unorganized. Instead, it's connected and organized into an information fabric that allows businesses to harness the power of data to make better business decisions.
Data is no longer just an asset or liability; it becomes the lifeblood of your business and enables you to adapt quickly to changing market conditions while remaining competitive.
Data is no longer just something you own; it's something you leverage through partnerships with others across your organization that contribute to your success.
(This article was inspired by The data-driven enterprise of 2025 | McKinsey)Top 5 Customer Experience Books of 2022
Etc.
Links to Industry news, ideas, insights, and the DCX Thought Leader Linkedin Profile of the week
Empathetic Customer Experience : Long-term Value, Innovation as an Asset - Soul Machines (Whitepaper - registration required)
There is currently an empathy deficit in the digital Customer Experience (CX). Humanized AI can create future-facing competitive advantage and value for businesses including insights about customers’ needs, preferences, and emotional states with the brand. Global brands have already invested in Soul Machines’ Digital People to realize this advantage.
The authors Greg Cross and Fergus Hay, CEO of Elysian Fields, discuss why businesses should invest in assets enabling empathetic CX. They say businesses should also invest in AI to create value out of long-term relationships with customers.
The best customer experience, data and design books of 2022 | MyCustomer
A look at 15 of the best customer experience management books and what makes them so special by Ricardo Saltz Gulko.
Top 5 Financial Customer Experience Trends for 2023 and Beyond (thefinancialbrand.com)
The report says that customer experience should be a top priority for executive leadership in every industry, and two-thirds of consumers say financial companies they do business with need to improve the way they serve customers.
To accomplish this, financial institutions must eliminate silos and accelerate digital transformation to shift their focus from communicating with customers to creating experiences for them. A great customer experience goes beyond not making errors and can result in diminished trust and loyalty.
While more than three-quarters of banking leaders say that their organization views customer experience as a critical business priority. Still, fewer than half consider their organizations to have a high level of CX maturity.
This week’s DCX Newsletter is Supported by:
DCX Thought Leader Linkedin Profile of the week
Every week, I share a person's profile from Linkedin that I think will benefit your life and career.
This week, I’d like to introduce you to, the “Godfather of CX,” Bruce Temkin, Head of Qualtrics XM Institute.
Bruce Temkin leads the Qualtrics XM Institute and is widely viewed as an experience management (XM) visionary. He has helped executives across many of the world's leading brands dramatically improve business results by engaging the hearts and minds of their employees, customers, and partners.
Given his work in establishing the discipline of CX, Bruce is often referred to as the “Godfather of Customer Experience.” He co-founded and was the initial chair of the Customer Experience Professionals Association.
Prior to joining Qualtrics, Bruce ran Temkin Group, a renowned research and advisory firm, and was a VP at Forrester Research, where he led many parts of the research organization, including CX, eBusiness, financial services, and B2B. He was the most-read analyst at Forrester for 13 consecutive quarters.
Check out his profile, follow him for his perspective and insights.
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