Managing the complexities of customer data across the organization

Data is the lifeblood of an organization. Customer centricity is the focus. Data is the crucial enabler. A "one moment of truth" is crucial. "But where should the customer related data sit in the organization?" asks your client. This seemingly simple answer bears a lot of complexity. This article helps you think through all the related issues holistically and guide you in providing a valuable answer to your client.

It requires specific inputs of numerous functions to serve the customer. No function owns the client, nor has all the data to serve the client!

Serving a customer is about supporting specific people on their individual journeys. Companies need to identify and support each touchpoint from beginning to end. It requires collective support of numerous business functions across the organization: Marketing, Sales, Customer Services, Finance, Procurement, Supply Chain, Aftersales, IT and more. Further, it requires the support of many teams, business units and individuals within those functions. All leverage their relevant data, insights and expertise to serve individual customers. There is no business function that "owns" the individual customer. No business function can serve a customer alone. No business function has all necessary data & information to serve customer across the entire journey.

Each organization works in unique ways. Data solutions are dependent on the individual client situations and business aspirations

Organizations have grown over time. While two organizations may list the same functions and call processes by same names, their organizational structures, processes, work/ information/ data flow, roles, KPIs often notably differs across organizations.

Each organization is unique: It serves different markets, offers different products & services, pursues own strategy, business model and operating model to market/sell, engage/ serve customers, leverages different approaches and channels, appeals to/ focuses on different market segments and customers.

Companies to evaluate the above and determine what data they need to achieve listed objectives.

Each organization has a large collection of systems, tools and myriad of deployed technologies already. Each client's situation is different and requires diligent analysis and customized solutions

Existing systems, tools and architecture may certain functionalities well, while falling short in other. A diligent examination of the technical situation is necessary for the present situation as well as for the near/ mid/ long-term future. In some situations, clients have missed to turn on already available features in existing systems. Often times existing technical infrastructure fulfills certain needs very well while not covering sufficiently enough others.

Systems, tools and existing architecture will support/ hinder necessary Data Life Cycle management through individual stages, tasks and use cases. The complex challenge is to work with the existing situation, examine objectives, identify improvements, build business cases & ROI, plan a sustainable roadmap. There typically several possible approaches and options. All have their particular pros and cons.

(Check out an example of how to empower customer engagement). Click on the picture below...

Recognize the present/ natural information & data flow across the organization

Existing systems and technologies are like trenches in the ground and data like water takes the path of least resistance to flow across the organization. Data then often collects in data lakes. Imposing new solutions may require notable work; effort may not justify results.

Information/ workflow differs. Process steps and tasks remarkably differ for employees across different organizations (due to underlying systems, degree of automation).

·       Systems, infrastructure, platforms, tools, API landscape and overall architecture is unique for each organization.

·       Each organization is in different stage of maturity from an IT, data, digital, cloud, people capability and myriad of other issues. Each organization is on a different journey.

·       What makes sense for organization A does not apply for organization B!

Do not centralize/ try to bring all data into one place! It may violate the law and other regulations.

Clients have understood the importance of having one "moment of truth" of their customers. However, they often wrongly assume that it requires putting all data in one place.

There are many regulations and laws that govern what kind of data and how data can be stored: 1) Certain data cannot be transferred among Europe, USA and China. 2) Industry specific regulations demand a separation of functions. For example in the Life Science / Health care industry, the data from the Medical Affairs group and the Marketing & Sales groups must be strictly separated.

Complexity of data suggests different data gathering, cleansing, storage and processing solutions. Modularization, data decentralization/ processing/ caching are necessary to support use cases and special data types.

Until a few years ago, data more static, defined and understood. Today we have an onslaught of challenges: Different types data requires/ favors different types of gathering processing and storage:

·       Structured, semi-structured, unstructured, high volume and real-time.

·       Confidential, restricted, public.

·       Internal/ external sources

·       Specific data for particular use cases

There are many different data architectures, systems, tools and other technologies to work with the different data types. The right choice brings technical and cost advantages. Improper use increases complexity and creates technical debt for the future.

See my related articles on systems and architecture.

Understand and manage the life cycle of individual data bits

Even pieces of individual data points may go through a life cycle. Data may be strictly confidential, but become public knowledge after a certain time. Example financial statements before and after announcement to the public. Also, data needs to be archived and then properly disposed according to clear company policies and regulatory mandates.

Many companies store too much information & data. They waste money on storage and administration. And even hurt themselves in litigation cases when needlessly accumulated data works against them.

Recognize that your solution is only correct for a period of time! Customer needs & expectations, market & completion, new products & services, new available tools and technologies, Merger & Acquisition change the present situation. You need to constantly monitor, align and change as necessary!

Your analysis and solution is always time bond. A change in market, customer, business model, service offering, technology advancement, new laws & regulations all can force notable change to your organization.

Customer:

·       Customer expectation may expect notable improvement across the customer journey. An organization may need better process & data alignment across key business functions.

·       Marketing & Sales need more, better and/ or higher quality data to gain to feed into AI & Analytics engines and to generate better/ faster insights. It may require tabbing/ purchasing of additional internal/ external data.

Competition:

·       Competitors may deploy real-time pricing analytics engines. Your organization may need to implement likewise to stay in business.

Technology:

·       A vendor roles out a new customer experience platform, Real time analytics/ pricing tools, CRM systems with new features that support some of your new key business use cases. Before the value was not compelling to make a change, but now it compels to get and implement it.

Recognize where you are in the journey and act as it makes sense to you. You may not need all the fancy stuff of other companies to serve your customers. Alternatively, you may have to act now in order to stay in business.

You do not need a bus to transport three people. You may not need certain data types to serve your customers well. You may not need to process certain data types in real-time, while for others you must to stay competitive. You may not need a 100% accuracy on data (example: unstructured market data), whereas you need it in financial reporting.

Where in the organization should the customer data sit?

The first reaction and response I hear from people is often that customer data should sit in the CRM. While this a first good answer, I submit additional few points:

First, let us think, what does customer data entail? It is certainly master data, but what about transactional and shipping data typically kept in ERP? What about real-time locational data in stores? IOT/ device/ tracking data? What about social media and other external data? Should CRM store and process structured, semi-structured, and unstructured data related customers? Are CRM systems built to handle all these data?

Analogy: In transportation there are different use case: One person taking out a car for a fast drive, two people driving to a business meeting, a family vacation and local football team. A large bus could solve many of the basic transportation needs, but would fall short as a sub-optimal solution for most use cases except of the football team. Similar applies to different systems, tools and technologies related to the data life cycle!

If you are interested in more detail in reading on data architectures, systems, and technologies you may refer to my other articles or contact me directly.

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Standard Legal Disclaimer: This article expresses my private opinion and does not represent my employer or earlier clients that I have worked for. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Nobody shall be responsible for any loss sustained by any person who relies on this publication.