How to survive the data revolution
Digital Transformation can mean a lot of different things to different people. But for everyone it means dealing with more data than ever before. Industry 4.0, Cloud, Big Data, Robotics, Artificial Intelligence, Machine Learning, blockchain, IoT (internet of things) and similar technological advancements are changing the very nature of commerce across every business sector in every market. Business leaders must find innovative ways to manage all this data and put it to use.
We are indeed in the middle of a revolution: The Data Revolution.
Through my personal experience with enterprises of all types, all over the globe, I have witnessed that businesses tend to move along a similar path on their digital transformation journey from a legacy state of multiple silos toward an integrated enterprise embracing relationship centricity then outward as a part of a connected ecosystem in a trust network.
Those who succeed in harnessing these advancements, at scale, will flourish. Their success will spawn disruptive innovation, new levels of customer experience, and unprecedented business value. Those who cannot leverage these new ideas, could find themselves stuck in a legacy quagmire of inefficiency and abrupt market irrelevance that will ultimately lead to their demise. The one constant in all this disruption is data. Once you embark on this digital transformation journey your data needs to be mastered.
Multiple silos create disparate data
As businesses strive to drive innovation and progress they continue to grow in complexity and foster fragmentation. The explosion of persona-based tech stacks (ERP, CRM, FinTech, MarTech, ABM, AdTech, etc.) offer unprecedented flexibility, but inherent in each new app is yet another silo. In each silo there is the potential to create another version of the same data. Separate departments, regions, channels and go-to-markets create separate data. These multiple systems and workflows create disparate data sources that lack internal standards. Globalization and frequent merger and acquisition activity tend to increase these operational barriers and create an inability to scale. Vital relationship types (such as customer, vendor, partner and prospect) as well as other important commercial entities (like brand, product and service) often have differing definitions across different parts of the same organization.
As businesses strive to drive innovation and progress they continue to grow in complexity and foster fragmentation.
For example, the CRM used by a local sales team might define a customer as individual locations, while the global finance department may view the full hierarchy of this same customer as a single entity. Both interpretations may be correct in their respective contexts but may appear inaccurate and irrelevant to each other; and in most cases they are not in sync. Imagine the frustration of that customer whose experience is negatively impacted by this lack of intercommunication.
Ask yourself: Do your systems know what your people know?
Most enterprises are in some form of this legacy state. This does not mean they don’t have modern technology and infrastructure. This simply means that they have not taken the holistic steps necessary to put relationships at the center of their processes – both strategically and systematically.
Establishing relationship centricity
All business is based on relationships. Although many organizations think they are “customer-centric” their data can hardly support that notion. Ask yourself: do your systems know what your people know? To create the innovative experiences that are at the core of digital transformation, enterprises must first embrace being relationship-centric. Relationship centricity can be achieved by creating a horizontal view of your relationships across the entire enterprise built upon standard definitions. A familiar approach involves the creation of a 360-degree view of customer, vendor, supplier or partner as well as product, brand and service.
Put relationships at the center of your processes – both strategically and systematically.
Compliance efforts around KYC (know your customer) and GDPR (general data protection regulations) or development activities like cross-sell/ up-sell require a consistent and shared view and definition of each unique relationship. The foundation of ABM (account-based marketing) is a solid, shared definition of “account.” This idea of the integrated enterprise, of being centric about relationships, is only achieved by establishing and governing a common version of the truth about those relationships.
Engage in trust networks
Once relationship centricity is embraced and becomes stable inside your enterprise, you need to look outward and participate in some form of trust network. A trust network is how you engage, how you interoperate, and how you seamlessly communicate with different partners across your different value chains. This can manifest itself in channel partner platforms, e-commerce customer self-service, supplier on-boarding systems, vertical industry identifiers/standards (such as UPC, Ad-ID, LEI) and programmatic marketing to name just a few.
Leveraging common data and syndicated processes allows interoperability in a trust network to scale.
Using the same standard data and definitions, or links to the same standard data and definitions, across verticals and markets provides the basis for seamless integration. Leveraging common data and syndicated processes between external parties allows the interoperability in a trust network to scale.
Ecosystems can only reach their full potential if they are built on accuracy and trust. In many cases our business dealings based on personal feelings and good faith can sour quickly if the data supporting that relationship is inaccurate, outdated and unstructured.
The solution is structured data
Since digital transformation runs on data, properly managing the diverse types and profuse quantities of that data will directly impact an organization’s ability to succeed and survive. Fortunately, there is a certain kind of data that can help unite and standardize all the other types of data: it’s called Master Data. Properly governed, Master Data can become the source of common business truth used between internal systems, applications, and processes as well as externally between enterprises.
“There are very few business leaders who understand the value or even recognize the existence of Master Data and the critical importance of enterprise data management, but all feel its effects.”
Concepts like “cleansing” and “freshness” and other data hygiene use cases are important, but they are hardly holistic and rarely strategic. Most data hygiene exercises are ad-hoc campaign-based projects isolated to a siloed use case. A real master data strategy will go well beyond the legacy lexicon in the enterprise data management space.
Data hygiene use cases are hardly holistic and rarely strategic.
There are broader, more fundamental political and cultural business changes needed if an organization wishes to fulfill the vision of true digital transformation. In many cases, the entire nature of how data is created, managed, curated, integrated, and aggregated must change to drive interoperability.
Creating a common protocol at the semantic layer for commercial entities, provides a much quicker time to value for any kind of enterprise data management and interoperability initiative. Standardized, expertly-governed master and reference data content can seamlessly integrate internally across methodologies, processes, workflows, apps and platforms, as well as externally between enterprises, value chains, and throughout market ecosystems. The consistent requirements across all these vectors are validated identity and a data structure.
Structured data helps other data integrate and processes interoperate. Both of those together help you scale. Structured data improves all types of reporting and analysis. Structured data works harder than unstructured data.
Most organizations, however, are stymied by the ROI exercise for mastering and structuring their data. Although rigorous cost/ benefit analysis is important for any investment, the primary business case for master data to support digital transformation is growing and scaling the very business you are in.
Structured data works harder than unstructured data.
In a digitally-transformed organization, data moves seamlessly from workflow to workflow and between external partners. Users can trust the results and spend their time improving the relationship experience rather than questioning the data. Your organization can move from silos to being centric and then to being part of a network.
Embracing digital transformation will lead to significant changes and unimagined opportunities. Some businesses will transform and deliver a staggering increase of value in spaces none of us ever thought existed, while others will find themselves isolated as they confront the possibility of extinction. Going forward, how you manage, master and structure your data will determine if you become empowered or devoured by digital transformation.
Will you scale or fail in this time of business disruption and non-stop data?
If you want to learn what you should do to better manage master reference data, here are the 5 best practices for RDM