Getting a comprehensive 360-degree view of customers in digital business processes involves the ability to connect customer master data with other master data entities, hierarchies, transactions, big data, and reference data.
As the diagram above shows, a connected and extended master data landscape (aka data hub) will give you the essential capabilities you need in order to understand your customers. Knowing your customers better allows you to develop better products, drive new streams of revenue, and deliver the best customer experience through hyper-personalization.
In a data hub encompassing master data, reference data, critical application data and more, data discovery can play a significant role in the continuous improvement of data quality and how data is governed, managed and measured along with an ever evolving business model and new data driven services.
Data discovery serves as the weapon used when exploring the as-is data landscape at your organization with the aim of building a data hub that reflects your data model and data portfolio. As the data maturity is continuously improved reflected in step-by-step maturing to-be states, data discovery can be used when increasing the data hub scope by encompassing more data sources, when new data driven services are introduced and the business model is enhanced as part of a digital transformation.
In that way data discovery is an indispensable node in maturing the data supply chain and the continuously data quality improvement cycle that must underpin your digital transformation course.
20 years ago, when I started working as a contractor and entrepreneur in the data management space, data was not on the top agenda at many enterprises. Fortunately, that has changed.
An example is displayed by Schneider Electric CEO Jean-Pascal Tricoire in his recent blog post on how digitization and data can enable companies to be more sustainable. You can read it on the Schneider Electric Blog in the post 3 Myths About Sustainability and Business.
Manufacturers in the building material sector naturally emphasizes on sustainability. In his post Jean-Pascal Tricoire says: “The digital revolution helps answering several of the major sustainability challenges, dispelling some of the lingering myths regarding sustainability and business growth”.
One of three myths dispelled is: Sustainability data is still too costly and time-consuming to manage.
From my work with Master Data Management (MDM) and Product Information Management (PIM) at manufacturers and merchants in the building material sector I know that managing the basic product data, trading data and customer self-service ready product data is hard enough. Taking on sustainability data will only make that harder. So, we need to be smarter in our product data management. Smart and sustainable homes and smart sustainable cities need smart product data management.
Enterprises are increasingly going to be part of business ecosystems where collaboration between legal entities not belonging to the same company family tree will be the norm.
This trend is driven by digital transformation as no enterprise possibly can master all the disciplines needed in applying a digital platform to traditional ways of doing business.
Enterprises are basically selfish. This is also true when it comes to Master Data Management (MDM). Most master data initiatives today revolve around aligning internal silos of master data and surrounding processes to fit he business objectives within an enterprise as a whole. And that is hard enough.
However, in the future that is not enough. You must also be able share master data in the business ecosystems where your enterprise will belong. The enterprises that, in a broad sense, gets this first will survive. Those who will be laggards are in danger of being left out of business.
Product information is the data a potential buyer of a product needs to make a purchasing decision. Today purchasing is more and more made by self-services as in e-commerce. The product information is usually obtained through a supply chain between trading partners stretching from the manufacturer to the end merchant.
The most common way of exchanging product information between trading partners is using spreadsheets. Spreadsheets are marvellous, because you can do almost anything you want with them. However, spreadsheets are also horrendous, because you can do almost anything you want with them. Therefore, trading partners are often stuck with manual, cumbersome and error prone processes on both the providing and receiving end.
At Product Data Lake we have developed a new mechanism that enables a whole new process for exchanging product information between trading partners. We have kept the flexibility of spreadsheets when it comes to choosing the data standards on the providing and receiving end but at the same time introduced automation and correctness when it comes to transferring, translating and transforming the data.
When telling about our service I am often asked if we have a nice feature for on-boarding spreadsheets. We don’t. Because the process is designed to omit the spreadsheets and transfer directly from the providers in-house product information data store(s) to the receiving in-house product information data store.
This reminds me of when we talk about using robots to substitute human labor. Then we often think about a machine that looks like a human. But effective industrial robots do not look like humans. They a designed to do a specific process much more effective than a human and will therefore not look like a human. The same is true in digitalization. When we redesign business processes to be much more effective they should not include spreadsheets.
Business ecosystems is an important concept of the digital age. The father of business ecosystems, James F. Moore, defined business ecosystems as:
“An economic community supported by a foundation of interacting organizations and individuals—the organisms of the business world. The economic community produces goods and services of value to customers, who are themselves members of the ecosystem. The member organisms also include suppliers, lead producers, competitors, and other stakeholders”.
The problem with data management methodologies and tools today, as I see it, is that they emphasizes on the needs inside the corporate walls of a single company without much attention to, that every single company is a member of one or several business ecosystems as examined in the post called MDM and SCM: Inside and outside the corporate walls.
Opening your data management, including your Master Data Management (MDM), up to the outside is scary business, as the ecosystems often will include your competitors as well as mentioned in the post Toilet Seats and Data Quality.
Nevertheless, if you want your company to survive in the digital age by building up your company’s digitilazation effort you have to extend your data management strategy to encompass the business ecosystems where you are a member.