Different Not Better
Change the way you think about change
It's time to re-set your expectations
Within an enterprise data architecture, what's in control? Is it the applications or the data? For most, it's the applications. But applications are useless without data. As a result data becomes the servant to the applications. As more applications are built, more data copies and more silos are created, leading to a never ending cycle of architectural complexity.
Moving towards collapse two steps at a time
An application led approach may seem like small steps forward for the business but in reality it's actually moving it closer and closer to collapse by complexity. Ultimately creating an unruly ecosystem of disparate applications tethered to disparate databases and repositories.
Getting out of this cycle requires thinking “differently”. It requires actually changing the way you change. It's realizing that a little better is not good enough and that traditional approaches should not be directing solution design.
Change the way you change
An example to ponder. Automobile manufacturers. Some think they are moving two steps forward by making combustion engines more fuel efficient. This "progress" doesn't solve the larger problems of cost, reliability and the reliance on fossil fuels. As a result, more versions of better will continue to be needed, little by little, over and over, stuck in the cycle of the traditional.
Some are taking a completely different approach. Electricity. Solving the problems of efficiency, emissions and fuel source in one fell swoop. This approach to change has also created brand new opportunities for growth, expansion and simplification. Why? Because these companies changed the way they thought about a single problem and as a result rid themselves of ALL the problems. They now live and operate in a world where old problems are irrelevant.
The net result. All cars will be electric in less than 10 years. Different wins because it is not focused on a little better. Companies not thinking differently now, will be left behind tomorrow.
What does this all have to do with your data architecture?
Different, not better is the way forward
What exactly does different mean in this context?
Let’s first start off looking at other common data management models and solutions on the market today. Data warehouses, data visualization, data lakes, data mesh and data fabric. All good models/solutions, yet all still act as a means to move only a few steps forward.
Why? Because with all of these solutions data remains under lock and key. It’s stuck in a world where applications are still in control of the data. As a result, all of these models require heavy integrations and there remains many different versions of data, all housed in silo’s. All this splintered data is difficult to govern. Access to the people who need the data to make good business decisions can often be completely blocked. There is nothing really "different" about any these approaches.
Data Collaboration is different.
It gets the enterprise into a new universe by first doing one thing completely different.
It integrates data without integration
A Data Collaboration Network liberates enterprise data and evolves it into a federated, fully collaborative network. Data being free, puts it in control. The paradigm of application control is flipped on its head and a new universe of how data interacts in the enterprise is created.
How does Data Collaboartion work in the enterprise?
CInchy's Data Collaboration Platform enables the enterprise to create data in an productized manner, one in which data structures can be defined independent of any one business solution planning on leveraging that data. The way the data is modeled is in a manner that is conducive to the business and how the business thinks about its information usage.
The Data Collaboration Platform in the enterprise can be generalized into 3 components:
Application owners can make data available to access and change via the dataset network with real-time 2-way synchronization. Bridging the gap between the legacy world and the new way the business can now manage their data. Bi-directional connectivity allows data to freely flow in and out of the dataset network as needed.
The dataset network is composed of individual domains of interconnected data sets or data products which can in turn can connect to one another. This environment now provides the capability of being able to model, protect and control data in a manner that is democratized, where many different teams can work in a completely federated way, building out this network of information. It is inherently evolving organically with protections that allow that evolution to take place without necessarily impacting the work that one team is doing when having a dependency on another. Data product owners can create and managedata products with granular access/ control policies enabling real-time data collaboration.
Project teams can use data products to access and change data to meet their project requirements without dependance on individual apps/teams. This allows the enterprise to interact with the data and further build additional application based capabilities as needed This is accompanied by a pre-built data browser as the main user interface also allowing users to be able to explore access, view, and change information inside of this entire environment subject to their level of access.
This entire package of these capabilities forms the solution known as the Data Collaboration Platform.
Dig deeper into data collaboration
Integration is Obsolete: A Survival Guide
Applications, Silos and Complexity, Oh My!
Enabling Data Mesh Principles and a Data Fabric Foundation
See Data Collaboration in action!
Connected data without the effort, time, and cost of traditional data integration.