Businesses across the globe in every industry desire to use data to drive insight that drives positive performance in their respective organizations. Effective integration, standardization gain the foundation to this insight, and consolidating the data and relating the data together to achieve knowledge and wisdom. Consolidating data has proven tough due to the limitations of many of the de-facto systems deployed within all organizations such as relational databases. In fact, much of the related technologies such as data transformation tools are so prevalent because of the shortcomings of relational databases, but still, even with all the tools, it's been elusive in using data to achieve meaningful relationships and contextually relevant insight.
Strict Schema Solutions
Relational databases, while having many strengths over their predecessors, have proven time and time again that they are difficult to create, rigid to change, and unable to support traversing n number of domains with ease or without having to replicate the data into various structures to gain insight.
Once the data is locked into a “hardened” model, along with the different strategies to optimize the performance of the applications, it becomes difficult to extend the model over time without impacting existing applications. Also, these structures are not very relationship oriented, in that the relationships of the entities across the tables are hidden within the tables of relational databases themselves making it harder to create or enhance later as new relationships are formed, or existing ones are updated or removed. The applications that depend on these strict schema structures to also become rigid over time as they are reflective of the underlining foundation.
Organizations have tried many methods to resolve these problems, but the most prevalent one is the creation of the operational data stores representing the use cases of the applications they support. The goal is to increase the quality of the data within that domain and provide an easier method to enable applications to gain access to the data. But what ends up happening is these data stores are lacking consistency, the quality of the data is not always at its best, and data is replicated all over the organization further resulting is the lack of trust.
To combat this one off operational data store, organizations adopted programs to master data in a single application where the quality of the data could be governed and trust in the data restored.
In fact, an MDM program is an operational data store that supports a given domain with prescribed models, queries, services, data management, data quality, data governance. Each supports the idea of simplifying the data management problem and creating a single view and the ability to link that domain to all other domains quickly.
The results have been lacking, and most organizations are not satisfied with the results. Why? Because these rigid data structures just do not lend themselves well to linking the multiple domains together, so solution providers tried to encapsulate multi-domain master data management applications, with the core focus being, say customer, and have a light version of other domains attached to the customer domain. These light versions just steer organization right into the train wreck they were looking to avoid by having a single data store that could house multiple domains to drive meaningful relationships.
Graph Databases = New Insights
But there is an answer to this problem and graph databases powered by solutions that have data quality as the focus, will enable businesses to build a comprehensive view of the data domain, to eliminate silos across data domains which ultimately result in getting to the single view right.
The customer is foundational in collecting, standardizing, consolidating them in a graph model where we can then integrate the various domains to identify relationships, uncover opportunities, and engage customers in a more meaningful and consistent way across the different channels.
The graph inherently supports the multi-domain and multi-contextual approach which is critical in making data fit for use across a much broader spectrum of knowledge to gain insight that leads to knowledge and wisdom.
Also, the flexibility of the graph database allows for businesses to grow and expand the data to include new and every changing requirements which improve the chances of success dramatically. This foundational change will certainly result in our ability to achieve the holy grail of the customer 360 view.
Moving Forward
With every new solution, there is a period of risk aversion and initially slow adoption. In working with the early adopters, we have achieved results that were just not possible in previous solutions.
Leveraging the graph, coupled with an agile solution stack, clients have achieved spectacular results in as little as six weeks. This insight leads to a consistent yet dynamic view of the customer, they are faster to market and deliver results at a much lower cost that in the past.
So why continue to struggle and spend millions of dollars on projects that provide lower returns because the integration, transformation, and consolidation of data are severely limited by its foundation? The plethora of data quality solutions was certainly the same used by organizations across the globe, but after seeing the results by making this shift from relational to a graph, customers are again excited about the future of gaining insight leveraging solutions that scale and ultimately meet the needs of our respective customers.
My advice is not to get left behind. Adopting graph databases is the wave of the future for data consolidation efforts.
