Businesses appear to have consensually agreed upon the value of a data-driven organization. Yet, studies have shown that very few businesses are maximizing the data opportunity. Senior Executives surveyed during an Economist Intelligence Unit (EIU) study, overwhelmingly missed out on “broad positive results”, with only 2% reaching this desired outcome.To put this into perspective, 70% of those surveyed indicated that data and analysis were high up on the organization’s agenda. So what’s going wrong?
There are multiple factors in play and the areas in which businesses need to improve are rarely black and white. However, one stand out approach to help make the most out of data, is a capable and efficient Enterprise Architecture initiative. Coupled with a robust Data Modelling strategy, data driven businesses should begin to thrive under the deeper insights offered by the approach.
Effective data management begins with capturing the structure and related metadata associated with your enterprise data assets. However, the native metadata available from most datasources is often cryptic. It’s also limited to a technical viewpoint, and very brittle in terms of providing a usable foundation for visibility, comprehension and analysis across the various stakeholders it concerns. These technical descriptors need to be augmented and integrated with additional viewpoints that describe the business purpose and usages of the data.
Historically, data modeling has been perceived as a design and deployment tool for creating databases. However, data models can deliver as much or more value and utility as a vehicle to discover, document, expose, analyze and govern data sources for the wide range of stakeholders involved in collaborative data management.
Data models are visual in nature, encapsulate multiple perspectives (conceptual, logical, physical) and enable the standardization of metadata to provide a consistent, contextual view of data across disparate platforms.
Additionally, data models allow organizations to de-couple from the production environment, enable scenario analysis and planning without risk to ongoing operations, and iteratively deploy “to-be” designs in an efficient and effective manner.
Being data-driven isn’t just about recording and examining as much data as possible. Businesses need to be active in how they leverage data, in order to provide insights into possible strategies going forward. But to be able to effectively leverage such data, businesses need to be smart about how data is stored, segmented and applied to decision-making.
The sheer volume of data, typically kept by data driven organizations can make it difficult to cipher through to what is relevant. This can give a false impression that the data being stored isn’t useful.
To streamline the data, making it more manageable and more insightful, businesses need to focus exclusively, on the data that’s relevant to the organization’s current aims, goals and objectives.
A well actioned Enterprise Architecture practice can achieve this, as a business’ current capabilities as well as its desired future state can be highlighted. From here, a business can work out what steps it can take, and which of those it will need to take in order to reach the desired future state. A good understanding here, should indicate which areas of data to drill down on.
Additionally, Enterprise Architecture tools supporting a View Manager can help further, still. Views are essentially a real-time snapshot of information, based on parameters decided by the user. Manipulating data into different views by changing the parameters can give an organization a better idea of what to prioritize (through sorting), but also give the organization a generally more manageable representation of data by removing everything that isn’t relevant.
In the section above, we talked about the merits of sifting through data, to de-clutter and help the information be deciphered more readily. But although data is far easier to understand in smaller, specific chunks, it needs to be whole in the first place to be truly accurate.
Many businesses cite wanting to improve communication across the organization – especially communication that happens between departments. Often, they want to improve communication in the literal sense – sharing ideas, updates and goals through some form of conversation. However, what some organizations are missing, is that these issues in communication are often rooted in misalignment of departments.
Businesses are forced into giving updates – verbal, written or otherwise – because the departments aren’t transparent enough for employees and other departments to see for themselves. Departments operate as siloed, independent areas of the business, instead of as smaller parts of one larger cohesive unit.
Data suffers because of this too. If data is fragmented across different departmental silos that aren’t implicitly open to one another, the data will never show the full picture.
Enterprise Architecture works to align these departments by instilling a common perspective of the current state of the architecture and wider business, as mentioned above. But it also helps align the business in its processes and systems, by highlighting areas of duplication (where two systems/processes are used in place of one), enabling the organization to move to one system, and unify the back end data contained in both.