Chances are the answers to this question have been quite varied, most likely due to the specific industry targeted in each article or presentation. While there may be varying answers, there is a common connection: Data, no matter the industry, is becoming increasingly valuable.
Different organizations are realizing different value using data, some of the bullets read like:
- Run more efficiently
- Be more competitive
- Evaluate performance
- Support key business decisions
- Monetizing the data
These problems have existed for long and data could/should have been used for generating the answers. So let’s put it another way – Data has always been valuable, we are just learning new ways of realizing that value.
The general feeling amongst executives is that we need to something with our data. The what and the how are continuously evolving as organizations derive more value out of their data. The next logical questions –
Where do I start?
Companies of all sizes want to take advantage of these benefits and unlock the maximum value from their data. The first step for an organization would be to understand the data and the business functions around that data. Connecting the data and developing this shared understanding of the data is one of the first ROI’s of the data governance initiatives within organizations.
Whether you are a small company starting from scratch or you have a seasoned team of data experts, it’s important for everyone to be on the same page. By starting with people and processes you will also have a better understanding of what technology solution will meet the needs of your organization.
How do I build that shared understanding?
The goal should be to start small and realize value quickly. Then re-iterate and expand the initiative with broader goals. There lies a tricky question while treading this path. While it is easy to get lost in the now, it is highly critical that you pave the path for the initiatives coming ahead.
Shared understanding across different stakeholders can be hard to achieve. Having a shared environment for stakeholders to access information and collaborate in curating it will help.
While it seems easy and appealing to start with a spreadsheet and many do so, such approach doesn’t promote sharing and collaboration, nor does it provide a path for growth and expansion of the governance practices. Data governance tools, on the other hand, can address both challenges. Using these tools also gets your organization to gain indirectly from the experience of the other data governance initiates and best practices prevailing in the field.
As a matter of fact recent results of a 2016 FIMA survey and several recent articles from consulting firms that specialize in best practices for data governance, confirm that there is a growing realization that having the right data governance software solution is a critical factor for effective data governance.
So, what to look for while choosing a tool?
When considering your technology requirements consider that most organizations have data coming in from a variety of sources and at a faster pace than ever before. Often, this results in mischaracterized data and more data silos.
Since data governance initiatives deliver increased value over time, it is important to invest in a technology which adapts quickly to this ever changing landscape of data sources. The choice of the right technical platform is critical for future proofing the data governance initiatives of an organization. A semantic approach to information management and data governance makes it easier to reconcile and standardize the data in a single, organized system.
A semantic system can help data stewards create new connections and relationships between data and add any information needed to describe it, document its sources and so on. As a result, organizations will no longer have to rely on a few individuals who know what data sources a particular field in a report was derived from or how each department defines a term.
In this new environment, all people who work with an organization’s data are far more empowered, resulting in faster, more accurate decision-making and timely action at every level.
One example of a major global bank taking advantage of semantic technology to dramatically extend their data governance capabilities is by using it to build a map of their application and data landscape. They are creating lineage records by loading information about thousand’s of applications and data flow dependencies into their semantics-based data governance solution.
In order to comply with regulations, financial services firms have to keep very detailed historical data. Increasingly, this firm is offloading historical data from relational databases to a data lake. However, in doing so, they need to ensure that data in the lake is stored in a way that makes it easy to find and use for reporting and analysis.
Semantics technology lets them connect their relational databases with big data assets by governing them in the same environment and even generating AVRO schemas for the data lake from the structure of relational databases. Overall, this unprecedented depth of integration provides the bank with a better global understanding of their information environment and is essential for regulatory compliance monitoring and reporting.
As mentioned earlier, it’s important for everyone to be on the same page to develop a successful data governance program. Thus, a data governance solution should enable all key data stakeholders in each department to better understand and connect with each other’s data needs. Fortunately, making connections are at the core of semantics-based technology, which makes the semantic information management approach a logical choice for any organization.
(About the author: Irene Polikoff is chief executive officer and co-founder of TopQuadrant)
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