Why Mid-Market Companies are Sitting Pretty When it Comes to Big Data
by Jason Solack, Vice President, Data Analytics, Gongos, Inc.
Organizations large and small have an abundance of data at their disposal and have access to even more. The differences, however, in large part are often due to corporate governance. In fact, an increasingly growing component of this is data governance. And, when it comes to permission and utilization of data, this is certainly a case where size matters—organizational size and capacity, that is.
But first, what is Data Governance? As a self-regulated competency, data governance ensures the control, security and quality of enterprise data within an organization. It requires the employment of both processes and tools to ensure the ongoing management, oversight, and integrity of the systems and data. As you can imagine, sustaining this level of control over complex systems and evolving data streams that are touched by multiple people is a weighty endeavor. Not to mention, automated processes and APIs programmed to dip into this data add an entirely new dimension of complexity. In fact, this is one of the many reasons companies like yours and mine keep their data on 24/7 lockdown.
Beyond data governance, when it comes to accessibility, tools and skillsets, there are three areas upon which large and mid-market companies differ. For the purposes of this compare-and-contrast exercise, below are broad stroke, yet persistent issues facing today’s organizations.
1. Gaining access to your own organization’s data requires getting permission from the data owners.
- While there is clarity among data ownership relative to individuals, teams and/or systems, it is challenging and even fatiguing to obtain approvals from the powers that be.
- Large investments in security ranging from servers and VPNs, to laptops and mobile devices create protective walls that limit access without layers of protocol.
- Data governance is further scrutinized and strictly enforced due to investor regulations, client/partner compliance, and large and geographically diverse employee base.
- With the politicization of big data, the tighter communications channels, collaborative teams and workplace proximity provided by smaller, privately held companies facilitate a more open ecosystem.
- Less hierarchical organizational structure expedites data access approvals more efficiently.
2. The tools to work with the data can be expensive, unless you leverage open source software.
- Using a more traditional software model (e.g. purchasing commercial software to address large-scale data needs) does not typically scale well financially with big data.
- Long-established organizations tend to be resistant to open source software, as it generally lacks the embedded enterprise support for which they’ve become accustomed.
- Ambiguity and experimentation are a welcomed—and often more solution-seeking—approach to companywide data-related endeavors.
- The “unfortunate” circumstance of limited IT budgets brings about the fortunate consequence to access and leverage the “free” world of open source systems.
3. To work with data one must wear a lot of hats – a single software package simply won’t do the trick.
- Many companies purchase full software stacks that complement and build upon one another, however, they don’t play well with other technology, thereby constricting the ability to weave in other necessary systems.
- Skillsets (and functional teams) are often silod, and often reside on separate campuses, making collaboration far more challenging and arduous.
- A more agnostic and generalist approach to problem-solving pairs well with the open source world where “fail fast” is a vital and encouraged component of learning.
- The notion of a “virtual employee” afforded by open sourcing is a finance officer’s dream (i.e. hiring this skillset in-house may otherwise be cost-prohibitive).
Through these chosen examples, it’s evident to see how mid-market companies have a leg up on the big dogs when it comes to corporate and data governance. Yet, the fire walls of access will not be torn down overnight. Large companies must find creative ways to overcome the barriers that inherently exist in long-established companies.
And while many Global 1000 companies may not embrace Samsung’s stance on open source software anytime soon, this is one case where an old dog is bringing new tricks to bear in the big world of data.
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