Two months after the Facebook data scandal broke shockwaves can still be felt. And, while the stock has bounced back, at the height of the news cycle the stock fell more than 24 percent, losing roughly $134 billion in market value. This epic data trauma event was directly tied to poor data governance and data management practices.
However, Facebook isn’t the only company that has suffered data trauma. Look at Equifax, Yahoo and Aetna. The list goes on and on. Yahoo’s failure to disclose its data breach drove its purchase by Verizon down by $1 billion.
However, beyond the immediate financial implications there are long term (and often irreparable) damage to a company’s brand and reputation. A new IBM Cybersecurity and Privacy survey shows that 78 percent of U.S. respondents say a company's ability to keep their data private is "extremely important" and only 20 percent "completely trust" organizations they interact with to maintain the privacy of their data.
Just as insidious as the lack of public trust, the organization itself no longer trusts its data, using it less, losing that key source of business and opportunity intelligence. All aspects of the business suffer, from customer relationships to new product/service/market development.
And now with the European Union’s General Data Protection Regulation (GDPR) in effect, we can expect to see many organizations in the headlines for failed data practices … and with steep financial penalties businesses will surely be feeling pain.
Before trauma, assume drama
The amount of data being created every second of every day is truly mind blowing. By 2020, an estimated 1.7 megabytes of new data will be created every second by every human on the planet, according to Bernard Marr in “Big Data: 20 Mind-Boggling Facts Everyone Must Read.”
From digital transformation to AI, with big data comes big problems. Every day, enterprises are struggling with data drama. The drama typically starts at the board or C-level with two questions: what data do we have, and where is it?
These seemingly easy questions often lead to a fire drill that trickles down across the organization … teams scrambling to find answers feeling stress, embarrassment and nervous promises of a future state when this merry-go-round stops.
Why all the drama? Simple. Organizations don’t have visibility and control of their data or architecture that delivers it. It is frustrating and painful. But how do you fix the drama?
Data nirvana is the end state where an organization has full visibility and control of its data so that it is properly and effectively used, trusted and impactful at all levels of the business. Data value and impact should also be easy to identify and measure.
Reaching data nirvana starts with proper data governance.
Data governance enables data driven business, it is also instrumental to a sound data privacy and security strategy. It’s also the enabling factor of the enterprise data management suite that ensures data quality, so organizations can have greater trust in their data. It ensures that any data created is properly stored, tagged and assigned the context needed to prevent corruption or loss as it moves through the production line – greatly enhancing data discovery.
In addition to improving data quality, aiding in regulatory compliance, and making practices like tracing data lineage easier, sound data governance helps organizations be proactive with their data, using it to drive revenue. They can make better decisions faster and negate the likelihood of costly mistakes and disastrous data breaches.
Data governance is a must for understanding critical data within a business context, tracking its physical existence and lineage, and maximizing its security, quality and value. So, how do you implement it as an enterprise initiative?
The five steps to data governance nirvana:
- Initiative Sponsorship. Without executive sponsorship, you’ll have difficulty obtaining the funding, resources, support and alignment necessary for successful DG.
- Organizational Support. DG needs to be integrated into the data stewardship teams and wider culture. It also requires funding.
- Team Resources. Most successful organizations have established a formal data management group at the enterprise level. As a foundational component of enterprise data management, DG would reside in such a group.
- Enterprise Data Management Methodology. DG is foundational to enterprise data management. Without the other essential components (e.g., metadata management, enterprise data architecture, data quality management), DG will be struggle.
- Delivery Capability. Successful and sustainable DG initiatives are supported by specialized tools, which are scoped as part of the DG initiative’s technical requirements.
Bringing it back to Facebook. How could they have avoided the data trauma?
With a more thorough data governance initiative and a better understanding of data assets, their lineage and useful shelf-life, and the privileges behind their access, Facebook likely could have gotten ahead of the problem and quelled it before it became an issue. Sometimes erasure is the best approach if the reward from keeping data onboard is outweighed by the risk.
The good news is that with these big triggering data news events and with GDPR in effect, businesses must make data governance an even greater priority. And in the end, the business will be able to keep track of its data, improve data quality for better decisions, monitor it and ultimately be able to answer what data do we have, and where is it?
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