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9 Data Governance Blog Posts Every C-Level Exec and Data Professional Should Read

In response to growing interest about data governance (DG), we’ve compiled a list of the data governance blog posts from 2017 you have to read.

Data industry analysts, thought-leaders and commentators largely agree that DG will have a significant influence on 2018 data trends.

Whether in response to tighter regulations (see our GDPR series here) or greater competition in data-driven business, data governance is undergoing an evolution.

Businesses are being encouraged to de-silo data governance efforts, moving the responsibility away from just IT to a more collaborative, company-wide approach. The evolution is overdue, as the siloed nature of DG is largely accredited for the failure of Data Governance 1.0.

Leaving IT to deal with data governance on its own led to a lack of context, gaps and poor data quality because the cataloging of data elements wasn’t carried out by the people who actually use the data.

We believe the following data governance blog posts will help you catch up on everything DG, so you can transition your business from Data Governance 1.0 to Data Governance 2.0.

Top 9 Data Governance Blog Posts

 

Data Governance 2.0: Collaborative Data Governance

Data Governance 1.0 has been too isolated to be truly effective, and so a new, collaborative data governance approach is necessary.

 

What Is Data Governance?

Dataversity gives its take on the definition of data governance and outlines some of its benefits.

 

Data Governance: Your Engine for Driving Results

As organizations seek to unlock more value by implementing a wider analytics footprint across more business functions, data governance will guide their journeys.

 

Data Will Change the World, and We Must Get Its governance Right

The Guardian writes that while the opportunities presented by ever-growing data are abundant, so too are the threats.

 

An Agile Data Governance Foundation for Building the Data-Driven Enterprise

The data-driven enterprise is the cornerstone of modern business, and good data governance is a key enabler.

 

He Who Rules the Data, Rules The World: A Brief History of Data Governance

According to Forbes, data rules the world, but who rules the data? The companies that collect it? The servers that store it? The cables and satellites that transmit it? Or the laws that keep it flowing into the right hands—and away from the wrong ones?

 

The Top 6 Benefits of Data Governance

It’s important we recognize the benefits of data governance beyond General Data Protection Regulation (GDPR) compliance, and we compile them here.

 

The Secret to Data Governance Success

For many organizations just getting started with DG, implementation will be reactionary because of its mandatory status under (GDPR). As such, businesses might be tempted into doing the bare minimum to meet compliance standards. But done right, data governance is a key enabler for any data-driven business.

 

Data Governance and Risk Management

As data continues to be more deeply intertwined in our day-to-day lives, the associated risks are growing in number and severity. So there’s increasing scrutiny on organizations’ data governance practices – and for good reason.

 

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Data Governance 2.0: Biggest Data Shakeups to Watch in 2018

This year we’ll see some huge changes in how we collect, store and use data, with Data Governance 2.0 at the epicenter. For many organizations, these changes will be reactive, as they have to adapt to new regulations. Others will use regulatory change as a catalyst to be proactive with their data. Ideally, you’ll want to be in the latter category.

Data-driven businesses and their relevant industries are experiencing unprecedented rates of change.

Not only has the amount of data exploded in recent years, we’re now seeing the amount of insights data provides increase too. In essence, we’re finding smaller units of data more useful, but also collecting more than ever before.

At present, data opportunities are seemingly boundless, and we’ve barely begun to scratch the surface. So here are some of the biggest data shakeups to expect in 2018.

2018 data governance 2.0

GDPR

The General Data Protection Regulation (GDPR) has organizations scrambling. Penalties for non-compliance go into immediate effect on May 25, with hefty fines – up to €20 million or 4 percent of the company’s global annual turnover, whichever is greater.

Although it’s a European mandate, the fact is that all organizations trading with Europe, not just those based within the continent, must comply. Because of this, we’re seeing a global effort to introduce new policies, procedures and systems to prepare on a scale we haven’t seen since Y2K.

It’s easy to view mandated change of this nature as a burden. But the change is well overdue – both from a regulatory and commercial point of view.

In terms of regulation, a globalized approach had to be introduced. Data doesn’t adhere to borders in the same way as physical materials, and conflicting standards within different states, countries and continents have made sufficient regulation difficult.

In terms of business, many organizations have stifled their digital transformation efforts to become data-driven, neglecting to properly govern the data that would enable it. GDPR requires a collaborative approach to data governance (DG), and when done right, will add value as well as achieve compliance.

Rise of Data Governance 2.0

Data Governance 1.0 has failed to gain a foothold because of its siloed, un-collaborative nature. It lacks focus on business outcomes, so business leaders have struggled to see the value in it. Therefore, IT has been responsible for cataloging data elements to support search and discovery, yet they rarely understand the data’s context due to being removed from the operational side of the business. This means data is often incomplete and of poor quality, making effective data-driven business impossible.

Company-wide responsibility for data governance, encouraged by the new standards of regulation, stand to fundamentally change the way businesses view data governance. Data Governance 2.0 and its collaborative approach will become the new normal, meaning those with the most to gain from data and its insights will be directly involved in its governance.

This means more buy-in from C-level executives, line managers, etc. It means greater accountability, as well as improved discoverability and traceability. Most of all, it means better data quality that leads to faster, better decisions made with more confidence.

Escalated Digital Transformation

Digital transformation and its prominence won’t diminish this year. In fact, thanks to Data Governance 2.0, digital transformation is poised to accelerate – not slow down.

Organizations that commit to data governance beyond just compliance will reap the rewards. With a stronger data governance foundation, organizations undergoing digital transformation will enjoy a number of significant benefits, including better decision making, greater operational efficiency, improved data understanding and lineage, greater data quality, and increased revenue.

Data-driven exemplars, such as Amazon, Airbnb and Uber, have enjoyed these benefits, using them to disrupt and then dominate their respective industries. But you don’t have to be Amazon-sized to achieve them. De-siloing DG and treating it as a strategic initiative is the first step to data-driven success.

Data as Valuable Asset

Data became more valuable than oil in 2017. Yet despite this assessment, many businesses neglect to treat their data as a prized asset. For context, the Industrial Revolution was powered by machinery that had to be well-maintained to function properly, as downtime would result in loss. Such machinery adds value to a business, so it is inherently valuable.

Fast forward to 2018 with data at center stage. Because data is the value driver, the data itself is valuable. Just because it doesn’t have a physical presence doesn’t mean it is any less important than physical assets. So businesses will need to change how they perceive their data, and this is the year in which this thinking is likely to change.

DG-Enabled AI and IoT

Artificial Intelligence (AI) and the Internet of Things (IoT) aren’t new concepts. However, they’re yet to be fully realized with businesses still competing to carve a slice out of these markets.

As the two continue to expand, they will hypercharge the already accelerating volume of data – specifically unstructured data – to almost unfathomable levels. The three Vs of data tend to escalate in unison. As the volume increases, so does the velocity and speed at which data must be processed. The variety of data – mostly unstructured in these cases – also increases, so to manage it, businesses will need to put effective data governance in place.

Alongside strong data governance practices, more and more businesses will turn to NoSQL databases to manage diverse data types.

For more best practices in business and IT alignment, and successfully implementing Data Governance 2.0, click here.

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Data Governance 2.0 for Financial Services

The tempo of change for data-driven business is increasing, with the financial services industry under particular pressure. For banks, credit card, insurance, mortgage companies and the like, data governance must be done right.

Consumer trust is waning across the board, and after several high-profile data breaches, trust in the way in which organizations handle and process data is lower still.

Equifax suffered 2017’s largest breach and the fifth largest in history. The subsequent plummet in stock value should have sent a stark warning to other financial service organizations. As of November, the credit bureau reported $87.5 million in expenses following the breach, and the PR fallout plummeted profits by 27 percent.

But it could be said that Equifax was lucky. If the breach had occurred following the implementation of the General Data Protection Regulation (GDPR), it also would have been hit with hefty sanctions. Come May of 2018, fines for GDPR noncompliance will reach an upper limit of €20 million or 4 percent of annual turnover – whichever is greater.

Data governance’s purpose – knowing where your data is and who is accountable for it – is a critical factor in preventing such breaches. It’s also a prerequisite for compliance as organizations need to demonstrate they have taken reasonable precautions in governing.

Equifax’s situation clearly implies that financial services organizations need to review and improve their data governance. As a concept, data governance for regulatory compliance is widely understood. Such regulations were introduced a decade ago in response to the financial crisis.

However, data governance’s role goes far beyond just preventing data breaches and meeting compliance standards.

Data Governance 2.0 for Financial Services

Data governance has struggled to gain a foothold because the value-adds have been unclear and largely untested. After new regulations for DG were introduced for the financial services industry, most organizations didn’t bother implementing company-wide approaches, instead opting to leave it as an IT-managed program.

So IT was responsible for cataloging data elements to support search and discovery, yet they rarely knew which bits of data were related or important to the wider business. This resulted in poor data quality and completeness, and left data and its governance siloed so data-driven business was hard to do.

Now data-driven business is more common – truly data-driven business with data at the core of strategy. The precedent has been set thanks to Airbnb, Amazon and Uber being some of the first businesses to use data to turn their respective markets on their heads.

These businesses don’t just use data to target new customers, they use data to help dictate strategy, find new gaps in the market, and highlight areas for performance improvement.

With that in mind, there’s a lot the financial services industry can learn and apply. FinTech start-ups continue to shake up the sector, and although the financial services industry is a more difficult industry to topple, traditional financial organizations need to innovate to stay competitive.

Alongside compliance, the aforementioned purpose of DG – knowing where data is stored and who is accountable for it – is also a critical factor in fostering agility, squashing times to market, and improving overall business efficiency, especially in the financial services industry.

In fact, the biggest advantage of data governance for financial services is making quality and reliable data readily available to the right people, so the right decisions can be made faster. Good DG also helps these companies better capitalize on revenue opportunities, solve customer issues, and identify fraud while improving the standard for reporting on such data.

These benefits are especially important within financial services because their big decisions have big financial impacts. To make such decisions, they need to trust that the data they use is sound and efficiently traceable.

Such data accountability is paramount. To achieve it, organizations must move away from the old, ineffective Data Governance 1.0 approach to the collaborative, outcome-driven Data Governance 2.0.

This means introducing data governance to the wider business, not just leaving it to IT. It means line-of-business managers and C-level executives take leading roles in data governance. But most importantly, it means a more efficient approach to data-driven business for increased revenue. A BCG study implies that financial services could be leaving up to $30 billion on the table.

Although the temptation to just meet regulatory compliance might be strong, the financial services industry clearly has a lot to gain from taking the extra step. Therefore, new regulations don’t have to be seen as a burden but as a catalyst for greater, proactive and forward-thinking change.

For more best practices in business and IT alignment, and successfully implementing data governance, click here.

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The Key to Improving Business and IT Alignment

Fostering business and IT alignment has become more important than ever.

Gone are the days when IT was a fringe department, resigned to providing support. But after so long on the sidelines, many businesses still struggle to bring IT into the fold, ensuring its alignment with the wider business. But this should be a priority for any data-driven enterprise.

On a fundamental level, it requires a change of perception and culture. The stereotype of basement-housed IT teams was widely acknowledged and satirized. It formed the basis of the popular British sitcom The IT Crowd, which focused on the escapades of three IT staff members in the dingy basement of a huge corporation. Often their best professional input was “turn it off and on again.”

Today, the idea of such a small IT team supporting a huge business is almost too ridiculous to satirize..

Bring IT Out of the Basement

In the age of data-driven business, IT now takes center stage. And it has been promoted out of the basement – at least in principle.

Although IT has moved away from its legacy of support and “keeping the lights on,” many businesses still have a long way to go in fostering business and IT alignment.

But the data-driven nature of modern business demands it. Not only is the wider business responsible for understanding, making use of and capitalizing on data; the business as a whole, including IT, is responsible for upholding the regulations associated with it.

Fostering Business and IT Alignment

The key here, then, is a collaborative data governance program. For business and IT to be sufficiently aligned, the business needs access to all the data relevant to its various departments, whenever it is needed.

This means the right data of the right quality, regardless of format or where it is stored, must be available for use, but only by the right people for the right purpose.

Therefore, the notion that IT can manage and govern data independently is unthinkable. It’s the business that will use data the most, and it’s the business that stands to lose the most when decisions are made based on bad data.

Companies had long neglected this reality. Past efforts to implement data governance programs (Data Governance 1.0) often fell short in adding value. When left solely to IT, Data Governance 1.0 was solely focussed on cataloging data. This, and the disparity between IT and the business meant the meaning of data assets, and their relationship within the wider data landscape, was unclear.

This is what Data Governance 2.0, and its innately collaborative nature aims to resolve. With Data Governance 2.0, the strategy encompasses defined business, IT and business-IT requirements.

Data Governance for Business and IT Alignment

Business Requirements: The business is responsible for defining data, including setting standards for the ownership and meaning of data assets so the organization can use data with a uniformed approach.

IT Requirements: IT manages data at the base level: from mapping data – which may exist across various systems, reports and data models – to physical data assets (databases, files, documents and so on). This, in turn, enables IT to accurately assume the impact of things like data-glossary changes across the enterprise. That’s a key enabling factor in enterprise architecture, allowing for cost-effective and thorough risk management by identifying data points that require the most security.

Business-IT Requirements: A joint effort allows IT to effectively publish data to relevant roles/people. This way, the business can readily use data that is meaningful and relevant to it across various departments, while maintaining compliance with existing and upcoming data protection regulations.

Additionally, those using data can follow data chains back to the source, providing a wider, less ambiguous view of data assets and thus reducing the likelihood of poor decision-making.

For more best practices in business and IT alignment, and successfully implementing data governance, click here.

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The Top 6 Benefits of Data Governance

It’s important we recognize the data governance benefits (DG) beyond General Data Protection Regulation (GDPR) compliance.

Data governance is mandatory for GDPR, so the incentive in implementing it before the May 2018 deadline is clear. However, the timeline’s pressures could also be viewed as somewhat of a double-edged sword.

On the one hand, introducing a mandate shines a spotlight on a practice many businesses have neglected. A First San Francisco Partners (FSFP) study found that only 47.9% of respondents have a DG program in place.

We are beginning to see the shift, though. The FSFP study also found that 29% of businesses are in the early stages of a DG roll-out, with an additional 19% at the research and planning stage.

The sword’s other edge is that much of this swing is reactionary, encouraged by the fast-approaching GDPR deadline.

By introducing a mandate for data governance on a timeline, many businesses will be tempted to do the bare minimum just to meet the standards for compliance.

Unfortunately, that means the following data governance benefits will be left on the table.

Data Governance

Data Governance Benefits

Better Decision-Making

One of the key benefits of data governance is better decision-making. This applies to both the decision-making process, as well as the decisions themselves.

Well-governed data is more discoverable, making it easier for the relevant parties to find useful insights. It also means decisions will be based on the right data, ensuring greater accuracy and trust.

Operational Efficiency

Data is incredibly valuable in the age of data-driven business. Therefore, it should be treated as the asset it is.

Consider a manufacturing business’ physical assets, for example. Well-run manufacturing businesses ensure their production-line machinery undergoes regular inspections, maintenance and upgrades so the line operates smoothly with limited down-time.

The same approach should apply to data.

Improved Data Understanding and Lineage

Data governance is about understanding what your data is and where it is stored. When implemented well, data governance provides a comprehensive view of all data assets.

It also provides greater accountability. By assigning permissions, it is far easier to determine who’s responsible for specific data.

Greater Data Quality

As data governance aids in discoverability, businesses with effective data governance programs also benefit from improved data quality. Although technically two separate initiatives, some of their goals overlap.

These include, but are not limited to, the standardization of data and its consistency. One way to clearly differentiate the two programs is to consider the questions posed by each field.

Data quality wants to know how useful and complete data is, whereas data governance wants to know where the data is and who is responsible for it.

Data governance improves data quality, because answering the latter makes it easier to tackle the former.

Regulatory Compliance

As mentioned in the introduction, if you haven’t yet adopted a data governance program, compliance is perhaps the best reason to do so. Hefty fines with an upper limit of €20 million or 4% or annual global turnover – whichever is greater – are nothing to baulk at.

That said, GDPR fines are only incentivising something you should already be keen to do. Data-driven businesses that aren’t enjoying the aforementioned benefits are fundamentally stifling their own performance.

It could even be argued that to be truly data-driven, data governance is a must.

Increased Revenue

Driving revenue should, in fact, be higher on the DG benefit list. However, it’s positioned here because the aforementioned benefits cumulatively influence it.

All the benefits of data governance addressed above help businesses make better, faster decisions with more certainty.

It means that less costly errors – in the form of false starts and even data breaches – are made. It means that you spend less money by managing risk, and closing the most vulnerable holes in your business’ security, instead of more money retrospectively, dealing with PR and financial fallout.

What You Need to Do

Considering the benefits and their accumulative real-term value , data-driven organizations can’t afford to leave data governance to IT alone. This is why Data Governance 1.0 has ultimately failed.

But even now, 23% of businesses in the FSFP study said information technology leads their data governance efforts.

In the current climate, this mind-set is inherently flawed. We’ve reached a new business age in which data is considered more valuable than oil. Yet many businesses are still reluctant in treating data with the same care as their physical assets.

This needs to change. If data is indeed this valuable, we need to treat data governance as a strategic initiative.

Data Governance 2.0 involves the entire enterprise, including department heads and C-level executives, who stand to benefit from data insights gained throughout the process.

For more data governance best practices and useful statistics, download our resource: Data Governance Is Everyone’s Business.

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Data Governance and Risk Management

Risk management is crucial for any data-driven business. Former FBI Director Robert Mueller famously said, “There are only two types of companies: those that have been hacked and those that will be.” This statement struck a chord when first spoken in 2012, and the strings are still ringing.

As data continues to be more deeply intertwined in our day-to-day lives, the associated risks are growing in number and severity. So, there’s increasing scrutiny on organizations’ data governance practices – and for good reason.

Governmental scrutiny, in particular, is gearing up. The General Data Protection Regulation (GDPR) introduces strict formality in the way data is governed across the European Union, including organizations outside the EU that wish to do business with its member nations.

But in certain sectors, public scrutiny is just as – if not more – important to consider. We’ve been talking since September about the data breach at Equifax, which has just been hit with a 50-state, class-action lawsuit.

And we just learned that Uber was hacked, resulting in the personal data of 57 million customers and Uber drivers being stolen. What’s more, the company concealed the breach for more than a year.

Whether we’re talking about financial or reputational damage, it’s absolutely clear that bad data governance is bad business.

Risk Management Data Governance

Risk Management for IoT

Think about the Internet of Things (IoT) for a moment …

IoT devices are gaining more stock in daily life – from the mundane of smart refrigerators and thermostats to the formidable of medical devices. Despite the degree of severity here, personal data is personal data, and the steps taken to mitigate security risks must be evidenced to be compliant.

Data governance is fundamental to risk mitigation and management. That’s because data governance is largely concerned with understanding two key things: where your data is kept and what it’s used for. Considering the scope of IoT data, this is no easy feat.

Estimates indicate that by 2020, 50 billion connected devices will be in circulation. Misunderstanding where and what this data is could leave the records of millions exposed.

On top of the already pressing need for effective data governance for risk management, we’re constantly approaching uncharted territories in data applications.

Lessons from Driverless Cars

The driverless car industry is one such example on the not-too-distant horizon.

Businesses from BMW to Google are scrambling to win the driverless car race, but fears that driverless cars could be hacked are well founded. Earlier this year, a Deloitte Insights report considered the likely risks of introducing autonomous vehicles onto public roads.

It reads, “The very innovations that aim to enhance the way we move from place to place entail first-order cybersecurity challenges.” It also indicates that organizations need to make radical changes in how they view cybersecurity to ensure connected vehicles are secure, vigilant and resilient:

  • Secure – Work on risk management by prioritizing sensitive assets to balance security and productivity.
  • Vigilant – Integrate threat data, IT data and business data to be equipped with context-rich alerts to prioritize incident handling and streamline incident investigation.
  • Resilient – Rapidly adapt and respond to internal or external changes to continue operations with limited business impacts.

The first thing organizations should take away is that this advice applies to the handling of all sensitive data; it’s by no means exclusive to autonomous vehicles. And second, security, vigilance and resilience all are enabled by data governance.

Data Governance Leads the Way

As discussed, data governance is about knowing where your data is and what it’s used for.  This understanding indicates where security resources should be spent to help mitigate data breaches.

Data governance also makes threat data, IT data and business data more readily discoverable, understandable and applicable, meaning any decisions you make regarding security investments are well informed.

In terms of resilience and the ability to rapidly respond, businesses must be agile and collaborative, points of contention in traditional data governance. However, Data Governance 2.0 as defined by Forrester addresses agility in terms of “just enough controls for managing risk, which enables broader and more insightful use of data required by the evolving needs of an expanding business ecosystem.”

As GDPR looms ever near, an understanding of data governance best practices will be indispensable. To get the best of them, click here.

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