erwin Expert Blog

The Secret to Data Governance Success

Data governance (DG) 1.0 has struggled to get off the ground, but now DG is required for General Data Protection Regulation (GDPR) compliance, so businesses need a new approach to achieve data governance success.

When properly implemented, data governance is an empowering tool for businesses. But 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.

The data governance success story

The first step in achieving data governance success is to define what it should look like. With clear goals, businesses can take the collaborative approach data governance requires – with the whole company pulling in the same direction – for proper implementation.

Data governance success typically manifests itself as:

  • Defined data: Consistency in how a business defines data means it can be understood across divisions, enabling greater potential for collaboration.
  • Guaranteed quality: Trusted data eases the decision-making process, allowing a business to make both faster and more assured decisions that lead to fewer false starts.
  • Compliance and security: With data governance, neither are sacrificed even as the volume of data and the accessibility of such data expands when silos are broken down. Of course, this is a key component of any business putting data at the heart of their operations.

With this in mind, your next steps should be to introduce Data Governance 2.0 by addressing the baggage of its predecessor, and learning from it. Two key lessons to take away: 1) treat data like physical assets and 2) treat data governance itself as a strategic initiative.

Treat data like physical assets

This year data went mainstream. In the two years prior, more data was created than in the whole of human history. With more and more businesses acknowledging the value of data insights, analysts correctly predicted that data would be considered “more valuable than oil” in 2017.

Businesses that have already experienced data-driven success recognized data’s potential value early on. Yet for the most part, data typically has been considered separate from physical assets. It has, therefore, been given subdued levels of vigilance compared to physical assets that are often tracked, maintained and updated to maintain peak operational performance.

Take the belt on a production line, for example. Lack of maintenance leads to faults, production delays, increased time to market and ultimately stifled profits and overall performance. Continuous neglect results in more costly repairs not to mention the costs related to down-time. The same is true for data.

If your data isn’t governed with due care, silos and bottlenecks easily develop, shutting off access to employees who need it and slowing down everything from data discovery to analytics.

Persistent neglect means your business will not understand where your most sensitive data is stored, making it more susceptible to breaches. As Equifax and Uber have demonstrated recently, such data breaches are costly enough without the fines that soon will be levied because of  GDPR.

Considering recent revelations surrounding the value of data, plus the imminent regulatory changes, it’s time businesses begin treating data with as much respect and care as their physical assets.

Treat data governance as a strategic initiative

The problem with historical data governance implementation is that it was seen exclusively as an IT-driven project. Therefore, governance was shoehorned through a collection of siloed tools with no input from the wider organization. More specifically, from line managers and C-Level executives to whom governed data is arguably most valuable.

In recent years, the problems with this approach have become further exacerbated by:

  • A demand for big data and analytics-driven growth
  • A need for digital trust in business dealings between organizations or between businesses and consumers
  • Upcoming personal data removal mandates with stronger individual privacy protections

In the current business climate, more than 35 percent of companies use information to identify new business opportunities and predict future trends and behavior. An additional 50 percent agree that information is highly valued for decision-making, and should be treated as an asset (

Clearly, it’s paramount that organizations view their data as a valuable asset, and the governing of their data as a strategic initiative in and of itself.

For more best practices in achieving data governance success, click here.

Data governance is everyone's business

erwin Expert Blog

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.

This rings especially true when considering the imminent implementation of the General Data Protection Regulation (GDPR). Compliance is required from all EU-based companies and those trading with the EU.

It’s extremely likely that your business falls under GDPR’s scope. Failure to comply will leave your company liable for penalties up to €20 million or 4% or annual global turnover – whichever is greater.

With the amount of data a modern business has to manage, and the copious access points, GDPR compliance will require everyone to sing from the same hymn sheet.

This is where Data Governance 2.0 comes in. As defined by Forrester, it is “an agile approach to data governance focused on 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.”

The principles of Data Governance 2.0 were designed with modern, data-driven business in mind. This new approach acknowledges the demand for collaborative data governance, tears down organizational silos, and spreads responsibilities across more roles.

Collaborative Data Governance

Collaborative Data Governance – Shattering Silos

As addressed above, modern businesses must deal with volumes of data that legacy systems and policies just weren’t designed to manage. This problem is exacerbated by the variety of data, both structured and unstructured, historically managed by different departments within an organization.

To shatter such silos, organizations can leverage a collaborative data governance approach to foster better data use and accountability. A governance tool that can sort, regulate and manage data access through secure checkpoints and assigned roles is key. Then the right data of the right quality, regardless or format or location, is available to the right people for the right purpose.

Such a data governance tool is paramount not only to help ensure GDPR compliance but also for effective business operations. It’s important to stress that data governance is a key revenue driver.

In this digital age, data is more valuable than oil. But as with oil, it must be refined.

Collaborative Data Governance – The Data Refinery

Data Governance 1.0 was mainly concerned with cataloging data to support search and discovery. However, it fell short in adding value because it neglected the meaning of data assets and their relationships within the wider data landscape.

Many of the IT professionals involved in data governance recognized this, but calls for business leaders to be more active in governance often fell on deaf ears. Now that data has become a more critical business asset, we’re starting to see a shift.

Collaborative data governance encourages involvement throughout the organizational hierarchy. This is especially important now that business leaders, from CMOs to CTOs, are intrinsically involved in data management on a day-to-day basis.

As the best placed individuals in an organization to advocate and implement change, bringing ranking business leaders into the fold helps inform and enable the effort’s return on investment – both in limiting data exposures and driving new opportunities.

In the case of the CMO, data analysis might indicate that email open rates exceed targets, but click-through rates are underperforming. The CMO then can adjust content strategy to provide prospects with more relevant information and calls to action.

To learn more about collaborative data governance and the tool to foster this approach, click here.

Data governance is everyone's business

erwin Expert Blog

Data-Driven Business Transformation: the Data Foundation

In light of data’s prominence in modern business, organizations need to ensure they have a strong data foundation in place.

The ascent of data’s value has been as steep as it is staggering. In 2016, it was suggested that more data would be created in 2017 than in the previous 5000 years of humanity.

But what’s even more shocking is that the peak still not may not even be in sight.

To put its value into context, the five most valuable businesses in the world all deal in data (Alphabet/Google, Amazon, Apple, Facebook and Microsoft). It’s even overtaken oil as the world’s most valuable resource.

Yet, even with data’s value being as high as it is, there’s still a long way to go. Many businesses are still getting to grips with data storage, management and analysis.

Fortune 1000 companies, for example, could earn another $65 million in net income, with access to just 10 percent more of their data (from Data-Driven Business Transformation 2017).

We’re already witnessing the beginnings of this increased potential across various industries. Data-driven businesses such as Airbnb, Uber and Netflix are all dominating, disrupting and revolutionizing their respective sectors.

Interestingly, although they provide very different services for the consumer, the organizations themselves all identify as data companies. This simple change in perception and outlook stresses the importance of data to their business models. For them, data analysis isn’t just an arm of the business… It’s the core.

Data foundation

The dominating data-driven businesses use data to influence almost everything. How decisions are made, how processes could be improved, and where the business should focus its innovation efforts.

However, simply establishing that your business could (and should) be getting more out of data, doesn’t necessarily mean you’re ready to reap the rewards.

In fact, a pre-emptive dive into a data strategy could in fact, slow your digital transformation efforts down. Hurried software investments in response to disruption can lead to teething problems in your strategy’s adoption, and shelfware, wasting time and money.

Additionally, oversights in the strategy’s implementation will stifle the very potential effectiveness you’re hoping to benefit from.

Therefore, when deciding to bolster your data efforts, a great place to start is to consider the ‘three Vs’.

The three Vs

The three Vs of data are volume, variety and velocity. Volume references the amount of data; variety, its different sources; and velocity, the speed in which it must be processed.

When you’re ready to start focusing on the business outcomes that you hope data will provide, you can also stretch those three Vs, to five. The five Vs include the aforementioned, and also acknowledge veracity (confidence in the data’s accuracy) and value, but for now we’ll stick to three.

As discussed, the total amount of data in the world is staggering. But the total data available to any one business can be huge in its own right (depending on the extent of your data strategy).

Unsurprisingly, vast volumes of data are sourced from a vast amount of potential sources. It takes dedicated tools to be processed. Even then, the sources are often disparate, and very unlikely to offer worthwhile insight in a vacuum.

This is why it’s so important to have an assured data foundation upon which to build a data platform on.

A solid data foundation

The Any2 approach is a strategy for housing, sorting and analysing data that aims to be that very foundation on which you build your data strategy.

Shorthand for Any Data, Anywhere, Anycan help clean up the disparate noise, and let businesses drill down on, and effectively analyze the data in order to yield more reliable and informative results.

It’s especially important today, as data sources are becoming increasingly unstructured, and so more difficult to manage.

Big data for example, can consist of click stream data, Internet of Things data, machine data and social media data. The sources need to be rationalized and correlated so they can be analyzed more effectively.

When it comes to actioning an Anyapproach, a fluid relationship between the various data initiative involved is essential. Those being, Data ModelingEnterprise ArchitectureBusiness Process, and Data Governance.

It also requires collaboration, both in between the aforementioned initiatives, and with the wider business to ensure everybody is working towards the same goal.

With a solid data foundation platform in place, your business can really begin to start realizing data’s potential for itself. You also ensure you’re not left behind as new disruptors enter the market, and your competition continues to evolve.

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