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State of DG: Shocking Number of Organizations Unprepared for GDPR, Is Yours?

The General Data Protection Regulation (GDPR) goes into effect in May, but a new study reveals that most organizations are overwhelmingly unprepared.

The State of Data Governance Report finds that only 6% of respondents consider themselves completely prepared for GDPR. That means a shocking 94% of the organizations surveyed are not ready for what is one of the most important data privacy and security regulations passed in recent years.

Failure to implement data governance (DG) to comply with GDPR will leave these organizations liable for fines of up to €20 million or 4% annual global turnover – whichever is greater.

But the news isn’t all bad; promising signs can be found. Although 46% of those surveyed indicate having “no formal strategy” in place for DG, 42% describe their data governance initiatives as a “work in progress.”

State of DG: Regulatory Compliance Driving Data Governance

Historically, data governance has left a lot to be desired. The value and ROI were insignificant to non-existent, and so executive buy-in and funding also has been low.

Business leaders usually left DG to their IT departments, but that created silos that cut off DG from it’s day to day “data owners” and “data stakeholders,” – in essence, everybody that uses data to drive business. With poor data discovery, lineage and context, data governance was largely abandoned or at least out of sight, out of mind.

Forty-two percent of the organizations participating in the State of DG Report survey indicate that lack of executive support is still a roadblock. But GDPR is spurring new interest in DG because companies must articulate what their data is, where it resides, what controls are in place to protect it, and the measures they will use to address mistakes/breaches.

An effective data governance initiative is critical for the data visibility and categorization needed to comply with GDPR. It also will help assess and prioritize data risks and enable easier verification of GDPR compliance to auditors.

Perhaps this is why 66% of those surveyed for the State of DG Report say understanding and governing enterprise assets has become more important or very important for their executives. And regulatory compliance is in fact the No. 1 driver for data governance.

State of DG: Implementing Data Governance for GDPR

It’s safe to say that organizations should be much further along with GDPR than they are.

The biggest challenge is to establish compliance with their current data architectures and then to build GDPR compliance into the processes for designing and deploying new data sources.

This requires visibility into the strategic roadmap and well-defined processes to govern new data deployments so that constant GDPR retrofits aren’t required.

Thankfully data governance has evolved from a siloed, IT-owned program primarily for data cataloging to support search and discovery. It has given way to proactive, enterprise-wide data governance to support regulatory compliance in addition to data-driven insights for achieving other organizational objectives.

Data Governance 2.0 understands that CTOs, CMOs and other C-level executives and business leaders across the enterprise are involved in data creation, management and use on a day-to-day basis. And GDPR compliance requires that all stakeholders be aware and empowered so that data governance is built in, and part of the culture.

By integrating data governance with enterprise architecture, business process and data modeling, you’ll have a GDPR compliance framework to:

  • Discover and harvest data assets
  • Classify data and create a GDPR inventory
  • Perform GDPR risk analysis
  • Define GDPR controls and standard operating procedures
  • Socialize and apply GDPR requirements across the organization
  • Implement GDPR controls into IT and business roadmaps for “compliance by design”
  • Prove compliance/respond to audits

Is your organization GDPR-ready?

Click here to get your State of DG Report to see how your organization compares to those we surveyed.

Of if you’d like to discuss how to improve your GDPR readiness with one of our solution specialists, click here.

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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.

 

Stay up to date with the latest in data governance by clicking 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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Continuous Business Improvement Depends on Data Governance

In my last post, I explained why organizations need to consider data as an asset rather than a cost center. When we deem something to be valuable, we then need to determine how and when we’ll use it as well as secure it. We do this by establishing standards, policies and processes to define how this asset will be utilized and protected.

Let’s look at the example of an office building. Furniture and equipment are inventoried and tracked. Employees are trained on safety and security, with some developing expertise in the use of specialized equipment. Office managers know which conference rooms and desks are available for use and their locations.

Keeping this office building clean, secure, comfortable and well organized adds to the productivity of its occupants.

Without such office governance, this office building could become unsafe, unsecure, unproductive and underutilized. Do you see the parallel between this office asset example and your data? Transforming data into an asset also relies on effective data governance.

Starting a Continuous Improvement Journey

Continuous improvement

Successful data-driven companies embrace and implement continuous improvement activities to enhance results, providing a structured approach for business improvement projects. Steps include problem identification, data collection, root-cause analysis, planning process changes, implementing the changes and monitoring the results. This cycle is known as the Plan-Do-Check-Act cycle of continuous improvement, or PDCA.

Organizations committed to a continuous improvement culture, based on the PDCA cycle, depend heavily on data at every step.  Business problems can be defined in terms of waste, delays and re-work. These problems need to be quantified with actual measurements to help analysis teams detect and prioritize the next set of improvement activities.

After improvement activities have been completed, it’s important to monitor the results through feedback. It provides evidence of success, and it also helps improvement teams learn about the processes on which to focus.

Data collected about improvement processes will show symptoms of inefficiencies and waste. The analysis team then carries out root-cause analysis to determine the “levers” that can be adjusted to reduce them.  Assumptions and hypotheses will be tested and validated to find the real forces at play so the appropriate management and operational levers can be adjusted accordingly.

Scaling and Sustaining the Improvement Cycle

Companies that implement a PDCA cycle of continuous improvement realize there will be challenges in scaling and sustaining the program across multiple business areas over time.

Data collection can be tedious, especially if the associated data management activities require significant manual activity. It is common that the data available from operating databases has many problems related to quality, security, confidence, accessibility and overall understanding. These are all roadblocks the will delay the improvement activities.

If data isn’t readily available, accessible, trusted or understandable, the analysis and improvement teams can’t do their jobs effectively. This will lead to a slowdown in momentum or cause companies to abandon the improvement approach altogether.  The necessary data to drive the improvement cycle must be in an “asset class” form to sustain the improvement cycle.

Scaling the PDCA cycle involves multiple teams working in different business areas to broaden the reach of the improvement activities. Processes for finance, human resources, operations, sales, supply chain, customer service and IT may all be under analysis and evaluation.

The path to operational excellence is based on the ability to scale and sustain continuous improvement.

How Data Governance Supports the Improvement Cycle

Consider a utility company that operates a physical network delivering energy to customers. The executive team wants to reduce the time it takes for newly constructed assets to go online and reap the financial benefits of commissioning them for service more quickly.

The business improvement team starts gathering performance data from previous construction projects to determine potential areas of improvement.

They soon realize a new work management system was implemented, and the conversion of historical construction data was deemed as “non-critical” to keep the project on schedule and in budget.

The implementation team didn’t view the historical construction data as valuable from an operational perspective, so they archived it rather than covert it to the new system. This decision was made within the context of a “local” project without considering the larger analytics needs of the company.

Unfortunately, data governance was not understood or in place at this utility. If it were, the historical construction data would have been cleansed and converted as part of the new work management system’s deployment. This company failed to recognize this data as an asset with downstream analytics applications.

In this example, the decision not to convert historical data was based on managing cost at the project level. A data investment was not considered. But well-governed data is a true asset. Quality, accessibility, timeliness and understandability are fundamental to the productivity and sustainability of continuous improvement processes.

If your company is implementing any form of program to improve results, such as specialized management systems, balanced scorecards, lean management concepts, Six-Sigma or total quality management, data governance sits is at the core of long-term, sustainable success.

Improvement programs require motivation, energy and commitment at all levels of the organization. To maintain momentum, governed data assets are the key enabler, making it easier and faster to detect and diagnose problems, improve processes and validate results. There’s a direct link between the quality of improvement programs and the data assets that power them.

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Data Governance: Your Engine for Driving Results

In my previous post, I described how organizational success depends on certain building blocks that work in alignment with common business objectives. These building blocks include business activities, data and analytics.

Governance is also one of the required building blocks because it provides cohesion in the standards to align people, processes, data and technology for successful and sustainable results. Although it has been somewhat of an abstract concept, data governance is foundational to helping organizations use data as a corporate asset.

Assets are acquired and used to help organizations execute their business models. Principles of asset management require that assets be cataloged, inventoried, protected and accessible to authorized people with the skills and experience to optimize them.

Assets typically generate more value if they have high levels of utilization. In the context of data, this means governed data assets will be more valuable if they strengthen existing operations and guide improvements, supported by analytics.

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

 A New Perspective on Data

Becoming a data-driven enterprise means making decisions based on empirical evidence, not a “gut feeling.” This transformation requires a clear vision, strategy and disciplined execution. The desired business opportunity must be well thought out, understood and communicated to others – from the C suite to the front lines.

Organizations that want to succeed in the digital age understand that their cultures and therefore their decision-making processes must become more proactive and collaborative. Of course, data is at the core of business performance and continuous improvement.

In this modern era of Big Data, non-traditional data sets generated externally are being blended with traditional data generated internally. As such, a key element of data-driven success involves changing the long-held perspective of data as a cost center, with few if any investments made to unlock its value to the organization.

Being data-driven, based on analytics, changes this mindset. Business leaders are indeed starting to realize that making data more accessible and useful throughout the organization contributes to the results they want to achieve – and must report to their boards.

If traditional asset management concepts are applied to data, then objectives for security, quality, cataloging, definition, confidence, authorization and accessibility can be defined and achieved. These areas then become the performance criteria of the new data asset class.

So transforming an organization’s leadership and the rest of its culture to perceive and treat data as an asset changes its classification from “cost” to “investment.” Valuable assets earn a financial return and fuel productivity. They also can be re-invested or re-purposed.

Data governance is key to this new perspective of data as an asset.

Data Governance Definition and Purpose

Data governance is important to the modern economy because it enables the transformation of data into valuable assets to improve top- and bottom-line performance. Well-governed data is accessible, useful and relevant across a range of business improvement use cases.

But in the early stages of implementing data governance, organizations tend to have trouble defining it and organizing it, including determining which tasks are involved.

At its core, data governance is a cross-functional program that develops, implements, monitors and enforces policies that improve the performance of select data assets.

Implementing data governance ensures that “asset-grade” data is available to support decision-making, based on advanced analytics. Using this rationale, potential objectives to meet the strategic intent of the organization can be defined to derive value.

Following is a list of possible objectives for a data governance program:

  • Improve data security
  • Increase data quality
  • Make data more accessible to more stakeholders
  • Increase data understanding
  • Raise the confidence of data consumers
  • Increase data literacy and determine the data-driven maturity level of the organization

Building a Data Governance Foundation

The scope and structure of a data governance program are important to determine and include responsibilities, accountabilities, decision rights and authority levels, in addition to how the program fits into the existing corporate structure in terms of virtual or physical teams.

Structural options include top-down command and control and bottom up collaborative networks. Executive accountability also should be outlined.

It’s common for a data executive, such as the chief data officer, to be identified as accountable for overall data governance results. Data owners are business leaders who manage the processes that generate critical data. They’re responsible for defining the polices that support the program’s objectives.

Data stewards report to the data owners and are responsible for translating data policies into actions assigned to data specialists. The data specialists execute projects and other workflows to ensure that the governed data conforms to the intent of the policies.

Data stewards form the backbone of a data governance initiative. They influence how data is managed by assigning tasks to the specialists. Data stewards are responsible for cataloging, defining and describing the governed data assets.

These roles may be full-time or part-time, depending on the scope of the work.

Key processes carried out by the data governance team include:

  1. Defining and planning the program’s scope
  2. Data quality improvement
  3. Data security improvement
  4. Metadata creation and management
  5. Evaluating the suitability of new data sources
  6. Monitoring and enforcing compliance to data policies
  7. Researching new data sources
  8. Training to improve data literacy of staff at all levels
  9. Facilitating and finding new data-driven opportunities to improve the business
  10. Leading and managing cultural change

Data governance is based on a strategy that defines how data assets should look and perform, including levels of quality, security, integration, accessibility, etc. The design and implementation of a data governance program should start with a limited scope and then gradually ramp up to support the overall business strategy. So think big, but start small.

The next post in the series explores how data governance helps implement sustainable business processes that produce measurable results over time. Click here to continue reading on.

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Why Data Governance Leads to Data-Driven Success

Searching for new ways to generate value and improve execution, organizations of all shapes and sizes are racing to embrace data-driven approaches that are enabled by advances in analytics.

A perfect storm of events that started in the mid-2000s has morphed into a disruptive force in the economy at an accelerating pace. Data-driven analytics has gained mainstream business adoption. Advances in communications, geo-positioning systems, sensors and computing technologies have combined with the rise of social media and the incredible growth of available data sources.

Leadership teams in the boardroom have become acutely aware of the potential opportunities available for driving innovation and growth.

Although opportunities are significant, many challenges exist that make it difficult to successfully adopt data-driven approaches.

We’re going to explore the rationale for becoming data-driven, how to frame success, and some of the critical building blocks required, including data governance.

Framing Data-Driven Success

Organizational impact helps us frame the concept of data-driven success. Impact is related to an outcome. An impact describes a changed condition in measurable terms. A well-defined impact is a proxy for value.

Stating that you want to “move the needle,” implies that the area of impact can be measured with a metric that represents that needle. By achieving impact in the right business area, incremental value is created.

When investments are considered for implementing new data-driven approaches, it’s essential to define the desired areas of impact. Evidence of impact requires knowledge of the condition before and after the data-driven approach has been implemented.

Areas of impact can be tangible or intangible. They might be difficult to measure, but measurement strategies can be developed that measure most areas of impact. It’s important to frame the desired area of impact against the feasibility of gathering useful measurements.

Examples of measurable impact:

  • Increase process efficiency by 5%
  • Reduce product defects by 15%
  • Increase profit margin by 10%
  • Reduce customer attrition by 15%
  • Increase customer loyalty by 20%

Impact measures relative changes in performance over time. The changes are directly related to incremental value creation. Impact can be defined and managed by organizations from all sectors of the economy. Areas of impact are linked to their mission, vision and definition of success.

Data-driven excellence describes the performance that exists when targeted areas of impact are successfully enabled by data-driven approaches.

Building Blocks of Data-Driven Approaches

Successfully becoming data-driven requires that desired impacts are related to and supported by four categories of building blocks.

Data-Driven Building Blocks

The first category describes the business activities that must be created or modified to drive the desired impact. These are called the “business building blocks.”

The second category describes the new information and insights required by the business building blocks based on analytic methods that enable smarter business activities. These are called the “analytics building blocks.”

The third category describes the relevant data to be acquired and delivered to the analytics methods that generate the new information and insights. These are called the “data building blocks.”

Success at an organizational level requires that all critical building blocks are aligned with shared objectives and approaches that ensure cohesion and policy compliance. This responsibility is provided by the fourth category called the “governance building blocks.”

The four categories form a layered model that describes their dependencies. Value-creating impact depends on business activities, which depends on analytics, which depends on data, which depends on governance.

The Governance Imperative

Data-driven approaches touch many areas of the organization. Key touch points are located where:

  • Data is acquired and managed
  • Insights are created and consumed
  • Decision-making is enabled
  • Resulting actions are carried out
  • Results are monitored using feedback

Governance at a broad level develops the policies and standards needed across all touch points to generate value.  As a form of leadership, governance sets policies, defines objectives and assigns accountabilities across the business, analytics and data building blocks.

Business activity governance ensures that proactive management and employee teams respond to new sources of information and change their behaviors accordingly. Policies related to process standards, human skill development, compensation levels and incentives make up the scope of business activity governance.

Analytics governance ensures that all digital assets and activities that generate insights and information using analytics methods actually enable smarter business activities. Policies related to information relevance, security, visualization, data literacy, analytics model calibration and lifecycle management are key areas of focus.

Data governance is focussed on the data building blocks. Effective data governance brings together diverse groups and departments to enable the data-driven capabilities needed to achieve success. Data governance defines accountabilities, policies and responsibilities needed to ensure that data sets are managed as true corporate assets.

This implies that governed data sets are identified, described, cataloged, secured and provisioned to support all appropriate analytics and information use cases required to enable the analytics methods. Data quality and integration are also within the scope of data governance.

Foundation for Success

Companies that are successful with data-driven approaches can rapidly identify and implement new ideas and analytics use cases. This helps them compete, innovate and generate new levels of value for their stakeholders on a sustainable basis.

Data governance provides the foundation for this success. Effective data governance ensures that data is managed as a true corporate asset. This means that it can be used and re-purposed on an on-going basis to support new and existing ideas generated by the organization as it matures and broadens its data-driven capabilities.

As organizations unlock more value by creating a wider analytics footprint, data governance provides the foundation necessary to support their journey.

The next post in this blog series dives deeper into data governance in terms of scope options, organization approaches, objectives, structures and processes. It provides perspectives on how a well-designed data governance program directly supports the desired data-driven approaches that ultimately drive key areas of business impact.

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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.

In recent years, we’ve seen startups leverage data to catapult themselves ahead of legacy competitors. Companies such as Airbnb, Netflix and Uber have become household names. Although the service each offers differs vastly, all three identify as ‘technology’ organizations because data is integral to their operations.

Data-Driven Business

As with any standard-setting revolution, businesses across the spectrum are now following these examples. But what these organizations need to understand is that simply deciding to be data-driven, or to “do Big Data,” isn’t enough.

As with any strategy or business model, it’s advisable to apply best practices to ensure the endeavor is worthwhile and that it operates as efficiently as possible. In fact, it’s especially important with data, as poorly governed data will lead to slower times to market and oversights in security. Additionally, poorly managed data fosters inaccurate analysis and poor decision-making, further hampering times to market due to inaccuracy in the planning stages, false starts and wasted cycles.

Essentially garbage in, garbage out – so it’s important for businesses to get their foundations right. To build something, you need to know exactly what you’re building and why to understand the best way to progress.

Data Governance 2.0 Is the Underlying Factor

Good data governance (DG) enables every relevant stakeholder – from executives to frontline employees – to discover, understand, govern and socialize data. Then the right people have access to the right data, so the right decisions are easier to make.

Traditionally, DG encompassed governance goals such as maintaining a business glossary of data terms, a data dictionary and catalog. It also enabled lineage mapping and policy authoring.

However, Data Governance 1.0 was siloed with IT left to handle it. Often there were gaps in context, the chain of accountability and the analysis itself.

Data Governance 2.0 remedies this by taking into account the fact that data now permeates all levels of a business. And it allows for greater collaboration.

It gives people interacting with data the required context to make good decisions, and documents the data’s journey, ensuring accountability and compliance with existing and upcoming data regulations.

But beyond the greater collaboration it fosters between people, it also allows for better collaboration between departments and integration with other technology.

By integrating data governance with data modeling (DM), enterprise architecture (EA) and business process (BP), organizations can break down inter-departmental and technical silos for greater visibility and control across domains.

By leveraging a common metadata repository and intuitive role-based and highly configurable user interfaces, organizations can guarantee everyone is singing off the same sheet of music.

Data Governance Enables Better Data Management

The collaborative nature of Data Governance 2.0 is a key enabler for strong data management. Without it, the differing data management initiatives can and often do pull in different directions.

These silos are usually born out of the use of disparate tools that don’t enable collaboration between the relevant roles responsible for the individual data management initiative. This stifles the potential of data analysis, something organizations can’t afford given today’s market conditions.

Businesses operating in highly competitive markets need every advantage: growth, innovation and differentiation. Organizations also need a complete data platform as the rise of data’s involvement in business and subsequent frequent tech advancements mean market landscapes are changing faster than ever before.

By integrating DM, EA and BP, organizations ensure all three initiatives are in sync. Then historically common issues born of siloed data management initiatives don’t arise.

A unified approach, with Data Governance 2.0 at its core, allows organizations to:

  • Enable data fluency and accountability across diverse stakeholders
  • Standardize and harmonize diverse data management platforms and technologies
  • Satisfy compliance and legislative requirements
  • Reduce risks associated with data-driven business transformation
  • Enable enterprise agility and efficiency in data usage.

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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.

Business and IT alignment - Data governance

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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 (BI-Survey.com).

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

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Digital Trust: Enterprise Architecture and the Farm Analogy

With the General Data Protection Regulation (GDPR) taking effect soon, organizations can use it as a catalyst in developing digital trust.

Data breaches are increasing in scope and frequency, creating PR nightmares for the organizations affected. The more data breaches, the more news coverage that stays on consumers’ minds.

The Equifax breach and subsequent stock price fall was well documented and should serve as a warning to businesses and how they manage their data. Large or small,  organizations have lessons to learn when it comes to building and maintaining digital trust, especially with GDPR looming ever closer.

Previously, we discussed the importance of fostering a relationship of trust between business and consumer.  Here, we focus more specifically on data keepers and the public.

Digital Tust: Data Farm

Digital Trust and The Farm Analogy

Any approach to mitigating the risks associated with data management needs to consider the ‘three Vs’: variety, velocity and volume.

In describing best practices for handling data, let’s imagine data as an asset on a farm. The typical farm’s wide span makes constant surveillance impossible, similar in principle to data security.

With a farm, you can’t just put a fence around the perimeter and then leave it alone. The same is true of data because you need a security approach that makes dealing with volume and variety easier.

On a farm, that means separating crops and different types of animals. For data, segregation serves to stop those without permissions from accessing sensitive information.

And as with a farm and its seeds, livestock and other assets, data doesn’t just come in to the farm. You also must manage what goes out.

A farm has several gates allowing people, animals and equipment to pass through, pending approval. With data, gates need to make sure only the intended information filters out and that it is secure when doing so. Failure to correctly manage data transfer will leave your business in breach of GDPR and liable for a hefty fine.

Furthermore, when looking at the gates in which data enters and streams out of an organization, we must also consider the third ‘V’ – velocity, the amount of data an organization’s systems can process at any given time.

Of course, the velocity of data an organization can handle is most often tied to how efficiently a business operates. Effectively dealing with high velocities of data requires faster analysis and times to market.

However, it’s arguably a matter of security too. Although not a breach, DDOS attacks are one such vulnerability associated with data velocity.

DDOS attacks are designed to put the aforementioned data gates under pressure, ramping up the amount of data that passes through them at any one time. Organizations with the infrastructure to deal with such an attack, especially one capable of scaling to demand, will suffer less preventable down time.

Enterprise Architecture and Harvesting the Farm

Making sure you can access, understand and use your data for strategic benefit – including fostering digital trust – comes down to effective data management and governance. And enterprise architecture is a great starting point because it provides a holistic view of an organization’s capabilities, applications and systems including how they all connect.

Enterprise architecture at the core of any data-driven business will serve to identify what parts of the farm need extra protections – those fences and gates mentioned earlier.

It also makes GDPR compliance and overall data governance easier, as the first step for both is knowing where all your data is.

For more data management best practices, click here. And you can subscribe to our blog posts here.

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