Tag Archive for: Data Governance

Data governance. This concept is emphasized differently among different stakeholders. IT representatives have always held a more restrictive and cautious approach towards enterprise data access, while the business users continue asking for more and more data to become available. 

As enterprise leaders continue to be energized by the transformational promise of the cloud, the need for a renewed strategy around who gets access to what data and how becomes obvious. In fact, 100 data and analytics leaders from middle-market companies reported in a survey that the eight mistakes most disruptive to their enterprise data strategy all have to do with data governance.

Data governance as a continual process

So what is data governance? Its central purpose is to improve trust in the data lifecycle. Trust in understanding where the data came from, how it’s been moved, if it’s been changed, and who can see it. How is this trust built? Through organizational policies and procedures that govern how data is managed at the enterprise.

What’s worrisome is that no phrase comes up more often with customers than, “I cannot trust the data.” The organization’s “currency” has not only become seemingless worthless, but also costly. Leaders and analysts alike cannot rely on what they’re seeing and decisions are being made blindly. Time is wasted arguing over which metrics are correct and the cycle is unending.

So, how can an organization avoid this total disruption of their cloud data strategy? Stop treating data governance as an end product – something to be completed – rather than a continual process. Data & Analytics leaders believe this is the biggest mistake enterprises make today – attempting to implement a whole data governance framework in one ‘bang bang’ and viewing it as a ‘one and done’ type of project.

Data management touches all aspects of an organization – which means a cultural shift is needed and this takes time to implement. Rolling out a comprehensive program should take a modular approach tactically and behaviorally.

Furthermore, effective data governance is never done. There will always be new data, new definitions, new technologies and new issues that arise over time. Without well-understood and trusted processes for addressing each of these areas, organizations run the risk of their program falling flat.

Building the right team is critical

While conversations about analytics tend to be saturated by technology, it is the people that make data governance work. A centralized team will define individual roles and responsibilities, ranging all the way from executive leadership to data stewardship. Business subject matter experts will work together with the data stewards to define key business processes and metrics, ensuring the consistency of data definitions to all areas of the organization. The data engineers and stewards are the key cogs to a governance operating model, which is why failing to establish these data owners in the business is considered a top risk to data strategy execution.

Technology powers successful implementation

While technology shouldn’t be the initial focus, leveraging modern governance tools will be essential in automating and scaling processes. 42% of the surveyed leaders were concerned with the successfulness of data governance policies if they’re introduced without the tools to implement them effectively

These tools can range from spreadsheets to data catalogs to enterprise governance SaaS platforms. Choosing the right technology is entirely dependent on where you are on the journey. Since a full data governance implementation is exhaustive, companies can quickly become overwhelmed by attempting to accomplish too much, too soon. This is why our recommendation is typically labeled “agile data governance.” Start with a focused set of initiatives (dictionary, quality, lineage) and then leverage technology to automate and monitor these procedures. This will enable teams to tackle other governance disciplines (master data management, metadata management) without being stretched too thin. It is the proper blending of people and technology that drives successful data governance.

To see the full graphic, download the whitepaper, Data Leadership: Top Cloud Analytics Mistakes – and How to Avoid Them

Training and data literacy translate to business value

Once the key personnel, processes, and technology are in place within your data governance organization, scaling literacy and democratizing data is how you turn all this hard work into business value. Educating users on the available governance documentation and technology enables them to quickly get up to speed on why, where, and how to access data. These training sessions are technical but also help to establish a data culture. Leaders must evangelize the governance program and generate buy-in from the users by reinforcing why these best data practices are needed to ensure they aren’t circumvented moving forward. 

Consolidated and centralized access to data slows the value creation process. When too much of the analytics workload falls on IT resources, it restricts an organization from building competitive advantages effectively. Securely distributing and democratizing data to users across the business curates a smarter, faster organization. These technical and business users can spend more time in data analysis and less time dealing with data bottlenecks.

Typically, self-service and governance interests appear at odds with one another – one is an open policy, while the other is closed. But truthfully, the exact opposite is true – governance empowers self-service to be successful. With an enterprise more literate in data and educated on its lifecycle, the more it can confidently build value from it.

Protecting customer data and complying with regulations

Establishing rules and standards for data privacy, protection and security has always been a key tenet of the IT operating model. Global, state, and industry regulations have further tightened the data-handling standards that organizations must meet. And when we discuss data governance with our customers, the first thing they’re concerned about is remaining compliant and protecting all sensitive data.

Nonetheless, the last mistake highlighted by the surveyed leaders was overlooking data protection and privacy impact assessments. An assumption could be made that this would be considered a higher risk if leaders weren’t already confident in the data-compliant and protective culture that has existed for many years within their IT departments. But even if certain portions of the business are well versed in the protection of personally identifiable information, as data access is opened up to more users, more education will be required.

Viewing governance as a path to larger returns

As the appetite for data consumption increases, so should diligence in governance – it is needed now more than ever. Organizations should stop viewing governance initiatives as costs to the bottom line and start viewing them as mechanisms to generate larger returns from its most important asset (its data). In Breaking away: The secrets to scaling analytics, McKinsey identified what they defined as “breakaway companies” and found this group is, “…twice as likely to report strong data-governance practices.” 

The organizational excuses for deprioritizing data management over other analytics initiatives is becoming less acceptable and more risky. External sources can be a catalyst for mid-market companies, providing the leadership and experience required to build their internal governance functions they need. There are many avenues to getting started with data governance – organizations just need to take one.

To learn more, read the whitepaper: Data Leadership: Top Cloud Analytics Mistakes – and How to Avoid Them

Your data needs are different from those of any other client we’ve worked with. Plus, they’re ever-changing. 

That’s why we’re fluid in our approach to creating your framework and why we ensure fluidity in the framework itself. 


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Whether your current investment in assessments, governance, and technology is heavy or light, we can meet you where you are, optimize what you have, and help you move confidently forward. 

These steps are all necessary, but don’t happen in a strict sequence. Each of them is an iterative process — taking small steps, looking at the results, then choosing the next improvement. You need to start with assessment and governance — unless you already have some progress in those areas. 

Analytics are constantly evolving, and the Modern Analytics Framework is designed to evolve more readily as users discover new insights, new data, and new value for existing data. There will be constant re-assessment of the desired future state, modifications to your data governance goals and policies, design of data zones, and implementation of analytics and automated data delivery. Making these changes small and manageable is a key goal of the Modern Analytics Framework.

Can we ask you a few questions?

The better we understand your current state, the better we can speak to your specific needs. 

If you’d like to gain some insight into how your organization can move most effectively toward a Modern Analytics Framework, please schedule a time with Geoff Speare, our practice director.

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Learn about our proven, streamlined approach to taking your current analytics framework from where it is to where it needs to be, for less cost and in less time than you might imagine.

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Check out the rest of the series.

At least weekly, I am granted the opportunity to meet and work alongside experienced professionals who serve in a corporate business intelligence (BI) leadership function. When they describe their role upon introduction, there is a common thread to the scope of influence and control which usually intersects one or more of these domains: Read more

Any discussion of Master Data Management automatically includes a discussion of Data Governance. The two go hand in hand. Successful MDM implementations require understanding data ownership, stewardship, and security, as well as determining business rules to be applied to the data. Specific business rules usually include rules for matching and consolidating data items as well as data quality checks. Read more

Governance is the ongoing process of creating and managing processes, policies, and information. This includes strategies, processes, activities, skills, organizations, and technologies for the purpose of accelerating business outcomes. It also involves creating organizations, roles and responsibilities to perform this management. In our experience, many organizations address governance once and often without completing the necessary tasks. Organizations that excel in data and analytics governance continuously manage the process on an ongoing basis. Read more

IT and business leaders share a common goal – to leverage the data available to them in order to make more informed business decisions. The first step to achieving that goal is to create a data & analytics roadmap, a task many companies find daunting. Where do you begin?


“Most organizations are ineffective in communicating data & analytics-related concepts across departments, resulting in suboptimal management and utilization of information.”

– Doug Laney, Gartner Blog Network

Read more

Data governance is a common need across organizations, and can be a very challenging subject to tackle. Understanding data governance’s components, what good governance looks like, and the drivers behind adopting it is essential to creating a successful governance effort. Read more

Creating a data and analytics roadmap is an important first step in helping your organization utilize data to achieve growth, understanding, and action. But often we hear from clients that their roadmap isn’t leading them to their desired destination. While many are successful in laying out the technical requirements, identifying the path to business outcomes, and building the right foundation, they still find themselves unable to make sufficient progress towards their goals. Read more

Master data management (MDM) efforts often get bogged down quickly due to business thinking that the entire process is owned by IT when in fact they need to set the standards. Knowing how to do that and what real MDM means will help make these efforts become collaborative and smooth to run. This article will answer several questions executives and other business leaders may have regarding MDM, in addition to proposing questions that should be used in order to leverage the full potential of MDM and illustrating real-world use cases. Read more

Ironside’s Crystal Meyers was featured in Vol. 19, No. 4 of TDWI’s Business Intelligence Journal. Her points are particularly relevant as business analytics begins a more pronounced shift toward a bimodal model in 2016.

Reprinted with permission of 1105 Media, Inc. As published in TDWI’s Business Intelligence Journal. Read more

Tag Archive for: Data Governance