Now that we’ve laid out the framework for what strong Data Governance strategies look like and the steps to achieve them, let’s round out this series by briefly going over a few common challenges you’re likely to face when you embark on your data governance journey. 

The Big Four 

We most commonly see four big hurdles to get over with data governance: 

1. Chasing Perfection 
2. Over-engineering 
3. Internal reluctance 
4. Clashing with current procedures 

We’ll define the problems below and pitch a few ideas on how to get around them. 

Chasing Perfection 

“Perfect” is the enemy of “good enough.” It is easy to become consumed with the never-ending challenge of precisely defining every KPI, having every director agree with said definition, then deploying the most perfectly optimized data stack that will solve all your problems and be widely adopted. 

You can’t. It’s a fool’s errand. 

All chasing perfection will get you is adoption paralysis. Clearly lay out the requirements for your data governance changes, do your research, find a good option and get to work. Iterate on your changes at a set cadence with standard reviews to see how things are going. If your “good enough” choices aren’t being good enough, swap them out. There’s no shame in that. 

The most important thing, always, is moving forward and making progress. 

Over-Engineering 

This one tends to be related to the chasing perfection roadblock. When developing your data governance strategy, be mindful to avoid over complicating the situation at hand.  Follow prior guidance to focus on base level needs first, while tackling additional capabilities as your practice matures. Additionally, make a habit of self-evaluating your plans to ensure you’re in the goldilocks zone of being able to keep up with your needs without over-investing. It helps to lay out a roadmap for the future to adequately pace your scaling. You can opt for a phased approach, but continue to make sure you’re not neglecting your base needs. Finally, make sure you’re right-sizing your path with accurate forecasting on a realistic growth path forward. 

Internal Reluctance 

Sometimes, change is a scary thing. Sometimes, there are budgeting concerns. Sometimes, decision makers are risk averse. There are any number of reasons that internal stakeholders might not wish to implement additional policies and procedures within their data stack. 

That’s why our biggest recommendation for overcoming internal reluctance is to be open about your progress and to have the research on your side. Lay out the benefits clearly in ways that show stakeholders how implementing your framework accomplishes your organization’s goals. Maintain proper trainings so users are empowered, and be consistent with what the end goal is and your teams progress towards achieving. 

The whole point is to alleviate any concerns as they arise and leave no room for questioning whether the project is worthwhile or not. 

Clashing With Current Procedures 

Organizations should perpetually evaluate the way teams are functioning in order to maintain an optimal operating state. Now, of course, that’s not to say that you should throw the baby out with the bath water, but you have to break a few eggs to make an omelet, to borrow two conflicting turns of phrase. 

Have a rock-solid proof of concept that clearly demonstrates the value your strategy will deliver. Identify opportunities to combine the old and the new in effective ways to not completely do away with solutions or experiences users are familiar with. Manage expectations about needed changes with clear lines communication.  Ultimately, be sure to own your strategy, be consistent, flexible and stay the course! 

Wrapping Up 

If you find that you’re facing down too many red flags to adequately address yourself, reach out to our team here so we can help you build a stronger data governance strategy. Many hands make light work, and we’re willing to have your back

The post Mastering Data Governance: Common Challenges appeared first on InterWorks.

Now that we’ve helped define and contextualize data governance in our previous blog post, let’s go over some steps we can take to ensure success. This time, instead of Maslow’s Hierarchy of Needs, we’re going to build from the bottom up as if we’re starting a brand-new company. 

This should help guide your path, no matter where you are in the process. If you are, in fact, starting a brand-new company, congratulations! We’ve got the whole roadmap here for you. If you’re already further along on your company’s data journey, you can double check the basics and make sure your fundamentals are taken care of before continuing down the list. 

Let’s get started! 

Define Goals 

If you don’t know what your end goal is, you’re going to find yourself wandering aimlessly, wasting time and just hoping that what you’re doing has a positive effect on your company’s trajectory. Our biggest recommendations on setting good goals start with collaboration. 

First, you should meet with key stakeholders and enable multi-domain collaboration and buy-in. Charting a course toward great data governance isn’t a one-person job — it’s a group project. It also helps to think of this as an opportunity to gather requirements and ensure data governance policies meet any broader organizational goals. Putting these projects into a global context and ensuring you’re in line with your company’s standards will help alleviate any potential headaches down the road. 

Finally, the goals you come up with should be outcome-based and SMART — Specific, Measurable, Achievable, Relevant and Time-Bound. If the SMART method is new to you, not to worry – there are endless guides, articles and videos out there to help define it in detail. 

Define Roles 

After you’ve mapped out your goals, it’s time to break down the “who’s doing what” of your gameplan. Defining data governance roles clarifies responsibilities and ensures accountability for the duration of your project. 

As a note: This does not, and in many cases should not, need to land entirely on one person or department. It should encourage multi-practice collaboration. Delegate as needed to minimize burnout. 

Here are some data governance roles we typically see and their duties:

• Governance Committee: Creates and maintains the data governance framework. Ensures policies are properly implemented across their domain, resolves conflicts on access to data, and regularly reviews performance of the data governance program. 
• Data Owner: Responsible for decisions around their data domain. Works with the governance committee to define policies. Works with data stewards to ensure proper use of data and policies for company goals. 
• Data Stewards: Responsible for the day-to-day data management. Ensures domain data is accurate and consistent, maintains data dictionary/definitions, and educates data consumers on proper usage. 

Define Policies 

Nailing down policies and procedures relating to data objects and assets is the next step on the ladder and, when done judiciously, helps enable your data governance practitioners to more effectively do their newly assigned roles. These policies should be well-documented and reduce confusion, rather than create it.  

Some example policies include: 

• Data validation policies on ingestion for required fields. 
• Data cleansing policies to remove duplicates and fix data types. 
• Data quality policies to ensure % data completeness. 
• Data privacy policies to mask PII fields in the data set and provide training on these. 
• Data security policies for monitoring access. 

Conduct periodic compliance audits to both make sure that the members of your team understand the policies you’ve outlined and ensure that the policies you have in place continue to make sense. What worked at the beginning may not work as your organization continues to expand, and adaptability is key. 

Select Tools 

With your goals outlined, roles assigned and policies defined, it’s time to review what tools make the most sense for your company’s needs. The tools you choose need to handle various aspects of your specific data governance ecosystem, and both building tools in-house or buying third-party tools are viable. Sometimes, specific tools are not always needed, and you can get by with the out-of-the-box features of established tools. 

In parallel with selecting the tools, a concerted effort should be made to foster a culture of good data governance. If you simply purchase the tools but don’t have a culture that enables potential users to use them, then these tools might be a waste of precious funds that could go to more worthwhile endeavors. 

Track Metrics 

If a tree falls in the woods and no one hears it, did it make a sound? Likewise, if you implement good data governance practices and have no way to track your progress, how can you know how effective they are? 

What this step looks like is going to differ greatly based on what your end goals are. The measures and metrics you lay out should be used to track the success of the data governance framework and make adjustments as needed. 

Be sure to specifically include the goals you set at the beginning of the process as key metrics to measure against so you’re always on track. 

Some examples of metrics to track would be percentage of unmasked PII fields, frequency of ad hoc access permissions and data pipeline downtime. 

Iterate Process 

Once you start to see success, it’s time to keep that success going. Ongoing iterations and improvements on the processes help keep your data compliant as your data governance requirements and data environment evolve. 

Don’t neglect your interpersonal processes as well. Maintain governance collaboration between cross-functional teams as laid out in the “Define Roles” section. As data governance practitioners or responsibilities change, ensure everyone is brought up to speed on their roles and duties to maintain good data governance practices. 

Example iteration tasks are: 

• Regular governance committee meetings to maintain framework. 
• Feedback loop from stakeholders, analysts, etc. 
• Roadmapping and data governance self-scoring. 
• Continued training and awareness sessions. 

Wrapping Up 

These six areas are key pieces of the framework for good data governance. In an upcoming blog, we’ll also lay out some of the challenges that you might face while building the data governance stack and culture you need to accomplish your goals. 

The post Mastering Data Governance: Building a Framework for Success appeared first on InterWorks.

Here’s a question for you: If you were asked to define “Data Governance,” how confident would you be in your answer?

Considering how quickly the data space is changing, we wouldn’t blame you if your confidence in that answer isn’t as high as you’d like it to be

For us, we’d say that data governance encompasses how organizations manage, protect, and contextualize their data assets across the entire data lifecycle and technology ecosystem.

We think that’s a fine definition, but it does beg the question: How do you actually do Data Governance? Given the wide breadth of our definition, that question can be quite daunting! With that said, we’d like to offer an analogy using one of our favorite Psychologist to help drive steer and focus.

Maslow’s Hierarchy of Needs

You might be familiar with this: American psychologist Abraham Maslow proposed in 1943 a way of quantifying an ascending order of needs for humans to achieve their full potential. Often portrayed as a pyramid-shaped graph, it outlines the most basic human needs all the way up to realizing one’s full potential at the top:

While we’re certainly not going to claim to be experts in human psychology, this does have a useful framework that we can step through to achieve understanding how we should tackle good data governance:

Let’s step through it, starting at the first step: Taking care of your base-level needs.

Step 1: Base Level

Providing the basic necessities of Data Governance lays the foundation for data governance, and are non-negotiables for a surviving data function

To that end, let’s break the basics down into three main parts:

• Data Acquisition: The processes and technologies used to collect and ingest data from various sources into the data ecosystem.
• Accessibility: The ability for authorized users to retrieve, view, and utilize data when needed. This includes physical access methods, query capabilities, and retrieval mechanisms.
• Availability: Ensuring data systems are operational and data is accessible when needed, including redundancy and reliability mechanisms.

Step 2: Safety and Security

Safety, in this context, are the protective mechanisms that secure data assets and ensure regulatory compliance, creating a trusted data environment. We’ll outline two essentials in this area, as well as two things you need to avoid.

The essentials:

• Role-Based Access Controls (RBAC) with automated provisioning
• Regular security audits and vulnerability assessments

The avoids:

• Not having formal, written policies about data access or sharing
• Lacking adequate data classification levels based on sensitivity

Step 3: Connection

Connections, in this case, are less technical and more about capabilities that create shared understanding of data and connect it to business meaning, fostering data communities and collaboration. This can be accomplished in a few ways.

Catalogs are a good way of maintaining an inventory of all your data assets, making them discoverable and understandable through technical and business metadata. Nailing down the Semantics is also going to help universalize the definitions and context that give your data unified meaning, including standardized terminology and data element definitions. Finally, establishing a Center of Excellence (COE) makes cross-functional groups that collaborate on data governance, sharing knowledge and best practices to improve data usage.

Step 4: Self Service

Self Service — the organizational and technical approaches that enable teams to independently access, understand and utilize data within a governed framework. We’re nearing the top of the hierarchy of needs, so it’s only natural that we’re close to reaching full data independence. This one has some must-haves and avoids as well:

Must-Haves:

• Ownership: Business users can independently access and analyze data
• Accountability: Domain-oriented data ownership with quality accountability

Avoids:

• Lack of Domain-Level Expertise: Some team level technical expertise needed
• Bottlenecks: Possibly due to centralized data team dependencies or inefficient products.

Step 5: Full Potential

This is the tippy top of the hierarchy pyramid: The full extent of what all the effort on the lower steps helps you achieve. This is using your data to the fullest. So, what does that look like?

It means achieving automation — being able to rely on technology to automatically enforce your policies, monitor compliance and remediate issues without manual interventions. It also includes data contracts, formal agreements between data producers and consumers that specify data structure, quality, delivery and usage terms. It also unlocks a current hot-button want for most companies: AI integration. This isn’t just using ChatGPT. It’s also the application of artificial intelligence to enhance governance capabilities, including anomaly detection, policy recommendation and adaptive controls.

To Be Continued

With the hierarchy laid out, our next blog is going to talk about actually building that framework piece by piece to make sure you can achieve your full data potential. Keep an eye out here for when that blog comes out, and if you have any questions so far, feel free to reach out.

The post Mastering Data Governance: Setting the Stage appeared first on InterWorks.

“Which is your favorite Christmas movie?” is a question often asked during the holiday season. And an even more convoluted question, “Is Die Hard really a Christmas Movie?” I decided to try and answer those exact questions by quantifying fan sentiment around movie rankings and juxtaposing that against a widely viewed list ranking those films.

IMDv: Internet Movie Data Viz

To start, I used IMDb’s list of the Top 100 Christmas movies and culled those titles down to a top 30. From there, I pulled IMDb’s titles and ranking datasets to build my quantitative comparison. These two sources were joined by Tableau’s data source tool and further refined the top 30 titles to be compared against IMDb’s authored list. By refining the data extract using a data source filter, I was able to drastically improve performance and limit the size of the dataset to the information relevant for my purpose below:

After that, I needed an accurate representation of overall fan sentiment and voting engagement by each title. Within the ranking dataset, there are fields for number of votes cast and average rating across the population of votes. By using Average Rating = Total Gross Votes/Number of Votes Cast, I was able to isolate Total Gross Votes in order to give a better representation of both fan engagement and sentiment rather than just total votes or average rating:

Now that we have our data point, we can now visually arrange the information to see how the fan-sentiment data from IMDb compares to their Top 30 Movies list curated by one of their authors. As you can see, Die Hard wins the day! Maybe it really is the best Christmas movie.

The post Analytics for the Holidays: The Top 30 Christmas Movies in Tableau appeared first on InterWorks.

As purveyors of analytics, we often come against the buttresses of change aversion. Nowhere is this more prevalent than in the lack of acceptance of Tableau tooltips. How often do you present your end users with a powerful and dynamic viz only to be met with, “But where is the legend?” This can be a disheartening conversation to have. Tooltips, however, can add dimensionality to your viz, free up space for content and allow you to convey your data story in the most pragmatic and impactful way possible.

A Tooltip Use Case

Let’s start by looking at a viz InterWorks recently used to display results from a VR racing module at our InnoTech booth. As you can see below, we deployed a tooltip for the race’s standings with further detail on each user’s traits and results. Within the tooltip, we are able to tell a more detailed story about that user and the data point without having to fill the page with ancillary artifacts.

By comparison, if we simply pull that detail contained within the tooltip into the viz, we’ve started to clutter the graphs. Also, our user’s eye is drawn to the information contained within each bar and away from the story we are trying to convey. Imagine if we had further detail within tooltip that would begin to spill outside the borders of each bar. What a mess!

We could also try to add context to each bar within each tooltip by adding color. Below, you can see we’ve created a legend for color assignment by Company so that the end user can visually identify which organization each participant represented.

Although this may speed up a viewer’s recognition, we’ve had to lose real estate for the inclusion of the legend, we’ve disrupted the overall flow of the viz and sacrificed formatting cohesion with the introduction of color. Imagine if this list was 20 bars long with 15 companies represented? The issue would only compound itself as your data becomes more and more complex.

Tooltip Alternatives

Let me wrap up by saying that tooltips are not a saving grace. You may often make a better case for other forms of context (color, shape, etc.) rather than displaying details in your tooltips. In many cases, you may even use a combination of the two. In illustrating the case above, it is important to convey the power of tooltips to your customers and have a compelling argument for their deployment over more conventional details. Explore the full InnoTech viz below to see these tooltips in action. 

The post Making a Case for Tableau Tooltips appeared first on InterWorks.