Getting Started with (Just Enough) Data Governance
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Learn the small steps you can take now to address practical, actionable steps to start your organization down the path to healthier data through data governance. We share real-world examples and discuss prioritizing data issues to identify a starting point, gaining support from leadership, choosing an initial team and building momentum. View our on-demand webinar and download this deck at: https://senturus.com/resources/getting-started-with-just-enough-data-governance/. Senturus offers a full spectrum of services across the BI stack plus training on Power BI, Cognos and Tableau. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
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Getting Started with (Just Enough) Data Governance
- 1. Getting Started with (just enough) Data Governance 1
- 2. 2 Hundreds of resources Visit the Resource Library on the Senturus website to download this presentation and explore other assets: senturus.com/resources 2
- 3. 3 Mike Bigenwald Consultant Senturus, Inc. Michael Weinhauer Director Senturus, Inc. 3 Introductions
- 4. Agenda • Introduction • Just enough data governance • Prioritize your data issues • Gain support • Pick your team • Execute & build momentum • Senturus overview • Additional resources • Q&A 4
- 5. Enjoy the full webinar presentation This slide deck is from the webinar Getting Started with (Just Enough) Data Governance To view the FREE video recording and download this deck, go to https://senturus.com/resources/getting-started-with- just-enough-data-governance/ 5
- 6. 6 Self Service BI: 2x Data Volumes +63% per month Data Lakes +237% Azure +775% Data Breach =$3,533 per employee $500,000,000,000 Some recent facts…
- 7. Components of a governance program Fin OPS SC CRM HR Data Hub Source systems, data files Monitoring tools, dashboards Data Governance Entity Data model Business definitions Process Information Data quality rules Data mapping Metadata Measurements Metrics Collection Data Dictionary
- 8. Components of a governance program Fin OPS SC CRM HR Data Hub Source systems, data files Monitoring tools, dashboards Data Governance Entity Data model Business definitions Process Information Data quality rules Data mapping Metadata Measurements Metrics Collection Data Dictionary
- 9. Data governance is a framework for ensuring the availability, accuracy and security of data across an organization. 9 What is data governance?
- 10. Data governance is a framework for ensuring the availability, accuracy, and security of data across an organization. 10 Agile concepts for data governance MVP: Minimum viable process Sprints: 2-3 week work plans Scrum: Daily ‘stand-up’ Backlog: Prioritized list
- 11. Getting started Prioritize your data issues Gain support Build your (virtual) team Execute & build momentum
- 12. 12 Prioritize your data issues
- 14. 14 Business Value Cross Department Regulatory Compliance Value Score IT Effort IT Complexity Feasibility Score NPS New Customer CLTV CAC Churn Retention MRR ARR Exercise - prioritization 14 Business value Breadth of use across departments Risk and regulatory compliance IT effort Complexity
- 15. 15 Exercise - prioritization 15 Business value Breadth of use across departments Risk and regulatory compliance IT effort Complexity Business Value Cross Department Regulatory Compliance Value Score IT Effort IT Complexity Feasibility Score NPS New Customer CLTV CAC Churn Retention MRR ARR
- 16. Like what you see? To view the video recording and download the slide deck go to https://senturus.com/resources/getting-started-with-just- enough-data-governance/ Visit our website to access our library of free BI knowledge resources including events, blogs, demos, whitepapers, other on-demand webinars and our dashboard gallery https://senturus.com/senturus-resources/ 16
- 17. 17 Business Value Cross Department Regulatory Compliance Value Score IT Effort IT Complexity Feasibility Score NPS 5 New Customer 4 CLTV 3 CAC 4 Churn 3 Retention 5 MRR 3 ARR 3 Exercise - prioritization 17 Business value Breadth of use across departments Risk and regulatory compliance IT effort Complexity
- 18. 18 Business Value Cross Department Regulatory Compliance Value Score IT Effort IT Complexity Feasibility Score NPS 5 1 New Customer 4 0 CLTV 3 0 CAC 4 1 Churn 3 1 Retention 5 0 MRR 3 1 ARR 3 1 Exercise - prioritization 18 Business value Breadth of use across departments Risk and regulatory compliance IT effort Complexity
- 19. 19 Business Value Cross Department Regulatory Compliance Value Score IT Effort IT Complexity Feasibility Score NPS 5 1 0 New Customer 4 0 0 CLTV 3 0 0 CAC 4 1 1 Churn 3 1 0 Retention 5 0 0 MRR 3 1 0 ARR 3 1 0 Exercise - prioritization 19 Business value Breadth of use across departments Risk and regulatory compliance IT effort Complexity
- 20. 20 Business Value Cross Department Regulatory Compliance Value Score IT Effort IT Complexity Feasibility Score NPS 5 1 0 1 New Customer 4 0 0 1 CLTV 3 0 0 2 CAC 4 1 1 1 Churn 3 1 0 1 Retention 5 0 0 1 MRR 3 1 0 1 ARR 3 1 0 5 Exercise - prioritization 20 Business value Breadth of use across departments Risk and regulatory compliance IT effort Complexity
- 21. 21 Business Value Cross Department Regulatory Compliance Value Score IT Effort IT Complexity Feasibility Score NPS 5 1 0 1 1 New Customer 4 0 0 1 1 CLTV 3 0 0 2 2 CAC 4 1 1 1 1 Churn 3 1 0 1 1 Retention 5 0 0 1 3 MRR 3 1 0 1 2 ARR 3 1 0 5 5 Exercise - prioritization 21 Business value Breadth of use across departments Risk and regulatory compliance IT effort Complexity
- 22. 22 Exercise - prioritization Business Value Cross Department Regulatory Compliance Value Score IT Effort IT Complexity Feasibility Score NPS 5 1 0 6 1 1 -2 New Customer 4 0 0 4 1 1 -2 CLTV 3 0 0 3 2 2 -4 CAC 4 1 1 6 1 1 -2 Churn 3 1 0 4 1 1 -2 Retention 5 0 0 5 1 3 -4 MRR 3 1 0 4 1 2 -3 ARR 3 1 0 4 5 5 -10
- 24. 24 24 Start here Risk Business impact Strategic alignment Exercise - prioritization
- 26. Active support for data governance is critical from three areas of the organization 26 Senior Leadership Business Users IT Teams
- 27. Active support for data governance is critical from three areas of the organization 27 Senior Leadership
- 28. Active support for data governance is critical from three areas of the organization 28 IT Teams
- 29. Active support for data governance is critical from three areas of the organization 29 Business Users
- 30. 30 Build your (virtual) team
- 31. Senior Leadership Business Users IT Teams Your initial data governance team will be small and represent your stakeholders 31 Core Data Governance Team
- 32. 32 RACI 32 Responsible • Person who performs an activity or does the work. Accountable • Person who is ultimately accountable and has the Yes/No/Veto. Consulted • Person that needs to provide feedback and contribute to the activity. Informed • Person that needs to know of the decision or action. Senior Leadership Data Owner Data Steward
- 33. Like what you see? To view the video recording and download the slide deck go to https://senturus.com/resources/getting-started-with-just- enough-data-governance/ Visit our website to access our library of free BI knowledge resources including events, blogs, demos, whitepapers, other on-demand webinars and our dashboard gallery https://senturus.com/senturus-resources/ 33
- 35. Execute & build momentum Identify Prioritize Define Research Document Deploy
- 36. Execute & build momentum Data Element Owner Definition SSOT Calculation NPS Lisa Q Net Promoter Score (NPS) is a customer loyalty and satisfaction measurement taken from asking customers how likely they are to recommend your product or service to others on a scale of 0-10. Promoters rate 9 or 10, Detractors rate 0-6. 7-8 are passives not counted. Qualtrics NPS = % Promoters - % Detractors New Customer Barb D A Customer that has not made a verifiable purchase from Acme in 36 months or longer. Sales Time from last purchase >36 months. CLTV Bob J Customer Lifetime Value is the estimated revenue you can generate from a given customer over the lifetime of their account. Finance CLTV = (Avg Sale)*(# Transactions)*(Retention Time)*(Margin) CAC Bob J Customer Acquisition Cost measures how much an organization spends to acquire new customers. The total cost of sales and marketing efforts, as well as property or equipment, needed to convince a customer to buy a product or service. Finance CAC = Total Spend on Customer Acquisition / # Customers Acquired in Period Customer Churn Bob J Customer Churn is the percentage of customers that stopped using your company's product or service during a certain time period. Sales Customer Churn = (Lost Customers ÷ Acquired Customers) x 100% Retention Dan D Customer Retention refers to a company’s ability to turn customers into repeat buyers and prevent them from switching to a competitor. Marketing Retention = ((# Customers at end of period - # New Customers acquired during period)/ (# Customers at start of peroid)) *100. MRR Bob J Monthly Recurring Revenue is a measure of your monthly predictable revenue stream for a term subscription business. Finance MRR = (New Monthly Revenue) - (Account Loss) - (Downsize) - (Price Change) ARR Bob J Annual Recurring Revenue is a measure of your annual predictable revenue stream for a term subscription business. Finance ARR = MRR * 12 Owner of this Document: Jack Smith
- 37. Getting started Prioritize your data issues Gain support Build your (virtual) team Execute & build momentum
- 38. A note about agile
- 39. Are you ready to get started? 39 Identify an area to address Make your case with stakeholders Recruit help Build momentum Are you ready to get started?
- 40. Data governance is a framework for ensuring the availability, accuracy, and security of data across an organization. 40 What now? Governance health assessment MVP: minimum viable process Prioritized governance programs
- 41. Get assistance with governance •Getting started/minimally viable process •Full implementation •Assessment •https://senturus.com/services/business-analytics-data-governance-data-security/ 41
- 42. Upcoming event •Cognos Data Module Architectures and Use Cases •Pros, cons and real-world scenarios •Thursday, Feb. 11, 2021, 11am PT/2pm ET 42
- 43. The authority in Business Intelligence 43 Exclusively focused on BI, Senturus is unrivaled in its expertise across the BI stack.
- 44. Decisions and actions Business needs Bridging the gap 44 Analysis-ready data
- 45. Full spectrum BI services •Dashboards, reporting and visualizations •Data preparation and modern data warehousing •Hybrid BI environments (migrations, security, etc.) •Software to enable bimodal BI and platform migrations •BI support retainer (expertise on demand) •Training and mentoring 45
- 46. A long, strong history of success • 20+ years • 1600+ clients • 3000+ projects 46
- 47. Expand your knowledge 47 Find more resources on the Senturus website: senturus.com/senturus-resources
- 48. Complete BI training 48 Instructor-led online courses Self-paced learning Mentoring Tailored group sessions
- 49. Additional resources 49 Insider viewpoints Technical tips Unbiased product reviews Product demos Upcoming events More on this subject
- 50. © 2020 by Senturus, Inc. This presentation may not be reused or distributed without the written consent of Senturus, Inc. www.senturus.com 888 601 6010 info@senturus.com
Notes de l'éditeur
- The first question we usually get is “Can I get a copy of the presentation?” Absolutely! It’s available on Senturus.com. Select the Resources tab and then Resources Library. Or you can click the link that was just posted in the GoToWebinar Control panel. Be sure to bookmark the resource library. It has tons of valuable content addressing a wide variety of business analytics topics.
- Joining us today is…..Mike Bigenwald Mike’s 30-year career has been focused on helping organizations worldwide make better decisions through the use of data, information and analytics. Mike joined Senturus from Slalom Consulting, where he led the Chicago Information Management and Analytics practice. Before Slalom, Mike was part of IBM’s acquisition of SPSS, where he held a variety of global leadership roles in the SPSS professional services, training and partner channel organizations.
- There has been a dramatic cultural shift with the explosion of data being created and available in democratic, self service environments in organizations of all sizes from startups to the largest enterprises. This shift has caught many firms off guard, leading to mismanaged data assets, poorly defined metrics and uncontrolled access authorizations that cause delay, confusion, poorly informed decisions and in many cases data breaches with the accompanying legal liabilities. **The sources for these figures are in the notes.** According to Markets and, the self-service BI market size is estimated to double in size (USD 3.61 Billion in 2016 to USD 7.31 Billion by 2021), at a Compound Annual Growth Rate (CAGR) of 15.2%. https://bit.ly/3svcrWY New Survey from Matillion and IDG: data volumes are growing at an average of 63% per month, with 12% of organizations reporting over 100% percent growth every month. https://bit.ly/3smW6DI Reportlinker.com reports "Data Lakes Market CAGR 27.4 2019-2024 (+237%) https://bit.ly/3p9L6b3 Ponemon found that organizations with between 500 and 1,000 employees had an average data breach cost of $2.65 million, or $3,533 per employee as opposed to large organizations, which averaged $204 per employee. Microsoft has reported that just since the pandemic started, they have witnessed over 775% increase in demand for cloud services. https://bit.ly/3sK7zh5 The cloud migration services market was valued at USD 119.13 billion in 2019 and is expected to reach USD 448.34 billion by 2025, at a CAGR of 28.89% over the forecast period 2020 - 2025. https://bit.ly/3p9L6b3
- Because you registered and made the time to attend this webinar, I will assume that you have in interest in introducing data governance in your organization. Whatever your role is, you can play a part. You may be CTO of a regional manufacturer that is expanding rapidly and needs to address customer master issues before they get any more complicated; or maybe you are a business analyst tasked with figuring out why your reporting does not agree across departments and data sources. It is possible that you may even be a compliance officer concerned that too many people have access too much business information across your bank. I also assume that you may have investigated starting a governance program in the past, and it just seemed too complicated, involved, time consuming and expensive. And you were right. A Data Governance program can be, and often appropriately so, a complex undertaking involving dozens of people across all levels of the organization.
- …. But, if the title of today’s webinar caught your attention, you probably joined us because you feel like this cat. It all seems like a lot, and you want to hear our ideas about how much is “Just Enough” data governance. So… what we are going to talk about in the remainder of this hour is how to take some simple, actionable steps to start your organization on a data governance journey. To be clear, we at Senturus believe a robust, well structured Data Governance program is essential for firms to stay competitive in this data driven economy. What we are sharing today is a starting point to make incremental progress toward your long-term data goals.
- So, we see a pretty typical definition of Data Governance here on the screen. A “framework for ensuring….” While frameworks are an important and useful way to break down a large program into manageable areas of focus, we will not spend a lot of time on that today. Instead, we will take lessons we have learned from over 20 years of helping companies grapple with their toughest data issues and offer some useful ways to leverage the key components of a Data Governance framework to get started on managing your data toward governed.
- I had a manager once that summed up getting started on a daunting task like this: She said, “You don’t need to boil the ocean to make a hot cup of tea.” I will stop there and not subject you to an extended tea preparation metaphor, but the concept has merit. In the Agile develop methodology, there exists this concept of a Minimum Viable Product, or an “MVP”. That’s just enough to prove out a hypothesis and make incremental progress toward a larger goal. Agile has many applications outside of software development. You can think of this light framework as the Governance Process “MVP” – Minimum Viable Process. This gives us just enough to prove value, and / or course correct early in the development of a full Governance initiative. Additional Agile concepts we adopt include Sprints, where we break the project down into 2-3 week work plans with the goal of delivering a working solution to the items in the sprint at the end of that sprint cycle. Scrum, a daily 15-minute meeting of the core team to discuss what was accomplished yesterday, what is being worked on today, and anything blocking progress and Backlog, which is a prioritized list of items to be worked on in each sprint during the project timeline.
- Our light framework consists of four areas of focus to get started with “just enough” Data Governance. We will spend most of the remainder of our time discussing these. So let’s start with Prioritizing your data issues…
- … In a typical Data Governance program, you would establish the need for governance, gain support, build a team and THEN work on issue prioritization. Because this is a governance MVP, and not a full-throated governance initiative (yet), you should start with a short list of topics/issues/concerns to create a case for support and involvement of resources from other areas of your organization.
- In order to prioritize your data issues, you need to develop a list to be prioritized. 1. Metric definitions – differing numbers with same name 2. Dimension/hierarchy/roll-up definitions and Master Data Management 3. Where to go for the right data: Different databases with slightly different data sets 4. Establish Single-source truth 5. Data quality and cleanliness 6. Roles & Responsibilities 7. Data access/security issues/Privacy 8. Compliance issues Privacy - GDPR, HIPAA and PPI/PII Sponsorship & Organizational issues Today, we will follow the simple example of standardizing a set of metrics around Customer Success to create a data dictionary with governed definitions and roll those out to the reporting and systems where it resides. Let’s say this is a mid-sized software vendor struggling with customer churn, even as they grow new subscribers. A classic leaky bucket scenario,
- What is the most important criterion for prioritizing governance activities? Next most important, etc.? 1. Identify and prioritize to be governed / defined 2. Establish quantitative prioritization criteria 3. Map prioritizations to simple 4-box matrix. Value and Feasibility
- Business Value On a scale of 1-5 in this case, - Are there direct impacts to customers? Channel? Operations? - Includes production or process. - Should consider the alignment with company strategy overall
- Business Value - Are there direct impacts to customers? Channel? - How many groups does this impact internally and externally? Can impact a small number of people but have a large impact on business too. - Includes production or process. - Should consider the alignment with company strategy overall
- Breadth of Use How many groups does this impact internally and externally? Can impact a small number of people but have a large impact on business too. Broad use across the organization does not immediately qualify as a high priority. How broadly across the organization is the data element used in reporting, planning or other decision-making applications? This is where you may find the same metric name attached to various definitions across departments
- Risk & Regulatory Compliance Are we out of compliance? Will there be a significant impact for being out of compliance? Are there new regulations that need to be codified? This is usually a must do. We should assess this for each metric, but it does not necessarily need to be a driving criteria.
- IT Effort - Feasibility - The amount of time it will take, measured in hours, to research, document and communicate the impact of changes
- Complexity - This reflects the relative complexity of bringing a data element into Governed. It is the people, systems, reports and processes impacted by changes to the data element. Different than effort.
- …and these scores can be mapped onto a simple 4 box matrix. The upper right box, sometimes referred to as the “magic quadrant” contains the elements that the exercise identified as being highest value to the organization while also being relatively easy to accomplish.
- As you can see, we are able to stratify out the data elements, in this case Customer Success metrics, by the two criteria of Business Value and Feasibility/Effort.
- Once your priorities are quantified and mapped to the matrix, review and make final prioritization of the list through the lens of: Risk (to doing and not doing), business effort weighed against benefits, and alignment to organizational data strategy and overall strategic direction.
- At this point, with your prioritized list of Data issues, you are equipped with the tools and evidence you need to work on gaining support for this Data Governance MVP. There may already be grassroots support for fixing obvious or glaring data issues. But there are a few groups of stakeholders that all need to be supportive of this effort for it to succeed….
- While you may believe strongly in your analysis and want to get started on the items in that “magic quadrant”, but you cannot go it alone. For any Data Governance initiative- even an MVP like we are discussing here- must have active support from at least these three groups of stakeholders. Likely you are from one of these groups…so it is a matter of identifying the remaining stakeholder representatives.
- When you are talking to Senior Leadership, remember that the key to success in a Data Governance program, like many initiatives and certainly something we see in data-related programs, is active Senior-Level support. To gain this, : Emphasize that people’s behaviors are governed, not data. This can be lightweight. Today this is an MVP. Even in a full DG program, you don’t have to blow up current organizational charts to do this, just provide structure and support to aid people to do this better. This doesn’t need to cost a lot of money. Incremental improvements can be made by improving processes and supporting people in the job they are already doing. Senior leadership will want to know if the expense in time, opportunity cost and real dollars justifies the effort – so be prepared to talk numbers as well In our example, Senior Leaders will want to know that our Customer Success metrics are accurately representing our success or challenges in reducing churn and growing our Net Promoter Scores.
- IT has access to source data and systems. They are an important partner in this. When talking with IT Teams: Clearly defined accountabilities and responsibilities. With clearly defined roles around data assets, IT can prioritize… IT will want to understand the impacts to workloads, other priorities IT is held responsible for and who will do the hard work of rolling a change out (updating reporting, training & communication) Something else.
- Business Users: Business users will have a vested interest in data quality, as they use this information every day to make decisions for the business. They will also be interested in how this impacts their ability to do their job, how this impacts resolving known issues and what will be expected of them in the process.. Make better, faster decisions with more certainty. Reduced risk through data quality, consistency and security drives better, faster decision making Something else.
- In building support for data governance, you have started the process of building your team. The same groups of stakeholders that you gained support from for this effort will have representation in the process.
- Three key areas that will represent stakeholders ID roles /responsibilities Finalize selection of Subject Area to start/ what order Assign data owners & data stewards Data Stewards – from whatever group has responsibilities for day to day management of data resources. They are the Data producers – they know the systems and how to access. Responsible for the quality of a defined dataset on day-to-day basis Work with Data Owners to address data governance related issues and activities Data Owners – They are the primary data users & consumers Representatives from data domain/subject area Create, review, adhere to data definitions Stakeholders accountable for quality of assigned data sets. Work with Data Stewards to address data governance related issues and activities Senior leadership has oversight responsibilities, primarily through membership on a Data Governance Committee Strategic senior level representatives from business areas and IT Primary issue resolution body responsible for coordination, cooperation and communication Additionally, the most senior organization leadership serve as the Executive Steering Committee Most Senior Enterprise Leadership – typically an existing group that meets on a variety of steering topics Support & Sponsorship of DG initiatives Ultimate decider
- A good way to think about who does what is by creating a RACI matrix to identify key roles and responsibilities for the data governance effort. In a formal DG program this document can look complicated, but even in this MVP it is a good idea to have clarity on who does what… In this Example…
- At this point in the Data Governance MVP, you have identified an area to focus on, gained support for this initiative from key stakeholders in the organization and recruited a few partners in this effort from other departments. Now is the time to execute on this work- put it into action…
- You have Identified your target data elements, prioritized where to start, decided on one consistent definition for your organization. You know and have documented what and who will be impacted by the change on reports and other uses for the data elements. Last step in the MVP for Data Governance is to deploy the newly Governed Customer Success metrics into your operational systems. This will involve those groups that maintain data sources of record, Data Marts and your Data Warehouse, generate and update reporting, maintain data sources for self service… really anywhere that these metrics touch the organization. This is where the hard work up to this point pays off. You can see the tangible output in the use of the governed metrics.
- When you get to this point – execution - remember this is a MVP. You don’t need to produce the array of documentation required of a full Data Governance program. Here you want to focus on one deliverable. In this case it is our Customer Success data dictionary. This data dictionary is the foundation for building a Governance program. You can use this to demonstrate the value and tangible output of a Governance program to leadership and other decision makers. And it should be used to actually deploy the agreed to definitions into your reporting and other uses.
- By following this simple Data Governance Framework MVP Now build momentum for more by sharing what has been accomplished. Measure impact. Quantify the return on an investment in Data Governance. Show the benefits, and propose your next area for governance. This is where you can continue to build on the MVP to slowly ramp up Data Governance, or take these learnings and drive toward a true Data Governance program for your organization.
- Agile is not about doing more with less people. It is about doing more in less time. Today’s discussion is not about Agile, but as mentioned, we at Senturus have adopted some key Agile practices for our project work. Taking this approach is a good way to keep the effort on track and will set you up for relatively seamless expansion of this Minimum Viable Process into a more robust Data Governance Program. These come out of Agile: - The concept of an MVP – Minimum Viable Process is just enough to give structure to the effort without creating a burden of process. - Two-week sprints – keeps the amount you “bite off” manageable and keeps the team nimble when blockers threaten to slow progress. Daily scrums – 15 minutes each day (ideally first thing in the morning) Yesterday-Today-Blockers Backlog – Your prioritized list of work to be done. This list is dynamic as the project progresses, and is managed through a process called backlog grooming where the order and priority of work effort is updated with each sprint.
- We just walked through how to get started with a lightweight, MVP approach to testing if an investment in Data Governance is right for your organization. We have shared pieces of our playbook on approaching Data Governance. Try it for yourself. Or let us help you. This is what we do.
- Other events we are working on: Feb 25: How BI Teams Build Good Reports Consistently March 11: Synapse vs. Snowflake March: 25 Ways to Publish & Share in Tableau
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