What is Data Governance and why it’s crucial for PropTech
•Als PPTX, PDF herunterladen•
0 gefällt mir•55 views
Customer, client, supplier, internal, private, third-party data… The amount of data available to real estate companies is truly outstanding! With the push of digitization and the changes Covid demands of real estate, businesses want to put high-quality data into the hands of the people who use it to do their jobs. That means organizations must build processes that define who owns what data, how it can be used, how to maintain it meaningful to their business, and how to make it consumable by everybody in the organization, not just the data experts. Data governance is no longer a choice in this data-filled, fast-moving space. Join us to learn about: What is Data GovernanceHow to manage, improve and control the quality of the dataHow to empower a common understanding of data through your organization
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Ähnlich wie What is Data Governance and why it’s crucial for PropTech
Ähnlich wie What is Data Governance and why it’s crucial for PropTech (20)
Mehr von Precisely
Mehr von Precisely (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
What is Data Governance and why it’s crucial for PropTech
- 1. Data Governance Carmen Adame| Product Marketing, PropTech Ruslan Sultanov | Product Marketing Empowering PropTech through Data
- 2. Why Data Governance? • The real estate market is undergoing significant and exciting change – driven by technology and data. • The amount of data from multiple sources can be overwhelming. • Companies in a transformational journey of digital data and technology depend on the accuracy, relevancy, and completeness of their data. • The risk of having data that provides no business value is too high.
- 3. What is Data Governance?
- 4. Data Governance Data governance is everything you do to ensure data is secure, private, accurate, available, and usable. It includes the actions people must take, the processes they must follow, and the technology that supports them throughout the data life cycle.
- 5. Benefits of Data Governance 5 Manage risk & Enhance Compliance Understand exposure of sensitive data, security breaches and avoid risks associated with non- compliance Establish trust. Understand customer trends, suppliers, and partners. Enhanced customer care Access data needed to improve products and services and seize opportunities for new revenues Better decision making 5
- 6. How to get started?
- 7. Good Data - Good Owners Data Governance is the convergence of People, Processes, and Technology. It is about consensus building, ownership, and overcoming barriers. It not only aligns IT and business functions to leverage the benefits of data, but also defines data ownership and policies, decision rights, and escalation procedures. Presentation name 7
- 8. Focusing on what matters (Critical Data) Data Selection of data maintained at the system level (tables and fields) Information Information required to run the business and conduct daily operations KPIs / Performance Measures / Analytics Measuring process effectiveness & enabling sound business decisions Actionable Insights & Business Value Strategic enterprise and organizational business value drivers CRITICAL DATA
- 9. Not all data is created equal 95% of business results 5% results Data Governance programs that prioritize critical data have 5x faster time-to-value ~5% critical data 95% all other data
- 10. Mapping data governance business value Goal Org Stakeholders Expected Outcomes DG Objective DG Capabilities Improve personalization of customer engagement and Brand recognition Marketing Sales Finance • Increase NPS by 5% • 17%+ repeat customer purchases • 11% reduced churn • Establish a common view of trusted customer data assets • Data Catalog • Data Lineage • Approval Workflow • Data Quality rules Increase sales and revenue through faster speed-to-market Marketing R&D Finance • $15M+ top-line revenue • 25% increased deployment speed • Establish stage gates, rules, policies, and quality measures from Ideation through Commercialization • DQ rules • Business process monitoring • Data quality metrics Increase user productivity by improving time- to-insights Business Analytics IT Data Office • Improve decision- accuracy by 22% • Reduce time-to- insight by 45% • Launch data literacy campaign across business data SMEs • Data lineage • Data Catalog • Automated workflow Reduce supply chain costs associated with errors in orders Vendor Management Finance Supply Chain • Reduce COGS by 4% • Improve OTIF by 15% • Establish common semantics view across order fulfillment data • Impact analysis • DQ rules • Business process monitoring
- 11. Pain Killer Vs Vitamin Goal DG Objective DG Capabilities Improve personalization of customer engagement and services • Establish trusted view of customer data assets • Data Catalog • Data Lineage • Approval Workflow • Data Quality rules Increase sales through faster speed-to-market • Establish stage gates, rules, policies, and quality measures for Commercialization process • DQ rules • Business process monitoring • Data quality metrics Increase user productivity by improving time-to- insights • Launch data literacy campaign across business data SMEs • Data lineage • Data Catalog • Automated workflow Reduce supply chain costs associated with errors in orders • Establish common semantics view across order fulfillment data • Impact analysis Centralized collection of customer data elements used for marketing and promotion Data profile providing additional context on volume, counts, location, and contents Data lineage flow of upstream/downstream relationships Impact analysis to business processes, metrics, and analytics Approved governance ownership indicating data is certified for access and use Automated approval workflow to grant access to data at source Data integrity metrics to indicate data that is accurate, consistent, and trusted Quality monitoring to trigger notifications below acceptable values P A I N K I L L E R “ M u s t H a v e s ” V I T A M I N “ B o n u s ”
- 12. Benefits of a business-first approach Accelerate program roll-out by 18-40% Increase likelihood of reinvestment by over 75% Generate 2-7x greater ROI
- 13. Data governance is no longer a choice in this data-filled, fast-moving space.
- 14. Takeaways • Data Governance helps put quality data in the hands of the people who use it • Enables you to make faster decisions, improves customer engagement and avoid risks • Data Governance helps you achieve business goals and drive value faster
Hinweis der Redaktion
- Without effective data governance, data inconsistencies in different systems across an organization might not get resolved. For example, customer names may be listed differently in sales, logistics and customer service systems. That could complicate data integration efforts and create data integrity issues that affect the accuracy of business intelligence (BI), enterprise reporting and analytics applications. In addition, data errors might not be identified and fixed, further affecting BI and analytics accuracy. Poor data governance can also hamper regulatory compliance initiatives. That could cause problems for companies that need to comply with the increasing number of data privacy and protection laws, such as the European Union's GDPR and the California Consumer Privacy Act (CCPA). An enterprise data governance program typically includes the development of common data definitions and standard data formats that are applied in all business systems, boosting data consistency for both business and compliance uses.
- To sum things up, successful Data Governance determines who owns the data, how and by whom data is created and updated, and who arbitrates decisions when disagreements or needs arise.
- Now, how do we understand what is our critical data? Well, if you start from the base of this pyramid as just kind of a working visual or an idea that might represent 100% of all the data that we have in the organization and almost every organization, regardless of the industry, the 100% of the data that that organization has, the only use about 40% to run the business. So the 40% of the information that's used to run the business could be information that is about our key customers, or are important suppliers or vendors, or are most profitable products or services, or a charts of accounts? It's about 10% of that 40% subset. So of 100% of all the data, 40% of it is the information that's used to run the business, only about 10% of that 40% is then used for insights and KPIs and critical processes in the organization. And at the very top of the pyramid, about 5% is a critical data. In that 5% of the critical data, again is data that's used across our three silos. So take a look at actionable insights and business value across risk mitigation across analytics and insights and across running the business more effectively from an operations perspective.
- People ask me all the time. This is a common concern. They say, You know, Ross, we have all of this data. How are we gonna govern it all? The data is moving so quickly, we have new stakeholders that have new needs every day. How do we get our hands around the data that in a way that is scalable and allows us to adapt over the long term? The fact that matter is that the majority of your data doesn't need to be governed. It might have utility, it might be useful, but it doesn't mean that it necessarily needs to be governed. So if we prioritize effectively, we can provide 5X faster time to value than if we were to focus on you know the 20 or 50 or you know even greater percentage of data that we have in the organization. And this is I think a key point in the way that we think about having access to the data that's in organization versus what we should govern and what we should manage and what we should look after from a data quality standpoint because only about 5% of the critical data that we have ultimately ends up driving about 95% of the the business results in our organization.
- So if you take a look to the left here, you might notice some goals that are fairly common. Might be familiar to your own organization, such as improving how personalized are products and services are for our customers, making them more contextual for our customers, or increasing sales and revenue by getting the market faster and beating our competition or increasing our user productivity for data consumers by helping them help self service to the data that exists in the organization. These might be goals as a part of the digitization or data transformation initiative. Each of these goals have their own organizational stakeholders, teams, regional stakeholders, and then also expected outcomes that get measured in the form of dashboards and scorecards and KPIs and analytics. We need to make it really crystal clear how our data governance objectives tie back to these organizational initiatives and stakeholders, and the reason is because people don't have time to make the connection themselves. They're already focused on their day-to-day work. They're already spending nights and weekends trying to get their business goal achieved or their particular project completed and so we don't want to put the extra step to make people think we want to make data governance relevant to the things that they're already bought into as opposed to asking them to think outside of their goal or initiative and to start to adopt our terminology and explain to them what our rules and policies and things like that. So governance immediately becomes important if it serves their initiatives and their needs versus the other way around. It also gives us insight into the capabilities and the tools that we need to provide to the organization. And oftentimes, one of the things that we see is organizations will go out and they'll just kind of buy the regular tooling that they think is the standard tooling that they need for data governance program without really thinking about where the key capabilities and the context of their business goals. And this comes to the second point, which is thinking about the solutions and the capabilities from a product, but also from a process perspective.
- And IN the terms of painkillers and vitamins, or the must haves versus the bonus or or the nice to have capabilities? You know, if you're thinking about improving your overall health and well being, you're not gonna be really focused on going to the gym if you have some sort of ailment or pain. So if you have a headache or a stomach ache, or if your knee hurts or your shoulder hurts, you're not going to be really focused on going to the gym that day. You're going to be focused on getting some rest and recovering until you feel better. And oftentimes, partitioning and in prioritizing these capabilities helps. When we think about what are the capabilities that are must haves to get the core business goal or initiative off the ground. So as an example for thinking about the personalization of customer goods and services with customer data, we might want to establish a foundation initially of centralizing a collection of really critical customer data elements that we usually use for marketing and promotion activities. Then we would establish a data lineage flow from source to target so that we can see how that information moves upstream and downstream. Then, once we understand what is the critical data and the affected systems within that upstream and downstream process, we would define a governance process that would establish ownership for indicating what data is certified for access and for use. And then we can overlay data quality checks on top that can indicate if the data is accurate, consistent and trusted. If we do those things from a foundational perspective, it's already a big win for the organization, but then we can come over top and overlay vitamins or or bonus capabilities so to speak in the form of providing additional data profiles, contextual insights or impact analysis that show how the customer data also impacts our business processes. Or we can automate our approval workflows or we could establish data monitoring or machine learning that will help us gain insights into why the data quality in some areas is more trustworthy than other areas that we can establish and understand those patterns.
- Some final stats to bring it home. In our experience, because we identified the goals that are most important to us, identified the critical data that impacts those goals – we want to communicate this with Value Metrics around our BUSINESS FIRST APPROACH methodology that our stakeholders care about: Our customers accelerate data governance program roll out by 18-40% Our customers generate as much as 7X greater ROI with better governed data And over 75% of our customers expanded or reinvested in their data governance programs when they realized the benefits of a business first approach
- The objective is to deliver value to the business via data. And for that data to mean something, it needs to be consumable by everybody in the organization, not just the data experts. Today, more and more businesses want to put high-quality data into the hands of the people who need it to do their jobs. That means organizations must build processes that define who owns what data and how it can be used.
- The take away of all this is that we really need to link data governance program initiatives to the higher level business goals, to the stakeholders, the business outcomes and then back into the capabilities that we need to deliver from a data governance perspective. This really helps us create the initial business case. It makes it clear for our stakeholders why the governance program is going to be essential, but also more importantly, how it will help to accelerate them to their expected result or the outcome that they're looking for. We can also prioritize painkillers and vitamins, so to speak, or the nice to haves and the must haves to protect and grow the business and ultimately launch the the program forward. So as a first step, we want to link the data governance program to the overarching business goals and initiatives of the organization. Companies no longer simply talk about being “data-driven;” they are actively implementing practices and processes. For BI projects to be effective, business users need to be able to find relevant data. Data governance is key to making data to actionable across the organization. Data governance is all about understanding, trusting and accessing quality data. And being a data governance expert requires passion for people, processes and technology. Data governance is more than simple tools. It is about getting data into the hands of the people on the front lines, and putting the data to work.
- So that concludes our session for today. I appreciate everyone listening and hope that you learned something new. Ross and I are happy to answer any questions or respond to any comments in the chat. So we'll go ahead and open it up for questions and comments at this point. Seed Questions Who in an organization usually initiates a data governance initiative? What is the most common reason Telcos initiate a data governance program How large is the typical data governance team within an organization? We have a data catalog – what’s the difference between a data catalog and data governance?