Data Integrity: The Baseline for Innovation

Editor's Notes

• #3: Welcome. My name is Susan Pawlak and I am the Product Marketing Manager for Precisely. Thank you for joining us today for this IDC Event. 
• #4: With the pandemic and other unpredictable market conditions, data leaders such are yourself, are accelerating innovation in their data strategies. Key business initiatives in 2022 revolve around transforming customer experiences, applying AI to proven business cases to derive new insights and increase efficiency, leveraging the power of location to solve new problems, and ensuring that the business is secure and compliant.  Some compelling statistics about business trends:  IDC reports that 83% of CEO’s want their organization to be more data driven and  Digital Transformation investments will top $6.8 trillion globally by 2023  Global data infrastructure spending is expected to reach $200B this year, and 68% of Fortune 1000 businesses now have CDO’s.  Each of these initiatives is heavily dependent upon integrated, clean, accurate, contextualized, enriched data in order to deliver the maximum benefit to the organization. These all support the fact that data is the fuel for decision-making today – and the conclusion is that Data Integrity must be a baseline for Innovation. 
• #5: There has been no consistent definition of data integrity that the market all uses, and the definitions out there often focused on collections of technical capabilities related to data quality. But that is not good enough. Today’s businesses need a definition of data integrity based on their need for data that is not just accurate and consistent, but is also rich in context. And that’s why Precisely has redefined data integrity as data with maximum accuracy, consistency, and context for confident business decision-making.
• #6: Let’s break this down a bit. Data integrity is a journey. That’s one thing we hear loudly and clearly when we talk to our customers. Everyone is on a journey to continuously improve the integrity of their data, better understand their business, and ultimately better make better business decisions and better serve their customers. What we have learned from our customers is that there isn’t a standard, linear journey to data integrity that works for everyone… and the days of large corporate initiatives are dead. Customers told us that their business and IT teams are working more closely together than ever… jointly identifying the specific scope that delivers meaningful business impact. And as a result, they tackle data integrity through distinct projects that give them business value… no matter where those steps fit into this journey… and then plan their next move. And not surprisingly, that means they want solutions that give them the freedom to make those choices.   Data integrity is a journey. It’s continuous. And it requires best-in-class solutions working together to deliver value to the business.  There are many different steps along the path to data integrity…. like integrating siloed data, measuring its quality, adding location intelligence, and enriching it with 3rd party data to name just a few. One of the hottest topics in data strategy today is data governance. But... 
• #7: The point of data governance is not data governance. We find that overwhelmingly, the DRIVERS for data governance fall into three general categories: Manage Risk around ever-increasing complex regulations and new environments Accelerated and timely decision making for more profitable growth Increased operational efficiencies to improve performance and reduce costs Data governance for data governance sake – or even the perception of that – makes no sense. The TRUE point of data governance is DELIVERING BUSINESS OUTCOMES AND CONNECTING BUSINESS GOALS, OBJECTIVES AND VALUE WITH MEASURED IMPACTS & RISKS.  The value of data governance is to deliver the RIGHT DATA, to make the RIGHT DECISIONS, at the RIGHT TIME with the RIGHT CONTEXT. 
• #8: Let’s look at a few examples of each of these drivers:  Reporting & Compliance  Improve accuracy, consistency, and completeness of internal Reporting  Increase visibility and compliance around Privacy Regulations like GDPR, CCPA and other that are popping up everyday  Industry specific regulations and reporting   HIPAA/Basel/Solvency II  Reduce risk around financial reporting NAIC MAR/SOX  Improve operationalizing ESG information Analytics & Insights  Create 360° View of the Customer  Leverage AI/ML  Incorporate Internet of Things  Expand Global Visibility  Expedite real-time data for analytics  Operational Excellence  Enhance customer satisfaction  Reduce operating costs  Accelerate systems migration & consolidations  Improve working capital  Streamline strategic sourcing  In order to achieve these business goals & outcomes, what should a successful data governance program include?
• #9: First and foremost, data governance should be designed with a business value centric approach. Data governance should link governed data to the KPI’s, goals, and objectives that impact data users up and down the organization – including strategic leaders, operational, AND technical teams Business goal tracking provides real-time views into how governed data supports business processes, compliance events, reports, and metrics. We describe this as a “top down, bottom up, middle out.” approach based on proven expertise with hundreds of companies across all industries. Successful data governance programs MUST measure the value of data governance to document the continuing return on your investment – but also to continue to inform and validate to the data users that the work effort is paying off for them with real business value around their own objectives and goals.
• #10: We all know that data is exploding and growing at an exponential rate. Yet data that directly impacts the business goals of a company is estimated to be as little as 5% of the data that exists at an organization. We’ve heard more than one unfortunate story where a company felt that cataloging ALL their data was the first and most important step – but for what purpose? And is that really sustainable? The second key component around successful data governance programs is focusing on what matters. Strategically determining what data sets will directly impact your defined business goals – from the Top Down, Bottom Up and Middle Out – will set you up for a sustainable and manageable data governance initiative. In this chart, you will see that from the volumes of data that exist, it’s usually a small sliver that is truly critical data....Still, that critical data can come from everywhere in the organization. From tables and fields that exist at the system level, to Master Data – the foundational data that is required to run the business and conduct daily operations.....through data that feeds KPI’s, performance measures and analytics – and data that delivers actionable insights around defined business objectives and business drivers.
• #11: We all use information to gain intelligence and insight but it’s only useful if the data is accurate! The third premise for successful data governance programs is closely integrated Data Governance and Data Quality. Data Quality processes and tools to not only clean the raw data, but to illustrate data errors, peculiarities and issues, in order to help compile the best standards, establish key metrics, and monitor the data quality over time. Data Quality needs appropriate Data Governance processes and practices to ensure the data is cleaned and maintained within an appropriate data framework which is relevant and pertinent to the business needs and users. The symbiotic relationship of Data Governance integrated with Data Quality is imperative in successful data governance programs to: Build Trust with Visibility into Metrics & Rules Measure value accurately around tangible business outcomes Create confidence and accountability by operationalizing data governance & data quality contributions from the data user community at large.
• #12: As further proof of the importance of this relationships, Research from Drexel University’s LeBow College of Business, shines a light on how data governance programs have a direct impact on data quality - with two-thirds of organizations reporting improved data quality as a "leading benefit“ Additionally, 75% of respondents also acknowledging that data quality is their organization’s “top concern,” the report delivers a compelling message for businesses looking to better understand and trust their data.
• #13: I wanted to come back to my original challenging statement: Data Governance is NOT the goal – The goal is delivering better business outcomes. The keys to delivering better business outcomes include the ability to: Connect Business Value to Data Governance Focus on What Matters Remember that DG & DQ are intertwined on your journey to Data Integrity
• #14: As we mentioned in the beginning, Data integrity is a journey. Today we talked about how data governance & data quality is critical…but The Precisely Data Integrity Suite is modular and delivers value at every step of the data integrity journey. From integrating all your critical data to connect today’s infrastructure with tomorrow’s technology To verifying that data governance and data quality are built into your data-centric processes to ensure accuracy and consistency By leveraging location intelligence for enhanced and actionable insights And enriching your data for decision-making with expertly curated, up-to-date business, location and consumer data
• #15: Precisely is proud to be the leader in data integrity where our software, data enrichment products, and strategic services deliver accuracy, consistency, and context in your data, powering confident business decisions. 12K clients 100 countries 97/Top 100 2500 Employees
• #16: I know we have a few questions What’s the difference between data governance and data catalog? DG + processes DC subset of DG, Inventory What are the biggest challenges in implementing data governance? Lack of ROI Cultural adoption What’s in it for me without link to MY VALUE Not business friendly enough: Natural language search, technical data lineage and metadata, inflexible metamodel POLL QUESTION