Strategies for Overcoming Data Management Challenges

Your SAP AI is only as good as your Data infrastructure. No clean data → No business impact. SAP is making headlines with AI innovations like Joule, its generative AI assistant. Yet, beneath the surface, a critical issue persists: Data Infrastructure. The Real Challenge: Data Silos and Quality Many enterprises rely on SAP systems - S/4HANA, SuccessFactors, Ariba, and more. However, these systems often operate in silos, leading to: Inconsistent Data: Disparate systems result in fragmented data. Poor Data Quality: Inaccurate or incomplete data hampers AI effectiveness. Integration Issues: Difficulty in unifying data across platforms. These challenges contribute to the failure of AI initiatives, with studies indicating that up to 85% of AI projects falter due to data-related issues. Historical Parallel: The Importance of Infrastructure Just as railroads were essential for the Industrial Revolution, robust data pipelines are crucial for the AI era. Without solid infrastructure, even the most advanced AI tools can't deliver value. Two Approaches to SAP Data Strategy 1. Integrated Stack Approach:   * Utilizing SAP's Business Technology Platform (BTP) for seamless integration.   * Leveraging native tools like SAP Data Intelligence for data management. 2. Open Ecosystem Approach:   * Incorporating third-party solutions like Snowflake or Databricks.   * Ensuring interoperability between SAP and other platforms. Recommendations for Enterprises * Audit Data Systems: Identify and map all data sources within the organization. * Enhance Data Quality: Implement data cleansing and validation processes. * Invest in Integration: Adopt tools that facilitate seamless data flow across systems. * Train Teams: Ensure staff are equipped to manage and utilize integrated data effectively. While SAP's AI capabilities are impressive, their success hinges on the underlying data infrastructure. Prioritizing data integration and quality is not just a technical necessity → It's a strategic imperative.

Most #datagovernance challenges stem from systems and processes that were designed without explicit data management principles. The larger, older, and more complex the organization is, the larger the problems tend to be. Organizations have tried to retroactively implement data governance for legacy systems and processes. I spent several years in the trenches of such programs, tracking down elusive system owners, and digging through outdated ETL scripts and technical metadata – it’s a grind. The approach involves extensive efforts to untangle #data flows, working with sometimes no longer supported technologies, and imposing governance standards on outdated or non-compliant systems. Engineers and consultants that were responsible in days past have long gone, further complicating efforts. Faced with mounting costs and frustration, many organizations simply give up. Savvy data leaders have begun to focus on “designing in” data governance from the start of new transformations, marking a move from remediation to prevention, and from reactive to proactive. The key to integrating data governance into the fibers of your data #architecture is to ensure that your organization’s transformation lifecycle methodology includes data governance commandments. Having an organizational transformation lifecycle ensures that any transformation of a certain impact or size is automatically brought into scope. This simplifies the task for data governance leaders, who would otherwise have to go out into the organization to identify the initiatives where data governance should be playing a role, each time then having to advocate for its inclusion – often a thankless task. The data governance considerations listed in the attached might sound heavy and time-consuming, but if done right, they can actually accelerate transformation, instead of delaying it. For example, using a data catalogue and adopting existing data products can save time finding and preparing data, and using an existing enterprise data model can prevent time being spent to create a new model from scratch. Trust me, the members of your data team will be much happier to enable new projects instead of to solve the mess caused by previous ones. Focusing on the #transformation lifecycle not only prevents the occurrence of new data governance issues but also yields a higher ROI. Designing systems and processes with data governance in mind from the outset reduces the costs and complexities of retrofitting governance later. Moreover, when data governance is embedded by design, the business users can leverage high-quality, well-governed data as soon as the transformation is complete. For more 👉 https://lnkd.in/e7nVGRGP 

Bad data isn't just a Marketing problem—it's an existential business threat. When leadership teams evaluate digital transformation failures, one factor consistently emerges: poor data quality. Don't let your strategy crumble against the reality of decayed, incomplete market intelligence. Take immediate action by implementing a data governance framework that assigns clear ownership of data quality across marketing, sales, and RevOps. Schedule monthly data health assessments with automated cleansing protocols for duplicate, outdated, and incomplete records. Deploy intelligent contact verification tools that automatically validate email deliverability, phone accuracy, and job title currency before any outreach begins. Integrate these verification steps directly into your sales engagement platform's workflow. Revolutionize your market opportunity sizing with dynamic territory planning tools that continuously ingest third-party data to identify accounts entering or expanding in your target markets. Create alerts for trigger events that signal buying readiness in your highest-value prospects. Meanwhile, market leaders are connecting their tech stack to DaaS platforms for continuous enrichment at the point of capture. Every new lead, every form submission, every website visit is instantly enhanced with rich firmographic and technographic data. In today's winner-takes-most marketplaces, enterprise-grade data isn't a luxury purchase—it's the table stakes for remaining relevant. Will your go-to-market strategy thrive on intelligence or perish from ignorance? #DataDriven #GTM #SalesLeadership #RevOps #B2BStrategy

Where do I start? This is arguably the question I’ve been asked the most by data leaders tasked with a large scale transformation initiative. The transformation could be a cloud migration, an ERP consolidation, or any large data-centric replatforming that involves a complex web of people, process, and technology. Quite often, many leaders have convinced themselves, or have been guided by a consultant, that taking a ‘bottoms up’ approach that starts with with an inventory of the data, often along with some form of a maturity assessment, is the right way to go. It’s not. The right way to go is to take an outcome-driven approach where you are rabidly focused on solving a very limited number of business problems. Each problem would have a well defined and limited scope, and would be accompanied by a business case where the financial benefits of that initiative are quantified, and aligned upon by your customers. Instead of focusing on all data, you’ll instead inventory, observe, govern, steward, master and integrate only the data needed to solve your immediate problem. Yes, some idea of the ‘future state’ must be defined and you need to ensure you’re building out an architecture that is scalable and flexible, but complete clarity on all aspects of every individual deliverable between now and that future state do not need to be defined. If you focus each of your phases around solving specifc problems, you will build the momentum and business support you need to get more funding, and slowly grow the program over time. Instead of taking a ‘framework driven’ approach that ensures your customers will have to wait 18+ months to see any value, your customers will get benefits now. Don’t be foooled into thinking that you need to catalog and govern everything in order to transform your data estate. You don’t. Focus on solving business problems and in time, you’ll catalog and govern what matters the most. What do you think? If you have different ideas on where to start, I would love to hear them? #cdo #datagovernance #datamanagement

Too many teams accept data chaos as normal. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North take a different path - eliminating silos, reducing wasted effort, and unlocking real business value. Here’s the playbook they’ve used to break down silos and build a scalable data strategy: 1️⃣ Empower domain teams - but with a strong foundation. A central data group ensures governance while teams take ownership of their data. 2️⃣ Create a clear governance structure. When ownership, documentation, and accountability are defined, teams stop duplicating work. 3️⃣ Standardize data practices. Naming conventions, documentation, and validation eliminate confusion and prevent teams from second-guessing reports. 4️⃣ Build a unified discovery layer. A single “Google for your data” ensures teams can find, understand, and use the right datasets instantly. 5️⃣ Automate governance. Policies aren’t just guidelines - they’re enforced in real-time, reducing manual effort and ensuring compliance at scale. 6️⃣ Integrate tools and workflows. When governance, discovery, and collaboration work together, data flows instead of getting stuck in silos. We’ve seen this shift transform how teams work with data - eliminating friction, increasing trust, and making data truly operational. So if your team still spends more time searching for data than analyzing it, what’s stopping you from changing that?

You join a 1000 person company as the head of data, and their current data infrastructure is a mess. What do you do? Here is where I'd start. 1. Avoid the urge to start fire-fighting - Instead spend some time talking to the business and key stakeholders to understand what problems they are trying to solve. Why do they want access to data, also what does their data to day currently look like? I believe Ethan Aaron has referenced this a lot when he has discussed first taking over a data team. 2. Understand why the prior team or project failed - I have had a few instances now where I have been brought in to turn around data infra and strategy and I always ask, "why are you bringing in me, and where did the break down happen in the prior project". You can always ask these questions directly and sometimes the prior project may have been failing so long, the initial cause may have been forgotten so you will need to spend some time digging into the why. 3. Set Expectations - There is no point in promising some over the top and unrealistic outcomes from the data team if you don't believe it. It can be tempting to try to curry favor with the leadership team but you want to be honest how long things will take, costs, and perhaps and blockers that need to be removed. Also, come with a clear plan with milestones so the leadership team can also see that even if its a long road you know the bath. 4. Asses the technology stack - The first thing you should do when you're turning around a data team isn't to try to make a bunch of changes to the data stack. In fact, for one project I was working on I realized that no one was using the dashboards and data warehouse anyways. Meaning we could put it off to the side while we assessed what the business and stakeholders actually wanted. Don't get me wrong sometimes you have a data stack that just requires some small improvements. But you won't know fully until you know what use cases will benefit the business. 5. Update leadership about wins - And if you can start putting out tangible outcomes that they can "touch" so to speak, do it. It'll help build trust and show them that you are trying to do more than just build a fancy data stack but you also want to get them unstuck and access to the data they have been waiting for. You also have to ensure that the leadership team has your back and wants to actually change the data situation. Sometimes, even when they say they want to be "data-driven" they might not be willing to provide the resources or unblock you and you need to figure that out fast. What other advice do you have for people just taking over data teams?

Navigating the Challenges of Data Architecture: A Journey to the Summit Imagine you’re scaling a mountain of data—what obstacles might you encounter along the way? Data architecture is the backbone of intelligent decision-making and innovation, but it’s often fraught with challenges. Here’s a snapshot of what you might face and how to navigate these hurdles: 1. Data Integration Heterogeneous Data Sources: Integrating data from various formats and structures requires robust ETL processes. Data Quality: Maintaining accuracy and consistency with validation checks and cleansing routines. 2. Scalability Growing Data Volumes: Leverage scalable cloud solutions and distributed computing frameworks like Hadoop and Spark. Performance Optimization: Use indexing, caching, and query optimization techniques. 3. Data Governance Data Lineage: Implement tools to track data origin and transformations. Compliance: Establish strict governance policies to adhere to GDPR, CCPA, and other regulations. 4. Data Security Access Control: Use role-based access control (RBAC) and fine-grained permissions. Encryption: Protect data at rest and in transit with robust encryption mechanisms. 5. Data Storage Choosing the Right Storage: Balance between data lakes, warehouses, or hybrid solutions based on needs. Cost Management: Implement tiered storage and cost-monitoring tools. 6. Data Processing Real-time vs. Batch Processing: Select based on use cases—real-time for time-sensitive, batch for periodic data. Pipeline Management: Ensure robust, fault-tolerant pipelines with monitoring and alerting systems. 7. Analytics and BI Tool Selection: Choose tools based on ease of use, integration, and advanced features. Skill Gaps: Continuous training and upskilling are essential for using advanced analytics tools. 8. Infrastructure Management Resource Allocation: Efficiently manage compute, memory, networking, and storage resources. Monitoring and Maintenance: Use observability tools for insights into system health and regular maintenance. Let’s discuss and share our experiences to climb the data mountain together! CC: Deepak Bhardwaj 🏔️ #DataArchitecture #BigData #DataIntegration #Scalability #DataGovernance #DataSecurity #Analytics #DataStorage

Data complexity increases as volume, velocity, and variety expand. Today, most organizations measure more things than they did in the past and struggle to manage all their data. In my #analytics consulting career, I’ve seen data teams approach data complexity in two ways. 1️⃣ 'Pass along’ approach Essentially, analytics teams relay the data complexity to business teams and stakeholders. Over time, more data complexity means more data products and more complicated offerings. 👉 A basic dashboard becomes more detailed with multiple tabs and advanced filters. 👉 A simple 10-page report turns into a 60-page one. 👉 A single access point for customer information expands to five disparate systems. I remember talking to an analytics executive who bragged that his organization had over 20,000 Power BI reports or dashboards. While he might have been impressed by this number, I don’t think the business teams at his organization would have been as enthusiastic. The ‘pass along’ approach deters data adoption rather than encouraging more people to use data. End users become increasingly overwhelmed by the expanding number of increasingly complex data products. This approach is focused on production rather than business outcomes. 2️⃣ ‘Focused and streamlined’ approach These data teams realize a ‘pass along’ approach only transfers the data complexity to business users and doesn’t directly address it. While it may not be possible to offset the increasing data complexity completely, these analytics teams strive to mitigate it as much as possible. They understand data products can be enriched with more or better information, but that doesn’t mean business users should be burdened with excessive amounts of data and increasingly complicated tools. These analytics teams realize they can expand data adoption by offering focused, meaningful information and streamlining how it is delivered. Their goal is to make the data as accessible and useful as possible, not overwhelming or confusing.   Some #data professionals will push back on this optimized approach. They may feel business teams won’t appreciate their ‘behind the curtain’ contributions to making data easy to access and use. I disagree. When you streamline the ability for business teams to access relevant, useful data, the value your team delivers will be clearer and more tangible to them. Success in analytics is about driving business outcomes—what you accomplish with the data—not the quantity or wizardry of the data products you produce. As a final point, these two approaches will use AI very differently. The 'pass along' approach will use it to shovel more data at a faster pace, piling on to the information that is already being ignored. The other will use AI to simplify the data complexity and help more business users extract better insights, which will expand user adoption. Do you agree with my take? What approach is your analytics team using?

Do you know what I have found to be one of the biggest hurdles in CRM data hygiene? 🕰️ Keeping information fresh and relevant! In the world of CRM, data ages like milk, not wine.🥛😬 Trying to run campaigns or outbound efforts using outdated data is like trying to navigate with an ancient map - you might end up in uncharted territory or, worse, completely lost. 🗺️ This challenge reflects our human struggle with time and change. Just as cultures evolve and adapt, so does the information about our customers and prospects. So, how do we keep pace with the relentless march of time? Here are some best practices: ✔️ Regular Data Audits: Schedule periodic reviews of your CRM data. It's like spring cleaning for your digital house! 🏠🧼 ✔️ Leverage Automation: Use HubSpot's tools to automatically update certain fields like workflows moving contacts through the lifecycle stages. Let the robots do the heavy lifting! 🤖💪 ✔️ Encourage Customer Input: Create opportunities for customers to update their own information. It's a win-win - they get better service, you get accurate data! ✔️ Train Your Team: Data hygiene is a team effort! Make data updating a part of every customer interaction Fresh data is the lifeblood of effective sales and marketing. ⏳By keeping your HubSpot CRM up-to-date, you're not just maintaining a database - you're preserving a living, breathing record of your customer relationships. 💖 #DataFreshness #HubSpotCRM #DataManagement #DataHygiene --- 👋🏼 Hi, I'm Omi, co-founder of Diaz & Cooper, a Platinum HubSpot Solutions Partner helping B2B companies create efficient revenue operations. I'm on a mission to bring the human back to HubSpot. Need some help with your data hygiene practices? Let's talk!

I've had a half-dozen executive leaders tell me their "data governance" program isn't working. When I speak to the data leaders responsible for these data management programs, they do an enormous amount of work and are frustrated by the lack of sponsorship. So, where's the disconnect? For me, it's as simple as metrics. As the data leader, have you put together a set of metrics for your data management program that others easily understand and that you publish weekly? Are metrics assigned to people? Do gaps have names assigned to them? Are there dates for deliverables? Is there evidence of progress? Your word only carries you so far, and for the person responsible for data, do you use data to justify your impact? No, not always... I'm always surprised that data leaders sometimes need help figuring out what to measure. Here are some simple metrics to get you started: 1) # of people within your data community 2) data communities with and without sponsors 3) # of use cases and $ benefits with owners 4) # of business terms with stewards with metrics for completeness and approvals 5) # of data elements inventoried and mapped to business terms with gaps assigned to stewards 6) an inventory of solutions with # of users and $ cost From here: 1) you can measure the ROI of a use case 2) you can align metrics to multiple use cases 3) you can get technology's focus to fix upstream platform issues 4) you can highlight sponsorship gaps Taking it further: 1) you can allocate ROI to individual business terms 2) you can assign value to data 3) you can figure out where competing business needs and inconsistent metrics are impairing data quality and what the cost is So, what are the metrics of your data management program? Do you have any? If you need help, shoot me a note, and we can chat. Remember: People use confusion as an excuse to disengage from the data program - which is often complex and boring. Don't get me wrong, there is an element of people genuinely not caring, but that doesn't mean stack the odds further against your success. To manage data, how are you measuring it? Best of luck in your data journey!