Best Practices for Data Management

If your CEO asks for deal updates in Slack, don’t expect reps to update Salesforce. You can throw all the tech, training, and sales ops resources you want at CRM adoption - but if leadership isn’t leading by example, none of it will stick. Here's the tl;dr: Reps don’t hate updating Salesforce because they’re lazy. They hate it because they know no one actually uses it. When leaders bypass the CRM - asking for updates in Slack, emails, or meetings - they send a clear message: “This system doesn’t matter. Your notes don’t matter. Just tell me directly.” And that’s how $100k+ Salesforce investments turn into glorified Rolodexes. So, how do you fix it? 1. Top-down adoption Start with the CEO. If they want deal updates, they need to ask for them in Salesforce. Chatter, Slack integrations, whatever it takes...but it has to flow through the system. 2. Make sales managers accountable Reps won’t change unless their managers enforce it. Run pipeline reviews directly from Salesforce dashboards. No exceptions. If it’s not in Salesforce, it doesn’t exist. 3. Quantify the pain Show reps how missing data costs them deals. Lost follow ups, misaligned hand offs, deals slipping through the cracks...all because the CRM isn’t up to date. 4. Reward the right behaviors Sales culture loves to celebrate closers. But what about the reps who close and keep a clean pipeline? Make data hygiene part of what gets recognized (and compensated). The reality is that CRM adoption isn’t a sales ops problem - it’s a leadership problem. If the top isn’t setting the example, the bottom won’t follow. And until that changes, you’ll keep throwing money at Salesforce while your reps keep their real pipeline in a Google Doc.

Data quality is a holistic process. If you ignore one practice, the rest of your effort may be worthless. Some practices are essential. You can't ignore them: ⚡Profiling the data to understand its structure ⚡Data stewardship to establish communication between data engineers and business users to decide what is good data ⚡Data quality issue tracking to react to and track problems Other practices can be ignored, but you will have to spend twice as much effort on other practices to fix them. ⚡Missing data contracts will not make the data publishers accountable ⚡Missing data observability will delay the detection of issues until users see them ⚡Missing data quality testing will make users test the data ⚡Without reporting, you cannot prove your effort in data quality and show which datasets are reliable over time ⚡Without standards and practices, you will apply different metrics and data quality testing methods across data domains ⚡No automation means that setting up data quality is time consuming There are other practices that you can also implement, such as: 🔸Validating data at the source 🔸Automated data cleansing 🔸Data lineage tracking #dataquality #datagovernance #dataengineering

Data Governance: Understand Key Focus Areas 🎯 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 & 𝐊𝐏𝐈𝐬: 🔘 Data Quality: Measure accuracy, completeness, and consistency. 🔘 Stakeholder Satisfaction: Ensure data governance efforts meet stakeholder expectations. 🔘 Security: Track how well your data is protected against breaches. 🔘 Operational Efficiency: Assess the effectiveness of your data processes. 🔘 User Adoption: Gauge the extent to which data tools and processes are utilised. 🔘 Data Value: Quantify the business value derived from data. 🔘 Data Risk: Identify and mitigate potential data-related risks. 🔘 Compliance: Ensure adherence to relevant laws and regulations. 🔍 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 & 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: 🔘 Data Quality Management: Maintain high standards for data accuracy and reliability. 🔘 Regulatory Compliance: Stay compliant with laws and regulations to avoid penalties. 🔘 Cost Efficiency: Optimize data-related costs for better financial management. 🔘 Data Stewardship: Assign responsibility for data management and policies. 🔘 Data Usability: Ensure that data is accessible and usable for stakeholders. 🔘 Data Transparency: Promote openness in data practices and policies. 🔘 Data Ethics: Uphold ethical standards in data collection and usage. 🔘 Decision-Making: Use data to inform strategic decisions. 🔘 Data Security: Protect data from unauthorised access and breaches. 🔘 Data Ownership: Clearly define who owns and is responsible for data. 🔘 Data Integrity: Maintain the accuracy and consistency of data over its lifecycle. 🔘 Data Auditing: Regularly review data and governance practices to ensure compliance and performance. 👥 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬: 🔘 Executive Leadership: Drive data governance strategy and ensure alignment with business goals. 🔘 Data Owners: Responsible for specific data assets and their quality. 🔘 Data Stewards: Manage data policies and quality. 🔘 Data Users: Utilise data for various business functions. 🔘 IT Departments: Support data infrastructure and security. 🔘 Legal and Compliance Teams: Ensure data governance practices comply with legal requirements. 🔘 Business Analysts: Analyse data to derive business insights. 🔘 External Partners: Collaborate on data sharing and governance. 🛠 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 & 𝐓𝐨𝐨𝐥𝐬: 🔘 Data Dictionary: Defines data elements and their meanings. 🔘 Data Catalogue: Organises data assets for easy discovery and access. 🔘 Metadata Management: Manages data about data for better understanding and use. 🔘 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 🔘 𝐏𝐨𝐥𝐢𝐜𝐲 𝐚𝐧𝐝 𝐑𝐮𝐥𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 🔘 𝐃𝐚𝐭𝐚 𝐋𝐢𝐧𝐞𝐚𝐠𝐞 🔘 Reporting Tools: Generate reports to monitor and manage data. 🔘 Governance Dashboards: Visualise key metrics and governance performance. 🔘 Audit and Compliance Tools: Ensure data governance policies and regulations are adhered to. #DataGovernance #DataQuality #Compliance #DataSecurity #DataEthics #DataIntegrity #DataManagement #AI #DataScience #DataStrategy

The Dark Side of Social Media: A Costly Mistake Leading to Cyber Crime In today’s digital world, social media has become an integral part of our lives. We share our achievements, personal experiences, and even our daily activities without realizing the potential risks involved. A recent viral video serves as a stark reminder of how a single post can lead to disastrous consequences, turning an innocent mistake into a major cyber crime. The Incident: How a Simple Post Led to Trouble The video highlights an alarming case where an individual unknowingly became a victim of cyber fraud due to their own social media activity. By posting sensitive personal details—such as location updates, job changes, or financial milestones—they made themselves an easy target for hackers and fraudsters. Cybercriminals often scour social media for such information to manipulate, scam, or even impersonate individuals. In some cases, people fall prey to identity theft, phishing attacks, or even legal troubles due to reckless online behavior. What Went Wrong? Oversharing Personal Information – Details like travel plans, addresses, and financial successes can be exploited by cybercriminals. Lack of Privacy Settings – Many users leave their accounts open to the public, allowing strangers easy access to their data. Unverified Links & Messages – Clicking on unknown links or responding to suspicious messages can lead to hacking and data breaches. Posting Sensitive Work-Related Content – Sharing confidential company details can result in legal actions or job loss. Misinformation & Legal Risks – Posting defamatory, false, or sensitive content can lead to legal consequences. How to Protect Yourself from Cyber Crime? Limit Personal Information Sharing – Be mindful of what you post, especially sensitive data. Enable Privacy Settings – Restrict who can see your posts and personal details. Be Cautious of Links & Messages – Avoid clicking on suspicious links or sharing personal data with unknown sources. Think Before You Post – Understand the possible implications of your content before making it public. Report & Block Suspicious Accounts – If you notice any unusual activity, report it immediately to the platform. Conclusion Social media is a powerful tool, but if misused, it can lead to severe consequences. This viral video is a wake-up call for everyone to be more cautious about their digital footprint. One thoughtless post can open doors for cybercriminals, leading to financial loss, identity theft, or legal troubles. Stay alert, stay informed, and most importantly, think before you post! video courtesy - fellow LinkedIn member

After years building event-driven systems. Here are the top 4 mistakes I have seen: 1. Duplication Events often get re-delivered due to retries or system failures. Without proper handling, duplicate events can: • Charge a customer twice for the same transaction. • Cause duplicate inventory updates, messing up stock levels. • Create inconsistent or broken system states. Solution: • Assign unique IDs to every event so consumers can track and ignore duplicates. • Design event processing to be idempotent, ensuring repeated actions don’t cause harm. 2. Not Guaranteeing Order Events can arrive out of order when distributed across partitions or queues. This can lead to: • Processing a refund before the payment. • Breaking logic that relies on correct sequence. Solution: • Use brokers that support ordering guarantees (e.g., Kafka). • Add sequence numbers or timestamps to events so consumers can detect and reorder them if needed. 3. The Dual Write Problem When writing to a database and publishing an event, one might succeed while the other fails. This can: • Lose events, leaving downstream systems uninformed. • Cause mismatched states between the database and event consumers. Solution: • Use the Transactional Outbox Pattern: Store events in the database as part of the same transaction, then publish them separately. • Adopt Change Data Capture (CDC) tools to track and publish database changes as events automatically. 4. Non-Backward-Compatible Changes Changing event schemas without considering existing consumers can break systems. For example: • Removing a field might cause missing data for consumers. • Renaming or changing field types can trigger runtime errors. Solution: • Maintain versioned schemas to allow smooth migration for consumers. • Use formats like Avro or Protobuf that support schema evolution. • Add adapters to translate new schema versions into older ones for compatibility. "Every schema change is a test of your system’s resilience—don’t fail it." What other mistakes have you seen out there?

These are 9 data hygiene actions I've been using for accounts without HubSpot Operations Hub with the new OpenAI workflow actions. 1. Standardise Job Titles Correct messy titles like "Sr. Mktg. Mgr EMEA" → "Senior Marketing Manager". 2. Clean Company Names Strip suffixes like "Ltd." and remove unnecessary characters for consistent naming. 3. Correct Contact Name Capitalisation Turn "joHN o’neill" → "John O’Neill" automatically. 4. Validate Country from Full Address Confirm or correct the country by checking the full postal address. 5. Classify Email Type (Business or Personal) Identify Gmail/Yahoo addresses to protect B2B data quality. 6. Generate Company Description from Domain No description? No problem. Create a short, professional description based on the domain name. 7. Validate and Correct Phone Numbers (E.164 Format) Fix local numbers, format them internationally or flag them as invalid if unfixable. 8. Detect Seniority Level from Job Title Categorise contacts into Executive, Director, Manager, Staff, or Other automatically. 9. Assign Timezone Based on Address Set the correct timezone from a full address, which is vital for email marketing and sequences.

In the nonprofit world, showcasing impact is crucial—especially when corporate donors come knocking. But for a large section of NGOs, this is easier said than done. NGOs often find themselves at a loss when asked to provide concrete data on their work's impact. The reality is that many nonprofits carry out impactful work. But their lack of resources, expertise, and consistent data collection means they struggle to measure and communicate these achievements effectively. Moreover, the impact many nonprofits aim to create is often complex and long-term. This makes it difficult to fit into the short-term, quantifiable results that donors often seek. The result? A gap between the real-world impact and what is presented to potential supporters. This gap can be a major hurdle in securing funding and sustaining vital programmes. How do you think we can equip nonprofits to ensure that their stories of change are captured in the best possible way? . . . . #NGOMemes #Impact #SocialSector #CreativeAgency #Communications #SimitBhagatStudios

I am coming out of a data equity advisory call and needed to say this out loud for my nonprofit friends (especially the ones in leadership roles): you can spend millions on dashboards, AI tools, and surveys, but none of it matters if the leadership isn’t willing to listen. The biggest barrier to data equity isn’t technology. It’s the human ego (can we call it leadership’s?). I have seen this come up a bunch of times: ● A donor survey revealed that BIPOC donors feel disconnected from the organization’s messaging, yet leadership sticks to the same fundraising strategies because “this is how we’ve always done it.” ● A staff engagement survey highlights burnout and pay inequities, but the leadership team dismisses it as “an HR issue” instead of a systemic one. ● A program evaluation finds that specific marginalized communities aren’t benefiting as intended, yet the org keeps funding the same initiatives instead of reallocating resources. When leaders ignore, dismiss, or downplay uncomfortable data, they don’t just lose insights—they lose trust. Does any of this ring a bell? ● Dismissing data because it challenges the narrative built forever. ● Avoiding specific questions because you are afraid of the answers. ● Gatekeeping decisions instead of inviting community voices into the progress work. Can we change this? Yes, we can. Our leaders can. You can… Without going into my essay-writing mode, here are three top-of-my-head ideas: ● Make data actionable, not performative. If you are collecting data but not using it to drive change (even if slow) + communicate about that change, you might be engaging in performative transparency. Start sharing with the community why and what you collect that data for. ● Engage with your data – multiple times in multiple ways. Data listening is not a one-time event. Build mechanisms for continuous engagement with staff, donors, and community members through your collected data. Ask questions to that data, see if you are asking the right things, right way, at the right time. ● Build a culture where data is both accessible for celebration and challenge. It is likely a harmful system if data is only accessed and accepted to celebrate without cultural self-awareness. Leaders must be open to questioning their own biases and redistributing decision-making power based on what the data reveals. Data equity starts with leadership and cultural accountability. Is there a time when data work revealed something uncomfortable in your work? Did you act on it? Report a data harm you witnessed here: https://lnkd.in/gjQuNxrP And then let’s talk. #nonprofits #nonprofitleadership #community

What if your LLM could reuse work and respond 5-10× faster? That’s exactly what LMCache delivers. What is LMCache? It’s the open-source “KV cache layer” for LLMs — designed to store and reuse key/value caches across queries, sessions and even engines. Built for high-volume, long-context systems. Evaluations show up to 15× throughput improvements when paired with engines like vLLM. Why This Matters Right Now Latency kills UX. Every extra millisecond waits hit adoption. LMCache slashes response time by re-using caches. GPU cycles cost money. Re-computation means wasted resources. LMCache allows reuse across workloads, reducing GPU load. Context & multi-round workflows are exploding. RAG systems, agent pipelines, conversational contexts — LMCache fits them all. It’s production-ready and open-source. No black-box: you can inspect, integrate, extend. Typical Use Cases: -Agentic systems that make multi-turn decisions -RAG pipelines that reuse retrievalable contexts -Long-form applications (document processing + summarization) -Multi-engine inference clusters / cloud-scale deployments Plug into your engine and enable KV-cache reuse across queries & threads. If you’re building LLM-based systems for scale, this isn’t one more library — it’s a fundamental architecture upgrade. Mark this: The future of LLM inference isn’t just bigger models — it’s smarter reuse.

Is Process Management the Key to Strong Risk and Compliance Management? So many organizations struggle with Risk and Compliance management! A quick scan of the headlines and you'll see another organization getting in trouble with the regulators. I was a consultant in the banking industry for over 25 years and have seen the struggle first hand. In my opinion, the root cause of failure is the lack of a semantic structure (a framework that defines and organizes data in a meaningful way) which exhaustively identifies every process the organization performs to provide consistent business context. According to ISO 31000, risk is defined as the effect of uncertainty on an organization's objectives. How are objectives accomplished? Through Process, of course. Organizations that must manage risk have a risk repository, many times a GRC platform, which stores their risk data such as regulatory obligations, controls, etc. The core challenge is that they typically have a size fit all process taxonomy (such as APQC) for business context which doesn't capture the nuances of their business. The result is that risk data is built on interpretations and assumptions which makes it unreliable, risk reporting for executives is inaccurate, and there is massive confusion for everyone that has a role in risk management. To address this, organizations need to create and maintain an inventory of every process they perform in each organizational unit. This approach leads to Business Integrated Risk Management, where risk management is performed through a common business-oriented lens. The Benefits include: -      Clean risk data by aligning all risk types to a common language of "What" processes the organization performs across all risk types. -      Operational efficiency by defining processes in the 1st line (risk owners), 2nd line (risk oversight), and 3rd line (risk assurance) in a standardized way. -      Enhanced decision-making through accurate risk reporting, allowing stakeholders and the customer they serve to make informed decisions. -      Accurate risk reporting to leadership so they can make accurate risk mitigation decisions. This also sets up organizations to leverage the power of AI through Digital Twins and AI agents to continuously scan the environment and perform automated risk assessment which could eliminate many risk management challenges. This is such a common sense approach, why has this simple solution evaded many organizations? To learn more about this approach, check out my book Digital Transformation Success https://a.co/d/2QSq8qf