Big Data Analytics Implementation Issues

I had a reach out from an old friend who is a CIO, but she is having problems with the Data & Analytics function. It became an interesting conversation on the top blockers for a company getting value out of D&A. 1. Though data quality and lack of governance usually tops the list, these are symptoms, NOT root causes. Usually if these are lacking, it isn't tools, or technology, but culture and process. Tools can help, certainly, but tools without changing the culture and processes are a waste of money. 2. Waiting for perfection. Though it is tempting to wait until your 3-year strategy is complete, no one is going to wait that long. Get to the "good enough" and iterate. Introduce tools which allow self-service, even if there is a manual component, knowing they will improve over time. 3. Giving into impatience. The opposite of the last point is just giving up and just letting everyone have access to the raw data and letting everyone do what they want. Sure, it feels good, and there is lots of motion, BUT if your goal is one version of the truth, there is no progress. Allow SOME people to have access, and govern what becomes your golden reports. 4. Too much IT think. Data & Analytics is, fundamentally, at best a hybrid technical function. If you try to manage in the same way people order new PC's, D&A will not produce value. If the function assumes it knows best for the business, it will fail. If you think the number of tickets resolved matter over impact and satisfaction, you are doomed. 5. Not having an agreed too strategy or roadmap. One consistent theme between all the companies I work at is I keep having to explain who we can't go faster by just hiring consultant/SI XXX. Data & Analytic functions are built on building blocks. There are certain elements that MUST be in place before you can build the next level. Robert Heinlein stated it well in The Door into Summer (1957) "When railroading time comes you can railroad—but not before." A strategy and roadmap may not eliminate having to repeat this ... but at least you can mark the needed progress and demonstrate a consistency of the message (while unlocking the value you CAN). If you notice, the top five blockers are NOT about tools or technologies, but more about leadership and how to attain business value. Though most aspects of IT have recognized this more or less as important, for the D&A function, it is essential. Any other critical blockers you have seen?

Stop trying to leapfrog to advanced analytics while your data infrastructure is fundamentally broken. A venture fund partner told us: "We're preaching these tools to our portfolio companies, but our own internal adoption has quite a long ways to go." We're seeing this dilemma everywhere: teams selling solutions they can't implement internally. Most of the work isn't in prompt engineering and optimizing ML models. It's unsexy, dirty data plumbing. Here's what we see in every project: 80% of effort goes into getting documents and structured data into formats you can actually analyze. Most customer databases are complete disasters with duplicate records, empty fields, information scattered across systems that don't talk to each other. Standard connectors fail because the underlying data is too messy to connect cleanly. Sloppy data infrastructure kills more projects than model selection ever will. Why is it not more obvious? Vendors that focus on the application layer like to present nice demos over telling you what you need to get operationally ready. "Out of the box" is hardly ever the case, unless the solution is so stripped down that it's very light engineering work that's easier than you think. Vendors want you to sign contracts based on feature lists rather than integration complexity. Post-purchase budgets then go to licensing and training, not months of cleanup work required before anything functions. Sure, you may get a quick win, but will this tool even last more than 3 months in your stack? Our belief at Brainforge is that technology purchases don't automatically improve operations. Companies assume buying the right platform solves business problems. Leadership expects immediate results because demos made everything look plug-and-play. The real constraint isn't better algorithms but getting information to flow between the systems you already use. You can construct lead scoring insights, but if they can't flow back into Salesforce where your sales team works, you've purchase a really expensive calculator. Without getting results back into daily workflows, pilots plateau at low adoption. Even as we build more streamlined solutions for clients, we also face the same challenges internally. We had to build custom tools just to get meeting transcripts, project tickets, and communications flowing between Slack, Linear, and our CRM. The good news: you can fix this without starting over. Start with your most painful workflow. Map where information gets stuck, fix that connection first. Your existing systems probably have APIs you haven't explored. Before buying new platforms, audit what you own. The integration you need is often in a settings menu nobody's opened. Focus on moving one piece of data from where it lives to where decisions happen. Prove that value first. Companies winning with advanced analytics started by making their existing data usable.

📌 The 4 Layers of Data & BI Problems Most businesses approach data & BI problems by fixing symptoms instead of root causes. They have a problem-solving problem. They spend all their time fixing what’s visible without ever addressing what’s critical. → Dashboards break? They find a workaround. → KPIs are wrong? They update them on the go. → Reports load slowly? They try to optimize queries. But Business Intelligence problems live across four layers. You need to understand where problems really originate and how to solve them once and for all. 1️⃣ 𝐒𝐮𝐫𝐟𝐚𝐜𝐞-𝐋𝐞𝐯𝐞𝐥 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 These are the easiest to spot but the least impactful to solve in isolation: → Dashboards loading slowly → Incorrect KPIs → Data export errors Most teams mistakenly spend most of their time firefighting here. But fixing symptoms without addressing the root cause means these problems will resurface, again and again… 2️⃣ 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐚𝐥 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 This is generally the technical debt accumulated over time. You have to dig deeper. If your reports and visualizations frequently break, the issue lies in structural layers like: → Data pipelines failing silently → Unreliable ETL processes → Poor semantic models causing frequent manual adjustments Until you invest in solid data engineering practices and build reliable pipelines, your BI layer will remain unstable. 3️⃣ 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 Even with the best pipelines, your BI strategy will fail if the underlying business alignment is broken: ⤷ No standardized KPI definitions across teams (Finance defines “Revenue” differently from Sales). ⤷ Data silos block cross-department collaboration and create fragmented insights. ⤷ Critical systems aren’t integrated which leaves decision-makers blind to the full picture. This is where true data leadership comes in. Fixing this requires cross-functional alignment and establishing enterprise-wide data definitions. 4️⃣ 𝐂𝐨𝐫𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 At the deepest level, problems always boil down to: → Weak or non-existent data governance → Unclear ownership and accountability → Missing a "single source of truth" The hard truth is: You can’t fix a broken BI strategy with more dashboards. 1) Fixing only surface-level problems means symptoms will reoccur. 2) Structural and strategic layers demand clear communication and cross-team collaboration. 3) Core data problems require a robust data governance strategy Addressing the root issues will transform your BI strategy from constant firefighting to true strategic enablement. Which layer does your organization struggle with most? Let’s discuss below 👇 #BusinessIntelligence #DataStrategy #DataGovernance

A 6-hour pipeline. 14 minutes after refactoring. ⚡ Inherited a Spark pipeline on Databricks. Ran every night. Took 6 hours. The team's explanation: "Big data problem." The evidence told a different story. What I found: → Scanning 14 months of data (only 30 days required) → Date column existed but partition pruning was not applied → 47 small files per partition (compaction never configured) → Shuffle joins where broadcast joins were viable → Cluster running at 11% utilization 93% of I/O was waste. Every single night. What I changed: → Partition filter on ingestion date → File compaction to 128MB targets → Converted 3 shuffle joins to broadcast → Right-sized cluster with autoscaling → Moved one transformation upstream — it did not require Spark The result: → Runtime: 6 hours → 14 minutes (-96%) → Compute cost: -78% → Infrastructure changes: none The principle: Spark performance problems are rarely about cluster capacity. They are about: → Scanning only what is necessary → Managing file sizes effectively → Choosing the right join strategy for the data distribution Larger clusters do not fix architectural inefficiency. They accelerate its cost. The broader point: Most slow pipelines are not big data problems. They are partitioning problems. File sizing problems. Join strategy problems. The data is not too large. The architecture is not precise enough. If your nightly pipeline finishes at 6am, ask yourself: what decisions are being delayed because the data is not ready until noon? #DataEngineering #Spark #Databricks #ETL #PipelineOptimization #DataOps

Why Does Internal Audit Struggle to use Data Analytics? If I had a dollar for every Internal Audit department paying for three or more unused analytics licenses... Data analytics has been a part of Internal Audit for over 25 years, yet many teams still struggle to integrate it effectively into their processes. Too often, when Audit leaders invest in analytics technology, they believe that simply purchasing the tool is the solution. However, the reality is that so much more can go wrong. Success in analytics requires more than just the right application—it demands strategic planning, alignment with business needs, and a shift in capabilities across your team. If you're an Internal Audit leader looking to build a sustainable data analytics capability, be aware that many challenges can arise after the initial rollout. Keeping momentum beyond the initial use cases can be difficult. Here are some common reasons why data analytics efforts struggle to take hold: 1. Technology Misalignment: The analytics tools used by Internal Audit are not aligned with what the business is using, leading to compatibility issues and a lack of support by the business. 2. Access Barriers: Politics and bureaucracy make it difficult for Internal Audit to gain access to enterprise data. 3. Data Validation Issues: Ensuring the accuracy, completeness, and reliability of data can be a significant challenge. 4. Data Literacy Gaps: Audit teams struggle to interpret and analyze data effectively, limiting the impact of analytics. 5. Process Integration: Internal Audit methodologies and processes have not been updated to incorporate more time or steps needed. 6. Business Readiness: Business partners may not be prepared to consume and act on analytics-driven insights, limiting adoption. 7. Lack of Organizational Mandate: The use of analytics is not embedded in Internal Audit’s charter, mandate, or strategic objectives. 8. No Performance Metrics: There are no clear KPIs to measure the success or impact of data analytics in Internal Audit. 9. Blended Skill Sets: Data analytics is often lumped together with IT Audit or other specialties rather than treated as a distinct and necessary competency for all auditors. 10. Key Talent Risk: The one team member highly skilled in data analytics leaves for a role in the business, leaving Audit without the necessary expertise. 11. Hiring Practices: Internal Audit leaders do not specifically recruit for data analytics competencies, limiting the team's ability to scale analytics efforts. 12. Dependency on External Resources: When data analytics is co-sourced or outsourced, capabilities disappear when budgets are cut, leading to a loss of momentum. These are some of the key obstacles Internal Audit leaders must address to create a sustainable, impactful data analytics program—one that doesn’t fizzle out like so many others have. What other pain points have you encountered when trying to embed data analytics into #InternalAudit?

Today, I would like to share a common problem of *Broken Data Pipelines* that have encountered in the past in my career. This disrupts critical decision-making processes, leading to inaccurate insights, delays, and lost business opportunities. According to me, major reasons for these failures are: 1) Data Delays or Loss Incomplete data due to network failures, API downtime, or storage issues leading to reports and dashboards showing incorrect insights. 2) Data Quality Issues Inconsistent data formats, duplicates, or missing values leading to compromised analysis. 3) Version Mismatches Surprise updates to APIs, schema changes, or outdated code leading to mismatched or incompatible data structures in data lake or database. 4) Lack of Monitoring No real-time monitoring or alerts leading to delayed detection of the issue. 5) Scalability Challenges Pipelines not being able to handle increasing data volumes or complexity leading to slower processing times and potential crashes. Over the period, I and Team Quilytics has identified and implemented strategies to overcome this problem by following simple yet effective techniques: 1) Implement Robust Monitoring and Alerting We leverage tools like Apache Airflow, AWS CloudWatch, or Datadog to monitor pipeline health and set up automated alerts for anomalies or failures. 2) Ensure Data Quality at Every Step We have implemented data validation rules to check data consistency and completeness. Use tools like Great Expectations works wonders to automate data quality checks. 3) Adopt Schema Management Practices We use schema evolution tools or version control for databases. Regularly testing pipelines against new APIs or schema changes in a staging environment helps in staying ahead in the game 😊 4) Scale with Cloud-Native Solutions Leveraging cloud services like Amazon Web Services (AWS) Glue, Google Dataflow, or Microsoft Azure Datafactory to handle scaling is very worthwhile. We also use distributed processing frameworks like Apache Spark for handling large datasets. Key Takeaways Streamlining data pipelines involves proactive monitoring, robust data quality checks, and scalable designs. By implementing these strategies, businesses can minimize downtime, maintain reliable data flow, and ensure high-quality analytics for informed decision-making. Would you like to dive deeper into these techniques and examples we have implemented? If so, reach out to me on shikha.shah@quilytics.com

Your dashboard isn’t broken. Your data quality is. And the worst part? Most issues don’t show up in meetings, they start quietly inside your pipelines. Here are the 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗺𝗼𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗱𝗯𝘁 𝗰𝗮𝗻 𝗰𝗮𝘁𝗰𝗵 𝗲𝗮𝗿𝗹𝘆 : 𝟭. 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱𝘀 𝗜𝗻𝗳𝗹𝗮𝘁𝗲 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 The same entity appears multiple times, making revenue or user counts look bigger than reality. 𝙙𝙗𝙩 𝙛𝙞𝙭: Unique tests on primary/surrogate keys. 𝟮. 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗙𝗶𝗲𝗹𝗱𝘀 Nulls in things like user_id or order_id break analysis downstream. 𝙙𝙗𝙩 𝙛𝙞𝙭: not_null tests on essential columns. 𝟯. 𝗕𝗿𝗼𝗸𝗲𝗻 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗧𝗮𝗯𝗹𝗲𝘀 Join keys don’t match, leaving gaps in fact–dimension relationships. 𝙙𝙗𝙩 𝙛𝙞𝙭: Relationship tests for foreign-key integrity. 𝟰. 𝗦𝗶𝗹𝗲𝗻𝘁 𝗗𝗮𝘁𝗮 𝗟𝗼𝘀𝘀 𝗔𝗳𝘁𝗲𝗿 𝗨𝗽𝘀𝘁𝗿𝗲𝗮𝗺 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 Pipelines “succeed,” but row counts mysteriously drop. 𝙙𝙗𝙩 𝙛𝙞𝙭: Row counts + volume-based anomaly tests. 𝟱. 𝗟𝗮𝘁𝗲-𝗔𝗿𝗿𝗶𝘃𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗸𝗲𝘄𝘀 𝗥𝗲𝗽𝗼𝗿𝘁𝘀 Historical records never load, causing trend distortion. 𝙙𝙗𝙩 𝙛𝙞𝙭: Incremental models with lookback windows. 𝟲. 𝗦𝗰𝗵𝗲𝗺𝗮 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗕𝗿𝗲𝗮𝗸 𝗗𝗼𝘄𝗻𝘀𝘁𝗿𝗲𝗮𝗺 𝗠𝗼𝗱𝗲𝗹𝘀 A renamed column silently breaks your entire stack. 𝙙𝙗𝙩 𝙛𝙞𝙭: Schema + freshness tests across sources. 𝟳. 𝗦𝘁𝗮𝗹𝗲 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 Reports “look fine” but run on outdated tables. 𝙙𝙗𝙩 𝙛𝙞𝙭: Freshness tests inside SLAs. 𝟴. 𝗜𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗟𝗼𝗴𝗶𝗰 𝗔𝗰𝗿𝗼𝘀𝘀 𝗧𝗲𝗮𝗺𝘀 Teams calculate the same metric differently. 𝙙𝙗𝙩 𝙛𝙞𝙭: Centralize logic inside dbt models. 𝟵. 𝗜𝗻𝘃𝗮𝗹𝗶𝗱 𝗼𝗿 𝗢𝘂𝘁-𝗼𝗳-𝗥𝗮𝗻𝗴𝗲 𝗩𝗮𝗹𝘂𝗲𝘀 Negative revenue, impossible dates, or status mismatches. 𝙙𝙗𝙩 𝙛𝙞𝙭: Custom tests for ranges, enums, and rules. 𝟭𝟬. 𝗘𝗿𝗿𝗼𝗿𝘀 𝗙𝗼𝘂𝗻𝗱 𝗢𝗻𝗹𝘆 𝗔𝗳𝘁𝗲𝗿 𝗦𝗼𝗺𝗲𝗼𝗻𝗲 𝗖𝗼𝗺𝗽𝗹𝗮𝗶𝗻𝘀 Stakeholders notice problems long after the pipeline runs. 𝙙𝙗𝙩 𝙛𝙞𝙭: Run dbt tests on every job, every deployment. Most data issues aren’t engineering problems, they’re visibility problems. dbt turns silent failures into loud alerts before dashboards break.

Most modern data platforms run into trouble before a single line of code is written. I’ve seen it happen over and over. Teams invest in new tools, build pipelines, and light up dashboards, yet months later leadership asks the same question: “Is this actually working?” The problem rarely comes from technology. It starts with the fundamentals: → No clear ownership for datasets → Source systems ingested inconsistently → Production and analytics mixed together → Pipelines that fail silently → Models built without business context …and more Ignoring these early issues turns even the most sophisticated stack into a costly, fragile system that frustrates teams and slows decisions. This cheatsheet covers 8 critical fixes every data leader must address before building a modern data platform. So your investment creates reliable insights, smoother operations, and measurable business impact. Follow me for practical frameworks that help data teams build platforms that actually deliver results.