Tag: Data Analytics

Illustration of data cubes connected to a server, with text

Why Companies Collect Data but Still Fail to Use It

January 7, 2026

The quiet breakdown between information and action Most organizations do not suffer from a lack of data. They suffer from a lack of movement. Data is collected relentlessly—transactions, operations, customers, systems, sensors. Storage expands. Dashboards multiply. Analytics teams grow. And yet, when decisions are actually made, the influence of data often feels marginal. This paradox is rarely addressed head-on. Leaders sense it but struggle to explain why data usage remains stubbornly low despite years of investment. The issue is not availability; the issue is that using data forces choices—and most organizations are not designed to absorb those choices comfortably. Data Collection Is Passive. Data Usage Is Confrontational. Collecting data is easy because it is passive. Systems generate data automatically. Little judgment is required. No one has to agree on what it means. Using data is different. It is active—and confrontational. It forces interpretation, prioritization, and accountability. It exposes trade-offs. It surfaces disagreements that might otherwise remain hidden. This is why organizations unconsciously optimize for accumulation rather than application. Data can exist in abundance without disturbing existing power structures. Using it cannot. The First Breakdown: Decisions Are Vague In many organizations, decisions are framed broadly—improve performance, drive efficiency, optimize growth. These statements sound decisive but are analytically empty. When decisions are vague, data has nowhere to attach itself. Analytics teams produce insights, but no one can say with confidence whether those insights should change anything. Data usage rises only when decisions are explicit. Until then, data remains informational rather than operational. Here’s our latest blog on: Business vs IT in Data Initiatives—Bridging the Gap That Never Seems to Close The Second Breakdown: Incentives Are Misaligned Even when insights are clear, they are often inconvenient. Data may suggest reallocating resources, changing priorities, or acknowledging underperformance. These implications rarely align with individual incentives or established narratives. When incentives reward stability over adaptation, data becomes threatening. It is reviewed, acknowledged, and quietly ignored. This is not resistance to data—it is rational behavior within the system. Until incentives and expectations align with evidence-based decisions, data-driven decision-making remains aspirational. Ready to clarify this for your organization? Contact us today. The Third Breakdown: Accountability Is Diffused In organizations with low data maturity, insights are everyone’s responsibility and no one’s accountability. Analytics teams generate reports. Business leaders consume them. Outcomes drift. When results disappoint, blame disperses. Using data requires ownership. Someone must be accountable not just for producing insight but for acting on it—or explicitly choosing not to. Without this clarity, data remains commentary, not a driver. Why More Data Often Makes Things Worse When leaders notice low data usage, the instinctive response is to collect more data or build more dashboards. This usually backfires. More data introduces more interpretations, more caveats, and more ways to delay decisions. Instead of clarity, leaders face cognitive overload. Instead of alignment, teams debate nuances. Abundance without focus leads to paralysis. This is why organizations with modest data but strong discipline often outperform those with vast, underutilized data estates. How Leadership Behavior Shapes Data Usage Whether data is used or ignored is ultimately a leadership signal. When senior leaders ask for data but decide based on instinct, teams learn that analytics is decorative. When leaders tolerate inconsistent metrics, alignment erodes. When data contradicts a preferred narrative and is quietly set aside, a message is sent. Culture follows behavior, not intent. Organizations that truly use data make expectations visible. They ask not just, “What does the data say?” But what are we going to do differently because of it? The Role of Timing Timing is an often-overlooked factor. Data frequently arrives after decisions are already mentally made. When insights come too late, they become explanations rather than inputs. This reinforces a damaging loop: analytics is seen as backward-looking, which justifies ignoring it for forward-looking decisions. Breaking this cycle requires integrating data earlier into decision workflows—not adding more analysis afterward. What Actually Changes Data Usage Organizations that close the gap between data and action do not start with tools. They start by clarifying decisions. They reduce metrics aggressively. They assign explicit ownership. They close the loop between insight and outcome. Most importantly, leaders notice when data is not used—and ask why. Usage increases not because data improves, but because expectations do. The Executive Reality For CXOs, the most important realization is this: Data does not create value by existing Data creates value by forcing choices If choices are uncomfortable, data will be sidelined Organizations that accept this reality stop chasing volume and start building discipline. They recognize that unused data is not a technical failure but a leadership one. Once that shift occurs, analytics stops being a background activity and becomes an engine for action. Most organizations are not short on data. They are short on decision clarity, accountability, and reinforcement. Until those conditions exist, data will remain visible in meetings but absent in outcomes. The organizations that move beyond this trap are not those with the most data but those willing to let evidence challenge comfort. That is when data finally earns its place at the table. Start by redesigning decisions—not dashboards. Talk with us about aligning data, authority, and accountability at the leadership level. Get in touch with Dipak Singh: LinkedIn | Email Frequently Asked Questions 1. Why do organizations with strong data infrastructure still struggle to use data? Because infrastructure solves collection, not decision-making. The real barriers are unclear decisions, misaligned incentives, and lack of accountability. 2. Is the problem more cultural or technical? Primarily cultural and structural. Technical limitations are rarely the main constraint once basic analytics capabilities exist. 3. How can leaders tell if data is actually influencing decisions? By asking what changed because of the data. If decisions would have been the same without it, data is not being used—only referenced. 4. Why does adding more dashboards often reduce data usage? Because it increases cognitive load and interpretation ambiguity, giving teams more reasons to delay or debate decisions. 5. What is the fastest way to improve data

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Business vs IT in Data Initiatives — Bridging the Gap That Never Seems to Close

January 6, 2026

Nearly every CXO recognizes the tension Business leaders feel data initiatives move too slowly, cost too much, and deliver insights that arrive late—or worse, feel disconnected from real business needs. IT leaders feel requirements are unclear, priorities shift constantly, and accountability is unfairly placed on platforms rather than outcomes. Both perspectives are valid. Yet despite years of investment, tooling, and transformation efforts, the divide between business and IT in data initiatives remains one of the most persistent sources of friction in modern organizations. This is not a relationship problem. It is a structural design problem—one leadership often underestimates. Why This Tension Is So Persistent At its core, the conflict exists because business and IT optimize for fundamentally different risks. Business leaders are rewarded for speed, responsiveness, and results. Delay is visible, costly, and often unforgivable. IT leaders are rewarded for stability, security, and scalability. Failure is catastrophic, public, and difficult to recover from. When data initiatives launch without explicitly reconciling these competing risk models, friction is inevitable. Business pushes for quick answers. IT pushes for robust solutions. Data sits uncomfortably in the middle—serving both, fully satisfying neither. The result is a repeating cycle of frustration that spans projects, teams, and years. Why Data Sits at the Center of the Divide Unlike traditional IT systems, data initiatives are not purely transactional—they are interpretive. A system is considered successful when it works. Data is only successful when it is understood, trusted, and used. Its value depends on context, definitions, and decision-making relevance. This makes ownership inherently ambiguous. Business assumes IT “owns the data” because it owns the systems. IT assumes business “owns the data” because it defines meaning and usage. Both assumptions are partially correct—and collectively ineffective. Without clear joint ownership, data initiatives drift. Platforms are delivered. Dashboards are built. Adoption lags. Accountability dissolves into blame. If your organization has invested heavily in data platforms but still debates numbers, struggles with adoption, or feels analytics never quite scales—this tension is likely structural, not executional. Contact us to realign your data initiatives around the decisions that actually drive impact. How This Divide Shows Up for CXOs For CEOs, the divide appears as stalled momentum—despite investment, analytics does not materially change how the organization decides. For CFOs, it surfaces as reconciliation fatigue and recurring debates over metrics that should already be settled. For COOs, analytics feels misaligned with operational reality—too slow, too generic, or too abstract to drive action. For CIOs, it manifests as a painful paradox: platforms delivered successfully, yet perceived as failures by the business. These are not execution errors. They are symptoms of misaligned accountability. The Hidden Flaw: Success Is Measured Differently One of the least discussed reasons the gap persists is that business and IT define success differently. Business considers a data initiative successful when it changes decisions or improves outcomes. IT considers it successful when the solution is delivered, stable, secure, and scalable. Both definitions are reasonable. Together, they create a gap. A dashboard can be technically flawless and operationally irrelevant. A rapid analysis can be insightful and operationally unsustainable. Without a shared definition of success, dissatisfaction becomes inevitable. Here’s our latest blog on how to Assess Your Organization’s Data Readiness in 30 Minutes Why “Better Collaboration” Rarely Fixes the Problem Organizations often respond by encouraging closer collaboration—more meetings, more workshops, and more alignment sessions. While well-intentioned, this approach treats the issue as interpersonal. It is not. The problem is not communication. The problem is that data initiatives lack a shared decision anchor. When initiatives are framed around reports, systems, or features, priorities remain subjective, and alignment becomes endless. When initiatives are anchored around specific decisions that must improve, alignment becomes concrete and measurable. What Mature Organizations Do Differently Organizations that successfully bridge the business–IT gap do not eliminate tension—they channel it productively. They start data initiatives by explicitly naming the decisions that must improve. Business owns the why. IT owns the how. Both are accountable for whether it worked. They establish joint ownership models where critical metrics and data products have both a business steward and a technical steward. This resolves ambiguity without overburdening either side. Most importantly, leadership stays visibly engaged until behaviors change—not just until systems go live. This signals that data is a business capability, not an IT service. The Role Leadership Often Underplays The business–IT divide cannot be solved at the middle-management level. CEOs must frame data as central to how the organization decides. CFOs must enforce consistency in metrics and definitions. COOs must ensure analytics reflects operational reality. CIOs must resist being positioned as sole owners of outcomes they do not fully control. When leadership alignment is weak, the divide widens—regardless of team effort. A Simple Diagnostic for CXOs Leadership teams can assess the health of their business–IT dynamic by asking: Are data initiatives described in terms of decisions or deliverables? Is success discussed in business outcomes or system metrics? When adoption is low, do we revisit ownership or simply add more features? Do data initiatives feel easier—or harder—to execute over time? If initiatives grow more complex and less impactful, the divide is structural, not situational. The Executive Takeaway For CXOs, the insight is uncomfortable—but liberating: Business vs IT is a false opposition Data initiatives fail in the space between ownership and accountability Shared decisions require shared stewardship When leadership clarifies who owns meaning, who owns enablement, and who owns outcomes, the gap narrows naturally. Data stops oscillating between speed and safety—and starts delivering consistent value. Bridging the divide is not about forcing alignment. It is about designing it. If your data initiatives are technically sound but strategically underwhelming, it’s time to rethink how ownership, accountability, and success are defined. Ready to turn data into decisions that drive real impact? 👉 Contact us to start designing initiatives that align leadership, execution, and measurable outcomes. Get in touch with Dipak Singh: LinkedIn | Email Frequently Asked Questions 1. Why does the business–IT gap persist despite modern data platforms? Because platforms solve technical problems,

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How to Assess Your Organization’s Data Readiness in 30 Minutes

January 5, 2026

A leadership-level reality check Most organizations delay meaningful progress in analytics, automation, or AI for one familiar reason: they believe they are “not ready.” The data is messy. Systems are fragmented. Teams are stretched thin. Eventually, someone suggests a formal readiness assessment—typically a multi-week effort that results in a dense report confirming what everyone already suspected. What is rarely acknowledged is this: data readiness is not a technical state. It is a leadership condition. And it can be assessed far more quickly than most organizations believe—if leaders are willing to look in the right places. This article is not about auditing platforms or scoring architecture maturity. It is about understanding whether your organization is ready to use data to make decisions today. That reality can surface in a single, focused leadership conversation lasting less than 30 minutes. Why Most Data Readiness Assessments Miss the Point Traditional readiness assessments focus on infrastructure, data quality, tooling, and skills. These factors matter—but they are downstream. From a CXO perspective, readiness does not fail because data is imperfect. It fails because decisions cannot be made with confidence. Many organizations with incomplete or messy data move decisively. Others, despite sophisticated platforms, remain paralyzed. The difference is coherence—whether leaders agree on what matters, trust the same numbers, and understand who owns which decisions. Readiness, therefore, is less about capability and more about alignment under constraint. This is why assessments that avoid uncomfortable organizational questions feel thorough but rarely change outcomes. What “30 Minutes” Really Means The 30 minutes is not about speed for its own sake. It is about signal clarity. In a short, honest leadership discussion, patterns emerge quickly. Hesitation, disagreement, and defensiveness are as informative as precise answers. What matters is not perfection, but convergence. If a leadership team cannot align on a few fundamentals in 30 minutes, the organization is not ready for advanced analytics—regardless of technology investments. 1. Do We Agree on the Decisions That Matter? Begin with a deceptively simple prompt: “What are the five recurring decisions where better data would materially improve outcomes?” This question exposes whether the organization has a shared decision model. Often, answers diverge immediately. The CEO focuses on strategic bets, the CFO on capital allocation, the COO on operational trade-offs, and business leaders on growth priorities. Diversity of perspective is healthy. Lack of convergence is not. When leaders cannot quickly align on a small set of critical decisions, data initiatives scatter. Analytics teams are asked to support everything—and end up supporting nothing well. Readiness, at its core, is the ability to focus. 2. Do We Trust the Same Numbers in the Same Room? Next, probe one or two enterprise-level metrics—revenue, margin, service levels, or cash flow. Ask how they are defined, calculated, and interpreted across functions. What matters is not technical precision, but confidence and consistency. When leaders reference “their version” of the metric or heavily qualify their answers, trust is fragmented. When definitions vary subtly, debates become inevitable. This is where organizations confuse data quality with data trust. The former can improve incrementally. The latter is binary at decision time. If leadership meetings routinely spend time validating numbers, the organization is not ready to rely on analytics at scale. 👉 Pause here and try this:Schedule a 30-minute leadership discussion. 3. What Happens When Data Conflicts with Intuition? This is the most uncomfortable—and most revealing—question. Ask leaders to recall a recent instance where data challenged a strongly held belief or preferred course of action. What happened next? Was the data interrogated constructively? Did the decision change? Or was the data set aside due to timing, context, or “experience”? Every organization claims to value data. Few are willing to let it override hierarchy or habit. Readiness is revealed not by how often data is cited, but by what happens when it creates friction. If data is primarily used to justify decisions already made, readiness remains superficial. Here’s our recent blog: https://intglobal.com/blogs/the-difference-between-data-strategy-and-data-projects/ 4. Is Ownership Explicit or Assumed? Ask who owns the organization’s most critical end-to-end metrics. Not who prepares the report.Not who maintains the system. Who is accountable for the metric’s integrity, interpretation, and implications? In low-readiness organizations, ownership is implicit and role-based. When issues arise, responsibility diffuses quickly. High-readiness organizations make ownership explicit. This does not eliminate debate—but it shortens it. Ownership, more than tooling, determines whether analytics can scale. 5. Where Does Finance Spend Its Time? This question cuts through abstraction. If finance spends most of its time reconciling numbers across systems and stakeholders, the organization lacks a stable analytical foundation. If finance focuses on analysis, scenarios, and foresight, readiness is materially higher. Finance often acts as the shock absorber for low data maturity. When reconciliation dominates, it signals that alignment is missing elsewhere. No advanced analytics initiative can compensate for this imbalance. 6. Can We Name a Decision That Changed Because of Data? Finally, ask for a concrete example. When was the last time a decision materially changed direction because of data or analysis? This is not about frequency—it is about credibility. If examples are vague or historical, analytics is informational rather than operational. Data is being consumed but not used. Readiness exists only when data has demonstrably influenced outcomes. What This 30-Minute Exercise Usually Reveals Most leadership teams walk away with two realizations: They are often more technically capable than they assumed. Systems may be imperfect—but usable. They are often less aligned organizationally than they believed. Decisions are unclear, ownership is blurred, and trust varies by context. This gap explains why analytics investments feel underwhelming. It is not a readiness gap—it is an alignment gap. For CXOs, the most important insight is this: Data readiness is not achieved. It is demonstrated. If decisions converge quickly, readiness exists. If decisions stall, no platform will fix it. Organizations do not need perfect data to move forward. They need to decide what matters, agree on how it is measured, and hold themselves accountable for using it. That can be assessed in 30 minutes.The rest is

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The most common symptoms of low data maturity text on a dark background.

The Most Common Symptoms of Low Data Maturity

December 29, 2025

Low data maturity rarely announces itself as a data problem. In most organizations, it shows up in far more familiar ways: delayed decisions, recurring disagreements in leadership meetings, endless reconciliations, and a quiet frustration that despite “having all the data,” clarity remains elusive. What makes this especially difficult for CXOs is that these symptoms are often attributed to execution gaps, people issues, or market volatility. In reality, they are structural signals of how data is—or is not—working inside the organization. Understanding these symptoms matters because organizations do not fail at data due to lack of intent. They fail because the warning signs are misunderstood. Why Low Data Maturity Is Hard to Recognize From the outside, many low-maturity organizations look sophisticated. They have invested in business intelligence, hired analytics teams, and launched multiple data initiatives. Dashboards are produced regularly, and review meetings are numerically rich. The problem is that activity is mistaken for capability. Low maturity does not mean the absence of data. It means data does not reliably reduce uncertainty at the point of decision. When that happens, friction quietly creeps into leadership workflows. 5 Levels of Data Maturity: Where Most Companies Actually Stand Symptom 1: Leadership Meetings Spend More Time Debating Numbers Than Decisions One of the clearest indicators of low enterprise data maturity is how leadership time is spent. When meetings repeatedly drift into questions like “Which number is correct?” “Why does this differ from last month’s report?” “Can we reconfirm this before deciding?” Data is not serving its purpose. For CEOs and executive teams, this creates a subtle but persistent drag. Decisions slow down, not because leaders are indecisive, but because the foundation for confidence is unstable. Over time, leaders begin relying more on experience and intuition, using data only as a secondary reference. Symptom 2: Finance Spends More Time Reconciling Than Analyzing In low data maturity organizations, the finance function often absorbs the pain first. Instead of focusing on forward-looking analysis, scenario planning, or performance insights, finance teams are consumed by: Reconciling numbers across systems, Aligning departmental reports, Defending figures during reviews. From a CFO’s perspective, this is not just inefficient—it is strategically limiting. When finance is trapped in reconciliation mode, it cannot play its intended role as a decision partner to the business. Symptom 3: The Same KPI Means Different Things to Different Teams Misaligned metrics are one of the most underestimated symptoms of low data governance. Revenue, margin, service level, utilization—these terms appear consistent on paper. In practice, definitions vary subtly across functions. What sales optimizes for may conflict with operations. What operation measures may not align with finance? For COOs and business heads, this creates execution friction. Teams appear to be performing well locally, yet enterprise outcomes disappoint. The issue is not effort—it is misaligned measurement. Symptom 4: Dashboards Are Reviewed, but Rarely Acted Upon Many organizations proudly showcase their dashboards. Few can confidently say those dashboards change decisions. At low maturity levels, dashboarding becomes a reporting ritual rather than a decision tool. Numbers are reviewed, explanations are offered, and meetings conclude with little change in direction. Over time, this conditions leaders to view analytics as informative but optional. The organization becomes “data-aware” without becoming data-driven. By this point, most CXOs recognize at least a few of these symptoms in their own organizations. The important question is not whether these issues exist but how deeply embedded they are in decision-making, governance, and accountability structures. Organizations that address low data maturity early prevent years of decision drag. rework, and stalled transformation. If these symptoms feel familiar, the next step is not another tool or dashboard. It is a clear-eyed assessment of how data supports—or obstructs—your most critical decisions. 👉 A structured data maturity assessment helps leadership teams move from recurring. Symptom 5: Heavy Dependence on a Few “Data Heroes” Every organization knows who they are—the individuals who understand the spreadsheets, the logic, and the workarounds. While these people are invaluable, their existence is also a warning sign. When insight depends on specific individuals rather than institutional processes, maturity is fragile. From a CXO standpoint, this creates operational risk. Knowledge concentration makes scaling difficult and succession planning risky. Mature organizations build systems and ownership models that outlive individuals. Symptom 6: Decisions Are Frequently Deferred “Until More Data Is Available” Low analytics maturity often leads to a paradox: more data, but less decisiveness. When data is not trusted or aligned, leaders delay decisions under the guise of seeking more information. In reality, the issue is not data availability—it is data confidence. This is particularly damaging in fast-moving environments, where delayed decisions carry real opportunity costs. Symptom 7: Post-Mortems Are Common, Preventive Insights Are Rare Organizations with low maturity are very good at explaining outcomes after the fact. What they struggle with is identifying leading indicators early enough to intervene. Root-cause analysis happens once results are known. Lessons are documented, but similar issues recur. For senior leaders, this creates a sense of déjà vu. Problems feel familiar, even when data investments are increasing. Symptom 8: Data Initiatives Restart Every Few Years Another telltale sign is the cyclical nature of data transformation efforts. New tools are introduced. New teams are formed. Expectations reset. Eighteen to twenty-four months later, momentum fades and the cycle begins again under a new label. This pattern is not caused by poor execution. It is caused by the absence of a clear data strategy anchored in business decisions rather than projects. Why These Symptoms Persist Low data maturity persists because it is rarely owned end-to-end. IT owns platforms. Analytics teams own models. Business teams own outcomes. No one fully owns the intersection where data becomes decisions. Without clear ownership, governance feels bureaucratic, and accountability diffuses across functions. Technology becomes the default solution, even when the root causes are structural and behavioral. What CXOs Should Take Away For senior leaders, the most important insight is this: low data maturity is not a failure of ambition or investment. It is a failure of alignment. A

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Choosing the right business intelligence consulting partner (2025 guide).

Choosing the Right Business Intelligence Consulting Partner (2025 Guide)

November 18, 2025

Business Intelligence Consulting: How to Choose the Right Partner As data complexity increases and in-house capabilities hit roadblocks, many enterprises turn to business intelligence consulting services to accelerate insight-driven growth. The right BI consulting partner can help architect scalable systems, streamline analytics pipelines, modernize legacy data stacks, and enable self-service dashboards for decision-makers. But how do you choose the right BI consultant from a sea of options? Let’s walk through what BI consultants actually do, why you might need one, and how to evaluate potential partners strategically. Business intelligence consultants help design, implement, and optimize your BI and data infrastructure. The right partner should offer industry expertise, platform-agnostic solutions, and strategic alignment with your goals. Key evaluation factors: technical skill, domain experience, delivery model, support, and customer reviews. BI consulting can accelerate data lake integrations, dashboarding, ML readiness, and ROI on data tools. 👥 What Does a Business Intelligence Consultant Do? A BI consultant brings the expertise and experience needed to design and implement effective BI solutions that empower data-driven decision-making. Core Responsibilities: Assess current BI architecture and identify gaps Recommend tools and platforms (e.g., Power BI, Tableau, Looker, Snowflake) Build ETL/ELT pipelines and data models Integrate data lakes, warehouses, and real-time data streams Create dashboards, reports, and KPI frameworks Implement data governance and security best practices What does a BI consultant do? 👉 A BI consultant advises organizations on data strategy, builds analytics solutions, and helps teams extract insights from structured and unstructured data. 🧹 When Should You Hire a BI Consulting Partner? You might need a BI consulting firm if: You’re migrating to the cloud or modernizing your data platform Your current BI tools are underutilized or lack adoption You need industry-specific analytics (e.g., pharma, retail, utilities) You lack in-house talent for scalable dashboarding or AI integration You’re building a data lake or lakehouse architecture from scratch Consulting firms can bring cross-industry patterns, governance expertise, and full-scale implementation resources to fast-track your BI initiatives. 🧪 How to Evaluate a BI Consulting Firm Choosing the right partner is not just about tech stack familiarity. It’s about long-term alignment, technical credibility, and the ability to scale with your vision. 1. Technical Expertise & Certifications Are they certified in your preferred BI tools and cloud platforms? Do they understand modern architectures (e.g., lakehouses, serverless BI, event-driven ingestion)? 2. Industry Experience Do they have experience in your domain (e.g., healthcare, finance, or retail)? Can they advise on industry-specific KPIs and compliance requirements? 3. Platform-Agnostic Consulting Do they push one vendor or offer neutral, best-fit recommendations ? Can they integrate both open-source and enterprise tools? 4. References & Case Studies Do they have real-world examples, use cases, or client testimonials? Can they show measurable impact (e.g., reduced reporting time, increased adoption)? 5. Delivery Model & Support Do they offer hybrid or remote consulting? How do they handle knowledge transfer, documentation, and post-delivery support? How do I choose a BI consulting firm? 👉 Look for proven experience, tool and platform flexibility, strong references, and alignment with your business goals. 🚀 Looking for expert guidance on BI modernization, cloud data lakes, or dashboard strategy? Explore our BI Consulting & Implementation Services to drive ROI from your data stack. 📊 Benefits of Hiring a BI Consulting Partner Whether you’re a startup or a large enterprise, a BI consultant can: ✅ Accelerate time to insights ✅ Reduce technical debt ✅ Improve data literacy across teams ✅ Ensure scalable, future-ready BI architecture ✅ Enhance data governance and compliance ✅ Enable real-time and predictive analytics Bonus: They bring cross-industry best practices and the latest tech trends—AI-powered BI, semantic layers, data mesh, etc. What are the benefits of business intelligence consulting?👉 Faster implementation, strategic planning, and reduced risk for BI initiatives—especially during modernization or platform migration. 🌊 Ready to unlock the full potential of your data with BI? Book a free BI discussion with our strategy team, or download the BI Vendor Comparison Checklist to get started. ❓ Frequently Asked Questions Q1. What is business intelligence consulting? BI consulting helps organizations design, implement, and optimize their data and analytics ecosystem to support informed decision-making. Q2. Who needs a BI consultant? Startups, SMBs, and enterprises undergoing digital transformation, cloud migration, or facing analytics bottlenecks benefit from BI consulting. Q3. What tools do BI consultants use? Common platforms include Power BI, Tableau, Looker, Qlik, AWS QuickSight, dbt, Snowflake, Azure Synapse, and open-source tools like Superset. Q4. Can a BI consultant work with my in-house team? Yes. Most BI consulting services offer collaborative engagement models, including staff augmentation, hybrid teams, and training. Q5. How much do BI consulting services cost? Costs vary based on scope, duration, tools involved, and delivery model—ranging from project-based pricing to monthly retainers.

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Rise of parametric insurance

The Rise of Parametric Insurance: Paying Out Based on Data, Not Damage

August 27, 2024

Use cases for data analytics in insurance have evolved considerably in recent years. One of the biggest such innovations is parametric insurance that is steadily rising in importance, with the increasing unpredictability of the world and environment today. Instech reports have already highlighted how parametric solutions saw record growth in 2022 and the trend is set to continue over this decade as well. What is Parametric Insurance? Often labeled as climate risk insurance or disaster insurance, parametric insurance can be classified as the following: Why Parametric Insurance is Beneficial? Insurtech players have already started realising the value of parametric insurance solutions, particularly for helping communities build financial resilience in the light of unpredictable and volatile climate risks. Some of the key benefits include the following: Are There Any Disadvantages? While the advantages of parametric insurance clearly outweigh the disadvantages, there are still a few moot points in this case. How It All Stacks Up Parametric insurance is an innovative product that is well-positioned to take-off in the current global scenario, despite its potential drawbacks. Technology will play a vital role in data-based payouts in the future. This will include real-time tracking from ground-based sensors and satellite/radar imagery and data. The aim of the insurance product in this case is to minimise risks as much as possible based on advanced data and technological prowess. In fact, it can well transform into a more effective and robust climate risk insurance model, thereby incorporating the costs of climate change in a better way. With the certainty of these products, a higher portion of premiums that buyers spend on coverage come back to them as claims, instead of being spent through disputes and frictional expenditure. Parametric insurance is also customisable to various industries and corporate clients, since it is not concerned about the type of asset, rather wider financial losses due to triggering events. Clients in the corporate sector can thus tackle the bigger financial impact on operations in case of climate events, including vendors, suppliers, customers, and logistics. IoT and other new technologies will keep powering parametric solutions, with warning systems that can inform customers about potential risks. This will help them take preventive measures in advance. LLMs or large language models are also positively impacting the sector and will completely change underwriting in the future. They will process vast datasets swiftly, including claims history and historical occurrences, thereby enabling underwriters to predict future claims more efficiently. So, to sign off, it can be said that parametric insurance should be around in the coming decade and even beyond. With more insurance companies, underwriters, customers, and brokers recognising it as a good solution for risk transfer, there will be more evolution into secondary complexities and events like floods, hail, thunderstorms, wildfires, and more. With data being leveraged comprehensively for pricing, this form of insurance will make underwriting easier and lower the time to quote and finalise policies or even settle claims. This will help insurance companies save more time and money, which will prove hugely beneficial in the long run. FAQs 1. What is Parametric Insurance and How Does It Differ from Traditional Insurance? Parametric insurance is a form of insurance where payouts are triggered based on certain pre-determined thresholds/parameters. It is different from traditional insurance, since it is not concerned with the actual loss/damage. 2. How Does Parametric Insurance Work? In case of any event/disaster that triggers any specific thresholds/parameters like wind speed, earthquakes of a certain magnitude, and so on, pre-fixed payouts will be given to policyholders, irrespective of their actual losses and whether they have suffered losses or not. Claims processing will be near-automatic due to pre-fixed agreements. 3. What Are the Benefits of Parametric Insurance for Policyholders? Communities and people without access to conventional insurance can benefit from higher financial resilience against climate change with parametric insurance. It enables swift payouts and more agile operations, while saving time and resources. It also lowers the chances of disputes with policyholders and fraudulent claims. 4. What Types of Events Are Covered by Parametric Insurance Policies? Parametric insurance policies can cover a wide range of events including earthquakes, poor crop yields/harvests, natural disasters like hurricanes and cyclones, and so on. 5. How is the Payout Determined in Parametric Insurance? The payouts in parametric insurance are determined based on whether specific thresholds have been triggered in the case of any event. There is no consideration for the actual loss of the policyholder. If these parameters are triggered, then payouts are released to policyholders and are pre-fixed amounts. These amounts are estimated by policyholders in most cases, depending on their calculations of the potential financial cost of damages due to varied natural disasters and other such events.

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Data Analytics in Health Insurance for Better Risk Management

2024 Trends: Data Analytics in Health Insurance for Better Risk Management

January 8, 2024

The health insurance industry has witnessed a rapid evolution in terms of digitisation, which has also swiftly transformed several other sectors worldwide. One of the biggest contemporary trends in health insurance (and poised to last for the foreseeable future) is the harnessing of data analytics to ensure superlative risk management among other benefits. Pharmacy managers, insurance companies, healthcare providers, and other industry stakeholders are already leveraging analytics to tackle issues of fast-growing healthcare costs. Insurance companies are already using them to identify high-risk patients and operate accordingly. Why Data Analytics is Indispensable From an operational standpoint, data analytics is indispensable for health insurance companies. They keep generating huge data volumes internally through sales and engagement but also get sizable chunks from varied external sources. When this data spreads out through multiple systems, it becomes an uphill task for insurers to effectively use and track the same. This is why analytics-based solutions that can fuse and consolidate data from multiple touch points and sources have gained relevance in recent times. A centralised data gathering system with consistent analytics and actionable insights is thus one of the key trends in health insurance currently. How Data Analytics Enables Better Risk Management Data analytics is undoubtedly contributing towards enabling insurers to manage their risks better. Here are some aspects that should be highlighted in this regard. These benefits have gained even more traction in recent years if one considers how insurers lose a whopping $40 billion per annum on account of fraudulent claims as per Gartner reports. Many insurance companies also estimate 10-20% of claims to be fraudulent while identifying less than 20% of the same. Suspicious and fraudulent behaviour and patterns can be identified with data analytics, with insurers creating diverse models to enable swift detection based on historical data and activities. For instance, analytics has reportedly helped the Czech Republic’s Allianz Insurance to save a whopping US$4.5 million annually by lowering fraudulent claims paid by it. This is just one of the instances that testify to the need for insurance companies to rapidly adopt data analytics into their operational frameworks. Analytics can be used to conduct risk evaluation in real-time which will help organisations respond swiftly in volatile scenarios. For example, in the case of auto insurance, an accurate assessment of risks posed by specific drivers will help insurers create more competitive premiums. Cars connected to the internet will help them gather large volumes of data accordingly. Insurance firms can now predict the chances of drivers being involved in accidents by analysing driving habits and behavioural data. Some other advantages of data analytics include easier customer lifetime value (CLV) prediction and prospective claim forecasting. FAQs How can data analytics contribute to more precise risk assessment in the health insurance industry? Data analytics can enable more accurate risk assessments in the health insurance sector. It can help identify fraudulent behavioural patterns and flag the same for review before the actual payout. What impact do 2024 trends in health insurance data analytics have on customer experiences and personalised offerings? 2024 trends in health insurance data analytics will have a positive impact on customer experiences. Insurers will be able to personalise their offerings for customers based on their behavioural data and also offer custom premiums with incentives for recommended and healthier actions.

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The Impact of AI and Data Analytics in Pharma Research

The Impact of AI and Data Analytics in Pharma Research

January 3, 2024

AI in pharma research has the potential to be a veritable game-changer for the entire sector. Data analytics in pharmaceuticals along with other innovations like data-driven research and AI/machine learning in pharma have made it comparatively easier to develop new drugs and tackle emerging diseases. Biopharma research remains expensive and lengthy although AI can play a vital role in enabling higher probabilities of success and boosting productivity.  How AI and Data Analytics are Indispensable for Pharma Research Here are a few ways in which AI in pharma research can be indispensable for the industry soon. AI in pharma research will enable the creation of feedback loops for further refining the predictive abilities and stability of AI algorithms. They will also inform experimental design functions accordingly. Through analytics and data science tools, pharma can capture the entire value of the present portfolio and create mechanisms and IP for driving research in the future. AI-drug discovery is already taking place with several companies building their pipelines. Biopharma entities are also developing top-down and executive strategies where AI-backed discovery can be a vital indicator and enabler of performance in the future. Automated image analysis or lead optimisation will be bolstered along with the collection of experimental data in a reusable manner, automated screening algorithms linking molecular descriptions with hits or desired outputs, blueprinting, enabling better testing and learning solutions for product delivery and designing new screening protocols. AI is already transforming the research space through the application of machine learning and data science to huge data sets, enabling swifter discoveries of newer molecules. It enables cross-referencing of published scientific literature with alternate sources of data (clinical trial data, conference abstracts, public databases, and unpublished data) to surface therapies that are promising. Medicines can be delivered in months at times instead of several years as a result. AI can also help lower clinical trial costs and cycle times while enhancing overall clinical development outcomes considerably. ML and AI are already being used for automatically generating study protocols while NLP (natural language processing) is being used to scale up manual tasks. AI algorithms can also ensure continual clinical data cleaning, coding, aggregation, management, and storage. Through automation and centralisation of intakes for adverse event reports backed by AI-backed technologies like NLP and OCR (optical character recognition), case documentation workloads are considerably reduced for expediting investigative processes. These are only a few of the widespread benefits that data analytics, AI, and ML can bring to the table for life sciences and pharmaceutical companies, especially in terms of research and development. FAQs What role will AI play in optimising clinical trials and research methodologies, and how is this expected to impact the pharmaceutical industry in 2024? AI will play a huge role in the optimisation of research methodologies and clinical trials in the future. This will have a positive impact on the pharmaceutical industry in 2024 and beyond since AI will optimise patient recruitment, predict the efficacy of treatments, automate data analysis, and boost safety tracking. It will also accelerate trial procedures while lowering costs and enhancing data quality. This will lead to more personalised and successful clinical trials. How will integrating AI and data analytics accelerate drug discovery processes within the pharmaceutical industry in the upcoming year? Drug discovery processes within the pharmaceutical industry can be accelerated in the upcoming year through the integration of data analytics and AI. This will be possible through the prediction of drug-target interactions, evaluation of the safety and efficacy of drugs that are repurposed, and identification of newer options for treatments. Potential biomarkers can be identified while researchers can easily analyse big data sets and design new molecules while forecasting the efficacy levels of potential drug candidates accordingly.

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Enhancing Underwriting Precision: The Role of Data Analytics in Insurance Broker Decision-making

Enhancing Underwriting Precision: The Role of Data Analytics in Insurance Broker Decision-making

December 28, 2023

Data analytics in insurance has been a veritable game-changer for the industry in recent times. It has become a major solution for several issues while enabling a more personalised experience for customers. Insurance brokers are steadily embracing data-driven insurance intending to bolster their decision-making process. Let us look at some of the biggest advantages of deploying analytics in the insurance sector. Benefits of Data Analytics in Insurance for Brokers Insurance broker decision-making can improve considerably with the deployment of data analytics. Here are some of the biggest advantages worth noting in this regard. These are some of the core benefits that can boost insurance broker decision-making greatly. Analytics can transform the operational side of the business while freeing up brokers to focus on strategising for future growth without worrying about the operational and administrative aspects of the sector. FAQs What is the role of data analytics in enhancing underwriting precision for insurance brokers? Data analytics can greatly enhance underwriting in terms of accuracy for insurance brokers. It can estimate risks better and predict premiums more accurately for customers who are more prone to the same. In what ways can insurance brokers leverage data analytics to tailor insurance solutions for individual clients? Insurance brokers can seamlessly leverage data analytics for tailoring insurance solutions for their clients.  Data-driven insights will enable better customer understanding and an idea of preferences. This will help insurance brokers come up with genuine recommendations and more personalised products/services that will help customers meet their needs better. What types of data sources are most valuable for insurance brokers seeking to enhance underwriting precision through analytics? Insurance brokers who are looking to enhance their underwriting processes through analytics rely on varied data sources. Some of these sources include social media platforms, demographics, lifestyle, age, medical data, and more.

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2024 Trends: Data Analytics in Health Insurance for Better Risk Management

2026 Trends: Data Analytics in Health Insurance for Better Risk Management

December 20, 2023

The health insurance industry has witnessed a rapid evolution in terms of digitization, which has also swiftly transformed several other sectors worldwide. One of the biggest contemporary trends in health insurance (and poised to last for the foreseeable future) is the harnessing of data analytics to ensure superlative risk management, among other benefits. Health insurance predictive analytics enables insurers to anticipate risks, while analytics in health insurance improves decision-making across underwriting, pricing, and claims management. “Health insurance data analysis plays a key role in improving decision-making and driving effective health insurance analytics.” Predictive analytics in health insurance enables insurers to derive actionable insights from data, helping them design more profitable and customer-focused insurance products. “Data analytics in health insurance is transforming the industry by enabling insurers to make smarter decisions through advanced data analytics insurance practices.” Analytics in health insurance are rapidly evolving as new insurance analytics trends enable more accurate risk assessment, personalized pricing, and improved patient outcomes. Predictive analytics in health insurance leverages big data and health insurance systems to analyze large volumes of patient, claims, and behavioral data in order to predict risks, reduce costs, and improve decision-making. Pharmacy managers, insurance companies, healthcare providers, and other industry stakeholders are already leveraging analytics to tackle issues of fast-growing healthcare costs. Insurance companies are already using them to identify high-risk patients and operate accordingly. Health insurance predictive analytics is becoming increasingly important as insurance industry data analytics evolves to improve risk assessment and customer outcomes. Health insurance data analysis plays a crucial role in health insurance analytics by transforming raw claims and member data into actionable insights that improve cost management, risk assessment, and patient outcomes. Why Data Analytics is Indispensable From an operational standpoint, data analytics is indispensable for health insurance companies. They keep generating huge data volumes internally through sales and engagement but also get sizable chunks from varied external sources. When this data spreads out through multiple systems, it becomes an uphill task for insurers to effectively use and track the same. Big data in health insurance is transforming the industry by enabling advanced data analytics in health insurance to improve risk prediction, fraud detection, and personalized member care. This is why analytics-based solutions that can fuse and consolidate data from multiple touchpoints and sources have gained relevance in recent times. A centralized data-gathering system with consistent analytics and actionable insights is thus one of the key trends in health insurance currently. Data analytics in health insurance is rapidly evolving as organizations increasingly rely on data science in health insurance to improve risk assessment, pricing accuracy, and patient outcomes. Health insurance data analytics plays a crucial role in improving patient outcomes and reducing costs, as data analytics in health insurance enables insurers to identify trends, assess risks, and optimize coverage plans effectively. How Data Analytics Enables Better Risk Management Data analytics is undoubtedly contributing towards enabling insurers to manage their risks better. Here are some aspects that should be highlighted in this regard. By leveraging big data in health insurance, companies can enhance customer analytics in health insurance to deliver more personalized and cost-effective care solutions. Data analytics in health insurance is becoming increasingly powerful, especially as predictive analytics helps insurers identify risk patterns and improve patient outcomes. These benefits have gained even more traction in recent years if one considers how insurers lose a whopping $40 billion per annum on account of fraudulent claims as per Gartner reports. Many insurance companies also estimate 10-20% of claims to be fraudulent while identifying less than 20% of the same.  Suspicious and fraudulent behaviour and patterns can be identified with data analytics, with insurers creating diverse models to enable swift detection based on historical data and activities. For instance, analytics has reportedly helped the Czech Republic’s Allianz Insurance to save a whopping US$4.5 million annually by lowering fraudulent claims paid by it. This is just one of the instances that testify to the need for insurance companies to rapidly adopt data analytics into their operational frameworks.  Analytics can be used to conduct risk evaluation in real-time which will help organisations respond swiftly in volatile scenarios. For example, in the case of auto insurance, an accurate assessment of risks posed by specific drivers will help insurers create more competitive premiums. Cars connected to the internet will help them gather large volumes of data accordingly. Insurance firms can now predict the chances of drivers being involved in accidents by analysing driving habits and behavioural data. Some other advantages of data analytics include easier customer lifetime value (CLV) prediction and prospective claim forecasting.  FAQs How can data analytics contribute to more precise risk assessment in the health insurance industry?  Data analytics can enable more accurate risk assessments in the health insurance sector. It can help identify fraudulent behavioural patterns and flag the same for review before the actual payout.  What impact do 2024 trends in health insurance data analytics have on customer experiences and personalised offerings? 2024 trends in health insurance data analytics will have a positive impact on customer experiences. Insurers will be able to personalise their offerings for customers based on their behavioural data and also offer custom premiums with incentives for recommended and healthier actions. 

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