{"id":3448,"date":"2025-07-22T15:45:07","date_gmt":"2025-07-22T07:45:07","guid":{"rendered":"https:\/\/www.rzautoassembly.com\/?p=3448"},"modified":"2025-07-22T15:50:43","modified_gmt":"2025-07-22T07:50:43","slug":"how-ai-is-reshaping-the-future-of-business-intelligence","status":"publish","type":"post","link":"https:\/\/www.rzautoassembly.com\/ko\/how-ai-is-reshaping-the-future-of-business-intelligence\/","title":{"rendered":"How AI is Reshaping the Future of Business Intelligence"},"content":{"rendered":"

\"\"<\/a><\/h3>\n

As organizations race to build resilience and agility, business intelligence (BI) is evolving into an AI-driven, forward-looking discipline\u2014focused on automated insights, trusted data, and a robust data culture.<\/p>\n

For decades, BI was defined by static dashboards: rear-facing tools that merely reflected the past. It relied on dedicated analysts to wrangle historical data into reports, leaving executives to review yesterday\u2019s performance. Today, that model is obsolete. Fueled by cloud computing, exploding data volumes, and advances in AI, BI has transformed from a retrospective tool into a proactive, predictive, and increasingly autonomous engine for decision-making.<\/p>\n

\u201cModern BI isn\u2019t about rearview mirrors\u2014it\u2019s about satellite navigation systems,\u201d says Maurizio Garavello, senior vice-president for Asia-Pacific and Japan at Qlik. \u201cTraditional dashboards gave us hindsight. Now, BI delivers foresight, context, and even autonomy. We\u2019re moving from dashboards to decisions, from tools you log into to intelligence that stays with you.\u201d<\/p>\n

This shift is reshaping industries. Experts agree: BI\u2019s purpose now extends beyond reporting. It empowers everyone\u2014from C-suite leaders to frontline staff\u2014to not just understand data, but to interact with it, question it, and use it to shape what comes next.<\/p>\n

Business Drivers for Modern BI<\/strong><\/span><\/p>\n

The adoption of modern BI is fueled by urgent needs. In today\u2019s volatile world, leaders can no longer rely on gut instinct. \u201cThey need timely, trusted data to make critical decisions with confidence,\u201d says Nate Nichols, vice-president of product at Tableau.<\/p>\n

Organizations also turn to BI to build resilience amid uncertainty. \u201cWhether optimizing operations, enhancing customer experiences, or navigating supply chain shocks, analytics let them respond faster and smarter,\u201d notes Garavello.<\/p>\n

And as businesses strive to do more with less, BI identifies inefficiencies to boost productivity and cut costs, adds Luca Spinelli, ASEAN managing director at SAS<\/p>\n

These benefits deliver tangible results:<\/strong><\/span><\/p>\n

CIMB Singaporeused SAS Viya to unify customer data, slashing time spent hunting for data\u2014from 80% of employees\u2019 workdays to just 20%\u2014while unlocking deeper customer insights.
\nNissin Foods Holdings(Japan) modernized data systems with Qlik, transforming inventory planning to enable real-time decisions and a data-driven culture.
\nIn India, IBM partnered with the\u00a0State Bank of Indiato cut report generation from days to minutes, delivering real-time operational insights.
\nThe Analytics Maturity Journey<\/p>\n

To achieve such outcomes, organizations progress through stages of analytics maturity\u2014each building value by moving from describing the past to prescribing action. Patrick Kelly, senior director at Databricks, outlines the path:<\/p>\n

Descriptive analytics (What happened?): The foundation. It tracks historical performance, spots trends, and ensures operational transparency.
\nDiagnostic analytics (Why did it happen?): Digs into root causes via drill-downs or data mining, helping teams fix issues or scale successes.
\nPredictive analytics (What might happen?): Uses AI and historical data to forecast\u2014say, shifts in demand or emerging risks\u2014enabling proactive planning.
\nPrescriptive analytics (What should we do?): The most advanced stage. It recommends actions via simulations or algorithms, empowering data-backed choices that maximize gains and reduce risks.
\nThe Data Paradox: Rich in Data, Poor in Insights<\/p>\n

Despite its promise, BI maturity faces a critical hurdle: the \u201cdata paradox,\u201d as Nichols calls it. \u201cOrganizations need faster data-driven decisions, but they\u2019re stuck with massive, scattered, unreliable data. They\u2019re data-rich but insight-poor.\u201d<\/p>\n

This paradox stems from three core issues:<\/strong><\/span><\/p>\n

Data silos and integration struggles
\nData lives in legacy systems, multiple clouds, and countless apps. Integrating it is a nightmare. Leading companies solve this with unified infrastructure\u2014tools like Databricks\u2019 Lakeflow Connect (for seamless data ingestion) or Tableau\u2019s Zero Copy Network (which queries data where it lives, no migration needed).<\/p>\n

Weak data quality and governance
\nEven the best AI fails with bad data. \u201cTrust in analytics starts with data quality and governance,\u201d says Garavello. This requires coordinated effort\u2014often led by a chief data officer (CDO)\u2014to build clear stewardship, reliable pipelines, and governance frameworks. As IBM\u2019s Anup Kumar notes: \u201cData quality isn\u2019t a one-time fix. A CDO\u2019s sustained focus drives consistent progress.\u201d<\/p>\n

Low adoption and cultural barriers
\nA BI tool only matters if people use it\u2014and adoption stalls without trust or usability. \u201cThe biggest barrier isn\u2019t technology\u2014it\u2019s earning trust, ensuring tools fit how people work,\u201d says Garavello. Kumar agrees: \u201cTrust in data is key to adoption.\u201d Cultural resistance, low data literacy, and failure to show value (users may see tools as \u201cextra work\u201d) all hold progress back.<\/p>\n

AI: From Analysis to Conversation<\/span><\/strong><\/p>\n

AI is solving these challenges\u2014redefining BI from a tool to a collaborative experience. \u201cAI isn\u2019t replacing BI; it\u2019s making it conversational,\u201d says Garavello. \u201cWe\u2019re moving from dashboards to co-pilots.\u201d<\/p>\n

This \u201cconversational BI\u201d goes by many names (augmented BI, agentic analytics), but its goal is simple: make data analysis as easy as talking.<\/p>\n

Take\u00a0agentic analytics: Users collaborate with AI agents to automate analysis. Databricks\u2019 AI\/BI Genie lets anyone \u201ctalk to data\u201d\u2014ask, \u201cWhy did sales spike in April?\u201d\u2014and get instant, governed answers, no coding needed.<\/p>\n

This lowers barriers, boosting adoption. For example, Japan\u2019s NTT Docomo uses Databricks to analyze LLM usage, cutting manual work by 90%. Teams now use AI\/BI Genie for natural-language insights, sparking innovation.<\/p>\n

Evolving Skills for the BI Future<\/strong><\/span><\/p>\n

As AI automates data prep and analysis, BI roles are shifting. Skills now blend technical know-how with business acumen, critical thinking, and communication.<\/p>\n

\u201cWe used to ask, \u2018Can you code?\u2019 Now we ask, \u2018Can you interpret and challenge AI?\u2019\u201d says Garavello. \u201cYou don\u2019t need to be a data scientist\u2014just able to read data, question it, and tell a compelling story.\u201d<\/p>\n

Nichols adds: \u201cAI handles tedious work like cleaning data, freeing analysts to ask big questions, review AI insights for relevance, and solve problems machines can\u2019t.\u201d<\/p>\n

The future belongs to \u201cdata natives\u201d\u2014professionals who pair domain expertise with curiosity. As Kumar puts it: \u201cThey understand industries like finance or healthcare, turn business problems into AI-ready questions, and act on insights.\u201d<\/p>\n

Building a Data-Mature Organization<\/p>\n

Experts agree on steps to succeed:<\/span><\/strong><\/p>\n

Start with clarity, not tech: \u201cIdentify key decisions to improve, then build from there,\u201d says Garavello. Pair this with a strong data strategy.
\nModernize data infrastructure: A robust \u201cdata fabric\u201d\u2014ensuring data is high-quality, accessible, and reliable\u2014is non-negotiable. \u201cWithout it, even great tools fail,\u201d says Kumar.
\nDefine maturity benchmarks: Assess capabilities across people, processes, and tech. Measure success via metrics like business performance, analytics productivity, and user satisfaction.
\nUpskill everyone: Train more than just data teams. Offer role-specific training, encourage data-driven questions, and celebrate wins to embed a data culture, advises Spinelli.<\/p>\n

Ultimately, BI\u2019s future lies in human-machine collaboration. AI processes vast data; humans bring curiosity, expertise, and storytelling. Together, they move beyond understanding the past\u2014to shaping the future. In a world drowning in data, as Garavello says, \u201cmeaning is the new superpower.\u201d<\/p>\n

What is the market price of an insulin pen assembly machine?<\/a><\/span>
\nWhat is the working principle of an insulin pen assembly machine?<\/a><\/span><\/p>","protected":false},"excerpt":{"rendered":"

As organizations race to build resilience and agility, business intelligence (BI) is evolving into an AI-driven, forward-looking discipline\u2014focused on automated insights, trusted data, and a robust data culture. For decades, BI was defined by static dashboards: rear-facing tools that merely reflected the past. It relied on dedicated analysts to wrangle historical data into reports, leaving [\u2026]<\/p>","protected":false},"author":1,"featured_media":3450,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,124],"tags":[],"class_list":["post-3448","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-technology"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/posts\/3448","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/comments?post=3448"}],"version-history":[{"count":0,"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/posts\/3448\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/media\/3450"}],"wp:attachment":[{"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/media?parent=3448"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/categories?post=3448"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.rzautoassembly.com\/ko\/wp-json\/wp\/v2\/tags?post=3448"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}