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Assemble:2026年数据战略技术与供应商选型决策基准报告:依托同行洞察实现更精准投资(英文版)(27页).pdf

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1、Decision Intelligence Benchmark|Q1 2026Data StrategyTechnology and VendorDecisions for 2026Making Smarter Investments with Peer Insights Enterprise data strategy leaders enter 2026 carrying the heaviest tech stacks in Assembles benchmark and every single one plans to reshape whats there.More than ha

2、lf of data strategy teams run 9to 14 tools,and 24%manage 25 or more heavier stacks than any other functional area in the benchmark.With that architecture comes a workload:71%of leaders spend 10 or more hours per weekmanaging the stack,and 24%spend 25 hours or more.Not one describes the experience as

3、 frictionless.No one is holding still on tech stack investments,either.Zero leaders report a stable with no changes posture for 2026,and 71%are pursuing selective replacements,with another 18%planning majorconsolidation.Leaders know what they want to swap.What theyre optimizing for sets the function

4、 apart.New capabilities,including AI leads tech stack objectives at 30%ahead of efficiency and time savings at 21%and cost reduction at 7%.Mostother functions in Assembles benchmark put efficiency at the top;data strategy puts capability there instead.That ambition meets a familiar wall:94%describe

5、tool approvals as anywhere from mixedbag to very difficult procurement processes calibrated to a slower pace of change than the data infrastructure world of today.Where investment is moving tracks the architecture:semantic and data modeling(87%),data catalogs(82%),data governance,MDM,and data lineag

6、e(76%each),and data quality(71%)all rank amongthe strongest growth signals.But peer recommendation scores in core categories MDM(6.6/10),ETL/ELT(6.3/10),and data quality(5.5/10)cluster at qualified satisfaction,not enthusiasm.Thestory for data strategy in 2026 is one of complexity.The stacks are the

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1. **技术栈复杂度高**:53%的数据策略团队使用9-14个工具,24%使用25+个工具,为基准中最高;71%每周管理工具超10小时。 2. **主动重塑技术栈**:0%保持稳定,71%计划选择性替换工具,18%计划重大整合,优先追求AI新能力(30%)而非效率(21%)。 3. **投资聚焦基础设施**:88%计划增加AI/GenAI投入,87%投入语义建模,82%投入数据目录,76%投入数据治理/MDM/血缘。 4. **采购流程滞后**:94%认为工具审批流程“混合至非常困难”,与快速迭代需求矛盾。 5. **平台集中但工具分散**:云基础设施AWS(77%)、Azure(69%)主导;数据平台Databricks(67%)、Snowflake(53%)领先;BI工具Power BI(73%)占优。
数据栈有多重? AI优先还是效率优先? 工具审批有多难?
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