{"id":199029,"date":"2026-07-16T04:34:20","date_gmt":"2026-07-16T08:34:20","guid":{"rendered":"https:\/\/innowise.com\/?p=199029"},"modified":"2026-07-16T04:34:43","modified_gmt":"2026-07-16T08:34:43","slug":"top-data-modernization-companies","status":"publish","type":"post","link":"https:\/\/innowise.com\/sv\/blog\/top-data-modernization-companies\/","title":{"rendered":"De 10 b\u00e4sta f\u00f6retagen inom datamodernisering 2026"},"content":{"rendered":"\t\t
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Best 10 data modernization companies in 2026<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\r\n Philip Tikhanovich<\/a>\r\n <\/div>\r\n\r\n
\r\n Jul 16, 2026<\/span>\r\n 10 min read<\/span>\r\n <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Key takeaways<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  • Data modernization companies<\/span> help enterprises replace slow legacy systems, scattered databases, and unreliable reporting. They set up platforms built for analytics, governance, and AI.<\/span><\/li>
  • This ranking of the<\/span> top data modernization companies 2026<\/span> compares providers based on their delivery focus, main strengths, industry experience, technology background, and public reviews.<\/span><\/li>
  • The<\/span> best IT services companies for data modernization <\/span>start by assessing the current data setup before recommending a new platform. They review legacy databases, ETL processes, reporting methods, ownership issues, data quality, and security needs.<\/span><\/li>
  • The best partner depends on your project size. Large firms like PwC and Capgemini are well-suited to enterprise-wide projects. Companies that need more hands-on technical work may prefer engineering-focused providers like Innowise and EPAM.<\/span><\/li>
  • Top companies for enterprise data modernization cover migration, cloud data platforms, lakehouse architecture, BI updates, data governance, system integration, and post-launch support.<\/span><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Businesses often start looking for <\/span>data modernization companies<\/span> when the same issues keep happening. Reports might be slow, data from different systems might not match, or a new AI project might be held back by outdated pipelines. At this stage, choosing the right vendor becomes a practical concern. Who can understand your current setup, fix the weak spots, and keep daily reporting running smoothly during the transition?<\/span><\/p>

    In this guide, I\u2019ll compare the <\/span>top data modernization companies 2026 <\/span>from this perspective. You\u2019ll find out which providers are best for large legacy systems, which ones focus on hands-on engineering, and what to consider if your project involves cloud migration, BI modernization, governance, or building data foundations for AI.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    Why data modernization is essential for AI and digital transformation<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    The hardest part of any AI project is often the data behind it. You can add a new analytics tool, connect an AI assistant, or build forecasts, but if the data foundation is weak, even the best tools will struggle to produce outputs the business can trust. With data modernization, you can turn scattered legacy data into cleaner flows, shared definitions, clear ownership, and access rules the business can use daily.<\/span><\/p>

    Below, I\u2019ve broken down the main reasons this matters for business.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    Cleaner data for AI systems<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    AI projects use data from systems such as CRM, ERP, product, finance, support, and operations. If the data is duplicated, outdated, or poorly labeled, those issues show up in the results. Modernization helps teams clean up source data, standardize definitions, and add quality checks before the data is used in AI workflows.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    Faster access to business information<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Legacy data setups often rely on overnight processing, manual exports, and reports that arrive too late for daily decisions. Modern data platforms speed up the flow from source systems to analytics tools, so teams can work with fresher numbers and spend less time fixing spreadsheets.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    One view across departments<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Sales, finance, operations, and product teams often use different systems and rules for the same metric. Data modernization connects these sources and makes it easier to manage shared definitions. This way, teams talk about the same numbers instead of debating different versions.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    Stronger governance and security<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Modernization helps companies create better access rules, track where data comes from, clarify ownership, and document how data is used. This is important for regulated industries and for any business working with customer, financial, employee, or operational data.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    Better cloud and analytics outcomes<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    A good modernization plan considers where data comes from, how it moves, who owns it, and how teams use it after migration. This approach turns a platform upgrade into a real business improvement, with faster reporting, clearer accountability, and a stronger base for analytics and AI.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    Need cleaner data for AI and analytics?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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