Remove Business Analytics Remove Business Intelligence Remove Data Architecture Remove Data Quality
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Convergent Evolution

Peter James Thomas

Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. Of course some architectures featured both paradigms as well.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 119
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How Data Management and Big Data Analytics Speed Up Business Growth

BizAcuity

Big Data technology in today’s world. Did you know that the big data and business analytics market is valued at $198.08 Or that the US economy loses up to $3 trillion per year due to poor data quality? quintillion bytes of data which means an average person generates over 1.5 Poor data quality.

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A Simple Data Capability Framework

Peter James Thomas

The flip side is that making the necessary investments to provide even basic information has been at the heart of the successful business turnarounds that I have been involved in. The bulk of Business Intelligence efforts would also fall into this area, but there is some overlap with the area I next describe as well.

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The latest edition of The Data & Analytics Dictionary is now out

Peter James Thomas

Business Glossary (contributor: Tenny Thomas Soman ). Data Architecture – Definition (2). Data Catalogue. Data Community. Data Domain (contributor: Taru Väre ). Data Enrichment. Data Federation. Data Function. Data Model. Data Operating Model. Chart (Graph).

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

Today, the way businesses use data is much more fluid; data literate employees use data across hundreds of apps, analyze data for better decision-making, and access data from numerous locations. This results in more marketable AI-driven products and greater accountability.