Enterprise. Orchestrated.


Enterprise Metadata >

Data Roadmap

A structured data warehouse provides a central access point to company data. It standardizes data and allows for more tight security controls (data security), Multidimensional analysis (MDA) abilities, accuracy and ease of use (structure and speed). We focus on the Enterprise Data Warehouse (EDW) as a single source for conformed enterprise data (central intelligence; enterprise-wide computations). It is important for enterprise data relationships and data science use across the enterprise. Below is a roadmap for improving and advancing the EDW. The ability to trend data to understand past performance, comparing and projecting.

Enterprise Operating System (EOS)

Companies have many systems surrounding their Enterprise Resource Planning (ERP) system. While competitors may use the same ERP and the same or similar surrounding software and hardware systems (usually industry specific), all companies have their own way selecting systems and integrating those systems into Company processes and operating. It is their unique EOS. We assist with selecting and integrating software and hardware solutions to refine your Company's EOS for better performance.

Like a Company's EOS, the Enterprise Data Warehouse (EDW) is built and integrated internally with EOS data and other needed data to improve overall Company performance. Conformed dimensions and facts are key for enterprise information intelligence and coordinated enterprise activities for better performance.

Creates New and Computation Metadata

Data Science working closely with Data Management for structured analytic data models. Data Science activities range from working in R and Python to SQL data models for computational statistics. For example, range from Market Basket Analysis (MBA) to Statistical Process Control (SPC).

Data Roadmap Cont.

Users access EDW data (tables and columns) with Analytics Tools, Custom Applications and more. Exports can also be achieved.

Users build many formulas on the frontend to achieve their desired dashboard, visualization and/or report for viewing and exports. Structured is best; however, still requires work on the frontend. Raw data compounds the issue.

A Company's Data Dictionary needs Dimension/Attributes, Raw Numbers and Calculations in one place (known Company Data Elements). Many Calculations live in frontend displays (slower and enterprise unrelatable). The dilemma is management needing analytics quickly and the IT workload or ability to structure and deliver quickly. Frontend work can be similar to R&D and is important and should be considered for a move to the EDW. Mashups and Calculations drive much of the frontend work.

Enterprise Metadata >

DATA Raw (needs Structure for the EDW)

Raw data comes from many sources (databases and files). When included in the EDW it is conformed to enterprise master data and dimensions where possible. Raw data does reside in most EDWs for display through the Company's EDW analytic tools. Main sources for the EDW are Customer Relationship Management (CRM), ERP, HR and Operational systems. External data is included as well. Companies have a "Data Dictionary" to understand the metadata in the EDW, ELT/ETL Transformations and for Sources. How do you move to a Modern EDW?

DATA Database (Structured)

The EDW provides the ability to see data across the Enterprise. Major processes like Order to Cash (ERP). If it is Campaign to Cash, CRM data is involved (Structured Data - Conformed). The diagram below shows a process from data staging, ETL and the EDW.

DATA Descriptive Statistics (Key for Projections and Comparisons)

Management and many people gauge their progress toward Company Goals (Plan) with applications and analytics. For example, marketing, sales, production and logistics (% to Plan, # Units and $ Currency). Many methods are used. The Modern Data Warehouse allows for aggregating, trending and comparing different metrics among each other for relationships (EDW tables versus frontend work - quicker and more accurate).

Computational Statistics

Computational statistics, or statistical computing, is the bond between statistics and computer science. It means statistical methods that are enabled by using computational methods. It is the area of computational science (or scientific computing) specific to the mathematical science of statistics. This area is also developing rapidly, leading to calls that a broader concept of computing should be taught as part of general statistical education. - Wikipedia

There are many visualization tools today and it is easier to mashup data and produce business analytic displays. This has also fragmented data and its use (Inhouse and Clouds for Data and/or Analytics).

DATA Historical Comparisons - Connect-the-Dots on Relationships and gauge strength overtime

Historical Comparisons require Descriptive Statistics to see advanced trend analysis and to make metric comparisons.

DATA Projections (Estimating as Accurately as Possible)

Historical Descriptive Statistics are also needed to make better projections for planning (look ahead). There are planning applications, MS Excel and other methods for projecting. We take an enterprise approach at a larger scale.

DATA Projected Comparisons (Connect-the-Dots on Projections)

Projections can also be compared for future periods. Top-level projections can go up-and-down relative to trending and future projections can be trended and compared like historical data (business approach).

Machine and External Data

With Enterprise Data crossing many applications and databases, understanding current business data and machine data is key to bridge the business-side to varied formats of technical data. From simple text, time and numerics to digital data.

Modern EDW

Contact us to learn more about improving your Company's Data Roadmap and EDW. Our focus is more tables and columns (data centric) and use along with host software and hardware.

Enterprise. Orchestrated.


Enterprise Business Processes and Technical Processes for Systems and Data.

Align key processes across the Enterprise to achieve optimal performance. Sync Enterprise Applications and Analytics.

Time intelligence speeds operations and provides clear communication with Associates, Customers, Partners and Suppliers.

Formal Lean Six Sigma or streamlined methods and techniques.

Define, Measure, Analyze, Improve and Control

Statistical Process Control (SPC): Run Charts, Control Charts and Design of Experiments

Gain the process advantage and include learning in the EDW and Data Lake.


Enterprise Visualizations, Dashboards and Reports.

Data Science

Leverage Desktop Excel, SQL, R, Python and Scala code. Hyperscale activities.

Integrate Data Science activities in the EDW for Enterprise-wide computations.

Data Mgmt.

Enterprise Applications, Other Data, EDW and Data Lake.

Data Roadmap >

Enterprise Operating System (EOS)



Data Science

Data Mgmt.

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