Enterprise. Orchestrated.

SAP SAC Planning >

Popular software: Tableau, Power BI, SAP Analytics Cloud, Qlik, Google Looker, AWS QuickSight, Oracle BI & APEX, Sisense, ThoughtSpot, HTML/CSS/JavaScript, Spotfire, RStudio, R, Python and Scala.


Designs, Deployments, Enhancements, Training and Maintenance

Enterprise Analytic systems are vital to many organizations and businesses. They are the glue that keeps organizations connected and coordinated on processes, activities, financials and more. The advantage is that people can easily access Analytics and data in their browser through reports, visualizations and ad hoc abilities. For example, one User can upload a spreadsheet, create a report and publish for thousands of Analytics Users to view and use (media). Larger companies are starting to include Industrial Internet of Things (IIoT) data in Data Lakes and Enterprise Data Warehouses (EDWs).

We use business management, lean, six sigma, data science and big data methods, techniques and computations in Enterprise Data Warehouses and Data Lakes.

Main Data Areas

Enterprise Application User Example

Below shows the use of two Enterprise Applications with entry screens and analytics. To get combined information, Analytics are used from the Enterprise Data Warehouse (EDW).

Software Applications receive (User or integrated entry) and store data. Users can make quick data inquiries and make updates. May have Imbedded Analytics.

Enterprise Analytics

Large companies typically have a large consulting firm, large integration company and multiple larger analytic and data management suppliers (many moving organizations and parts). We can assist when designs, cost and timelines are challenged by working independently with your organization and suppliers.

Typical large enterprise analytic systems have thousands of company shared (corporate) and personal dashboards, visualizations and reports.

Major parts of a Analytics or BI system

Visualizations, Dashboards and Reports

Traditionally, BI and analytics has been focused on users being able to run standard reports with prompt interactivity and even “ad hoc” abilities or a “self-service facility” to build reports. Dashboards evolved from these systems and gained popularity in the last 10 years with Mobile BI, BI Search and Interactive Visualization tools following in recent years. In addition, statistical client tools have been used throughout all these periods and continue to be a powerful ad hoc tool that complements enterprise analytic efforts to assist with in-database models and computations.

Analytics Server

Front-end visualizations, dashboards and reports typically depend on the Analytics Server (2) which sits in the middle of the overall BI Landscape. The Analytics Server is used to aggregate reasonable data sets and present those data sets to Frontend Visualizations for OLAP or static use. The server acts as a broker between the Users and the Database. It provides a place to administer users and to organize content or report and visualization objects (typically thousands of users and thousands of objects).

The key is to use the Analytics Server as a pass-through for the hard work performed at the Database layer (quick data throughput to users). The database should perform filtering, aggregations and computations where possible. In addition, Analytic Server Meta Data should follow the same rule and should complement or be a pass-through for Metadata at the Data Warehouse layer.

Experience Curve

Alexicon provides over 23 years of experience helping customers with progressive enterprise process, analytics and data management solutions across a variety of industries and functional company areas.

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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