SAP Datasphere >

Enterprise. Orchestrated.


Data Roadmap >

Enterprise Metadata >

Popular software: R, Python, Scala, MySQL, Snowflake, SQL Server, SAP HANA, Oracle, Informatica, SAP Datasphere, Trifacta, Tableau Prep Builder, Teradata, PostgreSQL, Hadoop, Spark, Windows, Linux, Google Cloud Platform (GCP) - (Cloud Storage, BigQuery and BigQuery ML), AWS (S3 Data Lake, Glue, Redshift, Athena, EMR and SageMaker) and Azure (Power BI dataflows, Blob Storage, Data Lake Storage G1/2, SQL and Data Warehouse).

Data Management

Smart Enterprise

Like our personal smartphones, companies have applications, collective processing power (compute) and memory (storage). Switching phones or hardware and software means moving data which needs to be done quickly. We assist with existing or new data efforts. Go-lives have a timeline and usually involve data. While analytics are typically used for data analysis, data is being exchanged more among partners and also analytics and custom applications to provide performance metrics. Also, IoT and Machine data is being requested more to be include in Enterprise Business Dashboards and Visualizations (machine KPIs and Metrics).

Data can be used in many ways for Analytics. The main data types are "Entries" and "Events". For Ecommerce companies tracking realtime events and accumulating data that impact their store is key. Major companies have the same with their enterprise systems and web applications. Events are part of larger processes or a large process (Sales Order Line Item Delivered).

Below are Entry and Event areas for data. Organizing events and tracking are key for analytics.

Orchestrated EDW™ (Sync/Summarize) | EDW²™ (Describe/Square/Project)

Compute and Store has become a cloud and onsite cost to consider. Large database reporting is typically built overtime with plans at the time. Repetitive low-grain queries can cost with Compute and time. Also, if the database is not completely synced on time and "company dimensions" for full IDE multidimensional use, improvements or a new structure can be used.

Seven Levels for gauging Enterprise Data Maturity and Optimization

Orchestrated EDW | EDW² >

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.

Plant Operations >   Industry 4.0 >   IIoT >   HMI >   SCADA >

Experience Curve

Alexicon has a 23 experience curve with progressive enterprise applications, data management and analytics solutions.

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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