Analytics

Analytics

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

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Analytics

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 easy 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).

"Analytics has become a key driver of how value is created in most businesses, and the finance function is now drowning in data. CFOs will need to lead the finance function in expanding analytical capabilities, roles and processes and help business stakeholders to understand, interpret and use financial data to make sound operational decisions."  Gartner - Top Priorities for Finance Leaders in 2021

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 or timelines are challenged by working independently with your organization and suppliers.

We use business, management, lean, six sigma, data science and big data methods, techniques and computations in EDWs.

Typical large enterprise analytic systems have thousands of company shared (corporate) and personal reports. Our first step is to catalog these systems which provides visibility on the As Is Environment. After this, change is possible toward achieving the designed To Be landscape.

Below are major parts of a BI or Analytics system

1. 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.

2. Analytics Server

Frontend 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 Meta Data at the Data Warehouse layer.

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Experience Curve

Alexicon provides over 20+ years of experience helping customers with progressive enterprise process, analytics and data management solutions across a variety of industries and functional company areas.