Advanced Analytics

& Big Data

Not analyzing and extracting value from your data effectively puts your company in a vulnerable position. Data analytics should be thought of as an investment in your company’s productivity and success. However, this type of in-depth analysis and representation requires sufficient staff, well-defined processes, a clear business strategy, and leadership support which often times may be lacking within a company due to other priorities. Don’t let that keep you from investing in the success of your company. EPE Innovations has years of experience delivering results through intricate business intelligence visualizations that add value to your business.

Types of Data Analysis

Reporting & Alerts:

Most common use of BI includes: simple or production-ready reports, parameter-driven reports, drill-down reports or alerts. Alerts signal monitoring events or data values sent via email or messaging app.


Single-page visualizations which combine data charts, tables or grids, and advanced JavaScript charts or animations. Allows drill down and drill across. Data updated on regular schedule, on demand via users, or triggered by data update.

OLAP Analysis:

Ad-hoc analysis, but queried from a multi-dimensional (OLAP) cube. Users can drill down a dimensional hierarchy or drill across multiple dimensions (pivot), or both.

Predictive Analysis:

A type of advanced analytics, which is commonly employed by data scientists to create predictive models or forecasts. These models can be used in visualizations or data discovery. Sometimes referred to as data mining or machine learning, proper usage of which will help business leaders gain insights into trends and subtle business patterns, optimize decision making, and assess risks.

Performance Management:

Measures business in key areas (or KPIs) at regular intervals to identify trends and detect problems. Performance management is typically a management tool for leveraging BI to manage and improve business processes.

Data Discovery:

A type of ad-hoc analysis to create a mashup of data visualizations and dashboards to present data. Recent excitement in this BI style comes from innovative tools such as Tableau and Qlik Sense, which incorporates a self-service graphical user interface (UI) to ease the data selection and BI mashup.

Data Visualization:

Data visualization, inclusive of dashboards, is a visual communication of data through charts, plots, tables, and statistical models to convey custom information effectively to information consumers.

Big Data Analytics:

This is BI for large sets of data, both structured and unstructured. Current relational databases and BI applications deal well with structured data, regardless of the size. Nevertheless, alternatives to current relational databases and BI tools are often necessary to analyze textual data apart from social media content, email messages, phone records, sensor data collected from the Internet of Things (IoT), etc.

Big Data

Businesses generate more data in the last few years than all the previous years combined.

  • How can companies extract insights and benefit from the growing amounts of data?
  • How can companies ceate a data-centric and evidence-based business culture?
  • How can companies manage Big Data and provide line-of-business managers with actionable insights?