Business Intelligence & Data Modernization

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How Do You Turn Data into Pro-Growth Decisions?

Every minute, your company produces hundreds of points of data. Some of that data isn’t relevant, but the majority of it is very useful.

Do you know the difference between useful data and background noise?

CIO Advise works with the leadership and internal IT organizations of corporations to discover previously undiscovered relevant data sources and to leverage existing data sets for analysis and predictive modeling. With this information in hand, you’ll be able to make boardroom decisions with confidence.

What Are the Possible Sources of Data in a Company?

In today’s device-heavy business environment, the sources for data are almost limitless. Here are a few of the more traditional sources.

  • CRM Systems
  • ERP Systems
  • Sales Force
  • HR
  • Marketing
  • Production
  • Direct Customer Engagement

How Does the CIO Advise Data Modernization Process Work?

  1. Assess current data sources
  2. Securely connect to those data sources and extract the required data
  3. Model the data according to your unique needs
  4. Create visualization using data analytics/business intelligence for informed, real-time decisions.

Just four steps.

Sounds simple, doesn’t it?

Well, that’s the 10,000-foot view.

When you get closer to where the action is, you discover that Data Modernization and Business Intelligence is a finely-tuned dance that requires the management of automation and integration processes that funnel real-time data to machines that can learn and provide predictive analysis for C-suite decisions.

Why Bother to Use Business Analytics and Modern Data Strategies? Can’t We Just Do it the Old Way?

There are many reasons why CIO Advise IT professionals work with companies to improve the use of data within the organizations we serve, but the most important ones are:

  • Analytics without unbiased data gathering and well-crafted algorithms result in muddy data reports that are subject to interpretation.
  • The old way usually depended on hearsay and anecdotal evidence for decision making – not hard facts and trustworthy data.
  • When data is used to create predictive models based on machine learning, you avoid the very real risk of people manipulating analytics for their personal agendas and gain.


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