Business Analytics/Intelligence has remained the top application/technology (a clear standout) since 2003. Companies value the ability to analyze data/information to gain insights as they compete to rapidly and accurately advise internal and external decision-makers.

Some call data the new oil. Others call it the new gold. Philosophers and economists may argue about the quality of the metaphor, but there’s no doubt that organizing and analyzing data is a vital endeavor for any enterprise looking to deliver on the promise of data-driven decision-making.  With the future of IT being driven by these technologies (in marketing, R&D, HR, legal...), every organization should be obtaining demonstrable value from implementing an effective data driven innovation strategy where big data means bigger and better decisions. Every organization needs to have a team of effective data scientists.

In addition to formidable process improvements, the focus is now on revenue generating initiatives. Information will be to the 21st century what steam, electricity, and fossil fuel were to prior centuries..

However, focusing just on data, or on technical considerations will not lead to demonstrable business value.  Understanding the data value chain is essential:

DATA QUALITY PYRAMID

• Guidance and expectations for data quality originate from the top of the pyramid.

• Activities related to data quality are primarily located at the base of a data governance pyramid.

• Data Governance Policies should establish guidelines for maintaining data quality.

• The actual implementation of data quality begins at the Data Standards level by defining requirements based on conventions.

• Data practices outline your approach to managing day-to-day activities that ensure data quality.



• The bottom operational layer involves tasks to address data quality issues, rectify errors, and develop new rules to ensure data quality.

The amount of data generated by businesses today is unprecedented. As this data growth continues, so do the opportunities for organizations to derive insights from their Data and Analytics initiatives and derive sustainable competitive advantage. Google’s chief executive, Eric Schmidt, observed that “There were 5 Exabyte’s of information created by the entire world between the dawn of civilization and the early 2000's. Now that same amount of information is being created every 2 days.” An Exabyte is equivalent to 1 billion gigabytes. While perhaps slightly exaggerated, an undisputable fact is that humanity is awash in data. The premise of big data is that all of this information can yield (is yielding) powerful insights. The difficulty is in how to harness the value of data.

A report from MIT says, digitally mature firms are 26% more profitable than their peers. McKinsey Global Institute indicates that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain customers and become 19 times more profitable.  Overall, Data and Analytics today are the next frontier for innovation and productivity in business. But achieving a sustainable competitive advantage from Data and Analytics is a complex endeavor and demands a lot of commitment from the organization. Gartner says only 20% of the Data and Analytic solutions deliver business outcomes. A report in VentureBeat says 87% of Data and Analytics projects never make it to production.

The focus of these courses is to address how organizations can get value from Data and Analytics. Specifically, how can enterprises leverage the data, AI (Artificial Intelligence) and BI (Business Intelligence) for competitive advantage? 

 Addressing the 4-V’s of Big Data have become fundamental for data scientists:



• Volume: The integration of existing enterprise data with Social, Mobile, Cloud, and Internet of Things is driving the data explosion
• Variety: Capturing all of the structured and unstructured data that pertains to the enterprise decision making processes
• Velocity: The rate at which data arrives and the time required to process and understand it
• Veracity: The quality and trustworthiness of the data
  

Furthermore, IT for all companies has traditionally focused on building reports about events that happened in the past. Big data and business analytics is now shifting the focus of IT. Instead of just looking backward, IT can develop (and the business can leverage) the capabilities for looking forward. To be able to take advantage of these new capabilities, organizations must recognize that the conventional model requiring data in the warehouse to be 'clean' and 'structured' must change. Organizations have to get comfortable with the idea that data can (and will be) 'messy' and unstructured', and that they will have to use external data sources (which have typically not been pulled into enterprise data warehouses) in new innovative ways. The complexity of this requirement is compounded by the traditional exponential growth of data in concert the growth of data brought by the internet of things.

Business Intelligence versus Data Science

The World Economic Forum estimates that over 130 million jobs will be created globally in new professions, where demand for data scientists, software engineers and a myriad of roles requiring digital skills are growing rapidly. In addition, successful managers and leaders increasingly require a strong working knowledge of digital technologies, as well as 21st century leadership skills including the ability to be adaptable, innovative and creative.