About Analytics

For those who work on Data Warehousing and Business Intelligence (BI) projects, the term analytics is already part of daily vocabulary. However, though it’s easy to get an idea of what it represents, several software vendors and IT professionals refer to analytics not always in the same perspective, and sometimes focusing on their own product strengths.
For a better understanding of the Analytics concept let’s do some research on its emerge.
Google Trends shows how was the evolution of the search for the word “analytics” in terms of popularity (not in quantity). Notice that between 2006 and 2012 popularity has grown consistently, and in the following years the interest did not drop much (left side of Figure 1), showing that the topic is still of great interest.

Figure 1 – Popularity evolution on Google search engine for “analytics” vs “business intelligence”

On the contrary, “Business Intelligence” has slowly been losing popularity but that does not mean that BI is less important for organizations, “BI/Analytics” is still on top of investment priorities.
As a consequence of this trend, Gartner updated the designation of its annual report about Business Intelligence. In 2012 its title was “Gartner Magic Quadrant for Business Intelligence Platforms” and in 2013 it changed to “Gartner Magic Quadrant for Business Intelligence and Analytics Platforms”. The company analysts, whose publications are reference for information technology research and advisory, justify this change to emphasize the growing importance of analysis capabilities to the information systems that organizations are now building.
Gartner also refers to Analytics as a term without a consensual definition: “Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (…). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen (…)”.
Forrester, another influential research and advisory firm, presents analytics as functionalities to go beyond traditional BI: “As traditional BI functionality (e.g., reporting and OLAP) becomes commoditized, buyers need to look for other differentiation, such as in the area of big data capabilities. BI on Hadoop; predictive, streaming, and text analytics; data exploration; and other features related to big data are now key strategic enterprise BI platform selection criteria”.
The current trend in the market is also confirmed by Forrester, its research highlights the increasing importance of analytical capabilities within organizations: “While reporting, including historical and operational reporting (proving the answers to the “what” questions), remain important, industry leaders leverage more analytical applications (providing the answers to the “why” questions) such as OLAP, dashboards, data visualizations, and analytical reporting more than industry laggards.”
Forrester does not present a clear view on defining analytics, instead introduces a different vision – systems of insight which represent an evolution of BI & Analytics: “business intelligence (BI) and analytics must evolve into systems of insight, where traditional BI, Agile BI, and big data converge to deliver actionable insights necessary to win, serve, and retain customers.”
Often, Business Intelligence and Analytics are differentiated in terms of time target. While the first has most of its focus on past data (reporting), the latest focus on future insight by developing predicting models and other analytical capabilities that help managers to make even more reliable data-driven decisions.
Among current BI trends, those who are most associated to Analytics are:

• Fast generated data becomes promptly available to provide action towards real time events.
• Self-service BI evolves to self-service analytics – advanced analytics are widespread, anyone in the organization may use advanced statistical capabilities without being an expert.
• As access to data is democratized, end-users feel empowered to dig into their own questions and initiatives.
• Cloud Analytics solves data architecture scalability and financial barriers to small and mid-size companies.
• Data visualization, interactive and collaborative applications reinforce a data-driven culture.
• Demand for people with a particular set of skills: data scientists.

The price of light is less than the cost of darkness – Arthur C. Nielsen