The machine and human
race is on: BI evolution reflects mankind progression.
Data is abundance, here is some statistics: Every day, 2.5
quintillion bytes of data are created, with 90 percent of the world's data created
in the past two years alone; Data production will be 44 times greater in 2020
than in 2009. The volume of business data worldwide is expected to double every
1.2 years. Big data promises to be transformational. Business Intelligence and Data Analytics becomes top
priority in every forward-look business’s agenda.etc. However, this also created a lot of confusion about as now the
term "Analytics", some folks use terms like: Analytics, Business
Analytics, Predictive Analytics, Business Intelligence Data Mining, Pattern
Based Analytics interchangeably. Shall we spend sometime to keep track of the
30-year of BI evolution and clarify relevant analytics concept as
well?
1.
Analytics vs. Business Intelligence
Analytics and
Intelligence always boils down to only one thing – Data. Business Analytics
and Business Intelligence is all about collecting and making your raw data
speak sense to understand what happened & how it happened and predict
future activity and planning.
Business Analytics is
an overarching concept: It’s intended to indicate that business analytics
is an umbrella term, including data
warehousing, business intelligence, enterprise information management,
enterprise performance management, analytic applications, and governance, risk,
and compliance. The effective analytics intend to answers the questions: a) What happened? b) When? c) Who? d) How many? e) Why did it happen? f) Will it happen again? g) What will happen if we change x? h) What else does the data tell us that never thought to ask? ..etc. .
Classic Business
Intelligence functions are: reporting what happened, drill-down to how it
happened, and find out the cause of why it happened and set alerts to act as
soon key metrics move in the wrong direction. the format may include a) Reporting
(KPIs, metrics); b) Automated Monitoring/Alerting (thresholds) c) Dashboards; d)
Scorecards e) OLAP (Cubes, Slice & Dice, Drilling) Ad hoc
query f) Statistical/Quantitative Analysis; g) Data Mining; h) Predictive
Modeling i) Multivariate Testing
Predictive Analytics
could be defined as Advanced Computational Statistics. It’s a complex
discipline that uses large cleansed historical data sets to find patterns in
order to forecast what has a high probability to happen in the future. It’s
here where expert data scientists, are necessary to not only to apply the right
algorithms but also to interpret the results. Applications include, customer
acquisition and retention, risk management, fraud detection, sentiment analysis
and demand driven forecasting among others.
Business analytics is
the first part of a decision making system. BA includes collecting data and
analyzing it to create information which if relevant and useful would be
business intelligence. BI is an input into the decision making process which is
final part of a decision making system. Relevant BA leading to useful BI should
result in better decisions being output from the decision-making process. To
make things simpler, analytics mainly relates to understanding what has
happened by drilling down to the root cause,
or what will happen and how to make certain decision. Intelligence caters to
predictions based on the analysis done on the historical data gathered over the
said time period.
2. All Flavors of Data Analytics
As computing resources have evolved, with advancing
capabilities to better handle data size and complexity, companies stand to reap
many more benefits from analytics. The evolutionary journey of data analytics
reflects the trend moving from analyzing historical perspective into capturing
business foresight, from operational perception to customer insight. etc. More
specifically: - Descriptive Analytics to capture hindsight: It intends to analyze what happened based on the historical data sets, to capture what happened and why, who are the most valuable customers;
- Diagnostic Analytics to gain insight: It further analyzes the root cause upon why it happened;
- Predictive Analytics to win foresight: What will happen? Which customers are most likely to respond to my next offer? What do customers want for the next cycle of products/services?
- Prescriptive Analytics to lead decision making: What should I do? Which offer should I make to customers? Prescriptive Analytics Defined a set of analytical capabilities that:
- Define a preferred course of
action
- By calculating expected outcomes
of alternative decision options
- The consulting firms also summarize evolution of BI into statistic intelligence, reactive intelligence, interactive intelligence and adaptive intelligence. Whereas business intelligence has long been associated ad hoc query, analysis and reporting—activities that explore and, perhaps, extrapolate based on historical data—advanced analytics apply statistical and predictive algorithms to come up with calculated, predictive measures, scores or models. Advanced analytics are far more sophisticated, supporting techniques such as statistical analysis, forecasting, correlation and prediction
The competition between machine intelligence and human
wisdom is still on, from information scarcity into data abundance, from industrialization to digitalization, from
evolution to revolution, the world can be pull up and pushed through to the new
era of high intelligence.
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