Data Requirements for advanced analytics

NUMBER ONE

Use advanced analytics to discover relationships and anticipate the future

This involves discovering relationships, anticipating the future, and adapting to change. Working with the right data in the right condition is key to achieving these goals.

Discover relationships. Whether advanced analytics is based on data mining, statistics, artificial intelligence, or complex queries, it can help you discover and quantify important relationships that you may have been unaware of. These relationships can reveal fraud, define customer segments, group products of affinity, and link field conditions that lead to product failures. The newly discovered relationships, in turn, help you reduce fraud and its
costs, target marketing campaigns more accurately, develop effective merchandizing strategies, and improve product quality.

Anticipate the future. Predictive analytics can produce scores and statistics through which you can predict the likelihood of various outcomes of certain situations. for example, predictive models quantify a customer’s proclivity to churn, thereby giving you an opportunity to retain the customer. Predictive models can assist with various types of forecasting. likewise, predictive analytics can quantify future risk for pragmatic applications.

Understand and adapt to change. on the one hand, advanced analytics can help you understand change in the form of rising costs or new customer behaviors. on the other hand, the discoveries made through analytics can lead to positive changes that help your business adapt to an evolving world.

NUMBER TWO
Scale up data integration to handle large analytic data volumes

NUMBER THREE

Realize that reporting and analytics have different purposes and needs

Reporting and analytics are two different practices that have different goals, methods, sponsors, funding, and enabling technologies. yet many people confuse the two, platforms for business intelligence (BI) include functions for various types of reporting and summarized analysis in the form of online analytic processing (OlAP)

NUMBER FOUR
Distinguish between data warehouses, data marts, and analytic databases

	Enterprise Data Warehouse	Data Mart	Analytic Database
Business Method	Single version of the truth for enterprise performance.	Single subject area(s) for application-specific purposes.	Test bed for exploring change and opportunity.
Optimization	Multiple update speeds, high performance,workload management, in-database analytics.	Regularly updated data for reporting, performance management, and OlAP.	Unpredictable data sets about changing markets, costs, customers, risks, etc.
Data Attributes	high standards for production data, plus inclusion of experimental data.	carefully transformed, cleansed, modeled, and audited.	less cleansed and modeled. often just raw source data.
Data Models	3NF data model to model the enterprise with views for application flexibility.	Relational models for reporting. Multi-dimensional models for olAP.	3nf of source data. Models demanded by analytic tools. Predictive models and scores.
Data Lifecycle	Permanent history with transient, elastic logical marts.	Permanent history of enterprise performance.	Data tends to be transient, as analytic needs change.
Data Acquisition	Well-governed process with the flexibility for self-provisioning elastic logical marts.	Slow process due to data Transformation,cleansing, modeling, audit trail, etc	Load data fast with little prep and start analysis immediately, regardless of state of data.

NUMBER FIVE
Design a data warehouse architecture that accommodates analytics

NUMBER SIX
Prepare data to meet the needs of the analytic method you’ve chosen

NUMBER SEVEN
Preserve analytic data’s rich details, because they enable discovery

NUMBER EIGHT
Improve data after working with it, not before

NUMBER NINE
Apply the products of advanced analytics to BI and DW activities