Beyond Data Warehousing: What’s Next in Business Intelligence?

1. Beyond Data Warehousing:What’s Next in Business Intelligence? Matteo Golfarelli, Stafano Rizzi DEIS – University of Bologna Iuris Cella Gruppo Formula S.p.A. Summary: 1. Metric Driven Management 2. Business Performance Management 3. Proposed Architecture 4. Research and Technological issues 5. Conclusions DOLAP 04

2. History table for DWing • Late ’80s: the request for efficiently and effectively analyzing the enterprise data becomes a pressing matter for managers • Early ’90s: the birth of DWing and OLAP in the industrial world • Mid ’90s: academic world becomes interested in Dwing • Late ’90s: metrics driven management becomes a pressing matter for managers • Early ’00s: the birth of Business Performance Management in the industrial world • Mid ’00s: What happens in the academic world?

3. Metric Driven Management I • Process-oriented management couples the organizational structure with a set of inter-division processes. • The organizational structure is a hierarchy of divisions aimed at defining duties and responsibilities. At this level the global strategy of the enterprise is decided Strategic level It is composed by multiple divisions, each controlling a set of functions; the decisions taken here are related to the corresponding functions and must comply with the strategy defined at the upper level Tactical level Operational level Where the core activities are carried out; the decision power is limited to optimize specific production activities in accordance with the main strategy. InformationSystem

4. Metric Driven Management II • Process-oriented management couples the organizational structure with a set of inter-division processes. • Processes identify a set of related tasks performed to accomplish a defined goal. Tasks are carried out by different divisions and at different levels. Strategic level Processes focus on the global business goal rather than a single divisional task. Of course, employees involved in processes must share the business strategy in order to synchronize their behavior Tactical level Operational level InformationSystem

5. target values for indicators Strategy actions/ decisions Daily export sales =10000$ actions/ decisions current values for indicators Calls to prospects = 200 per day Order latency = 2days DW Metric Driven Management III • Metrics (KPI) are used to: • continuously measure process performances. • share the top-level business strategy with the employees in order to synchronize their behavior. Strategic level Tactical level Operational level InformationSystem

6. Business Performance Management (BPM) • The term BPM defines the new approach to management. The peculiarities that distinguish BPM from classical DW-based BI applications are: • Users: are decision-makers but at the tactical and operational levels. • Delivery time: decision at the lower levels must be faster than the strategic ones. BMP systems are not supposed to operate in real-time, but rather in right-time. • Information coarseness and lifetime: information are usually more detailed since concerns events related to specific tasks. Lifetime of information is limited according to right-time requirement. • User interface: information are mainly accessed via dashboards and reports as well as through automated alerts activated by business rules.

7. user interface OLAP report dashboard alert metadata repository BAM Data Warehouse Rule engine metadata repository KPI manager Mining tools ODS DW DDS ETL RTI EAI source data ERP other DBs business domain data streams Architecturual sketch for BPM • The technology implementing the BPM flow is often called: Business Activity Monitoring (BAM) Right-Time Integrator (RTI): integrates data from operational data sources at right-time Dynamic Data Store(DDS): is a repository capable of storing short-term data for fast retrieving KPI Manager: computes all the indicators necessary at different levels to feed dashboards and reports Mining tools: capable of extracting relevant patterns out of data streams Rule engine: that continuously monitors the events filtered by RTI or detected by mining tools to deliver timely alerts

8. Research and technological issues I Data latency reduction • Right-time constraint makes classical ETL and ODS approaches unfeasible • On-the-fly techniques are needed: • Cleaning techniques devised so far rely on the presence of a materialized integrated level. • Data stream manipulation still presents many challenges (i.e complex queries over data are performed offline, real-time queries are usually restricted to simple filters).

9. Input 2 KP3 Input 3 KP2 KP1 Input 1 KP5 KP4 Research and technological issues II Informative power • Complex KPI systems have been devised in the economic field (e.g. balanced score card) but the BI community has only partially faced the problem of their modeling and handling • System dynamic tools are used within the economic field but their exploitation within the BI tools has not been considered yet. • When the set of KPI is large (i.e. hundreds or thousand) it is hard to understand which their relationships are and if the targets are consistently defined. To this end simulation tools are needed.

10. Research and technological issues III Informative power • Most of the mining techniques devised so far are inapplicable in the right-time assumption. • Indicators and mining tools may need short-term information to compute complex information. Since simple buffering techniques are not appropriated, main-memory databases (real-time databases) must be considered.

11. Research and technological issues IV Interface • The classical paradigms of DW systems, namely reporting and OLAP, are no more sufficient to give users a full picture of the trend of their business in the short and medium term. • New paradigms, with different characteristics, must be merged into a common interface

12. Research and technological issues V Design • Right-time: the designer must determine what is the meaning of right-time for the specific business domain • Light-architecture: the requirements for KPIs change quickly depending on the pursued strategies that will bring to the foreground new functions and behaviors monitored by KPI. • Process design: with respect to DW, BPM also requires to understand processes and their relationships in order to find out relevant KPI and Business rules. • KPI design: capturing and modeling the relationships between different indicators has a primary role in this phase to ensure that effective and reliable information is delivered.

13. Conclusions and discussion • We summarized the requirements emerging from modern companies and discussed how they meet into a new architecture that promises to lead Business Intelligence beyond data warehousing. • We saw that different sophisticated technologies are involved in BPM. Most of these fields are not mature enough in terms of commercial products. • Gartner group indicates BPM and BAM as the emerging market in Business Intelligence till 2008. Is it just a passing fad ? Is it a technology matter only ? Is it the second era in BI ?