Insightful Analytics The next level

Transcription

1 Insightful Analytics The next level Afterspyre provides proprietary analytics with machine-learning capabilities to enable insight It combines Quantitative and Semantic Analytics Incorporates powerful capabilities similar to Graph Database Afterspyre provides a single point of all information (Catalog / Models)

2 Afterspyre Use Cases Discover and leverage your Organizational DNA Use Ranking Analytics for effective selection of: a. Strategic objectives b. Initiatives c. Market opportunities d. Vendors e. Technologies f. Process Evaluations g. Product Features

3 Afterspyre Use Cases Two Model Semantic Comparisons a. Process listings between two organizations during mergers and acquisitions b. Compare features between two or more software applications Affinity Analysis to identify possible cause and effects Correlation Analysis to understand strengths of relationships, e.g. production and errors

4 Introducing a Sample Company Dolbk Incorporated Multiple business divisions: Manufacturing, Technology, Healthcare with an in-house dedicated analyst team Analyst Team Mandated to Build and Leverage the organizational DNA

5 Examining Elements of Organization DNA Why you deliver to the market Perceived market needs (Market models) The business environment (Economic and other models) What you deliver to the market Product / Service Offerings (Product Models) The Processes Used (Process, Workflow and other action models) How you deliver to the market Supporting Organizational Enablers (IT Supporting Environment is here) The process enablers The DNA is captured in Afterspyre s Catalog

6 Enriching the Catalog with Attributes The analyst can enrich the collected data within Catalog Using the Attribution functionality Enumerations can be added or selected from the built in library

7 Why the Analogy-Structure defines Organizational DNA DNA is made up of a chain of base pairs of chromosomes Afterspyre utilizes collections, on pairs of information or Matrix Models E.g. Technology related to processes Form the base pairs of Organizational DNA. Done using the List / Matrix Model Functionality Processes - Technology Risks - Technology

8 Start with Relationship Base Pairs A suite of Matrices represents Organization DNA Matrices of Relationship Pairs can be created manually or imported by the Analyst Matrices relate to each other through rows/columns just like a genome

9 Afterspyre Discovery of Matrix Pairs Afterspyre Relationship Discovery Wizard enables the analyst to auto-discover relationships between Matrices in a unified Catalog. Afterspyre also has the ability to interpret these suite of models similar to using a Graph database

10 Viewing the Organizational DNA Like a Graph database, the analyst can navigate the combinations/linkages An analyst can easily generate and visualize these relationships Display features provide zoom capability to isolate and zoom into areas of key concern and interest

11 Organizational DNA Diagrams The analyst can step through and follow multiple paths In order to trace the connections between different key elements of the organizational structure And better understand misalignments and potential issues

12 Ranking using a Machine Learning Algorithm Once issues are identified, Improvement Actions, or Initiatives are required to address areas of concern Ranking Analytics, based on machine learning algorithms, enable analysts to effectively analyze alternatives, such as, initiatives, market opportunities, vendors, prioritize product features etc.

13 Alternate visualization of Rankings Analyze, visualize and compare using different combinations of rankings Visual representation, such as 4-Box analysis, highlight recommendations based on the selected attributes, enabling easy identification of value adding alternatives

14 Two Model Semantic Analysis Import multiple processes from the respective BPM tools into Afterspyre Compare processes with those of the target company Identify similarity/differences between two sets of processes Ensure the process are suitable for consolidation or integration

15 Evaluating Process Fit (Possible Acquisition) Things in A not B Things in B not A Items:

16 Semantic and Sentiment Analysis

17 Affinity Analysis - Which Processes based on Location use are Candidates for Improvement? 7 Materials Processes and 10 Locations Implication is that Identify Supplier and Deliver Supplies are commonly related as are Manage materials and Deliver Supplies So Deliver Supplies may be a point of improvement Machine Learning, Affinity Algorithm Capability

18 Correlation Matrix- Which highly correlated items would you focus on? 15 Processes and 7 performance factors The high correlation of cost and transport time indicates an opportunity for improvement opportunity in transport time. Machine Learning, Correlation Matrix Algorithm Capability

19 Business Analytic Wizards Supporting Core and Solution Management Methods Innovation SWOT Risk Management Root Cause Analysis Strategic Impact Force Field Analysis

20 Shared Catalog In-built Methodologies Uncover Hidden Opportunities Benefits Solve Business Issues Provides Quantitative & Qualitative Insights Leverage Semantics Trapped in Other Tools Robust Analytics Relate Subjective & Objective Attributes Draw Clear & Targeted Conclusions