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Organizations continue to drive towards becoming higher performing businesses in response to the increased economic and competitive pressures in today’s marketplace. To be successful, organizations are looking to reduce costs and grow revenue while gaining more value from their data, with faster response times and higher quality assurances. Business analytics are the enabler to address these challenges and opportunities.
Business analytics goals and objectives are to identify business opportunities which can be enhanced with analytics. This entails understanding the role of analytics in planned business opportunities. Next is assessing value and difficulty of analytics opportunities within larger business objectives. And finally prioritizing high-value analytics opportunities within the overall supply chain.
The ideal then becomes to gain the ability to leverage IT investments, while obtaining the opportunity to make better decisions. This ultimately leads to improvements in operational efficiency and thereby improved profitability.
Business Analytics Matrix
Imperative:
Leverage IT Investments
Alternative Solutions:
. Consolidate Operations
. Leverage Offshore Solutions (Service, Project)
. Reduce TCO (platforms, operations, licenses)
Imperative:
Better Decisions
Alternative Solutions:
. Fact Based Decisions
. Faster Reaction Time
. Automated Decisions where appropriate
Imperative:
Improved Profitability
Alternative Solutions:
. Target the right Customers
. Partner with right Vendors
. Reduced Risk exposure
Imperative:
Operational Efficiency
Alternative Solutions:
. Avoid Re-work / Interpretations
. Industry Focused Solutions
. Effective Flows /Activity Monitoring
Imperative:
Improved Analytics
Alternative Solutions:
. Leverage ERP Solutions
. Fact Based KPI’s
. Pre-Defined Business Content
The value opportunities are vast as the following is not an exhaustive list:
A. Reduce costs – Operational & Reporting
B. Customer Analytics/Insight
C. Personalized Marketing
D. Sales Force Enablement
E. Trading & Risk Management
F. Procurement
G. Inventory Control
H. Finance Planning & Analysis
I. Understanding Constituents
J. Departmental Mergers
K. Regulatory Compliance
L. Organizational Performance
M. Grants / Funding results
N. Workforce Analytics
O. Financial Management
P. Capitalizing on ERP data
Q. Corporate Performance
R. Exploiting new Sources of Growth (Product & Market & Channel Innovation)
S. Rapid Information Delivery
Many firms and supply chain executives believe that business Intelligence and data warehousing are the same. Yet in fact they are not the same at all.
Business Intelligence
Is the capability of collecting and analyzing internal and external data to generate knowledge and value for the organization. This includes business process decision support at the strategic, tactical, and operational levels.
Data Warehouse
A database populated with data from business transactional systems optimized for retrieval of information providing value in the areas of business projection, market trend analysis, and cost minimization.
“Business intelligence describes the enterprise's ability to access and explore information (often contained in a data warehouse) and to analyze that information to develop insights and understanding, which leads to improved and informed decision making. – Gartner
Our definition of business intelligence is: the technologies and process for extracting, integrating and analyzing data to support the decision-making process. – Ovum
Just like data warehousing, transactional processing is very distinct from analytical processing.
Transaction Processing Consists of:
. Business/Production Processing
. Capture the Data / Simple Query
. Organized by Application and Transaction
. Temporally Inconsistent
. Structured, Repetitive Usage
. Designed for Speed, Efficiency
Analytical Processing is:
. Strategic and Tactical Reporting
. Exploit the Data / Complex Query
. Organized by Relevant Subject Areas (who/what/where/when)
. Temporally Consistent
. Unstructured, Less Predictable Usage
. Designed for User Understanding and Query Performance
Like all successful implementations whether system or process driven there are mistakes that should be avoided. Here are several pitfalls that must be avoided to ensure a successful business intelligence program:
• Failure to produce a business case propositioning the true value of the effort
• Preoccupation with technology versus business capabilities
• Lack of understanding of user requirements from a business perspective
• Following a “build it they will come” approach
• Ineffective working relationship between technology and the business line
• Dependence on “bleeding edge” technology independent of the true requirements
• Implementation without solid data delivery methodology and project management
• Big bang implementation
• Lack of appreciation for the complexities involved in consolidating and standardizing data from different systems
• Lack of informed and committed executive champion
• Building of data warehouses without including data marts
• Missing a data stewardship role which defines business definitions (e.g., metrics, hierarchies)
• Unwillingness of departments or functions to share the data
If then, business analytics are the enablers to address the challenges and opportunities of driving out cost, increasing profit and becoming more efficient, are there tools in the marketplace to assist with the analysis? Of course there are. As one would imagine the tools range from systems that will perform “what if” analysis to minimal tools that will do simplistic calculations. During the research for this article a particular product was discovered. Not the typical i2 or Manugistics that is costly with protracted implementations, but a very robust and easy to use system called i-Lean from Chiron Technologies. The company comes with big name clients with good references.
Some of the features of the product are Design Analysis, Cost Analysis, and Pareto Analysis.
Design Analysis:
This feature introduces an interesting new concept called POU = point of use. In addition analyses can be run such as “what if a supplier changes lead time?”
Cost Analysis:
Next is cost analyses such as EOQ or economic order quantity, annual holding cost interest rate, order carrying costs, ordering cost from vendor to stockroom, storage, kitting, and stock room costs, as well as the typical unit cost and transportation cost. Finally, one often missed cost analysis is the cost of being out of stock. This product incorporates analyses of “missed opportunity”.
Pareto Analysis:
These come in the form of A, B, C product or category rankings. Pareto summaries, error and what is called “run” which looks at lead time, cost and volumetrics.
So as business continues to progress using knowledge as a basis for competition in the marketplace, and to gain knowledge requires data for sound decision making, then business analytics will soon be the next internal business opportunity.
References:
Gartner website Gartner
Accenture Business Analytics practice Accenture
Chiron Technologies, Inc. Chiron Technologies, Inc.
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Introduction
