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Posts tagged ‘Decision making’

9
Apr

Bloomberg on Business Analytics


Interested in slicing, dicing, measuring, and analyzing data for customer and business insights?

According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.

While businesses have embraced the idea of fact-based decision-making, a steep learning curve remains. Only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions. Data is not just ignored but often discarded in many organizations as the business users can’t figure out how to extract signal from data noise.

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5
Mar

ROI on Analytics – Now We Have Numbers


Return on InvestmentA recent study by the Nucleus Research says that Analytics pays back $10.66 for every dollar spent. The study is based on data from 60 case studies and relates to investments in Business Intelligence, Performance Management and predictive analytics. Not surprising are the areas where they saw ROI increase – revenue, gross margin and expenses.

Enterprises have used various metrics to track the effectiveness of Business Analytics. Cycle Time to Information (CTI) is a metric that measures the elapsed time between the occurrence of a significant event and the time this information is available to a decision maker who has to act on that information. Cycle Time to Action (CTA) is variation of this metric which measures the elapsed time to act on information after an event occurs.  These metrics are useful to track the efficiency of a Business Analytics infrastructure and the elimination of manual processes to increase productivity. As the volume of data increases in an enterprise, automation in data management will become more complex in the future. Read more »

28
Feb

Proctor & Gamble – Business Sphere and Decision Cockpits


English: Logo for Procter & Gamble. Source of ...

Data-driven DNA is about having the right toolset, mindset, skillset and dataset to evolve a major brand and seize today’s omni-channel opportunities. Whether it’s retooling and retraining for the multiscreen attention economy, or introducing digital innovations that transform both retail and healthcare, P&G is bringing data into every part of its core strategies to fight for the customer.

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Striving for market leadership in consumer products is a non-stop managerial quest.  In the struggle for survival, the fittest win out at the expense of their rivals because they succeed in adapting themselves best to their environment. 

CMOs and CIOs everywhere agree that analytics is essential to sales & marketing and that its primary purpose is to gain access to customer insight and intelligence along the market funnel – awareness, consideration, preference, purchase and loyalty.

In this posting we illustrate a best-in-class “run-the-business” with Data/Analytics Case Study at P&G. The case study demonstrates four key characteristics of data market leaders:

  1. A shared belief that data is a core asset that can be used to enhance operations, customer ser­vice, marketing and strategy
  2. More effective leverage of more data – corporate, product, channel, and customer –  for faster results
  3. Technology is only a tool, it is not the answer..!

  4. Support for analytics by senior managers who embrace new ideas and are willing to shift power and resources to those who make data-driven decisions

This case study of a novel construct called Business Cockpit (also called LaunchTower in the Biotech and Pharmaceutical Industry) illustrates the way Business Analytics is becoming more central in retail and CPG decision making.

Here is a quick summary of P&G Analytics program:

  • Primary focus on improving management decisions at scale – did the analysis to identify time gap between information and application to decision making
  •  “Information and Decision Solutions” (IT)  embeds over 300 analysts in leadership teams
  • Over 50 “Business Suites” for executive  information viewing and decision-making
  • “Decision cockpits” on 50,000 desktops
  • 35% of marketing budget on digital
  • Real-time social media sentiment analysis for  “Consumer Pulse”
  • Focused on how to best apply and visualize information instead of discussion/debate about validity of data
DatatoAnalyticsModel
mycockpit-pg
 

P&G Overview

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20
Aug

Do your KPIs Reflect Business Insights?


Obsolete KPIs can be Lethal

In the Aesopian fable of the one-eyed stag, a deer overcomes his visual handicap by grazing on a cliff near the sea with his good eye facing the land. Since all his known dangers were on land, this keeps him safe from predators for a very long time – until he is killed by a hunter in a boat.

The relevance of our KPIs can make or break our business. KPIs are often defined as static metrics for an enterprise and can easily become outdated. Economic uncertainty and competitive pressures are prompting questions on the validity of KPIs and performance management processes. To stay competitive requires a process of continually validating metrics with the business environment.

Another common challlenge with KPIs is that there are too many of them. Modern technology has gven us the ability to measure a very large number of parameters in the business. Some of these are more relevant than others. Jack Welch is known to have said, ”Too often we measure everything and understand nothing”. Monitoring some metrics and ignoring others are decisions we make based on our business perspective.

Relevance Enabled by Process

How do you decide on which KPI’s are most relevant to success? An often overlloked first step is to understand that primary business goals before looking at the technology solution. Avinash Kaushik  defines KPIs simply as “Measures that help you understand how you are doing against your objectives”. This fundamental aproach is a good way of weeding out items which are not relevant to what we want as a business and avoid adverse surprises. At a more deeper level, building a robust Business Analytics solution requires answers to questions such as:

1. What events have the greatest impact on the busiens and how are they measured?

2. How often do you validate that you are measuring the right parameters ?

3. What instrumentation do you need to create the right dashbords for your KPI’s ? Can this instrumentation be updatd as teh KPIs change?

4. What is the process for collecting, synthesizing, manipulating and presenting the data to represent thsese metrics? How does the process change when if the metric change?

5. What technologies and architecture are necessary to support those decision-making patterns? Is there need for a “single source of truth” or a federated model possible?

Centers of Excellence

Needless to say, this approach requires a tight inegration between the business owners and IT acrchitects. A recent study by Gartner says that ”IT collaboration initiatives fail because IT leaders hold mistaken assumptions about basic issues…..rather than making technology the starting point, IT leaders should first identify real business problems and key performance indicators (KPIs) that link to business goals.”

Many business executives believe that IT is unable to deliver results where it counts. At the same time, IT organizations spend an incredible amount of time, money and resources simply reporting obvious data within their business process and workflows.

An organizational solution to this problem is the creation of a Competency Center or Center of Excellence (CoE) with representation from from both business and IT and shared objectives. The CoE defines the blueprint for implementing BI, Performance Management and Analytics aligend with KPIs. Some of the obvious benefits include:

  • Cost savings from eliminating Silos
  • Better collaboration between Business and IT
  • Joint ownership of corporate objectives

There are other aspects of the CoE which make it a practical approach to creating an effective vehicle for deploying analytics solutions. The sheer volume and texture of busienss data is much more complicated than it has ever been in modern busienss history. The world’s data doubles every two years creating more opportunities for analyses. Understanding this data even at an aggregate level requires a business perspective combined with technological expertise. Furthernore, understanding technologies such as Big Data for unstrcutured data analysis requires business leaders and IT eimplementors to work together.

The CoE is the ideal structire to implement a Business Perspective Solution.  A well implemented Business Perspective Solution takes into account the key objectives of the busienss, leverages sophisticated analytics technologies and focuses on sustainable processes to support decision making in an organization.

Superior decisions based on business perspective separate winners from losers.

Are your KPIs in sync with your business perspectives? Please share your comments below.

 Further Reading

1. Six Web Metrics / Key Performance Indicators To Die For by Avinash Kaushik, Occam’s Razor

2. Practical BI – What CEOs want from BI and Analytics by Ravi Kalakota, Business Analytics 3.0

3. The Stupidity of KPIs in Business Analytics by Mark Smith, Ventana Reasearch

3
May

Executing a BI and Analytics CoE


Most Organizations are Data Rich and  Information Poor

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Data overload is becoming a huge challenge for businesses and a headache for decision makers.  Public and private sector corporations are drowning in data — from sales, transactions, pricing, supply chains, discounts, product, customer process, projects, RFID smart tags, tracking of shipments, as well as e-mail, Web traffic and social media.

I see this data problem getting worse. Enterprise software, Web and mobile technologies are more than doubling the quantity of business data every year, and the pace is quickening. But the data/information tsunami is also an enormous opportunity if and only if tamed by the right organization structure, processes, people and platforms.

A BI CoE (also called BI Shared Services or BI Competency Centers) is all about enabling this disciplined transformation along the information value chain:  “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.”  A BI CoE can improve operating efficiencies by eliminating duplication and streamlining processes.

In this posting we are going to look at several aspects of executing a BI CoE:

  • What does a BI CoE need to do?
  • Insource or Outsourcing the BI CoE
  • Why do BI CoE’s Fail?
  • BI CoE Implementation Checklist

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