Skip to content

Posts tagged ‘Business Analytics’

19
Jun

Organizing for BI, Analytics and Big Data: CoE, Federated or Departmental


  • Data-as-a-Service:  Data Provisioning, Management, Lineage, Quality
  • Reporting-as-a-Service:  Dashboards, KPIs, Drilldowns/Aggregates…. Descriptive
  • Analytics-as-a-Service:  Predictive  Modeling and BI… Prescriptive analytics
  • Information-as-a-Service:  Threshold based Alerts, Exceptions, Mobile Prompts
  • Insights-as-a-Service:   ML/AI based…automated learning – ambient intelligence, Next best Offer/Action

Which strategy are you implementing?

Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data is challenging.

So, if you are a senior leader in a Fortune 2000 company.  How do you structure your group to deliver effective BI, Analytics or Big Data projects? Do you have the right structure,  toolset, dataset, skillset and mindset for analytics and Big Data?

Organizing for effective BI, Analytics and Big Data is becoming a hot topic in corporations.  In 2012, business users are exerting  significant influence over BI, Analytics and Big Data decisions, often choosing analytics and visualization platforms and products in addition to/as alternatives to traditional BI platform (reporting and visualization tools).

Read more »

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.

Read more »

18
Mar

Making Money on Predictive Analytics – Tools, Consulting and Content


Here are just a few examples of analytics at work

  • Target predicts customer pregnancy from shopping behavior, thus identifying prospects to contact with offers related to the needs of a newborn’s parents.
  • Tesco (UK) annually issues 100 million personalized coupons at grocery cash registers across 13 countries. Predictive analytics increased redemption rates by a factor of 3.6.
  • Netflix predicts which movies you will like based on what you watched.
  • Life insurance companies can predicts the likelihood an elderly insurance policy holder will die within 18 months in order to trigger end-of-life counseling.
  • Con Edison predicts energy distribution cable failure, updating risk levels that are displayed on operators’ screens three times an hour in New York City.

Now you are interested.  So what about your organization. Do you have the right toolset, dataset, skillset and mindset for analytics? Do you want to enable end users to get access to their data without having to go through intermediaries?

The challenge facing managers in every industry is not trivial… how do you effectively derive insights from the deluge of data? How do you structure and execute analytics programs (Infrastructure + Applications + Business Insights) with limited budgets?

Read more »

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.

—————————

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

Read more »

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

18
Jul

Mobile BI – Business KPIs and Dashboards “on-the-go”


 

mobile-applicationsWho doesn’t want to achieve faster “time-to-information” and shorter “time-to-decision” for executives and managers with mobile BI?  Who doesn’t want to disseminate insights or KPIs to front-line employees, such as field sales representatives, line of business managers, and field service employees?

The question is not whether Mobile BI is a good idea but how to execute this program in a low-cost way?  How to design and deploy eye-popping “wow” apps? How to support, maintain and enhance these apps which are constantly changing?  What technology and infrastructure to put in for a national or global deployment? Who is going to fund all this plumbing – corporate, LoB or IT?

Business Analytics solutions for “always-on” 3/4G enabled mobile devices – iPads, iPhones, tablets, smart phones – are becoming prevalent as the form factor becomes appropriate for BI.   We are increasingly seeing firms build state-of-the-art dashboard solutions for iPads. The “post-desktop” apps provide senior management with intuitive interactive access to the company’s most important business KPIs and dealing with data overload.

Tablets, 4G Wireless and next gen displays (+gesture based, verbal interfaces) have enabled new productivity improvements and better ways to consume information, perform ad-hoc querying and scenario planning. Dashboard, heatmaps and scorecards on the iPad, iPhones and Androids are intuitive, attractive, powerful, available at any time and any place: a perfect mix for top managers, sales teams and even customers.

BI (and Information Management) is a natural fit for mobile devices.  Managers, blue and white workers spend a majority of their time away from their desks. Most are traveling, walking about or driving from site to site. And it’s these mobile workers who need the most up-to-date information. They need mobile BI to retrieve data to make on-the-spot decisions, monitor operational processes and review KPI, and work-in-process dashboards.

Read more »

12
Jul

Are you one of these — Data Scientist, Analytics Guru, Math Geek or Quant Jock?


“The sexy job in the next ten years will be statisticians…”
‐ Hal Varian, Google

Analytics Challenge — California physicians group Heritage Provider Network Inc. is offering $3 million to any person or firm who develops the best model to predict how many days a patient is likely to spend in the hospital in a year’s time. Contestants will receive “anonymized” insurance-claims data to create their models. The goal is to reduce the number of hospital visits, by identifying patients who could benefit from services such as home nurse visits.

The need for analytics talent is growing everywhere. Analytics touches everyone in the modern world. It’s no longer on the sidelines in a support role, but instead is driving business performance and  insights like never before.

Job posting analysis indicate that market demand for data scientists and analytics gurus capable of working with large real-time data sets or “big data” took a huge leap recently.  The most common definition of “big data” is real-time insights drawn from large pools of data. These datasets tend to be so large that they become awkward to work with using on-hand relational database tools, or Excel.

It’s super trendy to be labeled “big data” right now – but that doesn’t mean the business trend’s not real.  Take for the instance the following scenario in B2B supply chains. Coca-Cola Company is leveraging retailers’ POS data (e.g., Walmart) to build customer analytical snapshots, including mobile iPad reporting, and enable the CPFR (Collaborative Planning, Forecasting, and Replenishment) process in Supply Chain. Walmart alone accounts for $4 bln of Coca-Cola company sales.

Airlines, hotels, retail, financial services and e-commerce are industries that deal with big data. The trend is nothing new in financial services (low latency trading, complex event processing, straight thru processing) but radical in traditional industries.  In trading, the value of insights depends on speed of analytics.  Old data or slow analytics translate into losing money.

As data growth in business processes outpaces our ability to absorb, visualize or even process, new talent around Business Analytics will have to emerge. New roles such as Data Scientists, Analytics Savants, Quant Modelers are required in almost every corporation for converting the growing volumes of data into actionable insights.

Look at these data stats.

Read more »

%d bloggers like this: