My wife and I were shopping the other day and she asked,“ Why are these people always asking us if we want to get a credit card? They have to know we have a couple already, don’t they? ” While I can’t
pretend to know all the reasons why retailers train their customer - facing folks to push store credit cards, I do know that there are some informational advantages. Stores can use credit card data to determine spending pattern behavior. This in turn reveals the different charging patterns of different segments of customers, information that can be mapped to customer profitability. With the buying behavior captured, retailers can target their most profitable customers with a personalized campaign of couponing designed to stimulate behavior. Lot of retailers in India offer loyalty programs and cards. This program will tell you about purchases that were made at your store but Co-branded credit cards may also tell you about purchases that were made at your competition's stores.
All retailers have as one of their long - term goals the desire to keep profitable customers loyal to their store. Is there — via the wonders of analytics — a way for a retailer to know when a customer is on the verge of leaving your store and to do something to circumvent that decision? Analytically here you are asking two questions: How do I know if a customer is going to leave my store? What can I do about it?
Most people would develop an ad hoc report that says, “ Tell me all the people that provide $ X amount and if that sales volume has dropped by $ Y amount. So, if they used to buy $ 100 a week and now spend only $ 50 a week, maybe they ’ re leaving my store.” That was the traditional model. The challenge with that is that by the time that situation has occurred, that customer has likely already made the decision that they are leaving their store. What you want to do is to be able to predict that a customer is going to stop shopping at your store before it is noticeable in a loss of sales. So we did that in one of proof of concept at one the retailer.
We threw a whole bunch of data at the computer for a set of consumers who did lapse the store and for a whole bunch of people who haven’t. In this example, we did find that there was one key item that was a telltale product that if a customer used to buy this item, and then stopped buying this item, that there was a high percentage likelihood that this customer would stop shopping at that store. Do you have any idea what the product was? Believe it or not, it was salt.
Business Analytics helps you understand where the customer’s head at the time of contact/purchase. Understanding this would enable much more appropriate messaging and might enhance service recovery. Such situational awareness would allow airlines, when you check in at the kiosk, to say “ Oh, sorry about your delay yesterday, here’s a free Vada Pav or Idli or Coffee coupon. ”
Saturday, June 5, 2010
Saturday, April 17, 2010
Business Analytics Implementation Strategy - Part I
I had met several senior executives last month to help them create strategy for implementing business analytics framework. I would like to address some of frequently asked questions by them in this blog.
Q: How do I embark on Business Analytics Journey?
For companies just embarking on the analytical journey, a specific business problem may be a good initial target. Perhaps customers are complaining about service or quality, or performance benchmarks show that a business process is wasting resources, or a competitor has raised the bar and you need analytics to determine and execute a response.
For any analytics initiative to be successful, there are 3 pre-requisities
1. Definition of Problem
2. Availability of Good Quality data
3. Business Domain
There are several techniques available to address each business problem. Without having specific problem in mind, it is very difficult to determine which technique to apply on data. Quite often, organizations share their data with business analytics vendors and expect vendors to suggest suitable business analytics applications on their data. This approach is very time consuming and doesn't yield expected results as there is no problem definition.
Problem definition can be as simple as
1. Share of wallet is very low with existing customers
2. Frequent stock outs at stores or excess inventory in plant
3. Transportation cost is very high
Each of the above problems can be addressed using different analytics techniques.
To increase share of wallet, you need to do segmentation & use Cross Sell/Up Sell predictive techniques.
To prevent stock outs or excess inventory, you need to use different forecasting techniques to accurately forecast demand.
To optimize transportation cost, you need to use different optimization algorithms.
Q: I am still in process of implementing data warehouse. Can I implement Business Analytics framework without having data warehouse in place?
Yes. We can implement business analytics framework without having data warehouse in place. You need good quality data to perform any analytics. Richness of data is also very important to use any analytics techniques effectively.
You need to have single view of customer in place to use Cross Sell/Up Sell predictive techniques effectively. If you have duplicate customer information then you may end up sending two different offers to same customer.You need to have customer demographic information such as birth date and occupation filled up properly in your data to use segmentation techniques effectively. You need minimum 36 data points to use forecasting techniques effectively.
I shall address the following questions in my next blog
1. How do I uncover Analytics Problem? I do not have analytics expertise in house.
2. Should I outsource Analytics work or should I build that capability inhouse?
3. How do I go about setting up Analytics CoE?
I had received execellent response for my earlier blog "Business Intelligence Vs Business Analytics". Thanks all for your encouraging commments. Do let me know if you want me to address any questions/doubts that you may have about business analytics.
Q: How do I embark on Business Analytics Journey?
For companies just embarking on the analytical journey, a specific business problem may be a good initial target. Perhaps customers are complaining about service or quality, or performance benchmarks show that a business process is wasting resources, or a competitor has raised the bar and you need analytics to determine and execute a response.
For any analytics initiative to be successful, there are 3 pre-requisities
1. Definition of Problem
2. Availability of Good Quality data
3. Business Domain
There are several techniques available to address each business problem. Without having specific problem in mind, it is very difficult to determine which technique to apply on data. Quite often, organizations share their data with business analytics vendors and expect vendors to suggest suitable business analytics applications on their data. This approach is very time consuming and doesn't yield expected results as there is no problem definition.
Problem definition can be as simple as
1. Share of wallet is very low with existing customers
2. Frequent stock outs at stores or excess inventory in plant
3. Transportation cost is very high
Each of the above problems can be addressed using different analytics techniques.
To increase share of wallet, you need to do segmentation & use Cross Sell/Up Sell predictive techniques.
To prevent stock outs or excess inventory, you need to use different forecasting techniques to accurately forecast demand.
To optimize transportation cost, you need to use different optimization algorithms.
Q: I am still in process of implementing data warehouse. Can I implement Business Analytics framework without having data warehouse in place?
Yes. We can implement business analytics framework without having data warehouse in place. You need good quality data to perform any analytics. Richness of data is also very important to use any analytics techniques effectively.
You need to have single view of customer in place to use Cross Sell/Up Sell predictive techniques effectively. If you have duplicate customer information then you may end up sending two different offers to same customer.You need to have customer demographic information such as birth date and occupation filled up properly in your data to use segmentation techniques effectively. You need minimum 36 data points to use forecasting techniques effectively.
I shall address the following questions in my next blog
1. How do I uncover Analytics Problem? I do not have analytics expertise in house.
2. Should I outsource Analytics work or should I build that capability inhouse?
3. How do I go about setting up Analytics CoE?
I had received execellent response for my earlier blog "Business Intelligence Vs Business Analytics". Thanks all for your encouraging commments. Do let me know if you want me to address any questions/doubts that you may have about business analytics.
Wednesday, March 10, 2010
Loyalty Programs: Derive more value from loyal customers
Today, I have become a member of loyalty program of a big retailer. I was very happy to be member of loyalty program as they were offering instant discount of 10% on my total bill. This is my 8th such membership. I am already a member of Kingfisher, Jet Airways, Cross word, Lifestyle, Shoppers Stop, Pantaloon and Park Avenue loyalty programs.
Today, there is a wide array of reward programs in virtually every industry segment. It has become defacto standard in Airlines and Retail industries.As membership in such programs continues to increase, many firms are left wondering whether their programs buy loyalty and increase customer value, or simply add costs without securing repeat business.Lot of organizations have started offering this programs as their competition is also offering such programs.They do not know whether customer loyalty/ reward programs actually influence consumers to change their behaviors, and if so,which factors of a program have the greatest influence.
I am a loyal customer Life Sytle(Retail Chain in India) for past few years, and I am also a member of their loyalty program. I haven't seen any special treatment given to me as their loyal customer in past few years. You are treated like any other customer in the store. Probably, they are offering this program because their competition is also offering similar programs. I like to buy from Life Style because of variety of brands they keep in their stores. One of the striking difference between Life Style loyalty program and other programs is that there is no loyalty program tier like Gold, Platinum, Silver etc. I think program tiers can be powerful incentives and a good way to reward your best customers with the best rewards.
I am also a member of Pantaloon loyalty program in India. They do have various loyalty program tiers. They offer discount based on program tiers. I buy things from Pantaloon just because it is not available in a nearby retail store.They are offering discount on things which I have any bought from Pantaloon.Loyalty programs like this turns loyal customers into price sensitive customers,who are then more likely to defect for a lower priced offer.
Loyalty and reward programs are typically designed to achieve four objectives: increase customer spending, improve retention, maintain competitive position and capture new customer data. But do such programs actually achieve those aims? There’s no doubt that today’s programs yield useful customer data, but what about the other objectives?
In order to achieve other objectives of loyalty programs, you need to analyze transactional data of loyalty members and tweak programs so that you can achieve maximum results.
Typically, BI query and reporting system will help you answer the following questions.
How do people behave in a loyalty program over a long period of time? Do things change as they move through tiers? Does their spending accelerate or decelerate? How do these trends align with customer demographics?
You need business analytics system to address the following questions
"Which segment of customer is most profitable?"
"What products can you cross sell and up sell to this segment?"
"How do you retain most profitable customers and let go non profitable customers?"
"How do I efficiently attract most profitable customer to become member of loyalty program?"
The answers to above questions will help you build loyalty of your customers around brand and the buying experience they have with your organization. This will in turn help you increase revenue from loyal customers.
Today, there is a wide array of reward programs in virtually every industry segment. It has become defacto standard in Airlines and Retail industries.As membership in such programs continues to increase, many firms are left wondering whether their programs buy loyalty and increase customer value, or simply add costs without securing repeat business.Lot of organizations have started offering this programs as their competition is also offering such programs.They do not know whether customer loyalty/ reward programs actually influence consumers to change their behaviors, and if so,which factors of a program have the greatest influence.
I am a loyal customer Life Sytle(Retail Chain in India) for past few years, and I am also a member of their loyalty program. I haven't seen any special treatment given to me as their loyal customer in past few years. You are treated like any other customer in the store. Probably, they are offering this program because their competition is also offering similar programs. I like to buy from Life Style because of variety of brands they keep in their stores. One of the striking difference between Life Style loyalty program and other programs is that there is no loyalty program tier like Gold, Platinum, Silver etc. I think program tiers can be powerful incentives and a good way to reward your best customers with the best rewards.
I am also a member of Pantaloon loyalty program in India. They do have various loyalty program tiers. They offer discount based on program tiers. I buy things from Pantaloon just because it is not available in a nearby retail store.They are offering discount on things which I have any bought from Pantaloon.Loyalty programs like this turns loyal customers into price sensitive customers,who are then more likely to defect for a lower priced offer.
Loyalty and reward programs are typically designed to achieve four objectives: increase customer spending, improve retention, maintain competitive position and capture new customer data. But do such programs actually achieve those aims? There’s no doubt that today’s programs yield useful customer data, but what about the other objectives?
In order to achieve other objectives of loyalty programs, you need to analyze transactional data of loyalty members and tweak programs so that you can achieve maximum results.
Typically, BI query and reporting system will help you answer the following questions.
How do people behave in a loyalty program over a long period of time? Do things change as they move through tiers? Does their spending accelerate or decelerate? How do these trends align with customer demographics?
You need business analytics system to address the following questions
"Which segment of customer is most profitable?"
"What products can you cross sell and up sell to this segment?"
"How do you retain most profitable customers and let go non profitable customers?"
"How do I efficiently attract most profitable customer to become member of loyalty program?"
The answers to above questions will help you build loyalty of your customers around brand and the buying experience they have with your organization. This will in turn help you increase revenue from loyal customers.
Monday, March 1, 2010
Business Intelligence Vs Business Analytics
Last week I was presenting business analytics framework to a large audience at one of the partner organizations. There were a lot of questions around business analytics and business intelligence reporting. I think there is lot of confusion between business intelligence & business analytics. I would like to address some basic questions about business analytics in this blog.
What is difference between Business Intelligence(BI) & Business Analytics(BA)?
Business Intelligence word was first coined decades ago. Business intelligence converts data into information. It includes query & reporting, OLAP, interactive dashboards and alerts. It's about analysis on past events, and more reactive in nature. It helps you address the following questions
1. What happened?
2. How many, how often, where?
3. Where exactly is the problem?
4. What actions are needed?
This is a first step towards creating intelligent enterprise.Today, there are many big organizations who are struggling to establish enterprise wide business intelligence reporting platform. It's not enough to compete using BI in today's economic scenario. You need much more than BI to create differentiation against your competitors in today's market place.
Business Analytics converts information into knowledge.It's about predicting future using past data and current events. It's more proactive in nature. It helps you address the following questions
1. Why is this happening?
2. What if these trends continues?
3. What will happen next?
4. What's the best that can happen?
Business analytics can directly impact top & bottom lines. It helps you to
1. Identify segment of people who are more likely to buy your product
2. Identify the best offer among the list of potential offers
3. Identify potential customers who can buy more products and services from you
4. Retain most profitable customers
5. Optimize resources based on various contraints
None of the above is possible using business intelligence tools. All of the above requires usage of statistical algorithms and processes.
BI will give you information like number of stock outs in a store where as BA will give you knowledge like optimal quantity of stock that you need to keep in your store to prevent stock out situations and minimize inventory cost. BI will give information like amount of withdrawals and cash out instances of a particular ATM where as BA will give you knowledge like optimal amount of cash that you need to keep in your ATM based on location, and withdrawal patterns so that you prevent cash out situations and minimize cost. There are several such examples available.
Do you need a Data warehouse to implement Business Analytics framework?
No. It's not necessary to source data from data warehouse for business analytics. You can apply business analytics techniques on data which is directly extracted from source system. You do not have to wait till your data warehouse is implemented as it usually takes anywhere from 12 - 24 months. One of pre-requistite for business analytics is availability of good quality data. It's doesn't matter where it comes from.
How much data do you need to perform Business Analytics?
It's depends on type of analytics that you want to perform. Typically, business analytics techniques requires data from last 3 to 36 months. As mentioned earlier, you need to have good quality data to derive effective results out of business analytics techniques.
According to me business intelligence is a subset of business analytics framework. You must have strategy in place to implement business analytics framework to compete in today's economics conditions. Business Intelligence is just not enough. Be aware of vendors who supply query & reporting tools in name of "Analytics".
You can find more info about business analytics @
http://www.sas.com/businessanalytics/
What is difference between Business Intelligence(BI) & Business Analytics(BA)?
Business Intelligence word was first coined decades ago. Business intelligence converts data into information. It includes query & reporting, OLAP, interactive dashboards and alerts. It's about analysis on past events, and more reactive in nature. It helps you address the following questions
1. What happened?
2. How many, how often, where?
3. Where exactly is the problem?
4. What actions are needed?
This is a first step towards creating intelligent enterprise.Today, there are many big organizations who are struggling to establish enterprise wide business intelligence reporting platform. It's not enough to compete using BI in today's economic scenario. You need much more than BI to create differentiation against your competitors in today's market place.
Business Analytics converts information into knowledge.It's about predicting future using past data and current events. It's more proactive in nature. It helps you address the following questions
1. Why is this happening?
2. What if these trends continues?
3. What will happen next?
4. What's the best that can happen?
Business analytics can directly impact top & bottom lines. It helps you to
1. Identify segment of people who are more likely to buy your product
2. Identify the best offer among the list of potential offers
3. Identify potential customers who can buy more products and services from you
4. Retain most profitable customers
5. Optimize resources based on various contraints
None of the above is possible using business intelligence tools. All of the above requires usage of statistical algorithms and processes.
BI will give you information like number of stock outs in a store where as BA will give you knowledge like optimal quantity of stock that you need to keep in your store to prevent stock out situations and minimize inventory cost. BI will give information like amount of withdrawals and cash out instances of a particular ATM where as BA will give you knowledge like optimal amount of cash that you need to keep in your ATM based on location, and withdrawal patterns so that you prevent cash out situations and minimize cost. There are several such examples available.
Do you need a Data warehouse to implement Business Analytics framework?
No. It's not necessary to source data from data warehouse for business analytics. You can apply business analytics techniques on data which is directly extracted from source system. You do not have to wait till your data warehouse is implemented as it usually takes anywhere from 12 - 24 months. One of pre-requistite for business analytics is availability of good quality data. It's doesn't matter where it comes from.
How much data do you need to perform Business Analytics?
It's depends on type of analytics that you want to perform. Typically, business analytics techniques requires data from last 3 to 36 months. As mentioned earlier, you need to have good quality data to derive effective results out of business analytics techniques.
According to me business intelligence is a subset of business analytics framework. You must have strategy in place to implement business analytics framework to compete in today's economics conditions. Business Intelligence is just not enough. Be aware of vendors who supply query & reporting tools in name of "Analytics".
You can find more info about business analytics @
http://www.sas.com/businessanalytics/
Tuesday, February 23, 2010
Sentiment Analytics: Next Wave in BI
Business spends huge sums shaping brand image and promoting brand awareness. To gauge the effectiveness of particular campaigns, brand managers will study transactions, for instance sales made in response to direct mail or using coupons, web-page visits and ad click-through, etc. But study of past transactions is of limited use in understanding potential buyers who are not responding to market messaging, in understanding competitive positioning and in picking up on nascent trends. Surveys and social-media mining, especially for attitudinal indicators, can fill the gap.
Recently, I was talking to the marketing executive of a software firm. He said "We are spending huge amount of money and efforts to promote brand awareness. But we can't measure effiectiveness of the same. We have to heavily rely on surveys. By the time we receive survey results, it's too late to take any corrective action." I am sure there are lot of companies in the market who have similar pain.
There are several tools available in market today which can help you measure sentiment of people about your services, product & brand.What do customers, reviewers, the business community – thought leaders and the public – think about your company and your company's products and services – and about your competitors? What can you learn that will help you improve design and quality, positioning, and messaging and also respond quickly to complaints?
Recently, we did a PoV for a bank in India. We have found out that people are not very happy with their branch banking services. Things that they didn't like about bank were long queues & inadequet parking space around bank's branch. When we further drilled down to investigate, we found out that long queues were primarily due to very high number of customers in that area compared to number of branches. The bank took corrective action immediately. They have shifted branch to a location where in there is ample parking space, and opened one more branch.
One of the company is using sentiment analytics to determine which executives have the highest correlation to positively moving the stock price when they deliver positive news. They found that certain executives had a positive influence on the markets, while others actually had a negative influence because of the tone of their delivery. This is very interesting. Stock market is often driven by sentiments of people. I have noticed one thing since Obama took over as president of America. Whenever Obama delivers a public speech, there is a negative sentiment and decline in indian stock exchange index(SENSEX).
One of the bank is using sentiment analytics to determine whether their customers are happy with their services and products or not. If a customer is happy with bank's service then they are passing that lead to marketing dept so that they can cross sell/up sell more products.
One of the consumer electronics company is using sentiment manager to analyze feedback about their newly launched product on review sites such as Amazon & CNET. They use this feedback as input to their product development lifecycle.
There are several such examples of sentiment analytics. I think this is going to be next wave in business intelligence space.Sentiment analytics can help customers to make more sense out of their Business Intelligence reports and KPIs. If your sales is going down in one region then is it because
1. Your sales team is not efficient or
2. Your supply chain is weak which results into frequent stock outs at dealer place/store or
3. Your brand is percieved as expensive or low quality goods or bad customer service.
Recently, I was talking to the marketing executive of a software firm. He said "We are spending huge amount of money and efforts to promote brand awareness. But we can't measure effiectiveness of the same. We have to heavily rely on surveys. By the time we receive survey results, it's too late to take any corrective action." I am sure there are lot of companies in the market who have similar pain.
There are several tools available in market today which can help you measure sentiment of people about your services, product & brand.What do customers, reviewers, the business community – thought leaders and the public – think about your company and your company's products and services – and about your competitors? What can you learn that will help you improve design and quality, positioning, and messaging and also respond quickly to complaints?
Recently, we did a PoV for a bank in India. We have found out that people are not very happy with their branch banking services. Things that they didn't like about bank were long queues & inadequet parking space around bank's branch. When we further drilled down to investigate, we found out that long queues were primarily due to very high number of customers in that area compared to number of branches. The bank took corrective action immediately. They have shifted branch to a location where in there is ample parking space, and opened one more branch.
One of the company is using sentiment analytics to determine which executives have the highest correlation to positively moving the stock price when they deliver positive news. They found that certain executives had a positive influence on the markets, while others actually had a negative influence because of the tone of their delivery. This is very interesting. Stock market is often driven by sentiments of people. I have noticed one thing since Obama took over as president of America. Whenever Obama delivers a public speech, there is a negative sentiment and decline in indian stock exchange index(SENSEX).
One of the bank is using sentiment analytics to determine whether their customers are happy with their services and products or not. If a customer is happy with bank's service then they are passing that lead to marketing dept so that they can cross sell/up sell more products.
One of the consumer electronics company is using sentiment manager to analyze feedback about their newly launched product on review sites such as Amazon & CNET. They use this feedback as input to their product development lifecycle.
There are several such examples of sentiment analytics. I think this is going to be next wave in business intelligence space.Sentiment analytics can help customers to make more sense out of their Business Intelligence reports and KPIs. If your sales is going down in one region then is it because
1. Your sales team is not efficient or
2. Your supply chain is weak which results into frequent stock outs at dealer place/store or
3. Your brand is percieved as expensive or low quality goods or bad customer service.
Monday, February 1, 2010
Successful BI Strategy - Part II
A BI initiative is of no use if it is not driven by the objectives of the enterprise. Implementing a BI solution should help an enterprise achieve the objective of advancing business by making the best use of information.
Dos and Don'ts for successful BI/DWH project implementation:
Do not start with a big bang implementation approach. Iterative implementation approach works well with BI project. Identify a business objective and deliver it via BI/DWH within first two-three months. Longer you take to deliver your first output from BI/DWH, higher is the possibility of failure. It is very important to deliver first output from BI/DWH on time with good quality. This will also help in selling BI/DWH vision to business teams. The shorter implementation cycles would be quite beneficial for the end users as well in terms of cost and time as they would have a much better feel of the end product, they would be able to modify the scope based on what is implemented after each cycle.
Do not try to roll out BI/DWH to many departments/groups at a time in first phase. If possible choose either Sales or Finance department for first phase as these areas are more closer to heart of CEO/CFO of the organization. It is easier to gain acceptability of an initiative if it has C-level executives acceptability & support.
Do not over burden end users with lot of trainings initially. If end users have to go through multiple days of trainings to use new BI/DWH system then there is a high probability that they will not use the system. Always look at maturity of a user group before delivering reports to them. If a user group has been using excel based static reports for past few years then give them reports which has drill down and parameter selection criteria. If someone has been using parameters based reports then give them OLAP based reports which will allow them to slice & dice the data on the fly. If someone has been using OLAP based reports then give them access to adhoc reporting tool.These will help in reducing training efforts that are required to use new BI/DWH system.Also, it makes transition to the new system easier and smooth. Lot of time static report users are given access to OLAP cubes which requires huge training efforts and time.Also, it requires steep learning curve, and it often demotivates them from using new BI/DWH system. Do not drastically change the way they are consuming information now. The change has to be gradual.
Dos and Don'ts for successful BI/DWH project implementation:
Do not start with a big bang implementation approach. Iterative implementation approach works well with BI project. Identify a business objective and deliver it via BI/DWH within first two-three months. Longer you take to deliver your first output from BI/DWH, higher is the possibility of failure. It is very important to deliver first output from BI/DWH on time with good quality. This will also help in selling BI/DWH vision to business teams. The shorter implementation cycles would be quite beneficial for the end users as well in terms of cost and time as they would have a much better feel of the end product, they would be able to modify the scope based on what is implemented after each cycle.
Do not try to roll out BI/DWH to many departments/groups at a time in first phase. If possible choose either Sales or Finance department for first phase as these areas are more closer to heart of CEO/CFO of the organization. It is easier to gain acceptability of an initiative if it has C-level executives acceptability & support.
Do not over burden end users with lot of trainings initially. If end users have to go through multiple days of trainings to use new BI/DWH system then there is a high probability that they will not use the system. Always look at maturity of a user group before delivering reports to them. If a user group has been using excel based static reports for past few years then give them reports which has drill down and parameter selection criteria. If someone has been using parameters based reports then give them OLAP based reports which will allow them to slice & dice the data on the fly. If someone has been using OLAP based reports then give them access to adhoc reporting tool.These will help in reducing training efforts that are required to use new BI/DWH system.Also, it makes transition to the new system easier and smooth. Lot of time static report users are given access to OLAP cubes which requires huge training efforts and time.Also, it requires steep learning curve, and it often demotivates them from using new BI/DWH system. Do not drastically change the way they are consuming information now. The change has to be gradual.
Saturday, December 26, 2009
Successful BI Strategy - Part I
We often run into situations where major companies ask us to help develop a BI strategy. When we ask companies about the objective of implementing BI solution, we hear the following statements quite often
Lot of time, organizations also get into functional requirements such as the following during BI product evaluation cycle
As per Gatrner Report "Fatal Flaws in BI Implementation", it is very important to get buy in & active participation from business teams for a BI project to be successful. This requires a clear linkage between business strategies, the core business processes via which the strategies are executed, and BI-driven business improvement opportunities, which is the basis for a BI business case that is compelling to the business stakeholders.
Some examples of compelling BI system objectives can be as below,
When you develop a BI strategy, do not look at point solutions like reporting, data integration etc..It's always recommended to look at a business analytics framework which can help you improve your business processes and achieve your business goals. The framework will in turn comprises of set of solutions which can help you address your business problems. Point solutions like reporting, data integration etc will help you gain short term benefits but it will not help you gain long term benefits & business support.
- “…produce enhanced organizational capabilities to manage data and information as organizational assets.”
- “…provide a single version of the truth.”
- “…enable consistent and reliable access to accurate corporate-wide data.”
- “…provide more sophisticated reporting and analysis, faster turnaround, improved accessibility and enhanced quality.”
- “…a single touch point where detailed financial transaction information can be filtered on user-entered selection criteria, viewed online, downloaded in standard file formats and used to generate real time reports.”
Lot of time, organizations also get into functional requirements such as the following during BI product evaluation cycle
- The system shall provide the ability to drill down, drill across, and slice-and-dice.
- The system shall provide the ability to specify organizational hierarchies and display performance scorecards for each organizational unit.
- The system shall enable role-based access to information.
- The system shall provide capabilities to route alerts to business users according to user-defined parameters.
- The system shall enable integration of data from multiple disparate sources.
As per Gatrner Report "Fatal Flaws in BI Implementation", it is very important to get buy in & active participation from business teams for a BI project to be successful. This requires a clear linkage between business strategies, the core business processes via which the strategies are executed, and BI-driven business improvement opportunities, which is the basis for a BI business case that is compelling to the business stakeholders.
Some examples of compelling BI system objectives can be as below,
- " BI system will help reduce transportation cost by 5%".
- "BI system will help reduce cash out situations at ATMs to less than 3".
- "BI System will help reduce idle cash in ATMs by 40%"
- "BI System will help increase private label sell by 5% in 80% of retail outlets".
- "BI System will help reduce stock out situations to less than 2 per outlet for premium or fast moving items".
- "BI System will help increase share of wallet by 10%".
When you develop a BI strategy, do not look at point solutions like reporting, data integration etc..It's always recommended to look at a business analytics framework which can help you improve your business processes and achieve your business goals. The framework will in turn comprises of set of solutions which can help you address your business problems. Point solutions like reporting, data integration etc will help you gain short term benefits but it will not help you gain long term benefits & business support.
Sunday, December 20, 2009
Fatal Flaws in Business Intelligence Implementations
Lot of organizations, assumes that business intelligence(BI) projects are like any other project, are often surprised when their BI project spins out of control. The requirements appear to be a “moving target;” the schedule keeps slipping; the source data is much dirtier than expected and is impacting the ETL team; the staff does not have the necessary skills and is not properly trained; communication between staff members takes too long; traditional roles and responsibilities, and how they are assigned, seem to result in too much rework; the traditional methodology does not seem to work; and so on.
BI Projects are often political in nature as lot of people do not like when their performance is being tracked by their management. This requires culture change & creating awareness about benefits of BI within end user community. BI Project should be seen as business enabler rather than a performance tracking tool. They should use BI system to meet or exceed their KPIs.
I have been thinking about writing on this topic for a long time but then I came across a nice research paper on this topic from Gartner. I have shared below the details of Gartner report as is. I have personally experienced and seen some of the flaws mentioned below in lot of BI projects very recently.
Most failed business intelligence (BI) efforts suffer from one or more of nine fatal flaws, generally revolving around people and processes rather than technology, according to Gartner, Inc.
Gartner said the failure to achieve strategic results usually stems from one or more of nine common mistakes:
Flaw No. 1: Believing that “If you build it, they will come”
Often the IT organisation sponsors, funds and leads its BI initiatives from a technical, data-centric perspective. The danger with this approach is that its value is not obvious to the business, and so all the hard work does not result in massive adoption by business users — with the worst case being that more staff are involved in building a data warehouse than use it regularly.
Gartner recommends that the project team include significant representation from the business side. In addition, organisations should establish a BI competency centre (BICC) to drive adoption of BI in the business, as well as to gather the business, technology and communication skills required for successful BI initiatives.
Flaw No. 2: Managers “dancing with the numbers
Many companies are locked into an “Excel culture” in which users extract data from internal systems, load it to spreadsheets and perform their own calculations without sharing them companywide. The result of these multiple, competing frames of reference is confusion and even risk from unmanaged and unsecured data held locally by individuals on their PCs.
BI project instigators should seek business sponsors who believe in a transparent, fact-based approach to management and have the strength to cut through political barriers and change culture.
Flaw No. 3: “Data quality problem? What data quality problem”
Data quality issues are almost ubiquitous and the impact on BI is significant — people won’t use BI applications that are founded on irrelevant, incomplete or questionable data.
To avoid this, firms should establish a process or set of automated controls to identify data quality issues in incoming data and block low-quality data from entering the data warehouse or BI platform.
Flaw No. 4: “Evaluate other BI platforms? Why bother”
“One-stop shopping” or buying a BI platform from the standard corporate resource application vendor doesn’t necessarily lower the total cost of ownership or deliver the best fit for an organisation’s needs.
BI platforms are not commodities and all do not yet deliver all functions to the same level, so organisations should evaluate competitive offerings, rather than blindly taking the path of least resistance.
Integration between the application vendor’s ERP/data warehouse and BI offerings is not a compelling reason for ignoring alternatives, especially as many third-party BI platforms are as well integrated.
Flaw No. 5: “It’s perfect as it is. Don’t ever change “
Many organisations treat BI as a series of discrete (often departmental) projects, focused on delivering a fixed set of requirements. However, BI is a moving target — during the first year of any BI implementation, users typically request changes to suit their needs better or to improve underlying business processes. These changes can affect 35 per cent to 50 per cent of the application’s functions.
Organisations should therefore define a review process that manages obsolescence and replacement within the BI portfolio.
Flaw No. 6: “Let’s just outsource the whole darn BI thing”
Managers often try to fix struggling BI efforts by hiring an outsourcer that they expect will do a better job at a lower cost. Focusing too much on costs and development time often results in inflexible, poorly architected systems.
Organisations should outsource only what is not a core competency or business and rely on outsourcing only temporarily while they build skills within their own IT organisation.
Flaw No. 7: “Just give me a dashboard. Now”
Many companies press their IT organisations to buy or build dashboards quickly and with a small budget. Managers don’t want to fund expensive BI tools or information management initiatives that they perceive as lengthy and risky. Many of the dashboards delivered are of very little value because they are silo-specific and not founded on a connection to corporate objectives.
Gartner recommends that IT organisations make reports as pictorial as possible — for example, by including charting and visualisation — to forestall demands for dashboards, while including dashboarding and more-complex visualisation tools in the BI adoption strategy.
Flaw No. 8: “X + Y = Z, doesn’t it”
A BI initiative aims to create a “single version of the truth” but many organisations haven’t even agreed on the definition of fundamentals, such as “revenue” Achieving one version of the truth requires cross-departmental agreement on how business entities (customers, products, key performance indicators, metrics and so on) are defined.
Many organisations end up creating siloed BI implementations that perpetuate the disparate definitions of their current systems. IT organisations should start with their current master data definitions and performance metrics to ensure that BI initiatives have some consistency with existing vocabulary, and publicise these “standards”.
Flaw No. 9: “BI strategy? No thanks, we’ll just follow our noses”
The final and biggest flaw is the lack of a documented BI strategy, or the use of a poorly developed or implemented one. Gartner recommends creating a team tasked with writing or revising a BI strategy document, with members drawn from the IT organisation and the business, under the auspices of a BICC or similar entity.
“Simple departmental BI projects that pay an immediate return on investment can mean narrow projects that don’t adapt to changing requirements and that hinder the creation of companywide BI strategies,” said James Richardson, research director at Gartner.
Link to Gartner report:
http://www.gartner.com/it/page.jsp?id=774912
BI Projects are often political in nature as lot of people do not like when their performance is being tracked by their management. This requires culture change & creating awareness about benefits of BI within end user community. BI Project should be seen as business enabler rather than a performance tracking tool. They should use BI system to meet or exceed their KPIs.
I have been thinking about writing on this topic for a long time but then I came across a nice research paper on this topic from Gartner. I have shared below the details of Gartner report as is. I have personally experienced and seen some of the flaws mentioned below in lot of BI projects very recently.
Most failed business intelligence (BI) efforts suffer from one or more of nine fatal flaws, generally revolving around people and processes rather than technology, according to Gartner, Inc.
Gartner said the failure to achieve strategic results usually stems from one or more of nine common mistakes:
Flaw No. 1: Believing that “If you build it, they will come”
Often the IT organisation sponsors, funds and leads its BI initiatives from a technical, data-centric perspective. The danger with this approach is that its value is not obvious to the business, and so all the hard work does not result in massive adoption by business users — with the worst case being that more staff are involved in building a data warehouse than use it regularly.
Gartner recommends that the project team include significant representation from the business side. In addition, organisations should establish a BI competency centre (BICC) to drive adoption of BI in the business, as well as to gather the business, technology and communication skills required for successful BI initiatives.
Flaw No. 2: Managers “dancing with the numbers
Many companies are locked into an “Excel culture” in which users extract data from internal systems, load it to spreadsheets and perform their own calculations without sharing them companywide. The result of these multiple, competing frames of reference is confusion and even risk from unmanaged and unsecured data held locally by individuals on their PCs.
BI project instigators should seek business sponsors who believe in a transparent, fact-based approach to management and have the strength to cut through political barriers and change culture.
Flaw No. 3: “Data quality problem? What data quality problem”
Data quality issues are almost ubiquitous and the impact on BI is significant — people won’t use BI applications that are founded on irrelevant, incomplete or questionable data.
To avoid this, firms should establish a process or set of automated controls to identify data quality issues in incoming data and block low-quality data from entering the data warehouse or BI platform.
Flaw No. 4: “Evaluate other BI platforms? Why bother”
“One-stop shopping” or buying a BI platform from the standard corporate resource application vendor doesn’t necessarily lower the total cost of ownership or deliver the best fit for an organisation’s needs.
BI platforms are not commodities and all do not yet deliver all functions to the same level, so organisations should evaluate competitive offerings, rather than blindly taking the path of least resistance.
Integration between the application vendor’s ERP/data warehouse and BI offerings is not a compelling reason for ignoring alternatives, especially as many third-party BI platforms are as well integrated.
Flaw No. 5: “It’s perfect as it is. Don’t ever change “
Many organisations treat BI as a series of discrete (often departmental) projects, focused on delivering a fixed set of requirements. However, BI is a moving target — during the first year of any BI implementation, users typically request changes to suit their needs better or to improve underlying business processes. These changes can affect 35 per cent to 50 per cent of the application’s functions.
Organisations should therefore define a review process that manages obsolescence and replacement within the BI portfolio.
Flaw No. 6: “Let’s just outsource the whole darn BI thing”
Managers often try to fix struggling BI efforts by hiring an outsourcer that they expect will do a better job at a lower cost. Focusing too much on costs and development time often results in inflexible, poorly architected systems.
Organisations should outsource only what is not a core competency or business and rely on outsourcing only temporarily while they build skills within their own IT organisation.
Flaw No. 7: “Just give me a dashboard. Now”
Many companies press their IT organisations to buy or build dashboards quickly and with a small budget. Managers don’t want to fund expensive BI tools or information management initiatives that they perceive as lengthy and risky. Many of the dashboards delivered are of very little value because they are silo-specific and not founded on a connection to corporate objectives.
Gartner recommends that IT organisations make reports as pictorial as possible — for example, by including charting and visualisation — to forestall demands for dashboards, while including dashboarding and more-complex visualisation tools in the BI adoption strategy.
Flaw No. 8: “X + Y = Z, doesn’t it”
A BI initiative aims to create a “single version of the truth” but many organisations haven’t even agreed on the definition of fundamentals, such as “revenue” Achieving one version of the truth requires cross-departmental agreement on how business entities (customers, products, key performance indicators, metrics and so on) are defined.
Many organisations end up creating siloed BI implementations that perpetuate the disparate definitions of their current systems. IT organisations should start with their current master data definitions and performance metrics to ensure that BI initiatives have some consistency with existing vocabulary, and publicise these “standards”.
Flaw No. 9: “BI strategy? No thanks, we’ll just follow our noses”
The final and biggest flaw is the lack of a documented BI strategy, or the use of a poorly developed or implemented one. Gartner recommends creating a team tasked with writing or revising a BI strategy document, with members drawn from the IT organisation and the business, under the auspices of a BICC or similar entity.
“Simple departmental BI projects that pay an immediate return on investment can mean narrow projects that don’t adapt to changing requirements and that hinder the creation of companywide BI strategies,” said James Richardson, research director at Gartner.
Link to Gartner report:
http://www.gartner.com/it/page.jsp?id=774912
Friday, December 4, 2009
Intelligent Operational System
I just received an automated "telemarketing" SMS on my cell phone. Big deal and who cares, right? Well it isn't a big deal, and its doubtful anyone cares. But it did bring to mind an interesting reminder about the Intelligent Operational system when designing a customer experience.
The SMS was an automated SMS from Crossword. Those who do not know about Crossword, Crossword is a big book store chain in India. They didn't SMS me to sell anything. Instead, they were sending SMS to help me, which ultimately helps them.
The SMS was from the Crossword "Book Rewards" program. If you are not familiar with the Book Rewards Program, it is Crossword's customer loyalty program, where you scan a card at the point of sale and a percentage of your purchase counts towards an in-store credit or a gift voucher. Crossword mails you a gift voucher for the credit, and the credit can be used in the store.
The SMS was to alert me that my gift voucher, which I had forgotten about, would expire soon. It provided me with the details of my gift voucher, such as how much it was, and when it would expire. The information was provided a month ahead of the expiration date, which would allow me time to get a replacement voucher if I didn't receive the original, or provide me with ample time to schedule a trip or research a purchase. I wasn't thinking about going to Crossword in the near future, but I was considering buying a new book from Oxford Book Store which is located near our office. Now that I've been reminded that I have credit at Crossword, I'll buy it there.
It reminds us that we need to think about the "systems" in which our customers reside. These "systems" include technology, work, and social context components, and the interaction of these components provide opportunities, or limitations which we may not have considered. The automated SMS from Crossword provides a good example of the application of intelligent operational system, since it leveraged the capabilities of automated computer and telephony systems to reach into my "system" and gently provide a socially acceptable message indicating that "we miss your business, so please shop with us soon."
While designing applications, seriously consider the user's system, and the opportunities and limitations provided by the system. By applying these considerations to our designs, we can make applications which will ultimately be more helpful for our customers, as well as easier to use.
The SMS was an automated SMS from Crossword. Those who do not know about Crossword, Crossword is a big book store chain in India. They didn't SMS me to sell anything. Instead, they were sending SMS to help me, which ultimately helps them.
The SMS was from the Crossword "Book Rewards" program. If you are not familiar with the Book Rewards Program, it is Crossword's customer loyalty program, where you scan a card at the point of sale and a percentage of your purchase counts towards an in-store credit or a gift voucher. Crossword mails you a gift voucher for the credit, and the credit can be used in the store.
The SMS was to alert me that my gift voucher, which I had forgotten about, would expire soon. It provided me with the details of my gift voucher, such as how much it was, and when it would expire. The information was provided a month ahead of the expiration date, which would allow me time to get a replacement voucher if I didn't receive the original, or provide me with ample time to schedule a trip or research a purchase. I wasn't thinking about going to Crossword in the near future, but I was considering buying a new book from Oxford Book Store which is located near our office. Now that I've been reminded that I have credit at Crossword, I'll buy it there.
It reminds us that we need to think about the "systems" in which our customers reside. These "systems" include technology, work, and social context components, and the interaction of these components provide opportunities, or limitations which we may not have considered. The automated SMS from Crossword provides a good example of the application of intelligent operational system, since it leveraged the capabilities of automated computer and telephony systems to reach into my "system" and gently provide a socially acceptable message indicating that "we miss your business, so please shop with us soon."
While designing applications, seriously consider the user's system, and the opportunities and limitations provided by the system. By applying these considerations to our designs, we can make applications which will ultimately be more helpful for our customers, as well as easier to use.
Monday, November 30, 2009
Information Overload & BI
In these difficult times we live in, when resources seem scarce, there is still one thing that is widely and abundantly available: information. According to the most recent statistics, the amount of information created annually by businesses and organizations, paper and digital combined, is growing at a rate of more than 65%. The amount of digital information being created in the world and distributed in emails, instant messages, blog posts, new Web pages, digital phone calls, podcasts and so on, will increase 10-fold over the next five years. The one fact that stands out is this: The growth of information is relentless.
There is too much of infornation available in various forms. Is it information Overload? or is it failure of Information filter? There is so much of information out there that one can't browse through every possible bit of information. Business Intelligence systems can play a very important role here. It can act as a information filter. It can provide information which is very critical and must need your attention. In today's world when someone have hundred's of KPIs to monitor, BI system can help to identify only those KPIs which needs immediate attention.One can start his day with BI portal. Typically, one follows the following routine
There is too much of infornation available in various forms. Is it information Overload? or is it failure of Information filter? There is so much of information out there that one can't browse through every possible bit of information. Business Intelligence systems can play a very important role here. It can act as a information filter. It can provide information which is very critical and must need your attention. In today's world when someone have hundred's of KPIs to monitor, BI system can help to identify only those KPIs which needs immediate attention.One can start his day with BI portal. Typically, one follows the following routine
- Check Emails
- Check Calendar(Meeting Schedule)
- Check Important News/stocks
- Check Most critical KPIs
- Prepare To-Do List for a day
- Prepare/View status reports
- Collabrate with collegues using Enterprise messenger
Saturday, November 21, 2009
Analytical MDM Vs Operational MDM
I was having interesting coversation about MDM with one of my customer last week. It's mid sized bank and they are in process of evaluating MDM. When i asked him " How confident are you about quality of your data?" He said "Honestly, I do not know". Then i told him that for MDM one of prerequisite is to have good quality of data. If quality of your data is not good then MDM solution implementation is bound to fail.
I have seen quite a few organizations who would like to embark on MDM initiative without having good data quality system in place. Thanks to huge amount of marketing money spent by some of large IT product vendors. Quite often organizations fall in this trap and end up investing hugh amount of money and efforts.
There are two types of MDM solutions in market. Operational MDM and Analytical MDM.
Operational MDM is used to collect customer information at front desk. This solution is used to standardize the mechanism to capture customer information at various customer touch points in organization. Typically, organizations have 5-20 customer touch points. This solution provides customer information to various operational systems in organization.It ensures that any changes made in customer information at any of customer touch point are transferred to all operational systems. It will work in organizations which are still in process of implementing operational systems and have very few customer touch points. This approach requires discipline, and huge amount of training efforts. Currently most of banks in India have operational systems in place. These operational systems are built using old technology and captures customer information specific to their application.Enhancements to these oprational systems are very time consuming and lead to performance issues. Hence this solution will not be suitable for most of the large and mid sized organizations. It also requires huge amount of training efforts to train front desk staff on this solution.
Analytical MDM is used for historical and predictive analysis. This solution sources the data from transactional systems such as CRM, ERP, CBS, LOS etc...Analytical CRM can be updated once in a day or multiple times in a day. I have seen banks updating it once a day which is more than sufficient to cater to their current business requirements. This solution doesn't require retraining of front desk staff. However, it requires tight integration with transactional systems. Analytical MDM should be SOA enabled. This will enable source system to call web service and check whether new customer is already customer of bank or not. Analytical MDM will also provide information related to class of customer(Preffered, Gold etc) and behaviour based on past transactions. This will help to take decision about loan approval or issuing credit card or giving prefferential service to your customer.
The way Analytical MDM & operational MDM store the data is also different. Analytical MDM stores data in denormalized format so that it can be retrieved easily for analysis whereas Operational MDM stores the data in normalized format so that it can be updated quickly.
Operational MDM stores demograhic details such as age, birthdate, name, address whereas Analytical MDM stores information related to profibility, behaviour score, credit score and propensity to buy product apart from demographic details of a customer.
Both types of MDM solutions require strong data quality engine in backend. This data quality engine should be capable enough to address peculiarities in Indian addresses and names.
I have seen quite a few organizations who would like to embark on MDM initiative without having good data quality system in place. Thanks to huge amount of marketing money spent by some of large IT product vendors. Quite often organizations fall in this trap and end up investing hugh amount of money and efforts.
There are two types of MDM solutions in market. Operational MDM and Analytical MDM.
Operational MDM is used to collect customer information at front desk. This solution is used to standardize the mechanism to capture customer information at various customer touch points in organization. Typically, organizations have 5-20 customer touch points. This solution provides customer information to various operational systems in organization.It ensures that any changes made in customer information at any of customer touch point are transferred to all operational systems. It will work in organizations which are still in process of implementing operational systems and have very few customer touch points. This approach requires discipline, and huge amount of training efforts. Currently most of banks in India have operational systems in place. These operational systems are built using old technology and captures customer information specific to their application.Enhancements to these oprational systems are very time consuming and lead to performance issues. Hence this solution will not be suitable for most of the large and mid sized organizations. It also requires huge amount of training efforts to train front desk staff on this solution.
Analytical MDM is used for historical and predictive analysis. This solution sources the data from transactional systems such as CRM, ERP, CBS, LOS etc...Analytical CRM can be updated once in a day or multiple times in a day. I have seen banks updating it once a day which is more than sufficient to cater to their current business requirements. This solution doesn't require retraining of front desk staff. However, it requires tight integration with transactional systems. Analytical MDM should be SOA enabled. This will enable source system to call web service and check whether new customer is already customer of bank or not. Analytical MDM will also provide information related to class of customer(Preffered, Gold etc) and behaviour based on past transactions. This will help to take decision about loan approval or issuing credit card or giving prefferential service to your customer.
The way Analytical MDM & operational MDM store the data is also different. Analytical MDM stores data in denormalized format so that it can be retrieved easily for analysis whereas Operational MDM stores the data in normalized format so that it can be updated quickly.
Operational MDM stores demograhic details such as age, birthdate, name, address whereas Analytical MDM stores information related to profibility, behaviour score, credit score and propensity to buy product apart from demographic details of a customer.
Both types of MDM solutions require strong data quality engine in backend. This data quality engine should be capable enough to address peculiarities in Indian addresses and names.
Sunday, November 8, 2009
Task Based Intelligence
I was working on "Task Based Intelligence" concept few years ago. The idea was to integrate BI with operational systems. E.g when someone is creating a purchase order in ERP system, he will be able to see scorecard of a supplier without going to a seperate interface or application. The supplier scorecard is embedded into PO application. This will not only prevent PO going to a black listed supplier, but also gives flexibility to users to select supplier based on priority at that point in time. The supplier can be selected based on score which is determined based on the various parameters such as lead time, On time delivery performance, price & quality of supply(Rejection Rate).
The same concept can be applicable to banking industry as well. While granting a loan or credit card to an individual, the bank officer will be able to see application and behaviour score on LOS system. This will help him take more informed decision.
Discount coupons can be printed @ATM machines based on the amount withdrawn from ATM at that point in time. Just imagine a scenario wherein a discount coupon for a digital camera is printed @ATM machine when 10000 Rs is withdrawn from ATM. Competition is increasing day by day in every industry vertical. Margin is going down day by day. I won't be surprised if the bank starts selling cricket match ticket or flight tickets in near future to share the cost of infrastructure and therefore increase profitability of each branch & ATM.
In retail, discount coupons can be printed based on items bought by customer at that point in time. This will not only increase customer satisfaction but also ensure that the customer returns to store for more purchases in near future. Customer loyalty program is in very nascent stage in India. Hence such intelligence embedded into operational system will definitely help retailer to increase revenue per customer.
The same concept can be applicable to banking industry as well. While granting a loan or credit card to an individual, the bank officer will be able to see application and behaviour score on LOS system. This will help him take more informed decision.
Discount coupons can be printed @ATM machines based on the amount withdrawn from ATM at that point in time. Just imagine a scenario wherein a discount coupon for a digital camera is printed @ATM machine when 10000 Rs is withdrawn from ATM. Competition is increasing day by day in every industry vertical. Margin is going down day by day. I won't be surprised if the bank starts selling cricket match ticket or flight tickets in near future to share the cost of infrastructure and therefore increase profitability of each branch & ATM.
In retail, discount coupons can be printed based on items bought by customer at that point in time. This will not only increase customer satisfaction but also ensure that the customer returns to store for more purchases in near future. Customer loyalty program is in very nascent stage in India. Hence such intelligence embedded into operational system will definitely help retailer to increase revenue per customer.
Saturday, October 24, 2009
Green Business Intelligence
Green Business Intelligence is a new buzz word in BI world nowadays. More than one third of Gartner Survey respondents plan on spending more than 15% of their IT dollars on Green IT projects. Most of these projects fall into the "improve energy efficiency" category for short-term, immediate cost savings.
So, with 15% of IT dollars going toward green projects, can BI initiatives be a part of that? Absolutely. Key to the success of green projects is measuring and monitoring. If a project claims to reduce energy usage, that usage must be measured before the project begins and monitored afterwards through the payback period (and potentially beyond). Web based BI systems can help move companies to a paperless environment.
Reducing power consumptions in servers is one way to contribute to green initiatives of a company.BI Systems can help companies to measure and monitor usage of hardware resources(CPU, RAM, Network, Storage Etc..). It can forecast hardware resource requirements based on historical data & events. This will help companies to consolidate hardware resources & there by reducing power consumption.One of pre-requisite for such BI system is to have centralized IT data mart which can collect & store data from various performance & monitoring tools like CA Unicenter or Tivoli or Open View.
Inventory management is perhaps the most important step where BI can improve efficiency. Not having the right product in store will lead to lost sales and unhappy shoppers or excess inventory. Either is bad for the environment. When consumers don’t find what they are looking for, they make additional trips, increasing the carbon footprint of their shopping. Excess inventory leads to waste (especially in the case of perishable products), affecting both the environment and margins. Most progressive retailers have implemented perpetual inventory systems to keep track of what is on the shelf, and advanced replenishment systems to forecast demand and generate orders based on past trends and current factors. While these systems have helped run a more efficient operation, they are not perfect. Business intelligence can help retailers get smart by analysing issues in forecasting, understanding their root causes and preventing future exceptions. Accurate inventory visibility in store is a key input for upstream operations such as manufacturing and distribution, to reduce waste, cost and carbon footprint.
In the near future green reporting is going to be as mandatory as any other financial reporting. When tight controls are in place, people discover and reinvent more creative and efficient ways to save money. There is no better time than now to take action and allocate a piece of the budget toward serious and productive green IT. Environmental issues will shape the information management landscape for decades to come, affecting areas like data management and data governance. It also will have significant impact on areas such as competitive strategy, business intelligence marketing and even a company’s ability to attract and retain people.
So, with 15% of IT dollars going toward green projects, can BI initiatives be a part of that? Absolutely. Key to the success of green projects is measuring and monitoring. If a project claims to reduce energy usage, that usage must be measured before the project begins and monitored afterwards through the payback period (and potentially beyond). Web based BI systems can help move companies to a paperless environment.
Reducing power consumptions in servers is one way to contribute to green initiatives of a company.BI Systems can help companies to measure and monitor usage of hardware resources(CPU, RAM, Network, Storage Etc..). It can forecast hardware resource requirements based on historical data & events. This will help companies to consolidate hardware resources & there by reducing power consumption.One of pre-requisite for such BI system is to have centralized IT data mart which can collect & store data from various performance & monitoring tools like CA Unicenter or Tivoli or Open View.
Inventory management is perhaps the most important step where BI can improve efficiency. Not having the right product in store will lead to lost sales and unhappy shoppers or excess inventory. Either is bad for the environment. When consumers don’t find what they are looking for, they make additional trips, increasing the carbon footprint of their shopping. Excess inventory leads to waste (especially in the case of perishable products), affecting both the environment and margins. Most progressive retailers have implemented perpetual inventory systems to keep track of what is on the shelf, and advanced replenishment systems to forecast demand and generate orders based on past trends and current factors. While these systems have helped run a more efficient operation, they are not perfect. Business intelligence can help retailers get smart by analysing issues in forecasting, understanding their root causes and preventing future exceptions. Accurate inventory visibility in store is a key input for upstream operations such as manufacturing and distribution, to reduce waste, cost and carbon footprint.
In the near future green reporting is going to be as mandatory as any other financial reporting. When tight controls are in place, people discover and reinvent more creative and efficient ways to save money. There is no better time than now to take action and allocate a piece of the budget toward serious and productive green IT. Environmental issues will shape the information management landscape for decades to come, affecting areas like data management and data governance. It also will have significant impact on areas such as competitive strategy, business intelligence marketing and even a company’s ability to attract and retain people.
Sunday, September 13, 2009
Application Data Warehouse
The definition of data warehouse is changing in Indian Market. Earlier people use to build data warehouse to cater to MIS reporting need of an organization nowadays data warehouse is built to support various business applications such as
Today scenario has changed. Very recently, we had worked on two enterprise data warehouse RFPs wherein the end goal of implementing data warehouse was to support various business applications. Prospect had clearly stated objective of data warehouse in RFP. They wanted to built a data warehouse to support the following business applications
In today's economic conditions, it is very critical to build "Analytics" friendly data warehouse. Typically, you require historical data to do analytics. Hence you need to capture data related to analytical variables right from day one when data warehouse is implemented. I have seen lot of organization making a mistake of building MIS reporting data warehouse. There are several disadvantages of this approach.
- Cross Sell/Up Sell
- Retention
- Campaign Management
- Marketing Optimization
- Market Mix Modeling
- Basel II compliance
- Market Risk Analysis
- Op Risk Analysis
- Warranty Analysis
- Supply Chain Optimization etc...
Today scenario has changed. Very recently, we had worked on two enterprise data warehouse RFPs wherein the end goal of implementing data warehouse was to support various business applications. Prospect had clearly stated objective of data warehouse in RFP. They wanted to built a data warehouse to support the following business applications
- Basel II Compliance
- Market Risk
- Credit Scoring
- Cross Sell/Up Sell
- Retention
- Campaign Management
In today's economic conditions, it is very critical to build "Analytics" friendly data warehouse. Typically, you require historical data to do analytics. Hence you need to capture data related to analytical variables right from day one when data warehouse is implemented. I have seen lot of organization making a mistake of building MIS reporting data warehouse. There are several disadvantages of this approach.
- It takes significant amount of time & efforts to build such data warehouse. Your reporting requirements change by the time data warehouse is implemented.
- ROI generated from such data warehouse is not significant enough to justify it's investment.
- If right variables are not captured in the data model then it takes significant amount of time and efforts to incorporate them in data model at later stage. It involves change in data model, ETL & BI Strategies. Lot of time, it is not possible to incorporate such changes due to complexity of data model and ETL routines, and you end up creating a data mart to cater to Analytical requirements. This results in data de-duplication.
Saturday, September 5, 2009
New Products Forecasting
Yesterday Nokia officially launched X6 touch phone at Nokia world. This is a nice touch phone with features comparable to or better than iphone 3GS. It took them almost two years to release a phone which is comparable to or better than iphone. This phone is also very competitively priced (Rs 30,000). It's good to see that competition for Apple Iphone has finally arrived. Apple has been ignoring Indian market for long time now. Apple Iphone launch in India was a big failure due to very high pricing. India is suppose to be one of the largest mobile handset markets in the world. One can't afford to ignore them. I hope Apple learns from their past mistakes and launches Iphone 3GS at very competitive pricing in India.
Nokia comes out with a new product or model every month. Lot of time they launch a model which has overlapping features with existing model in market. I always wondered how they forecast inventory for their new phone.Typically, forecasting is done based on past data and events. There is no such data available for new products. Moreover, there are similar products available in market from the same manufacturer. Each of these products eat into each other's revenue. In high tech companies, typically there are 50-60% of mature or stable products, 35% of new products and 5% of "first-of-its-kind" products. How do you ensure that the new product doesn't impact sales of existing product in market?
Each product follows a particular life cycle. A new product launch should be decided in such a way that it doesn't cannablize revenue of other similar products in market. E,g Nokia X3 is a new music phone. Most probably, it will replace Nokia X5300 express music phone. Launch of Nokia X3 is planned when Nokia X5300 is reaching towards end of its life. If two models are going to co-exists than you need to ensure that messaging & target consumer audience is different for both the products. In case of Nokia, both Nokia N97 and N97 mini are going to co-exist. Both the products are meant for different consumer segment.
There are several new product forecasting techniques available. One of the most common one is a Bass diffusion technique. The bass diffusion technique requires 3 parameters
I am ardent fan of Apple iphone. I personally believe that competition is always good for end consumer. With Nokia X6 launch, Apple will not be complacent. It will accelerate innovation at both the places. Finally end consumers like me will get benefited.
You can find more details about Nokia X6@
http://www.mobilenewshome.com/2009/09/nokia-launches-mobile-cum-music-device.html
Nokia Phone Comparision
http://europe.nokia.com/find-products/phone-comparison
Nokia comes out with a new product or model every month. Lot of time they launch a model which has overlapping features with existing model in market. I always wondered how they forecast inventory for their new phone.Typically, forecasting is done based on past data and events. There is no such data available for new products. Moreover, there are similar products available in market from the same manufacturer. Each of these products eat into each other's revenue. In high tech companies, typically there are 50-60% of mature or stable products, 35% of new products and 5% of "first-of-its-kind" products. How do you ensure that the new product doesn't impact sales of existing product in market?
Each product follows a particular life cycle. A new product launch should be decided in such a way that it doesn't cannablize revenue of other similar products in market. E,g Nokia X3 is a new music phone. Most probably, it will replace Nokia X5300 express music phone. Launch of Nokia X3 is planned when Nokia X5300 is reaching towards end of its life. If two models are going to co-exists than you need to ensure that messaging & target consumer audience is different for both the products. In case of Nokia, both Nokia N97 and N97 mini are going to co-exist. Both the products are meant for different consumer segment.
There are several new product forecasting techniques available. One of the most common one is a Bass diffusion technique. The bass diffusion technique requires 3 parameters
- Lifetime expected sales - the total amount of units sold in its product lifetime. Also known as market potential
- P(Mass media) - influenced by the technical aspects of a product that drives a consumer purchase. Also known as coefficient of innovation.
- Q(Word of Mouth) - Reflects the internal dynamics of the consumer. Also known as coefficient of imitation.
- Find a cluster of like (similar) products. This is used to determine the historical data we can use from like products.
- Perform regression analysis on the cluster
- Use Bass diffusion model to determine the forecast of the new product
- Adjust or reforecast after we have some actual data
I am ardent fan of Apple iphone. I personally believe that competition is always good for end consumer. With Nokia X6 launch, Apple will not be complacent. It will accelerate innovation at both the places. Finally end consumers like me will get benefited.
You can find more details about Nokia X6@
http://www.mobilenewshome.com/2009/09/nokia-launches-mobile-cum-music-device.html
Nokia Phone Comparision
http://europe.nokia.com/find-products/phone-comparison
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