Showing posts with label BI. Show all posts
Showing posts with label BI. Show all posts

Tuesday, November 1, 2011

Is Siri future of BI?

Siri, a voice-activated "personal assistant" on the new iPhone 4S helps you send messages, set reminders, and search for information. You speak to Siri to ask it questions and give it commands, such as small tasks that you'd like it to complete. For example, ask Siri about the weather, and it will respond out loud with a short summary of the day's weather report and on-screen with a snapshot of the five-day forecast.

What will happen if we integrate Siri with BI applications? If we integrate Siri with BI application then conversation between BI end user and Siri will go like this

Siri: What can I help you with?
CEO: What is our sales revenue?
Siri: It's 300 mn till date. There is growth of 10% Y-o-Y.
CEO: Which is our worst performing region?
Siri: North. Sales revenue has declined 20% compared to the same period last year.
CEO: Text scott why is sales revenue declining? Can we meet tomorrow? (Scott - Regional Manager - North)
CEO: Set up sales review meeting with Scott at 9 am tomorrow.
Siri: Ok. I have set up a meeting at 9am.

Siri makes it so easy to consume the information and hence it is a ideal match for BI applications. Currently Apple doesn't allow third party developers to access Siri. When they open access to Siri, it will definitely change the way people use BI applications. Siri has potential to revolutionize BI application world.

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.

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/

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
  • “…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.”
These objectives doesn't excite business executives and managers as it doesn't articulate how business intelligence will be used within specific business processes to improve business performance.As a result, they underfund business intelligence, which limits its business impact. Very recently, we have faced a situation at one of the large organizations where in Business teams refused fund BI project. They didn't see any compelling reason & business case to implement BI solution.

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.
BI functional requirements like those listed above are standard features of commercially available BI tools.While it is important to know what your company needs BI tools to do, BI functional requirements typically say little about the kinds of business information, analytical techniques and decision support that are required or the specific core business processes that the company seeks to improve via business intelligence.

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%".
Each of the above BI objectives are linked to a business process. It clearly tells business team how implementation of BI system can help them achieve their goals. It becomes easier to get buy in from business executives and managers when BI is directly linked to business process improvement.

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

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.