(Please see Dashboard Design: Key Performance Indicators & Metrics By Thomas Gonzalez  BrightPoint Consulting. Our Server)

This article will focus on collecting and defining metrics and key performance indicators for executive and operational dashboards. While the techniques discussed here can be used across many different business intelligence requirements gathering efforts, the focus will be collecting and organizing business data into a format for effective dashboard design.

With the explosion of dashboard tools and technologies in the business intelligence market, many people have different understandings of what a dashboard, metric, and key performance indicator (KPI) consist of. In an effort to create a common vocabulary for the scope of this article, we will define a set of terms that will form the basis of our discussion. While the definitions below might seem onerous and require a second pass to fully understand them, once you have grasped the concepts you will have a powerful set of tools for creating dashboards with effective and meaningful metrics and KPIs.

Metrics and Key Performance Indicators:
Metrics and KPIs are the building blocks of many dashboard visualizations; as they are the most effective means of alerting users as to where they are in relationship to their objectives. The definitions below form the basic building blocks for dashboard information design and they build upon themselves so it is important that you fully understand each definition and the concepts discussed before moving on to the next definition.

 

Metrics: When we use the term metric we are referring to a direct numerical measure that represents a piece of business data in the relationship of one or more dimensions. An example would be: “gross sales by week.” In this case, the measure would be dollars (gross sales) and the dimension would be time (week). For a given measure, you may also want to see the values across different hierarchies within a dimension. For instance, seeing gross sales by day, week, or month would show you the measure dollars (gross sales) by different hierarchies (day, week, and month) within the time dimension. Making the association of a measure with a specific hierarchal level within a dimension refers to the overall grain of the metric.

Looking at a measure across more than one dimension such as gross sales by territory and time is called multi-dimensional analysis. Most dashboards will only leverage multi-dimensional analysis in a limited and static way versus some of the more dynamic “slice-and-dice” tools that exist in the BI market. This is important to note, because if in your requirements gathering process you uncover a significant need for this type of analysis, you may consider supplementing your dashboards with some type of multi-dimensional analysis tool.

 

Key Performance Indicators (KPI): A KPI is simply a metric that is tied to a target. Most often a KPI represents how far a metric is above or below a pre-determined target. KPI’s usually are shown as a ratio of actual to target and are designed to instantly let a business user know if they are on or off their plan without the end user having to consciously focus on the metrics being represented. For instance, we might decide that in order to hit our quarterly sales target we need to be selling $10,000 of widgets per week. The metric would be widget sales per week; the target would be $10,000. If we used a percentage gauge visualization to represent this KPI and we had sold $8,000 in widgets by Wednesday, the user would instantly see that they were at 80% of their goal. When selecting targets for your KPI’s you need to remember that a target will have to exist for every grain you want to view within a metric. Having a dashboard that displays a KPI for gross sales by day, week, and month will require that you have identified targets for each of these associated grains.

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Barriers to Performance Improvement
 

Here's a wish-list that I suspect many of us share in our work lives:

• If only we could measure an increase in mindshare.
• If only we measured what was really important.
• If only we could look at a customer's complete product portfolio and service history.
• If only we knew which improvements would have the strongest effect on revenue growth.
• If only management knew how little time we have to work on "priorities" because we get bogged down answering emails and attending meetings.

Methodical Approach
Organizations begin performance management initiatives at any point - for example, one may begin with defining the corporate strategy and determining KPIs to support it. Others will initiate a business intelligence or data warehousing project. For many executives, a scorecard without business intelligence behind it is their first step. To achieve the best results, however, every phase – reporting, management, and improvement should be done comprehensively. For example, if a business is reporting on its global financials, but doesn't take currency rate fluctuations into consideration, it is not seeing the whole picture. If business units are not aligned around corporate goals, improvement efforts (in the wrong direction) could be harmful. If analytics are applied to improve performance by accurately forecasting demand, but the information isn't shared with Sales, Marketing, and Customer Service as well as Supply – the outcome could be devastating. Regardless of where you begin, here are some of the gaps you might need to fill in. (Please see  Barriers to Performance Improvement By Becca Goren,  SAS. Our Server ).

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The Smart Business Intelligence Framework
 

(Please see  The Smart Business Intelligence Framework By Colin White B-EYE Network. Our Server).

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Putting the Business Back into BI

Although BI means “business intelligence,” it sometimes seems that the technology interests supersede those of the business. If your BI program gives more attention to dashboards, scorecards, OLAP, and data warehouses than to finance, R&D, marketing, operations, and customer support, then you likely need to put the business back into BI.

The sole purpose of business intelligence is to deliver information that makes a difference—substantial, bottom-line business impact that is achieved through increased revenue, reduced expense, and risks avoided. The challenge of BI lies in making the connection between these business goals and the information that is actually delivered. All too often, BI delivers the metrics that are available, obvious, and easy, and misses opportunities to deliver truly high-impact information. (Please see  Putting the Business Back into BI By Dave Wells TDWI.  Our Server.)

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Getting Started with Operations Analytics
 

 (Please see Getting Started with Operations Analytics By Bill Collins and Richard Keith DecisionPath Consulting. Our Server.)

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Business Intelligence - Beyond the Software

While there is no one definition of business intelligence, there appears to be general agreement on what it does: it converts operational data to knowledge, providing meaningful information that facilitates effective decisions aligned with firm strategy. Offering unlimited analytical potential, BI is most successful when implemented with the support of senior management as part of a change initiative, often in the areas of enterprise performance management that employs elements of the balanced scorecard.

Firms employing BI can effectively communicate strategy on a real-time basis firm wide through a combination of dashboards, event-driven reporting and report alerts reflecting specifically selected key performance indices (KPI) aligned with firm or business unit strategy. Strategy-linked performance measures guide individual firm members to take timely actions when actual results fall short of expectations. (Please see  Business Intelligence - Beyond the Software By Steven Campbell International Legal Technology Association. Our Server.)

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12 Tips for Generating Rich Data
Here, a guide to uncovering the bounty buried in your data warehouse.From CRM Magazine
 

Business intelligence (BI) applications have come a long way over the past 10 years. More commonly known as data mining a decade ago, analysts back then predicted a boom for this software market. Only today is that boom starting to materialize.

Already, industry pundits posit that 60 percent of companies have data-mining or BI systems installed. Of those 12 percent say they use their software at least once every hour, while 36 percent say they use it at least once each day, according to Ventana Research. It may come as no surprise that those who install data-mining software weren't necessarily getting the most out of it or the data, for a variety of reasons. Alton Adams, a partner at Accenture's CRM practice, says, "Even today people are still struggling to get it right. At its foundation data mining is all about having a good understanding and a single view of the customer to effectively market and sell to that customer. But we're just not there yet. There is probably a litany of things that people are doing wrong and things that they should be doing right. There's still so much work to be done."

Similar to mining for gold, when done well data mining can extract a treasure trove. Here are 12 tips experts and users say will help you uncover the precious elements in your data.

1) Share data with caution
As Robb Eklund, Oracle vice president for CRM product marketing, says data (both from internal and external sources) is your business's gold. And data about your customers is especially valuable and sensitive. If it's leaked or lost your business image as well as bottom line will suffer, something MasterCard International found out this past May when more than 40 million customers had their account information exposed. Aside from the obvious--building security features into your database and providing users and your database with a secure connection using trusted IP addresses--you can prevent problems by creating limits and rules for each user. Know who has access to what and why.

2) Look beyond transactional data
It's a given that your CRM and analytics programs will use data collected from transactional and application systems, but there's plenty of other nontraditional data out there that can bring added insight to your employees, according to Anne Milley, director of technology product marketing for SAS. You can purchase demographic and psychographic data from outside vendors and there's data you can collect on your own, such as market research, customer surveys, and focus group results. Another data source is full-text conversations from your customer service or call center. Today, there's software available from companies like Utopy, Nexidia, and CallMiner that can turn dialogue into reliable quantitative data that can be used to predict future customer service problems, as well as help agents with cross- and upsell techniques. "You have to get out of the database mentality," Milley says. "Transactional data is fine, but as far as analytical richness, it's very limited in what it can provide."

3) Clean your data regularlyThere are many kinds of dirty
data. Some of the most basic--having multiple entries for the same customer or misspellings--can be the most labor-intensive to remove. Other cleansing issues stem from organizational problems. Your marketing department might classify data one way with one naming convention, while your sales department uses another. But it all goes back to policies: Require all users to input data the same way, and clean data often, deleting mistakes and duplicates.

Kyle Lambert, vice president of information solutions for John I. Haas, a grower and supplier for the beer industry, says sometimes, dirty data can be the impetus to get a CRM or analytics project moving. "We found that exposing dirty data to executives was much more powerful than just cleaning our data," he says. "After we cleaned the data executives would come back and say 'The IS department can't deliver any meaningful information anyway.' But if we showed them we could deliver the data but it was dirty, they started to correct the processes [that made it dirty]. Executives love to change processes. They were able to contribute to the improvement. And over time they could see the numbers were getting better."

4) Distribute data at every level
You already know that your marketing and sales staff can benefit from your CRM data, but you probably don't realize how useful it can be to other personnel and departments, says Bill Stoughton, BI group leader with database marketing services provider Merkle. "People just aren't distributing data to the end touch points," he says. "Do you have enough information going out to the call center or to your Web site for customer self-service?"

Of course, someone working in the call center isn't necessarily going to understand a detailed report or have direct access to your database. To that end, being able to distribute reports in Microsoft Excel or Word documents is key, according to Ventana Research, which found that 81 percent of users wanted the ability to export data to Excel. "We see it all the time," says Patrick Morrissey, worldwide marketing director for Business Objects. "It makes sense for the end user to see a report or analysis in Excel, Word, or PowerPoint. It helps people use the data the way that they work."

5) Fund training and relearning
You've spent millions on your CRM implementation, but do your employees know enough about it to take advantage of the technology? In all likelihood the answer is no, says Accenture's Adams. In fact, between 60 and 80 percent of companies don't have adequate training budgets. "We find this is the biggest gap. Companies are not optimizing their spend and effectively operationalizing their CRM programs. Train those who will be working directly with the software, as well as those who will be using and benefiting from reports. Many vendors offer free or online training, which will keep your capital outlay low."

6) Balance server space with strong analysis
How much data do you have in your database? Three months' worth? Six months' worth? Your goal really should be 13 months' worth, according to Merkle's Stoughton, and at least three years' worth of contact data information. Some data should always be accessible. "People tend to have too much data so they aggregate it, but when you aggregate data, you're losing data somewhere. Keep point-of-time information accessible, for example, data that marks major events in a customer's life with you--when they became your customer, when you last marketed to them."

7) Aggregate, don't delete
Dr. Judy Bayer, director of advanced analytics for software vendor Teradata, agrees that you should keep a minimum of two to three years' worth of data in archived files, but in the best case you should have all of your data available somewhere. "I've worked with customers who had thrown out data about people who aren't their customers anymore," Bayer says, "but how can you figure out why they are not your customer if you don't have their data?" A rule of thumb: Analyze data before you aggregate it--never simply throw data away.

8) Standardize whenever possible
One of the first things Toshiba America Medical Systems (TAMS)did when it installed its new software was standardize everything related to its data. All the reports coming from the CRM program have the same look and feel, thanks to a template. Everything gets a time and date stamp. "Our vice president of marketing believes in this program. By standardizing, it was a way to make sure all of the information used in analysis came from our new data reports," says Diane Werner, a customer relationship specialist at TAMS. A few years ago TAMS took this strategy even farther by using standard file and document naming conventions that employ real language to bypass numbers in favor of descriptive document names. "It's very clear what each report is. We use descriptions by month, quarter, half. We don't use technical names," Werner says.

9) Talk to your users often
How can you decide what to measure and what reports to implement if you don't know what people need? Karen Williams, vice president of BI, product marketing, at Cognos, says one of the biggest mistakes she sees her customers making is that they don't create a partnership between the users and the IT group. "There should be a partnership early on that takes into account what business people want and what IT can deliver," she says. "Make users part of the purchase and the implementation. Identify requirements--what information they need to do their jobs."

Mark Lack, planning and financial analysis manager at manufacturing firm Mueller, constantly polls his users, asking what they need. This process also ferrets out which reports and analyses are unnecessary, saving you time and energy in the long run, according to Lack.

10) Get executive buy-in
Business change comes from above. One of the best places to start is your board, John I. Haas's Lambert says. "Interview your executives and find out what info they are looking at on a daily basis. I recently went to our board of directors and asked how they wanted to measure the company--what growth expectations were. Then I went to our executive management and said, 'This is what the board wants to see, how are you going to deliver that?' They told me their strategy. I asked, 'How do you measure your success against your strategy?'" Those fundamental questions start the ball rolling."

11) Create a continuity plan for your data
Barbara McMullen, director of the Institute for Data Center Professionals (IDCP) and project manager at Marist College's Center for Collaborative and On-Demand Computing, is in the process of implementing such a plan right now. The reason? The IDCP has been around for more than three years. In the beginning, it stored data using MySQL. Soon after, IDCP hired a new employee who didn't have experience using the database format and converted everything to Excel. Unfortunately, only some of the data was ported, and soon after the truncated database became the default database. All the data that wasn't converted was lost forever. "We lost information about potential customers," McMullen says. "We had a policy in place, but the person responsible for enforcing that policy left. Now, when I see some of those databases that used to be more robust, it really bothers me."

McMullen suggests having more than one main contact person for your data, and having a clear line of command. Your policy should also have strict guidelines about how it will be stored, deleted, and analyzed, she says. And everything--including where your data is stored--should be in writing. "You can't ask someone on their last day of work if everything was turned over."

12) Treat your partners like employees
Mueller's Lack says his implementation succeeded in part thanks to his software vendor, Cognos, and his consultant, the CD Group. "We had never done anything like this before. There was a lot of trust that had to go back and forth between [Cognos and CD Group] and our company. A good relationship with the folks you're working with is key. You never want to get into an adversarial relationship."

Lack says he assessed companies the way he would a friend. "Did the consultants have the same ethics and values that we do? We found that there was a certain genuineness to our project leads. It definitely came down to gut feelings in some cases."

Uncovering the treasure trove
Customers in the retail world who buy pretty floral Capri pants may not shop for delicate, fringed ponchos, but unless you have a strong CRM and analytics program, you wouldn't know that. Catalog retailer Newport News knows this firsthand. A decade ago the company had rudimentary information about its customer base--what it bought, what catalogs customers purchased from, and where customers lived. But with millions of customers who could be sliced into more than 800 segments--not counting a creditworthiness category--they needed a little more data.

The major problem was, analysts spent 90 percent of their time extracting data and only 10 percent actually analyzing it. As a result, Newport News wasn't getting the most out of its data. Customers weren't segmented as deeply as they could be, which detracted from sales, says Van Rhodes, Newport News' manager of marketing decision-support systems. And the analysis that was completed was often out of date. Even worse, the company, which like other catalog retailers buys mailing lists, never knew which lists were most beneficial. The company simply wasn't getting the most out of its advertising dollars. "In the catalog business we are forever buying each other's lists," Rhodes says. "We might buy a list from Chadwick's or Victoria's Secret. We're interested in how those lists are performing, but we just didn't know because we couldn't do the analysis."

In an effort to boost its analytics, Newport News contracted in 2002 with SAS, creating a full-blown data warehouse and installing SAS's analytical tools. Almost immediately the company saw big results. Although Rhodes doesn't have specific ROI metrics, he says his new system along with the best practices that he's implemented have paid for themselves already, especially a statistical modeling feature, which helps his company find and market to its strongest customers. "The number one benefit so far is the speed of getting [our users] the answers they need and the ability to give them data that they couldn't see before."

Today, Newport News employees can identify buying patterns as they are emerging, which lets them send out catalogs that are tailored to customer needs. "With statistical modeling you can slice the customer database so you can rank the entire database best customer to worst customer," Rhodes says. "And now you can slice the customer so much finer that you know, for example, why customer A is better than customer B."

Newport News also knows which products each customer is more likely to buy, which helps it mail out the right catalogs. It has helped with the company's email marketing programs, something that didn't exist only a few years ago. --K.B.

(Please see 12 Tips for Generating Rich Data.  Here, a guide to uncovering the bounty buried in your data warehouse. )

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THE ESSENTIAL INGREDIENT:
How Business Intelligence depends on data quality
Mat Hanrahan   A DCR Data quality resource

1.0 Executive summary
• Business Intelligence (BI) is about identifying competitive advantage from business data.
• BI tools give business analysts the opportunity to examine how changes to the cost, production and selling of a product or service can affect the margin of profit it supports.
• BI tools traditionally delivered long-term ROI for companies that could benefit from economies of scale. Today, however, a wide range of companies are accumulating large amounts of data on both their customers and their product lines and hoping to benefit from BI
technology.
• Despite all this growing interest in BI, many companies are still ignoring the fact that a BI tool is only as good as the quality of the data it is processing.
• Data quality problems are common throughout business, but BI is particularly sensitive to them. Poor quality data will undermine the integrity of the tools used by experienced analyst, while less experienced users have the potential to base important decisions on inaccurate data, with potentially disastrous results. Both problems will cripple any ROI estimates of the tool.
• Attempts from the suite vendors to position ‘embedded BI’ functionality into their products will be particularly susceptible to this problem.
• BI can only become a truly commodity product if the customer knows his data assets and what they can be used for.
• BI functions used alongside data quality tools can produce valuable commercial opportunities. BI can become an active danger when it is not using data of a known quality.

2.0 Business Intelligence: an introduction

The heart of Business Intelligence (BI) is the ability of an organisation to access and analyse information, and then exploit it to competitive advantage.
Competitive advantage is sometimes difficult to separate from the characteristics of a particular market and those who compete within it. Despite this, the commercial pressures of a mature market can be formidable, and competing companies must often adopt similar strategies if they are to survive. In this kind of market, commercial advantage will depend on striking a precise balance between three factors:

• the price and quality of a product or service
• the cost of producing and selling the product
• the margin of profit that can be supported by customer demand for the product

Business Intelligence and Business Analytics tools aim to help business analysts identify areas of competitive advantage through exploring how these three factors interact. The most typical approaches to improving efficiency are:

• Identifying products, customers and sales channels that return the highest profit margins, and moving resources to them from their low-margin equivalents.
• Identifying and adjusting costs in the production and provision of service.
• Recalibrating estimates of overheads and expenses in ways that are easier to define and control.
• Building a working model of the business across departmental divisions, and using it as a yardstick for improving efficiency.
• Forecasting variations in buying patterns across different types of customers, products and time lines, and adjusting marketing strategy and supply chain logistics accordingly.
 

BI tools are traditionally associated with specialist vendors that produce data-mining, data analysis, forecasting and decision support products. Decent BI tools:

• Provide full and independent access to data from across the full range of the business
• Have minimal or zero impact on operational IT infrastructure and require little support from the IT department.
• Are driven by business analyst rather than IT experts
• Have a flexible and extensive selection of features that can be quickly brought to bear on any problem.

Although BI tools often generate immediate return on investment (ROI) by identifying significant and costly inefficiencies during configuration, they deliver most value as a long term investment. In November 2002 IDC published a survey of ‘The Financial Impact of Business Analytics’ that stated BI implementations generated an average 5 year ROI of 431%, with over half (63%) of those studied delivering ROI in two years or less(1). This kind of significant long-term ROI comes from understanding how precise, incremental
changes can bring competitive advantage to a business. A decade ago, when storing and sharing data was a still a relatively expensive business, the companies that had the most to gain from the kind of subtle, incremental adjustments were those big enough to benefit from the economies of scale. Today, in our networked age of throwaway circuitry, even a mid-sized enterprise can accumulate mountains of data about their customers and product-line for almost negligible cost. It should be no surprise therefore that there is a new and growing interest from all sectors of industry in how this data can be exploited, and that BI is being seen as a must-have for any business with an eye on the future.

BI and analytic tools bring competitive advantage to the modern business in two key ways.

Enterprises are investing in CRM analytics(2) in order to consolidate and deepen the understanding they have of their customers as a result of loyalty cards, call-centres and Customer Relationship Management (CRM) systems.

At the same time, enterprises are trying to drive efficiency through both the supply chain and the sales and service channels by using technology such as Radio Frequency ID (RFID) to collect more and more data on the products that they sell.

The explosion in data collection, the growing ruthlessness and efficiency of competition and the plummetting cost of both hardware and software is also pushing BI into new markets. Where BI was once the tool of the high-level strategist, today the ‘suite’ vendors are marketting ‘embedded BI’ as a value-add commodity for the SME(3). BI features are being adopted by database products, CRM and human resources suites, and supply chain applications as vendors adopt increasingly predatory tactics in a changing market.

Yet, for all the undoubted drama and promise of BI, few commentators acknowledge how completely dependent it is on another aspect of business the industry prefers to ignore.

BI tools are only as good as the data which they process: if the data is of poor quality the results will be inaccurate. This can have major implications for business.

3.0 Data Quality and Business Intelligence

There are many reasons why data quality is the most overlooked issue in modern IT. Changes in regulatory compliance and the growth of ‘info-centric’ architectures may have recently made data quality issues a concern of the board(4), but in the past businesses often preferred to leave the job of sorting out occasional inaccuracies of their data to the individual discretion of staff. This often left the business analyst in a very difficult position.

Quality data is the most essential working material of the business analyst. A question over the integrity of some of the data used in a calculation will at the very least, turn a certainty into a mere probability. Cast doubt over the integrity of more than one source and the analyst is going to be hard put to produce anything that might not actually mislead.

There is real danger here: a company that gambles on a new commercial strategy that is underpinned with misleading intelligence can do itself irreperable damage.

Business analysts and BI users have always been aware just how dependent they are on the quality of the data they interrogate. However the low visibility of the data quality issue meant they were often left to act on their own initiative, and the workarounds they developed would often detract from the value of the investment in BI and analytic tools.

Much of the strategic attraction/value of a BI tool comes from the way it can bring visibility to interdepartmental processes without incurring the considerable headache and expense of investing in operational networks. Enterprise-wide visibility is a worthy and valuable goal, but although the concept is an easy one to sell putting it in place on the ground is a very different matter.

Data quality is key here. Data warehouses traditionally always needed co-operation from the data-owners before they could agree on a common structure with which to organise their data before sharing. Recent innovations in data profiling have made this task far easier than it was, but the focus still tends to be on the format and form of the data rather than the accuracy of its content. Inter-departmental politicking is common across businesses and many data-owners refuse on principle to admit the quality of their data is anything less than 100% accurate. Data quality remains a nettle that enterprise is often reluctant to grasp(5).

The result is that data quality problems are rampant. A survey from Price Waterhouse Coopers in 2001 stated 75% companies had data quality problems(6), while a report from The Data Warehousing Institute in 2002(7) estimated data quality problems cost US businesses in the region of $600 billion a year. Ted Friedman of Gartner stated that poor data quality was not only a major factor behind the failure of Business Intelligence, CRM and other data sharing initiatives, but that it was causing ‘constant levels of pain’ in enterprise even without taking these failures into consideration. He went on to claim Gartner estimated that by 2006, there was high probability that Fortune 1000 enterprises will lose more money in operational inefficiency due to data quality issues than they will spend on business intelligence, CRM, and ERP initiatives(8).

BI is particularly sensitive to poor data quality. Decent analysts, the kind that are capable of identifying a slender margin worth millions of dollars, need to have a low tolerance for inaccurate data. Those with a sharp eye for suspect figures may simply blame the BI tool and stop using it, taking reliable data offline and working on it in a private spreadsheet. Both choices dilute the effectiveness of the tool and cripple its ROI.

The analysts without the eye for bad data are an even bigger concern. Supply these people with a BI tool and order them to crunch data of unknown quality and there is a very real risk inaccurate information will start finding its way into important business decisions.

The new market for ‘embedded analytics’ increases the risk. BI functions are now being opened up to users who have a limited view of the business and minimal training, who can now produce re-assuringly precise statistics at the click of a mouse.

For the suite vendors ‘embedded analytics’ is a marketing necessity, a way of working themselves up the bank and into the richer margins of an adjacent pond. Their marketing campaigns present the data layer as an inert, passive resource at the bottom to be transformed,
manipulated and passed at will around by the business and application layers above(9).

Today, businesses are waking up to the fact that the data layer, far from being inert, is the primary business asset from which the overarching layers must take their orders – and that there are major business benefits from taking a systematic approach towards understanding the quality of the data they use(10). Little wonder then that Meta Group stated in a market sector analysis in
September 2003 that the data quality market would grow at a compound rate of 30% a year(11).

4.0 Why BI needs DQ

Data can go bad in many different ways.

It can be inaccurate, incomplete and out of date. It can be used out of context and or changed deliberately or accidentally.

Bad data already damages business. It can interfere with the processes that take place within a business and the processes that take place between businesses and customers. It can waste marketing resources, damage a company’s reputation and make it vulnerable to both litigation and fraud.

The following examples are based on actual incidents and are typical of the way poor quality data can damage a business..

The director of a promotions and marketing has been with a company for eight years. He originally started out in sales. He has a good understanding of the business and knows from past experience that the till scanners can generate anything up to a 10% margin of error on the official sales figures, and consequently has an instinctive distrust of the official sales data. When the time comes to write his quarterly report he uses his personal contacts within both sales and marketing to double check the facts, and then generates his figures on a spreadsheet – and continues to work in this way even when the BI tool is introduced. He is unaware that approximately 12% of his marketing and mailing budget is being wasted per year by mailing promotional offers to incorrect addresses.

The regional marketing officer is new to the business and still finding her bearings, but keen to make her mark in her new role. The Managing Director mentioned they were investing in a new BI tool during the job interview and the new appointee sees it as key to her future within the business. The BI tool is both her ticket to understanding the inner-workings of the business and a means of gaining ground on more experienced colleagues. The training in the tool will also look good on her CV if the job does not work out.

From the beginning the precision of the statistics the BI tools produces makes a strong impression on her, and takes the opportunity to interrogate the BI tools whenever possible. She takes all the statistics at face value, secure in the knowledge that, as a new employee, if there are any mistakes, they were made before she joined the company. She has already uncovered some interesting oversights: for some reason her predecessor has been ignoring the fact that 37.8% of the customer base are retired. She sees an opportunity to make her mark, and begins quietly using the BI tool to profile the spending habits of this valuable demographic and target them with a marketing campaign. She does not realise is that 70.4% of this demographic is a totally random grouping caused by call centre operatives choosing the ‘retired’ option in Date Of Birth drop down list because it is the quickest way to add a value to the field.

The most successful of the sales representatives is paid mostly on commission. He has a good mental picture of who his most valuable customers are, and knows that most of them have more than one customer record in the CRM system. He makes sure he consults all records before he goes out on his visits. He’s also aware that a rival company is targeting these customers with a high-profile promotional campaign and a competitively priced product. Like the director of sales and marketing he has to use his initiative to work the data, in this case by organising the multiple views of his high value customers into a single record, before he can try and identify a strategy that can stop them leaving. By the time he has done this, however, the competition has the third phase of its strategy in place, and a group of customers representing 60% of his commission and 10% of the companies overall turnover have already made their move.

The two most valuable customers were not slow to take the opportunity to move their business elsewhere. They had nursed concerns about its efficiency ever since they started receiving triplicate versions of promotional literature.

A managing director needs to replace his most senior advisor, who is retiring, and is hoping that technology will be able to take on part of his role. He has read a few articles in the press about the falling cost of analytics and bought an analyst report on the subject and, on the strength of this, has decided to invest in the BI, with the ultimate aim of developing a dashboard that would inform him in real-time about the success and sales of the business. Unfortunately the picture this dashboard gives is distorted by the poor quality of the data it aggregates.

Duplicates of customer records have swelled the customer base by 15%. ‘De-duping’ these into a single customer record will not only speed the increase the efficiency and agility of the company, it will also go some way in helping to protect both companies and customers from fraud.

Similarly 12% of the addresses recorded are wrong. Cleaning the mailing list he would save the marketing department 12% of their yearly mailing budget. Errors in his pricing database are haemorrhaging 2% off his production costs, while rethinking his pricing strategy could win him an extra 15% of business.

None of this is visible, even with the BI tool. Without a company-wide approach that sees data as a strategic business asset both problem and its solution remain hidden away in departmental working procedures and the personal agendas of the employees.

5.0 DATA QUALITY: THE KEY TO ANALYTICS

Business Intelligence tools are only as good as the data they use.

In the last two years the organisations that traditionally took the initiative in data-warehousing and BI analytics have started to recognise that the data within their systems is a strategic resource and a valuable business asset(12). Many of these initiatives started with the need to demonstrate compliance to legislation such as Sarbanes-Oxley or Basle II, yet the need to demonstrate compliance has provided both the incentive and momentum needed to drive the data quality and data integration agenda past the barriers that traditionally held it back. The result is that, far from being an overhead, for some companies demonstrating compliance has resulted a marked improvement of efficiency(13). Improved data quality has been an important factor here.

The rise of complex services that use data from multiple strategic partners has similarly driven interest in data quality. Customer-facing service providers are acutely aware of the value of being able to independently assess the accuracy of the data before passing it on to their customers(14). Similarly, success within a merger or acquisition activity is often dependent on the speed with which each party can understand and exploit the value of the new data assets.

Yet despite all this, it is BI - so long a hostage to poor data quality - that may well deliver the broadest and widest gains from a systematic approach to understanding data quality. Today, BI tools are more affordable than ever before. They are intuitively designed, flexible, fast and invariably packed with rich feature-sets.

BI has obvious potential as a strategic application, but this potential can only be realised if the customer knows his data assets and how they can be used.

Used in conjunction with data quality tools, BI has the potential to change the shape of a market. Used without, at best it is an expensive vanity - at worst, a misleading liability that can harm your business.

(Please see THE ESSENTIAL INGREDIENT: How Business Intelligence depends on data quality ) &  (Principles of Information Quality Improvement. By Larry P. English )

[19]

Designing Executive Dashboards, Part 1
By Thomas Gonzalez
 

Introduction: Corporate dashboards are becoming the “must have” business intelligence technology for executives and business users across corporate America. Dashboard solutions have been around for over a decade, but have recently seen a resurgence in popularity due to the advance of enabling business intelligence and integration technologies. Designing an effective business dashboard is more challenging than it might appear due to the fact you are compressing large amounts of business information into a small visual area. Every dashboard component must effectively balance its share of screen real estate with the importance of the information it is imparting to the viewer. This article will discuss how to create an effective operational dashboard and some of the associated design best practices. Dashboard Design Goals: Dashboards can take many formats, from glorified reports to highly strategic business scorecards. This article refers to operational or tactical dashboards employed by business users in performing their daily work; these dashboards may directly support higher-level strategic objectives or be tied to a very specific business function. The goal of an operational dashboard is to provide business users with relevant and actionable information that empowers them to make effective decisions in a more efficient manner than they could without a dashboard. In this context, “relevant” means information that is directly tied to the user’s role and level within the organization. For instance, it would be inappropriate to provide the CFO with detailed metrics about Web site traffic but appropriate to present usage costs as they relate to bandwidth consumption. “Actionable” information refers to data that will alert the user as to when and what type of action needs to be taken in order to meet operational or strategic targets. Effective dashboards require an extremely efficient design that takes into account the role a user plays within the organization and the specific tasks and responsibilities that user performs on a daily/weekly basis. Defining Key Performance Indicators: The first step in designing a dashboard is to understand what key performance indicators (KPI) users are responsible for and which KPIs they wish to manage through their dashboard solution. A KPI can be defined as a measure (real or abstract) that indicates relative performance in relationship to a target goal. For instance, we might have a KPI that measures a specific number, such as daily Internet sales with a target goal of $10,000. In another instance we might have a more abstract KPI that measures “financial health” as a composite of several other KPIs, such as outstanding receivables, available credit and earnings before tax and depreciation. Within this scenario the higher-level “financial” KPI would be a composite of three disparate measures and their relative performance to specific targets. Defining the correct KPIs specific to the intended user is one of the most important design steps, as it sets the foundation and context for the information that will be subsequently visualized within the dashboard.

(Please see Designing Executive Dashboards, Part 1 By Thomas Gonzalez,  BrightPoint Consulting. Our Server )

[20]

Designing Executive Dashboards, Part 2
By Tom Gonzalez

Introduction: In part one of this series we covered the basic requirements of a corporate dashboard solution and went on to discuss the first steps of the dashboard design process. The two main areas covered were determining the appropriate key performance indicators (KPIs) and how to design a dashboard with the five most common KPI visualizations: alert icons, traffic lights, trend icons, progress bars, and gauges. In this article we complete the design process and cover visualization of supporting analytics and the layout techniques used to create a visually efficient and compelling design. Supporting Analytics: Supporting analytics are additional data visualizations that a user can view to help diagnose the condition of a given KPI or set of KPI’s. In most business cases these supporting analytics take the form of traditional charts and tables or lists. While the scope of this article is not intended to cover the myriad of best practices in designing traditional charting visualizations, we will discuss some of the basics as they relate to dashboard design. When creating supporting analytics, it is paramount that you take into account the typical end user who will be viewing the dashboard. The more specialized and specific the dashboard will be the more complexity and detail you can have in your supporting analytics. Conversely, if you have a very high level dashboard your supporting analytics will generally represent higher level summary information with less complex detail. Below we will discuss some of the most common visualizations used for designing supporting analytics. Pie Charts: Pie charts are generally considered a poor data visualization for any data set with more than half a dozen elements. The problem with pie charts is that it is very difficult to discern proportional differences with a radially divided circle, except in the case of a small data set that has large value differences within it. Pie charts also pose a problem for labeling, as they are either dependent on a color or pattern to describe the different data elements, or the labels need to be arranged around the perimeter of the pie, creating a visual distraction. When to use: Pie charts should be used to represent very small data sets that are geared to high level relationships between data elements. Usually pie charts can work for summary level relationships but should not be used for detailed analysis.   Bar Charts: Bar charts are an ideal visualization for showing the relationship of data elements within a series or multiple series. Bar charts allow for easy comparison of values due to the fact that the “bars” of data share a common measure and can be easily visually compared to one another. When to use: Bar charts are best suited for categorical analysis but can also be used for small time series analysis (e.g. the months of a year.) An example of categorical analysis would be examining sales of products broken down by product or product group, with sales in dollars being the measure and product or product group being the category. Be careful in using bar charts if you have a data set that can have one element with a large outlier value; this will render the visualization for the remaining data elements unusable. This is due to the fact that the chart scale is linear and will not clearly represent the relationships between the remaining data elements.

(Please see Designing Executive Dashboards, Part 2 By Tom Gonzalez,  BrightPoint Consulting. Our Server.)

[21]

Tactical Issues and Best Practices
By Stephen Hunt  Accenture

Finance executives can gain immediate benefits from tactical solutions and best practices that enable operational managers to adopt forecasting and budgeting processes as key management tools.

Ask most CFOs and finance directors to describe an ideal forecasting and budgeting process, and they’ll likely portray it as part of an overall integrated performance management framework, ultimately driven by value-based measures. At the same time, however, they’ll admit that this vision involves a significant transformation to their current forecasting and budgeting processes, systems, and organization. Accenture’s experience shows it can take three to five years to fully implement and embed these changes.

Meanwhile, finance organizations face a more immediate problem. Legacy systems and processes that have been in operation for the past 10 years are often broken. Despite significant efforts, they can no longer support the dynamic changes affecting the business. Increasingly, then, the question becomes, “What practical steps can we take to improve or replace existing processes and systems?” — usually combined with “before we start the next budgeting cycle.”

The good news is that tactical solutions deliver significant and usually exponential benefits. However, tactical solutions should not detract from pursuing a longer-term strategic forecasting and budgeting solution that is aligned to the overall strategy and business requirements. In fact, tactical initiatives, delivering quick wins and visible benefits, are essential in obtaining support and sponsorship for an overall strategic initiative.

As with any longer-term solution, successful tactical initiatives also require strong executive sponsorship, a robust and proven approach, a persuasive business case, and a significant change to the way the organization views and operates the forecasting and budgeting process.

Articulating the Issues
Although issues with the existing forecasting and budgeting process and systems are often well-known, it is important to fully document and communicate their impact to gain executive sponsorship, drive momentum for change, and ensure that the benefits are understood (see Figure 1). This is especially true since many of the benefits are qualitative and focus on accuracy and accountability.

Figure 1: Budgeting and Forecasting Issues

Frequency and Timeliness
Annual forecasting and budgeting cannot keep pace with today’s dynamic business environment because the information produced is often out-of-date and irrelevant. Managers need to be able to understand and respond quickly to the impact of competitive forces and rapid changes affecting their business, yet most organizations fail to forecast the financial impact of these changes fast enough.

All too often, the end-to-end process takes too long. Quarterly forecasts take two to five weeks to finalize. Budgets are often not finalized until well into the actual year they are purported to budget. Similarly, the time taken to produce each iteration of the forecast or budget is too long, frequently taking days and sometimes weeks. In today’s environment, the impact of any change to the financials needs to be understood within the day or even the hour.

It is surprising that the need for faster delivery of forward-looking forecasts and budgets has not received more attention, especially in light of the time and effort spent implementing ERP solutions and the drive toward a faster close, which, by definition, provides backward-looking information.

Flexibility
Most forecasting and budgeting processes and systems lack sufficient flexibility to accommodate the reorganizations, divestitures, mergers, and acquisitions that have become the hallmark of contemporary business. These changes need to be modeled and reflected within forecasting and budgeting systems, both in the future and also retrospectively to ensure relevant prior-year comparisons. Without this flexibility, finance professionals spend significant time and effort restating the numbers.

In recent years, this effort has become so immense that more and more organizations choose not to make restatements, deciding instead to highlight them via footnotes within the forecast and budget documentation, which makes historical comparison and trend analysis of questionable value.

In addition, most systems are not flexible enough to accommodate the demand for multiple views of forecast and budget information. Consequently delivering slice-and-dice views of data and what-if analyses requires time-consuming, offline data manipulation.

Cost and Effort
The cost of existing forecasting and budgeting processes is significant and appears to be growing every year. Accenture’s Planning for Value research study, conducted in conjunction with Cranfield University, found that the budget process for lower-quartile companies takes longer than six months. Similarly, $1 billion companies take, on average, 25,000 man-days to complete their budget.

Accountability and Ownership
The finance function is so involved in forecasting and budgeting that it becomes the owner of the process rather than the facilitator. “These are not my numbers” is a regular cry heard when operational management reviews forecasts and budgets. This has much to do with last-minute changes made without the agreement of all those involved.

Transparency and Access
Lack of accountability also relates to the lack of transparency and access to information offered to operational management. Operational managers work hard to produce information but may receive little or no feedback after the numbers are submitted and, thus, cannot easily view the forecast and budget information presented to senior management. Often they are also unable to access the data for modeling or examination. As a result, they see the forecasting and budgeting process as an effort by the finance function to collate and aggregate bottom-up data, turning it into “just another management request for information.”

Accuracy
Forecasts and budgets are often inaccurate. Despite technological advances, most organizations use a patchwork of spreadsheet models to undertake their forecasting and budgeting, with multiple hand-offs and revisions throughout the process. Inaccuracies arise due to lack of version control, transposition of numbers, and unallocated numbers (“buckets”) with aggregated data not equaling the sum of their parts. The impact is significant, leading to a lack of confidence in both the numbers and the ability of the finance function to deliver.

This impact extends to the analyst community as well, creating potentially a far greater cost to the organization. Empirical research tells us that shareholder value is materially affected when companies fail to provide accurate projections of business performance.

Finance Skills and Morale
Trying to manage such a problematic process often takes a toll on those involved and has a negative impact on how the finance function is perceived. Though forecasting and budgeting is often managed and operated by highly qualified finance professionals, the function can be relegated to nothing more than a factory for producing numbers. Rather than focusing on delivering value-added analysis, the finance function spends a disproportionate amount of time and effort cranking the numbers through multiple iterations using ill-equipped mechanisms and processes.

In summary, these issues combine to deliver a forecasting and budgeting process that takes too long, costs too much, and is too manually intensive. To make matters worse, the resulting forecast or budget is typically inaccurate, lacks accountability, and is out-of-date by the time it is produced.

Applying Best Practices
Although much has been written about best practices in budgeting and forecasting, most of it has been academic, until recently (see Figure 2). Now, however, technological advances offer capabilities that enable many best practices to be delivered.

Figure 2: Budgeting and Forecasting Best Practices

The following best practices are increasingly being adopted by organizations to solve common forecasting and budgeting issues. Importantly, no one best practice is a panacea for all the issues mentioned. Only by implementing a combination of these practices can organizations really begin to overcome the problems they face.

Rolling Forecasts
Traditionally, the budget process has been a one-off event, albeit a long and arduous one, and the forecasts, though more frequent, remain as a series of one-off quarterly events.

However, significant gains can be made from eradicating this single period/annual mindset and moving to a rolling forecast approach. Operations do not switch off on Dec. 31 each year and start afresh on Jan. 1. Customers do not think of your business in this way, so why monitor and manage the business in such discrete timeframes?

The first step in implementing rolling forecasts is to define what is meant by a “true rolling forecast.” Figure 3 best illustrates the concept of a 12-month rolling forecast. As each additional month’s actual information is finalized, the forecast is updated to provide an additional month’s forecast, thus always providing a 12-month projection into the future.

Figure 3: A True Rolling Forecast — Blue bars indicate actual results.

The move to rolling forecasts provides a number of benefits, in particular:

• Reducing or eliminating the traditional approach of the previous period plus an uplift. This approach forces the individuals undertaking the forecasts to update their business projections each month and embed the activity in monthly procedures;
• Helping to eliminate the annual mind-set and focus on the current year, acknowledging that the business functions as an ongoing operation and needs to be managed accordingly;
• Providing a continual 12-month business outlook at all times, enabling management to take remedial action as forecast business conditions change;
• Eliminating the unrealistic December-to-January gap that appears when next year’s budget is calendarized for the first time. By undertaking rolling forecasts, the December-to-January forecast is no different than any other two-month period; and
• Reducing or potentially eliminating the annual budgeting process. At the normal budget time, management will already have a very good idea of what the following financial year will look like from their latest rolling forecast. For example, an organization operating a 15-month rolling forecast will already have, at the end of the third quarter, a complete projection of the next financial year.

An alternative to a true rolling forecast is a “fixed period rolling forecast,” with which a number of organizations operate. Although this approach has the benefit of ensuring that forecasts are updated monthly, the benefits just described are not fully realized because the forecast remains focused on the current period. The key problem with this approach is that the business still has a fixed horizon — with associated performance management implications.

Increasingly, top-quartile companies have moved or are moving toward rolling forecasts. This is no small achievement. Usually there is significant cultural attachment to the forecasting and budgeting process, so the transition to rolling forecasts should not be underestimated. A budgeting process, for example, that starts in March and ends in August can become a raison d’être for the finance organization during this time, with much political power and control associated with the process.

In transitioning an organization towards operating rolling forecasts, a number of practical issues must be addressed. Most importantly, it cannot be done in isolation. It is not simply a matter of repeating on a monthly basis what is currently undertaken quarterly or semi-annually. This message must be communicated early in the process, or managers will worry that they “won’t be doing anything else but forecasting all day.”

Transitioning to a 12-month rolling forecast immediately can prove difficult, especially if the new process involves operational managers who have not directly participated in the forecasting process before. If the organization conducts forecasts semi-annually or less frequently, moving to a quarterly forecast first is a sensible option. If the organization forecasts quarterly, an approach to transition would be to first move to a rolling forecast with the required detail for the first six months and then to quarterly totals for the next six months.

In reality, the organization may be unwilling to completely discard quarterly forecasting or annual budgeting activities. Indeed, more detail may be required for quarterly forecasting and annual budgets due to external reporting requirements. Rolling forecasts do not remove this need, but they do provide management with timely information to support business decisions. Over time, the existing spiked quarterly effort will — and should — reduce as the rolling forecast becomes embedded in the monthly management of the business.

Increased Participation
Driving down the forecasting and budgeting process to operational managers has gained more ground as the best way to ensure accurate and reliable forecasts. Historically, any suggestion of this approach would have been met with disbelief, giving rise to visions of even more data aggregation, longer cycle times and increased manual handovers. However, technological advances in recent years, most noticeably the Web, have given rise to a number of solutions that are highly scalable to hundreds and even thousands of end users, enabling the forecasting and budgeting capability to be placed in the hands of the business. The advantage of this is obvious — those who can produce the best projections of business activities are those who undertake and are responsible for those activities.

For example, consider a bank with a large branch network where forecasting and budgeting is likely to be done by the finance function at a regional or group level, using tools and techniques available only to them. Today’s Web-based solutions enable the process to be driven down to the regional or even branch manager by providing little more than access to an Internet browser.

Of course, as with any new initiative, delivering sufficient practical training to the end users is essential for successful adoption of the new solution. Training should not be limited to the new technical solution alone, but also to the underlying concepts of forecasting and budgeting. A recent example of a forecasting and budgeting implementation saw the users receive a half-day training session, only 15 percent of which was targeted at the use of the technical solution. The majority of the session was focused on such basic concepts as “What is a forecast?”, “What is the organization trying to achieve with the forecast?”, and “Where and how do you get the underlying information?”

Detail Linked to Accountability
Another best practice is to link detail to those items that end users are actually accountable for and which they control. In short, keep it simple and relevant. Traditionally, finance professionals have gained comfort from the detail. In fact, Accenture’s Planning for Value research study found that bottom-quartile companies budget for more than 250 lines of detail. Projecting at such a level of detail is not only unrealistic but also assumes a spurious level of detail. In contrast, by linking detail to accountability, accuracy will likely increase as operational managers forecast or budget items that they manage and discuss on a day-to-day basis.

Returning to the banking example, suppose that the regional finance function currently undertakes a forecast of regional and branch profitability. When driving down forecasting and budgeting to the branch management, there is little point in forcing branch managers to forecast profitability, since they have no control over the pricing of mortgages or savings products their branch sells or the cost of funds associated with them. What the branch or regional manager is accountable for, however — and acutely aware of — is the number of mortgages and savings accounts sold and managed by the branch.

Practically, the roles and responsibilities of operational managers should be assessed to understand what common elements of the business model they are accountable for and — just as importantly — for what elements they are not.

Driver-Based
Driver-based forecasting and budgeting enables the underlying business model to be encapsulated within a standardized and structured forecast and budget capability. The benefits can be significant and include:

• Releasing potentially hundreds of business users from building and maintaining individual, usually spreadsheet-based, forecast and budget models;
• Allowing common parameters to be incorporated within the models, eliminating the need for end users to forecast items for which they are not responsible;
• Ensuring transparency and providing modeling capabilities to operational managers; and
• Providing management with the confidence that forecasts and budgets are derived from one common modeling methodology and set of algorithms.

In addition, thought should be given to incorporating an upward reporting and governance process for forecasting and budgeting into the model. To support this, many of the new technical solutions provide for multiple hierarchies and online workflow control.

Using the banking example, a driver-based modeling capability provided locally to branch management would incorporate common information on price, cost of funds, and central allocations. Local branch management could then forecast the volumes of savings and mortgage products as well as branch costs, enabling branch profitability to be calculated. Similarly, individual branch profitability would then aggregate automatically through the reporting hierarchies to provide regional, divisional, and country profitability.

Practically, investment is required upfront in taking time and effort to talk to the various business stakeholders to ensure that the business model and processes are correctly understood and can be translated into the appropriate driver-based model.

End-User Analysis
Advances in forecasting and budgeting applications enable analysis and reporting capabilities — not just data collection — to be deployed to a larger and widely distributed base of operational end users. Previously, finance was the only function with access to modeling tools, such as spreadsheets and business objects, and the training and skills to use them.

In the banking example, a branch manager using a local forecast or budget model could undertake what-if analyses to assess scenarios for deploying branch staff to different activities. Providing analytical capabilities to local operational managers gives them tools to manage and track their local business. This helps empower local management and ensures buy-in to the new forecasting and budgeting process.

Again, this requires upfront investment to understand the business requirements of both operational management and senior management. This ensures that operational managers receive a model with reporting and analytical capabilities that help them run their local business. Building only the analysis required by the corporate center into the forecasting and budgeting tool will compromise the end users’ perception and successful adoption of the solution.

The Way Forward
While no one particular best practice solves all the issues, leveraging a combination of best practices enables operational managers to adopt forecasting and budgeting processes as key management tools.

To facilitate this greater level of involvement from operational management, forecasting and budgeting processes and systems must be timely, relevant, and useful to end users. No longer should the budget process be a one-off event that is rushed through as an administrative chore.

In an ideal world, forecasting and budgeting processes and systems become so embedded at the operational level that aggregating results for management is merely a byproduct of operational managers using forecasting and budgeting tools in their normal management routines.

Additional Information:
Stephen Hunt is a senior manager in Accenture's Finance and Performance Management Service Line in London.

(Please see Tactical Issues and Best Practices By Stephen Hunt Accenture. Our Server. )

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