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Wayne Eckerson

Welcome to Wayne's World, my blog that illuminates the latest thinking about how to deliver insights from business data and celebrates out-of-the-box thinkers and doers in the business intelligence (BI), performance management and data warehousing (DW) fields. Tune in here if you want to keep abreast of the latest trends, techniques, and technologies in this dynamic industry.

About the author >

Wayne has been a thought leader in the business intelligence field since the early 1990s. He has conducted numerous research studies and is a noted speaker, blogger, and consultant. He is the author of two widely read books: Performance Dashboards: Measuring, Monitoring, and Managing Your Business (2005, 2010) and The Secrets of Analytical Leaders: Insights from Information Insiders (2012).

Wayne is founder and principal consultant at Eckerson Group,a research and consulting company focused on business intelligence, analytics and big data.

Recently in Predictive Analytics Category

(Note: This is the sixth and final article in a series on advanced analytics.)

Model-making is at the heart of advanced analytics. Thankfully, few of us need to create analytical models or learn the statistical techniques upon which they're based. However, any self-respecting business intelligence (BI) professional needs to understand the modeling process so he can better support the data requirements of analytical modelers.

Analytical Models

An analytical model is simply a mathematical equation that describes relationships among variables in a historical data set. The equation either estimates or classifies data values. In essence, a model draws a "line" through a set of data points that can be used to predict outcomes. For example, a linear regression draws a straight line through data points on a scatterplot that shows the impact of advertising spend on sales for various ad campaigns. The model's formula--in this case, "Sales=17.813 + (.0897* advertising spend)"-- enables executives to accurately estimate sales if they spend a specific amount on advertising. (See figure 1.)

Figure 1. Estimation Model (Linear Regression)
Linear regression.jpg

Algorithms that create analytical models (or equations) come in all shapes and sizes. Classification algorithms, such as neural networks, decision trees, clustering, and logistic regression, use a variety of techniques to create formulas that segregate data values into groups. Online retailers often use these algorithms to create target market segments or determine which products to recommend to buyers based on their past and current purchases. (See figure 2.)

Figure 2. Classification Algorithms

Classification models separate data values into logical groups.

Trusting Models. Unfortunately, some models are more opaque than others; that is, it's hard to understand the logic the model used to identify relevant patterns and relationships in the data. The problem with these "black box" models is that business people often have a hard time trusting them until they see quantitative results, such as reduced costs or higher revenues. Getting business users to understand and trust the output of analytical models is perhaps the biggest challenge in data mining.

To earn trust, analytical models have to validate a business person's intuitive understanding of how the business operates. In reality, most models don't uncover brand new insights; rather they unearth relationships that people understand as true but aren't looking at or acting upon. The models simply refocus people's attention on what is important and true and dispel assumptions (whether conscious or unconscious) that aren't valid.

Modeling Process

Given the power of analytical models, it's important that analytical modelers take a disciplined approach. Analytical modelers need to adhere to a methodology to work productively and generate accurate models. The modeling process consists of six distinct tasks:

  1. Define the project

  2. Explore the data

  3. Prepare the data

  4. Create the model

  5. Deploy the model

  6. Manage the model

Interestingly, preparing the data is the most time-consuming part of the process, and if not done right, can torpedo the analytical model and project. "[Data preparation] can easily be the difference between success and failure, between usable insights and incomprehensible murk, between worthwhile predictions and useless guesses," writes Dorian Pyle in his book, "Data Preparation for Data Mining."

Figure 3 shows a breakdown of the time required for each of these six steps. Data preparation consumes one-quarter (25%) of an analytical modeler's time, followed by model creation (23%), data exploration (18%), project definition (13%), scoring and deployment (12%), and model management (9%). Thus, almost half of an analytical modelers' time (43%) is spent exploring and preparing data, although this varies based on the condition and availability of data. Analytical modelers are like house painters who must spend lots of time preparing a paint surface to ensure a long-lasting paint finish.

Figure 3. Analytical Modeling Tasks
Modeling Steps.jpg

From Wayne Eckerson, "Predictive Analytics: Extending the Value of Your Data Warehousing Investment," 2007. Based on 166 respondents who have a predictive modeling practice.

Project Definition. Although defining an analytical project doesn't take as long as some of the other steps, it's the most critical task in the process. Modelers that don't know explicitly what they're trying to accomplish won't be able to create useful analytical models. Thus, before they start, good analytical modelers spend a lot of time defining objectives, impact, and scope.

Project objectives consist of the assumptions or hypotheses that a model will evaluate. Often, it helps to brainstorm hypotheses and then prioritize them based on business requirements. Project impact defines the model output (e.g., a report, a chart, or scoring program), how the business will use that output (e.g., embedded in a daily sales report or operational application or used in strategic planning), and the projected return on investment. Project scope defines who, what, where, when, why, and how of the project, including timelines and staff assignments.

For example, a project objective might be: "Reduce the amount of false positives when scanning credit card transactions for fraud." While the output might be: "A computer model capable of running on a server and measuring 7,000 transaction per minute, scoring each with probability and confidence, and routing transactions above a certain threshold to an operator for manual intervention."

Data Exploration. Data exploration or data discovery involves sifting through various sources of data to find the data sets that best fit the project. During this phase, the analytical modeler will document each potential data set with the following items:

  • Access methods: Source systems, data interfaces, machine formats (e.g. ASCII or EBCDIC), access rights, and data availability.
  • Data characteristics: Field names, field lengths, content, format, granularity and statistics (e.g. counts, mean, mode, median, and min/max values)
  • Business rules: Referential integrity rules, defaults, other business rules
  • Data pollution: Data entry errors, misused fields, bogus data
  • Data completeness: Empty or missing values, sparsity
  • Data consistency: Labels and definitions

Typically, an analytical modeler will compile all this information into a document and use it to help prioritize which data sets to use for which variables. (See figure 4.) A data warehouse with well documented metadata can greatly accelerate the data exploration phase because it also maintains much of this information. However, analytical modelers often want to explore external data and other data sets that don't exist in the data warehouse and must compile this information manually.

Figure 4. Data Profile Document
Data Profile Document.jpg
A data profile document describes the properties of a potential data set.

Data Preparation. Once analytical modelers document and select their data sets, they then must standardize and enrich the data. First, this means correcting any data errors that exist in the data and standardizing the machine format (e.g. ASCII vs EBCDIC). Then, it involves merging and flattening the data into a single wide table which may consist of hundreds of variables (i.e., columns). Finally, it means enriching the data with third party data, such as demographic, psychographic, or behavioral data that can enhance the models.

From there, analytical modelers transform the data so it's in an optimal form to address project objectives and meet processing requirements for specific machine learning techniques. Common transformations include summarizing data using reverse pivoting(See figure 5), transforming categorical values into numerical values, normalizing numeric values so they range from 0 to 1, consolidating continuous data into a finite set of bins or categories, removing redundant variables, and filling in missing values.

Modelers try to eliminate variables and values that aren't relevant as well as fill in empty fields with estimated or default values. In some cases, modelers may want to increase the bias or skew in a data set by duplicating outliers, giving them more weight in the model output. These are just some of the many data preparation techniques that analytical modelers use.

Figure 5. Reverse Pivoting
Reverse Pivoting.jpg
To model a banking "customer" not bank transactions, analytical modelers use a technique called reverse pivoting to summarize banking transactions to show customer activity by period.

Analytical Modeling. Analytical modeling is as much art as science. Much of the craft involves knowing what data sets and variables to select and how to format and transform the data for specific data models. Often, a modeler will start with 100+ variables and then, through data transformation and experimentation, winnow them down to 12 to 20 variables that are most predictive of the desired outcome.

In addition, an analytical modeler needs to select historical data that has enough of the "answers" built in it with a minimal amount of noise. Noise consists of patterns and relationships that have no business value, such as a person's birth date and age, which gives a 100 percent correlation. A data modeler will eliminate one of those variable to reduce noise. In addition, they will validate their models by testing them against random subsets of the data which they set aside in advance. If the scores remain compatible across training, testing, and validation data sets then they know they have a fairly accurate and relevant model.

Finally, the modeler must choose the right analytical techniques and algorithms or combinations of techniques to apply to a given hypothesis. This is where modelers' knowledge of business processes, project objectives, corporate data, and analytical techniques come into play. They may need to try many combinations of variables and techniques before they generate a model with sufficient predictive value.

Every analytical technique and algorithm has its strengths and weaknesses, as summarized in the tables below. The goal is to pick the right modeling technique so you have to do as little preparation and transformation as possible, according to Michael Berry and Gordon Linhoff in their book, "Data Mining Techniques: For Marketing, Sales, and Customer Support."

Table 1. Analytical Models
Table 1.jpg

Table 2. Analytical Techniques
Table 2.jpg

Deploy the Model. Model deployment takes many forms, as mentioned above. Executives can simply look at the model, absorb its insights, and use it to guide their strategic or operational planning. But models can also be operationalized. The most basic way to do operationalize a model is to embed it in an operational report. For example, a daily sales report for a telecommunications company might list each sales representative's customers by their propensity to churn. Or a model might be applied at the point of customer interaction, whether at a branch office or at an online checkout counter.

To apply models, you first have to score all the relevant records in your database. This involves converting the model into SQL or some other program that can run inside the database that holds the records that you want to score. Scoring involves running the model against each record and generating a numeric value, usually between 0 and 1, which is then appended to the record as an additional column. A higher score generally means a higher propensity to portray the desired or predicted behavior. Scoring is usually a batch process that happens at night or on the weekend depending on the volume of records that need to be scored. However, scoring can also happen in real-time, which is essentially what online retailers do when they make real-time recommendations based on purchases a customer just made.

Model Management. Once the model is built and deployed, it must be maintained. Models become obsolete over time, as the market or environment in which they operate changes. This is particularly true for volatile environments, such as customer marketing or risk management. Also, complex models that deliver high business value usually require a team of people to create, modify, update, and certify the models.

In such an environment, it's critical to have a model repository that can track versions, audit usage, and manage a model through its lifecycle. Once an organization has more than one operational model, it's imperative it implements model management utilities, which most data mining vendors now support.


Analytical models can be powerful. They can help organizations use information proactively instead of reactively. They can make predictions that streamline business processes, reduce costs, increase revenues, and improve customer satisfaction.

To create analytical models is as much art as science. A well-trained modeler needs to step through a variety of data-oriented tasks to create accurate models. Much of the heavy lifting involved in creating analytical models involves exploring and preparing the data. A well designed data warehouse or data mart can accelerate the modeling process by collecting and documenting a large portion of the data that modelers require and transforming that data into wide, flat tables conducive to the modeling process.

Posted November 29, 2011 1:42 PM
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The previous two articles in this series covered the organizational and technical factors required to succeed with advanced analytics. But as with most things in life, the hardest part is getting started. This final article shows how to kickstart an analytics practice and rev it into high gear.

The problem with selling an analytics practice is that most business executives who would support and fund the initiative haven't heard of the term. Some will think it's another IT boondoggle in the making and will politely deny or put off your request. You're caught in the chicken-or-egg riddle: it's hard to sell the value of analytics until you've shown tangible results. But you can't deliver tangible results until an executive buys into the program.

Of course, you may be fortunate to have enlightened executives who intuitively understand the value of analytics and are coming to you to build a practice. That's a nice fairy tale. Even with enlightened executives, you still need to prove the value of the technology and, more importantly, your ability to harness it. Even in a best-case scenario, you get one chance to prove yourself.

So, here are ten steps you can take to jumpstart an analytics practice, whether you are working at the grassroots level or working at the behest of a eager senior executive.

1. Find an Analyst. This seems too obvious to state, but it's hard to do in practice. Good analysts are hard to come by. They combine a unique knowledge of business process, data, and analytical tools. As people, they are critical thinkers who are inquisitive, doggedly persistent, and passionate about what they do. Many analysts have M.B.A. degrees or trained as social scientists, statisticians, or Six Sigma practitioners. Occasionally, you'll be able to elevate a precocious data analyst or BI report developer into the role.

2. Find an Executive. Good sponsors are almost as rare as good analysts. A good sponsor is someone who is willing to test long-held assumptions using data. For instance, event companies mail their brochures 12 weeks before every conference. Why? No one knows; it's the way it's always been done. But maybe they could get a bigger lift from their marketing investments if they mailed the brochures 11 or 13 weeks out, or shifted some of their marketing spend from direct mail to email and social media channels. A good sponsor is willing to test such assumptions.

3. Focus Your Efforts. If you've piqued an executive's interest, then explain what resources you need, if any, to conduct a test. But don't ask for much, because you don't need much to get going. Ideally, you should be able to make do with people and tools you have inhouse. A good analyst can work miracles with Excel and SQL and there are many open source data mining packages on the market today as well as low cost statistical add-ins to Excel and BI tools. Select a project that is interesting enough to be valuable to the company, but small enough to minimize risk.

4. Talk Profits. It's very important to remember that your business sponsor won't trust your computer model. They will go with their gut instinct rather than rely on a mathematical model to make a major decision. They will only trust the model if it shows either tangible lift (i.e., more revenues or profits), or it validates their own experience and knowledge. For example, the head of marketing for an online retailer will trust a market basket model if he realizes that the model has detected purchasing habits of corporate procurement officers who buy office items for new hires.

5. Act on Results. There is no point creating analytical models if the business doesn't act on them. There are many ways to make models actionable. You can present the results to executives whose go-to-market strategies might be shaped by the findings. Or you can embed the models in a weekly churn report distributed to sales people that indicates which customers are likely to attrite in the near future. (See figure 1.) Or you can embed models in operational applications so they are triggered by new events (e.g., a customer transaction) and automatically spit out recommendations (e.g., cross-sell offers.)

Figure 1. An Actionable Report
Part V - Actionable Report.jpg

6. Make it Useful. The models not only should be actionable, they should be proactive. The worst thing you can do is tell a salesperson something they already know. For instance, if the model says, "This customer is likely to churn because they haven't purchased anything in 90 days", a salesperson is likely to say, "Duh, tell me something I don't already know." A better model would be one that detects patterns not immediately obvious to the salesperson. For example, "This customer makes frequent purchases but their overall monthly expenditures have dropped ten percent since the beginning of the year."

7. Consolidate Data. Too often, analysts play the role of IT manager by accessing, moving, and transforming data before they begin analyze it. Although the DW team will never be able to identify and consolidate all the data that analysts might need, it can always do a better job understanding their requirements and making the right data available at the right level of granularity. This might require purchasing demographic data and creating specialized wide, flat tables preferred by modelers. It might also mean supporting specialized analytical functions inside the database that lets the modelers profile, prepare, and model data.

8. Unlock Your Data. Unfortunately, most IT managers don't provide analysts ready access to corporate data for fear that their SQL queries will grind an operational system or data warehouse to a halt. To balance access and performance, IT managers should create an analytical sandbox that enables modelers to upload their own data and mix it with corporate data in the warehouse. These sandboxes can be virtual table partitions inside the data warehouse or dedicated analytical machines that contain a replica of corporate data or an entirely new data set. In either case, the modelers get free and open access to data and IT managers get to worry less about resource contention.

9. Govern Your Data. Because analysts are so versatile with data, they often get pulled in multiple directions. The lowest value-added activity they perform is creating ad hoc queries for business colleagues. This type of work is better left to super users in each department. But to prevent Super Users from generating thousands of duplicate or conflicting reports, the BI team needs to establish a report governance committee that evaluates requests for new reports, maps them to an existing inventory, and decides which ones to build or roll into existing report structures. Ideally, the report governance committee is comprised of Super Users who are already creating most of the reports users use.

10. Centralize Analysts. It's imperative that analysts feel part of a team and not isolated in some departmental silo. An Analytics Center of Excellence can help build camaraderie among analysts, cross train them in different disciplines and business processes, and mentor new analysts. A director of analytics needs to prioritize analytics projects, cultivate an analytics mindset in the corporation, and maintain a close alliance with the data warehousing team. In fact, it's best if the director of analytics also has responsibility for the data warehouse. Ideally, 80% to 90% of analysts are embedded in the departments where they work side by side with business users and the rest reside at corporate headquarters where they focus on cross-departmental initiatives.


Although some of the steps defined above are clearly for novices, even analytics teams that are more advanced still struggle with many of the items. To succeed with analytics ultimately requires a receptive culture, top-notch people (i.e., analysts), comprehensive and clean data, and the proper tools. Success will not come quickly but takes a sustained effort. But the payoff, when it comes, is usually substantial.

Posted November 21, 2011 7:34 AM
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The prior article in this series discussed the human side of analytics. It explained how companies need to have the right culture, people, and organization to succeed with analytics. The flip side is the "hard stuff"- the architecture, platforms, tools, and data--that makes analytics possible. Although analytical technology gets the lionshare of attention in the trade press--perhaps more than it deserves for the value it delivers--it nonetheless forms the bedrock of all analytical initiatives. This article examines the architecture, platforms, tools, and data needed to deliver robust analytical solutions.


The term "analytical architecture" is an oxymoron. In most organizations, business analysts are left to their own devices to access, integrate, and analyze data. By necessity, they create their own data sets and reports outside the purview and approval of corporate IT. By definition, there is no analytical architecture in most organizations--just a hodge-podge of analytical silos and spreadmarts, each with conflicting business rules and data definitions.

Analytical sandboxes. Fortunately, with the advent of specialized analytical platforms (discussed below), BI architects have more options for bringing business analysts into the corporate BI fold. They can use these high-powered database platforms to create analytical sandboxes for the explicit use of business analysts. These sandboxes, when designed properly, give analysts the flexibility they need to access corporate data at a granular level, combine it with data that they've sourced themselves, and conduct analyses to answer pressing business questions. With analytical sandboxes, BI teams can transform business analysts from data pariahs to full-fledged members of the BI community.

There are four types of analytical sandboxes:

  • Staging Sandbox. This is a staging area for a data warehouse that contains raw, non-integrated data from multiple source systems. Analysts generally prefer to query a staging area that contains all the raw data than each source system individually. Hadoop is a staging area for large volumes of unstructured data that a growing number of companies are adding to their BI ecosystems.

  • Virtual Sandbox. A virtual sandbox is a set of tables inside a data warehouse assigned to individual analysts. Analysts can upload data into the sandbox and combine it with data from the data warehouse, giving them one place to go to do all their analyses. The BI team needs to carefully allocate compute resources so analysts have enough horsepower to run ad hoc queries without interfering with other workloads running on the data warehouse.

  • Free-standing sandbox. A free-standing sandbox is a separate database server that sits alongside a data warehouse and contains its own data. It's often used to offload complex, ad hoc queries from an enterprise data warehouse and give business analysts their own space to play. In some cases, these sandboxes contain a replica of data in the data warehouse, while in others, they support entirely new data sets that don't fit in a data warehouse or run faster on an analytical platform.

  • In-memory BI sandbox. Some desktop BI tools maintain a local data store, either in memory or on disk, to support interactive dashboards and queries. Analysts love these types of sandboxes because they connect to virtually any data source and enable analysts to model data, apply filters, and visually interact with the data without IT intervention.

Next-Generation BI Architecture. Figure 1 depicts a BI architecture with the four analytical sandboxes colored in green. The top half of the diagram represents a classic top-down, data warehousing architecture that primarily delivers interactive reports and dashboards to casual users (although the streaming/complex event processing (CEP) engine is new.) The bottom half of the diagram depicts a bottom-up analytical architecture with analytical sandboxes along with new types of data sources. This next-generation BI architecture better accommodates the needs of business analysts and data scientists, making them full-fledged members of the corporate BI ecosystem.

Figure 1. The New BI Architecture
Part IV - BI Architecture of Future.jpg

The next-generation BI architecture is more analytical, giving power users greater options to access and mix corporate data with their own data via various types of analytical sandboxes. It also brings unstructured and semi-structured data fully into the mix using Hadoop and nonrelational databases.

Analytical Platforms

Since the beginning of the data warehousing movement in the early 1990s, organizations have used general-purpose data management systems to implement data warehouses and, occasionally, multidimensional databases (i.e., "cubes") to support subject-specific data marts, especially for financial analytics. General-purpose data management systems were designed for transaction processing (i.e., rapid, secure, synchronized updates against small data sets) and only later modified to handle analytical processing (i.e., complex queries against large data sets.) In contrast, analytical platforms focus entirely on analytical processing at the expense of transaction processing.

The analytical platform movement. In 2002, Netezza (now owned by IBM), introduced a specialized analytical appliance, a tightly integrated, hardware-software database management system designed explicitly to run ad hoc queries against large volumes of data at blindingly fast speeds. Netezza's success spawned a host of competitors, and there are now more than two dozen players in the market. (see Table 1).

Table 1. Types of Analytical Platforms
Part IV - Tools Table.jpg

Today, the technology behind analytical platforms is diverse: appliances, columnar databases, in memory databases, massively parallel processing (MPP) databases, file-based systems, nonrelational databases and analytical services. What they all have in common, however, is that they provide significant improvements in price-performance, availability, load times and manageability compared with general-purpose relational database management systems. Every analytical platform customer I've interviewed has cited an order-of-magnitude performance gains that most initially don't believe.

Moreover, many of these analytical platforms contain built-in analytical functions that make life easier for business analysts. These functions range from fuzzy matching algorithms and text analytics to data preparation and data mining functions. By putting functions in the database, analysts no longer have to craft complex, custom SQL or offboard data to analytical workstations, which limits the amount of data they can analyze and model.

Companies use analytical platforms to support free-standing sandboxes (described above) or as replacements for data warehouses running on MySQL and SQL Server, and occasionally major OLTP databases from Oracle and IBM. They also improve query performance for ad hoc analytical tools, especially those that connect directly to databases to run queries (versus those that download data to a local cache.)

Analytical Tools

In 2010, vendors turned their attention to meeting the needs of power users after ten years of enhancing reporting and dashboard solutions for casual users. As a result, the number of analytical tools on the market has exploded.

Analytical tools come in all shapes and sizes. Analysts generally need one of every type of tool. Just as you wouldn't hire a carpenter to build an addition to your house with just one tool, you don't want to restrict an analyst to just one analytical tool. Like a carpenter, an analyst needs a different tool for every type of job they do. For instance, a typical analyst might need the following tools:

Excel to extract data from various sources, including local files, create reports, and share them with others via a corporate portal or server (managed Excel).
BI Search tools to issue ad hoc queries against a BI tool's metadata.
Planning tools (including Excel) to create strategic and tactical plans, each containing multiple scenarios.
Mashboards and ad hoc reporting tools to create ad hoc dashboards and reports on behalf of departmental colleagues
Visual discovery tools to explore data in one or more sources of data and create interactive dashboards on behalf of departmental colleagues
Multidimensional OLAP (MOLAP) tools to explore small and medium sets of data dimensionally at the speed of thought and run complex dimensional calculations.
Relational OLAP tools to explore large sets of data dimensionally and run complex calculations
Text analytics tools to parse text data and put it in a relational structure for analysis.
Data mining tools to create descriptive and predictive models.
Hadoop and MapReduce to process large volumes of unstructured and semi-structured data in a parallel environment.

Figure 2. Types of Analytical Tools
Part IV - Types of Tools.jpg

Figure 2 plots these tools on a graph where the x axis represents calculation complexity and the y axis represents data volumes. Ad hoc analytical tools for casual users (or more realistically super users) are clustered in the bottom left corner of the graph, while ad hoc tools for power users are clustered slightly above and to the right. Planning and scenario modeling tools cluster further to the right, offering slightly more calculation complexity against small volumes of data. High-powered analytical tools, which generally rely on machine learning algorithms and specialized analytical databases, cluster in the upper right quadrant.


Business analysts function like one-man IT shops. They must access, integrate, clean and analyze data, and then present it to other users. Figure 2 depicts the typical workflow of a business analyst. If an organization doesn't have a mature data warehouse that contains cross-functional data at a granular level, they often spend an inordinate amount of time sourcing, cleaning, and integrating data. (Steps 1 and 2 in the analyst workflow.) They then create a multiplicity of analytical silos (step 5) when they publish data, much to the chagrin of the IT department.

Figure 2. Analyst Workflow

In the absence of a data warehouse that contains all the data they need, business analysts must function as one-man IT shops where they spend an inordinate amount of time iterating between collecting, integrating, and analyzing data. They run into trouble when they distribute their hand-crafted data sets broadly.

Data Warehouse. The most important way that organizations can improve the productivity and effectiveness of business analysts is to maintain a robust data warehousing environment that contains most of the data that analysts need to perform their work. This can take many years. In a fast-moving market where the company adds new products and features continuously, the data warehouse may never catch up. But, nonetheless, it's important for organizations to continuously add new subject areas to the data warehouse, otherwise business analysts have to spend hours or days gathering and integrating this data themselves.

Atomic Data. The data warehouse also needs to house atomic data, or data at the lowest level of transactional detail, not summary data. Analysts generally want the raw data because they can repurpose in many different ways depending on the nature of the business questions they're addressing. This is the reason that highly skilled analysts like to access data directly from source systems or a data warehouse staging area. At the same time, less skilled analysts appreciate the heavy lifting done by the IT group to clean and integrate disparate data sets using common metrics, dimensions, and attributes. This base level of data standardization expedites their work.

Once a BI team integrates a sufficient number of subject areas in a data warehouse at an atomic level of data, business analysts can have a field day. Instead of downloading data to an analytical workstation, which limits the amount of data they can analyze and process, they can now run calculations and models against the entire data warehouse using analytical functions built into the database or that they've created using database development toolkits. This improves the accuracy of their analyses and models and saves them considerable time.


The technical side of analytics is daunting. There are many moving parts that all have to work synergistically together. However, the most important part of the technical equation is the data. The old adage holds true: "garbage in, garbage out." Analysts can't deliver accurate insights if they don't have access to good quality data. And it's a waste of their time to spend days trying to prepare the data for analysis. A good analytics program is built on a solid data warehousing foundation that embeds analytical sandboxes tailored to the requirements of individual analysts.

Posted November 15, 2011 7:44 AM
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