November 7, 2016
Feature Story
BI-Enabled Business
Performance Management
Steve Williams, DecisionPath Consulting
BI and Analytics Strategist

Having helped over a dozen highly successful companies with their enterprise business intelligence (BI) and analytics strategies, I can attest that many companies still approach performance management in old-fashioned, manually intensive ways. They spend too much time figuring out what happened and not nearly enough time determining courses of action that have high chances of success.

The companies I've worked with are prominent in various industries—manufacturing, distribution, financial servies, transportation and logistics, retail, and energy. Regardless of industry, business performance management (BPM) is mainly about understanding the dynamics around customers, products and/or services, channels, and the business units responsible for achieving financial and operational results. In addition, performance is generally measured in comparison with prior-period results (directional trend) and against budgets and various operating plans or targets. Given this common framework, there are many key recurring performance management questions:

• How are we doing compared to prior periods (e.g., year, month, week, day of week) and budgets (e.g., annual budget, quarterly updated budget, get-well plan)?

• How well are we executing our operatonal plans and meeting targets (e.g., market share growth, channel penetration targets, planned product promotion, service cross-selling and upselling targets, and many others)?

• For which customers, products and/or services, and channels are revenues more than last year and for which are they less?

• For which customers, products and/or services, and channels are revenues ahead of target and for which are they behind?

• How are business units performing compared to last year and compared to plans?

Overcoming Analytical Complexity

Managers need timely and accurate answers to these key questions in order to manage performance efficiently and effectively. That said, all but the simplest companies have many customers and products and/or services, many operate in more than one channel and/or geographic area, and all manage performance via some combination of business units operating according to some sort of business plan. This creates analytical complexity when it comes to getting answers to our basic performance managements questions—and it's really multidimensional analysis you require. Here are a few examples of analytical complexity in different industries:

• Financial services companies often have millions of customers, hundreds of locations, dozens of services with various options, and three or more channels.

• Consumer packaged goods manufacturers often have hundreds or thousands of products sold to many different types of customers at thousands of locations, via hundreds of retailers (customers) in various channels.

• Grocery chains operate dozens to thousands of locations that stock upward of 40,000 items sold to many different types of consumers in a wide variety of combinations, with widely varying margins.

To substantially increase management and business analyst productivity and effectiveness around performance, BI in the form of dashboards, scorecards, and multidimensional slice-and-dice analyses is the perfect tool for the job. Yet, for some reason, companies spend lots of time and money each month on business analysts (or equivalent titles) who wrangle a combination of static reports, custom data sets, and data from spreadsheets, emails, and other sources, and then amalgamate all that into a static presentation deck and send it up the chain for review.

Summary: Fully Leveraged BI

In contrast, a well-designed custom performance management system leveraging BI can put executives, managers, and analysts in a position where just a few clicks of the mouse can tell them which combinations of customer and product or service and channel and business unit are having the most favorable and unfavorable impacts on business performance. Imagine being able to know this in minutes instead of days so that BI can be further leveraged to look ahead, model scenarios and economic impacts, evaluate options, leverage decision-support techniques, and decide on courses of action in days instead of weeks or months.

In this day and age, a BI-enabled performance management system should be the norm, not the exception. For more on this topic, please see chapter 6 in my recently published book, Business Intelligence Strategy and Big Data Analytics (2016, Morgan Kaufmann).

Steve Williams is president of DecisionPath Consulting. He has developed enterprise business intelligence and analytics strategies, business cases, and road maps for companies with annual revenues ranging from $500 million to $20 billion. Steve has also been a judge for TDWI Best Practices in Business Intelligence and Data Warehousing competition, and he has written articles for Strategic Finance magazine and the Business Intelligence Journal, among others.

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Business Intelligence Journal, Vol. 21, No. 3
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Big Data, Analytics, and the Cloud: Strategies for Success
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Best Practices for Data Lake Management
contents
Feature
BI-Enabled Business Performance Management

Positioning Your Data and Analytics Group for Greatest Efficiency
Feature
Data Warehouse Trends: Ripping and Replacing DW Platforms

Mistake: Storing Data Without a Business Purpose
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Flashpoint Insight
Positioning Your Data and Analytics Group for Greatest Efficiency

Companies must realize the strategic role of data and analytics (D&A) and establish an effective organizational structure that enables data-driven enterprises to reap business benefits.

To do so, D&A needs to be a centralized entity on an equal footing with other businesss functions such as sales, marketing, finance, and R&D. The D&A team operates best with a clearly defined mission—often including business-model innovation and transformation to a data-driven organization. This article discusses aspects of D&A—what it is, examples of its benefits, where it belongs in the organization and why—and a methodical approach for building successful core capabilities. The article also discusses key differences betweeen a successful D&A organization and other entities such as the BICC and the COE.

This article is designed for business leaders who wish to review their D&A team and position it for success. The right organizational structure will promote a proactive approach to leveraging D&A at all levels of the organization and should therefore propel adoption of analytics as well.

Learn more: Read the entire article by downloading the Business Intelligence Journal(Vol. 21, No. 3).

 
TDWI Research Snapshot
Data Warehouse Trends: Ripping and Replacing DW Platforms

Ripping out and replacing a data warehouse platform can be a viable modernization strategy—or part of a larger strategy combined with other approaches—when the platform is deficient or outmoded. However, rip and replace can be expensive and disruptive for both business and technical users. Despite those risks, some organizations are considering—or are already committed to—a DW platform replacement.

Yet how many organizations are involved and when might they execute the rip and replace? To quantify the situation, this report's survey asked: When do you anticipate replacing your current primary data warehouse platform? (See Figure 14.)

(Click for larger image)
Click to view larger

A third of organizations surveyed will not replace their DW platform. It speaks well for the capability and value of the average data warehouse that so many users have no plans to replace the current primary DW platform (32%).

One-tenth of respondents have already made the replacement. When we consider that an appreciable number of organizations have recently replaced the primary DW platform (9%), we see that the rip-and-replace strategy is actually happening in the real world.

A quarter of respondents plan to replace the DW platform now. Many users surveyed report that they will make the replacement in 2016 (24%).

Another quarter anticipate DW platform replacements in coming years. Respondents report plans for replacement in 2017 (16%), 2018 (3%), and 2019 or later (5%).

If we pull together the above information, we see that roughly half of organizations surveyed will make platform replacements within three or four years. Hence, the systems architecture of the average data warehouse (where the platform resides) will be quite different in the future....

Regardless of the direction taken by individual user organizations, vendors, or open source contributors, it's clear that in aggregate we're experiencing a dramatic and exciting evolution in the type and use of data warehouse platforms.

Read the full report: DownloadTDWI Best Practices Report: Data Warehouse Modernization in the Age of Big Data Analytics (Q2 2016).

 
Flashpoint Rx
Mistake: Storing Data Without a
Business Purpose

It's not just how you do something that's important; rather, it's whether you're doing something that matters.

You must prevent data redundancy during integration, for example, because it's foolish from a cost and maintenance standpoint to store data "just in case." Enterprises should integrate data into NoSQL because the data volume is high and speedy processing is critical.

Wherever you store data, you should have a business purpose for keeping the data accessible. Data just accumulating in NoSQL, without being used, costs storage space (i.e., money) and clutters the cluster. Business purposes, however, tend to be readily apparent in modern enterprises clamoring for a 360-degree view of the customer made intelligently available in real time to online applications. Digital communications, online catalogs, and profiles should be robust, updated, and available in real time. Mobile and Internet of Things (IoT) applications are also naturally supported by NoSQL solutions.

You should grow the data science of your organization to the point that it can utilize a large amount of high-quality data for your online applications. This is the demand for the data that will be provided by NoSQL.

Read the full issue: Download Ten Mistakes to Avoid in NoSQL (Q3 2016).