Make Your Analytics Projects Effective by Design

June 17, 2014 Ray Rivera



Ask an economist over a few glasses of beer about the difference between an MBA and a PhD, and he or she will tell you that an MBA can select the best decision from among a set of bad choices, while a PhD will prove that you never really had a choice in the first place.

Sassy as the punch line may be, it also provides a good short answer for the difference between analysis and analytics, but only after you alter the punch line somewhat.

In brief, analysis seeks to quantify the available choices, and select the best from among them. Analytics attempts to discover all the choices that could be available, especially the ones you never knew you had.

The longer answer is even more instructive. Examining how analysis and analytics come about reveals some worthwhile principles which we should observe when designing analytics solutions for business.

Same root word, different roots

We hear it increasingly from business users: “analytics is just like analysis, but with too much data and not enough results.” It is easy to understand such frustration. Yet we should not allow this sentiment to become a punch line that ends most conversations about analytics.

Analysis refers to a collection of quantitative techniques that define a business strategy and later renders it operational. Properly done, analysis leads to an outcome of a business decision, along with a framework for allocation of resources, quantification of risk, and establishment of control systems.

As any MBA will tell you, the aim of crafting business strategy is to create competitive advantage for the company. Since most competitors within the same industry produce similar goods as one another, the best decisions informed by the best plans typically determine the marketplace winners. For analysts, competitive advantage is achieved through determining the most valid business plan, at the core of which is doing something different from everyone else. The amount by which that difference is meaningful to the market is what we refer to as value, and is the ultimate measure of a successful business strategy.

Analytics, on the other hand, is a set of quantitative techniques borrowed from scientific research where the outcome is new knowledge that may inform a decision, but does not prefigure its execution like analysis does. The new knowledge may lead to insight, improved performance, or some otherwise unknown capability whose discovery often creates an immediate competitive advantage. Thus, analytics represents an evolution of the field of action research to an environment of big data.

An important concept that further separates the two is that although analytics makes use of scientific methods, the primary outcome is relevance rather than validity. In practical terms, analytics results must be immediately convincing and implementable by all stakeholders.

Competitive advantage is achieved from knowing something that competitors do not know and thereby doing something they cannot do, because they lack the same data. Value in analytics is somewhat more complicated, tending frequently to adhere to the dynamics of intangible assets.

Design principles for analytics solutions

1. The user must determine what data to ignore

Data available for business analysis is often incomplete and typically must be supplemented by additional data in order to yield valid results. Analytics data, on the other hand, is overabundant and needs to be reduced, compressed, or restructured in order to become relevant. In any case, the user will need to ignore the majority of the available data. Analytics solutions should include features that enable sound user judgment on what data to ignore.

2. Tests of findings are necessary to move forward

Analytics is characterized by both discovery and testing. Yet when analytics consists of only discovery, the results are no fundamentally different from gut feelings. Analytics solutions need to provide tests of sufficient rigor so as to transform discoveries into business knowledge, and not reinforce prevailing intuitions.

3. Every step forward involves simplification

Analysis is a process of elaboration and consolidation that leads to business decisions based on qualitative differences determined by the results. Analytics is a process of simplification that leads to new knowledge based on testing information discovered during the process. Each iterative step in an analytics solution should therefore move toward greater simplicity rather than elaboration.

4. Steps must be retraceable

In the spreadsheet environment commonly used in analysis, steps are not necessarily retraceable. For example, data cells can be altered without a subsequent ability to audit, thereby compromising data integrity. Analytics solutions need to log all user activity, allowing the user to retrace all steps performed.

5. Good enough is good enough

Good enough means knowing when to stop, and is achieved when the validity of the results matches their relevance. Therefore, an analytics solution should be designed with the primary aim of the user obtaining relevant results.

Be choosey

Good strategy remains at the core of business growth and profitability, and so the need for sound business analysis will not likely diminish anytime soon. But competitive advantage has become much more elusive as barriers to entry fall and data becomes an equalizer. To remain competitive, business cannot just settle for the best of a bad lot, no matter how brilliant the analysis. They need to discover the options that nobody knows yet exist, and get to them before everyone else finds out.

An earlier version was published on SAP Business Trends

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