In the course of our research, we talked with a variety of people experienced in embedding analytics into business processes, from first forays to applications with competitive impact. We'll leave you with a list, compiled from the insights of these seasoned practitioners, of seven of the most common obstacles or sticking points specific to embedded analytics implementations.

Specifications

Where do you start if the analytics are entirely new to the process and it's difficult to envision how they might work, or if nobody can really articulate how the decisions currently get made?

Undocumented decision methods are very common, often in cases where process performers are very experienced. Key to getting over this sticking point is the skill of your analysts - both working with people to understand their work methods and mental processes, and working with data to tease out its patterns and meaning.

Data

What do you do if important information is incomplete or unavailable, or if stakeholders don't agree on its meaning and format?

These are common business and systems problems, the bane of information management professionals everywhere. But they are especially damaging to embedded analytics initiatives because of their reliance on complete and high quality data. Long term, the key is to get data assets well organized into a robust representation of the business and to assess your data management methods on an ongoing basis. Short term, you may be cleaning up data and trying to analyze it simultaneously. Even the best-structured processes still have missing or dirty data on occasion; this is often the reason why semiautomated decisions get "kicked out" or delegated to human decision-makers.

Business Relationships

How do you articulate plans and progress to process owners and other senior managers and stakeholders who may not be experienced with business analytics or the experimental nature of analytical implementations?

This kind of explanation can often feel like a high-stakes game of charades. In particular, algorithm and model development may be a black box to key players (leading to a reluctance to make decisions based on the results), or they may expect to see progress and progress reports similar to those of more traditional projects. In these circumstances, several companies report success through careful communication and education efforts. Assess each player's understanding of business analytics and analytical initiatives, and let them know what to expect at each stage of the implementation game.

User Training

How do you wean people off decisions that they've long been making, enable them to trust the analytics, and make them comfortable with the fact that the automated decisions may be more consistent and better informed than theirs?

The key here is to incorporate the process performers as early as is realistic in designing and testing the new systems and process, given that they may be untrained and unsophisticated analytically. Then make sure you have feedback channels in place so process performers can be heard during and after implementation.

Rollout

If the business is decentralized, how do you roll out the new analytically enhanced systems, decision methods and process to organizations with widely varying analytical capability and attitudes toward analytics? Do you start where the environment is friendliest, the responsible manager most enthusiastic and committed? Where the revised process will get its most realistic exercising? Where the feedback from process performers and their management is going to be most useful?

You seldom find a location that meets all these criteria. Keeping in mind that the first implementation is also going to be an iteration of analytics and process design, we recommend placing more emphasis than usual on getting high-quality feedback.

Completeness

How do you know when you're done? When it's time to declare the implementation complete and shift from iterative construction to model management? Unfortunately, you can't stick a fork in an analytics implementation or throw one at the wall to see if it hangs there. If the analysts building the application or the businesspeople sponsoring it are perfectionists, then they may be tempted to tweak the application indefinitely. And the business process may continue to improve as a result, but have you reached the point of diminishing returns, and should your analysts be more productively engaged working on other processes and projects?

There are two ways to mark completeness. One is to set a process performance target, and when that is reached (or progress toward it slows), declare the implementation over. The other is to have renewable funding for each iteration of design and implementation. When the business sponsor does not anticipate enough added value to fund another round of development, then the project is done.

Transparency

How much of the analytical methods and application do you want to reveal or share, especially when the process involves customers or other business partners? This is a subtle and important point, especially when the analytical application is delivering high business value, perhaps competitive advantage.

The tendency has long been to treat such processes and information systems as highly proprietary. However, the trend in business relationships is strongly toward collaboration, including the integration of processes and systems with those of customers and suppliers. Ask yourself periodically whether you can unleash more business value by sharing your analytical capability than by holding it close - particularly if your own organization's analytical culture would be difficult to emulate easily. The details of your algorithms and models may remain trade secrets, but the process capabilities they enable may best be shared.

Organizations that effectively manage these sticking points, while embedding analytics into core processes and systems, begin to approach "process nirvana." These organizations thoroughly understand workflow, information flow and decision points for targeted business processes, especially those that are part of their distinctive capability. They leverage analytics to improve efficiency and flexibility simultaneously, and to deliver high performance in the eyes of the process's customers. Embedding analytics into business processes sends a clear signal to employees that analytics are important to the entire organization. The next step to institutionalizing an analytical capability is to embed analytics into the organization's culture. Of course, just as we don't often achieve nirvana in our personal lives, few organizations attain embedded analytical nirvana. But we must all strive for spiritual and analytical perfection.

Reprinted by permission of Harvard Business Press. Excerpt from "Analytics at Work" by Thomas Davenport, Jeanne Harris and Robert Morison.

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