Advanced analytics promises to unlock hidden potential in organizational data. If that's the case, why have so few organizations embraced advanced analytics in a serious way? Most organizations have dabbled with advanced analytics, but outside of credit card companies, online retailers, and government intelligence agencies, few have invested sufficient resources to turn analytics into a core competency.
Advanced analytics refers to the use of machine learning algorithms to unearth patterns and relationships in large volumes of complex data. It's best applied to overcome various resource constraints (e.g., time, money, labor) where the output justifies the investment of time and money. (See "What is Analytics and Why Should You Care?" and "Advanced Analytics: Where Do You Start?")
Once an organization decides to invest in advanced analytics, it faces many challenges. To succeed with advanced analytics, organizations must have the right culture, people, organization, architecture, and data. (See figure 1.) This is a tall task. This article examines the "soft stuff" required to implement analytics--the culture, people, and organization--the first three dimensions of the analytical framework in figure 1. A subsequent article examines the "hard stuff"--the architecture, tools, and data.
Culture refers to the rules--both written and unwritten--for how things get done in an organization. These rules emanate from two places: 1) the words and actions of top executives and 2) organizational inertia and behavioral norms of middle management and their subordinates (i.e., "the way we've always done it.") Analytics, like any new information technology, requires executives and middle managers to make conscious choices about how work gets done.
Executives. For advanced analytics to succeed, top executives must first establish a fact-based decision making culture and then adhere to it themselves. Executives must consciously change the way they make decisions. Rather than rely on gut feel alone, executives must make decisions based on facts or intuition validated by data. They must designate authorized data sources for decision making and establish common metrics for measuring performance. They must also hold individuals accountable for outcomes at all levels of the organization.
Executives also need to evangelize the value and importance of fact-based decision making and the need for a performance-driven culture. They need to recruit like-minded executives and continuously reinforce the message that the organization "runs on data." Most importantly, they not only must "talk the talk," they must "walk the walk." They need to hold themselves accountable for performance outcomes and use certifiable information sources, not resort to their trusted analyst to deliver the data view they desire. Executives who don't follow their own rules send a cultural signal that this analytics fad will pass and so it's "business as usual."
Managers and Organizational Inertia. Mid-level managers often pose the biggest obstacles to implementing new information technologies because their authority and influence stems from their ability to control the flow of information, both up and down organizational ladders. Mid-level managers have to buy into new ways of capturing and using information for the program to succeed. If they don't, they, too, will send the wrong signals to lower level workers. To overcome organizational inertia, executives need to establish new incentives for mid-level managers and hold them accountable for performance metrics aligned with strategic goals around the decision making and the use of information.
The Right People
It's impossible to do advanced analytics without analysts. That's obvious. But hiring the right analysts and creating an environment for them to thrive is not easy.
Analysts are a rare breed. They are critical thinkers who need to understand a business process inside and out and the data that supports it. They also must be computer-literate and know how to use various data access, analysis, and presentation tools to do their jobs. Compared to other employees, they are generally more passionate about what they do, more committed to the success of the organization, more curious about how things work, and more eager to tackle new challenges.
But not all analysts do the same kind of work, and it's important to know the differences. There are four major types of analysts:
- Super Users. These are tech-savvy business users who gravitate to reporting and analysis tools deployed by the business intelligence (BI) team. These analysts quickly become the "go to" people in each department to get an ad hoc report or dashboard, if you don't want to wait for the BI team. While super users don't normally do advanced analytics, they play an important role because they offload ad hoc reporting requirements from more skilled analysts.
- Business Analysts. These are Excel jockeys that executives and managers answer to create and evaluate plans, crunch numbers, and generally answer any question an executive or manager might have that can't be addressed by a standard report or dashboard. With training, they can also create analytical models.
- Analytical Modelers. These analysts have formal training in statistics and a data mining workbench, such as those from IBM (i.e., SPSS) or SAS. They build descriptive and predictive models that are the heart and soul of advanced analytics.
- Data Scientists. These analysts specialize in analyzing unstructured data, such as Web traffic and social media. They write Java and other programs to run against Hadoop and NoSQL databases and know how to write efficient MapReduce jobs that run in "big data" environments.
Where You Find Them. Most organizations struggle to find skilled analysts. Many super users and business analysts are self-taught Excel jockeys, essentially tech-savvy business people who aren't afraid to learn new software tools to do their jobs. Many business school graduates fill this role, often as a stepping stone to management positions. Conversely, a few business-savvy technologists can grow into this role, including data analysts and report developers who have a proclivity toward business and working with business people.
Analytical modelers and data scientists require more training and skills. These analysts generally have a background in statistics or number crunching. Statisticians with business knowledge or social scientists with computer skills tend to excel in these roles. Given advances in data mining workbenches, it's not critical that analytical modelers know how to write SQL or code in C, as in the past. However, data scientists aren't so lucky. Since Hadoop is an early stage technology, data scientists need to know the basics of parallel processing and how to write Java and other programs in MapReduce. As such, they are in high demand right now.
The Right Organization
Business analysts play a key role in any advanced analytics initiative. Given the skills required to build predictive models, analysts are not cheap to hire or easy to retain. Thus, building the right analytical organization is key to attracting and retaining skilled analysts.
Today, most analysts are hired by department heads (e.g., finance, marketing, sales, or operations) and labor away in isolation at the departmental level. Unless given enough new challenges and opportunities for advancement, analysts are easy targets for recruiters.
Analytics Center of Excellence. The best way to attract and retain analysts is to create an Analytics Center of Excellence. This is a corporate group that oversees and manages all business analysts in an organization. The Center of Excellence provides a sense of community among analysts and enables them to regularly exchange ideas and knowledge. The Center also provides a career path for analysts so they are less tempted to look elsewhere to advance their careers. Finally, the Center pairs new analysts with veterans who can give them the mentoring and training they need to excel in their new position.
The key with an Analytics Center of Excellence is to balance central management with process expertise. Nearly all analysts should be embedded in departments and work side by side with business people on a daily basis. This enables analysts to learn business processes and data at a granular level while immersing the business in analytical techniques and approaches. At the same time, the analyst needs to work closely with other analysts in the organization to reinforce the notion that they are part of a larger analytical community.
The best way to accommodate these twin needs is by creating a matrixed analytical team. Analysts should report directly to department heads and indirectly to a corporate director of analytics or vice versa. In either case, the analyst should physically reside in his assigned department most or all days of the week, while participating in daily "stand up" meetings with other analysts so they can share ideas and issues as well as regular off-site meetings to build camaraderie and develop plans. The corporate director of analytics needs to work closely with department heads to balance local and enterprise analytical requirements.
Advanced analytics is a technical discipline. Yet, some of the keys to its success involve non-technical facets, such as culture, people, and organization. For an analytics initiative to thrive in an organization, executives must create a fact-based decision making culture, hire the right people, and create an analytics center of excellence that attracts, trains, and retains skilled analysts.
Posted November 7, 2011 9:45 AM
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