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HSMAI Section: Building a Successful Analytics Team - Part Two

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March 01, 2017
HSMAI
Samuel Ayisi - sayisi@leumassolutions.com

©2017 Hospitality Upgrade
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A successful analytics team does not need to be made up of superstars who can do it all. Rather, the strengths of team members should be complementary for the greater good of fully optimizing the value of analytics to the organization. Part 1 of this article discussed how factors such as leadership, talent acquisition and role definition influence the success of the analytics team. The takeaways were:

  • The right leadership will guide the analytics team to success.
  • Diversity of skills, background and experience is key. Look for a winning combination of unique skills that complement each other for the greater good.
  • Ambiguous roles and responsibilities are a recipe for failure.
 
Compatible Organizational Structure
With the talent acquired and their roles and responsibilities clearly defined, the next logical question is, what is the best way to organize the analytics team? Should the team be centralized in one part of the organization or scattered across the enterprise? The choice you make depends on your existing corporate organizational structure, the maturity of your analytics initiatives, and the deployment model that adds the most value to the enterprise as well as to the analytics team. For example, a decentralized analytics team deployment might not thrive successfully within an enterprise that has a heavily centralized organizational structure. The optimal solution might be one that strikes a comfortable balance between close relationships with the business people while at the same time complementing each other toward the achievement of the strategic analytics goals.
Quite a number of organizational models have been implemented for analytic teams with various degrees of success. However, the most widely adopted implementations are outlined in a book by Thomas H. Davenport, Jeanne G. Harris and Robert Morison et al titled, “Analytics at Work: Smarter Decisions, Better Results.” A summary of the proposed models is shown in the diagram at this link.
 
If you are just starting to use analytics, the functional model might be the best option since analytics would most likely only be focused in a few areas of the business. As your analytics initiatives become more enterprisewide and complicated you can then migrate to a suitable model of your choice. My preference would be a hybrid of the centralized model (to take care of advanced analytics and corporate strategic projects) and the center of excellence model (made up of analytics teams embedded within the various functions and business units), with both models complementing each other. This hybrid model provides a comfortable balance between working closely with the business people while at the same time maintaining control and coordination at a strategic level.
 
Takeaway:
Organizational fit should not be compromised.
 
 

Conducive Analytics Culture
The right culture matters. It is probably the most difficult to change and manage, but perhaps also has the most significant influence on the success of the analytics team. Analytics success is much more than data crunching and fancy reports/dashboards. Of critical importance is a corporate culture that actually embraces analytics and supports the transformation of all the work the analytics team has done into valuable evidence-based decision-making. Simply inserting an analytics team into a non-conducive culture is no guarantee of analytics success and may even result in unintended negative consequences. A conducive analytics culture therefore helps you realize the full potential of your analytics team. Remember that your analytics is only as good as the value it adds to the business people and decision-makers. Thus, fostering the right corporate culture also helps your analytics team to be more successful.

Apart from the corporate culture, the culture internal to the analytics team is also critical for success. A team culture that encourages innovation, open communications, visibility and transparency is one that team members would like to belong to and contribute their utmost best. The onus therefore lies on the leadership of the analytics team to develop and maintain a conducive team culture if their team is to be considered successful. Factors such as recruitment, training, team setup and role definitions must all reinforce and reflect the desired team culture.
 
Takeaway:
Fostering conducive team and corporate cultures enhance analytics success.
 
 

Strong and Sustainable Credibility
One of the most important gauges of analytics success is the credibility of the analytics team. Without strong and sustainable credibility associated with the analytics team, all their hard work might be under-utilized and unappreciated. Your analytics leadership can achieve its biggest payoff by investing in sustainable credibility. The initial successes and quick wins of the analytics team should be developed in such a way that they are sustainable in the long term. This is not the time to be known as a one-trick pony.

Here are a few ways to build sustainable credibility for your analytics team:

  • The data used for analytics must be trustworthy. If people become skeptical of the data supporting the analytics, they will ignore the analytics insights and revert to their own ways.
  • Your analytics team must make the extra effort to understand and speak the language of the business people and work with them to ensure that the delivered analytics insights produce business value.
  • Start with simple, and if possible, small, quick wins. It is difficult to re-establish credibility after big, early failures. Conversely, if you establish early credibility via smaller, quick wins, future lapses are more easily forgiven.
  • Keep your audience in mind as you develop the various analytics insights. Your goal is to add value, not to get your audience overwhelmed and confused. While simplicity and clarity would work for some, others require complexity using the appropriate jargon. When using visualizations make sure that they convey the intended message, as interpretations of visualizations could differ. Effective analytics is as much about communication as it is about data crunching.
  • Avoid showing off your brilliance and focus your passion on fulfilling the needs of the business people instead. Resist the temptation to build something that you perceive to be excellent and then attempt to shove down the throats of the business people. However, this doesn’t mean that you shouldn’t be innovative and proactive.
  • Be as transparent as you can, especially when it comes to the data and models used for the analytics as well as the underlying assumptions. Remember that the use of your analytics could be perceived by some as a leap of faith, thus there must be very little reason for doubt. Transparency also compels the analytics team to strive for excellence since their work can be easily scrutinized.
  • Treat your analytics team and the various projects they undertake as a brand. Poor branding can lead to analytics failure.
  • Provide excellent customer service to the business people you support.
Takeaway:
Early failures can erode long-term credibility. Treat your analytics team and its projects as a brand and take the necessary actions to protect that brand.
 
 
 
Even though there are a various schools of thought on building a successful analytics team, I hope that the takeaways from this article will provide some valuable guidance for you. Take cognizance of the fact that building a successful analytics team requires some significant effort and commitment on your part. It’s not that easy.

1. The right leadership will guide the analytics team to success.

2. Diversity of skills, background and experience is key. Look for a winning combination of unique skills that complement each other for the greater good.

3. Ambiguous roles and responsibilities are a recipe for failure.

4. Organizational fit should not be compromised.

5. Fostering conducive team and corporate cultures enhance analytics success.

6. Early failures can erode credibility. Treat your analytics team and projects as a brand and take the necessary actions to protect that brand.
 

Samuel Ayisi is the head of analytics with Leumas Solutions. He can be reached at sayisi@leumassolutions.com.



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