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HSMAI Section: Building A Successful Analytics Team (Part 1)

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October 25, 2016
Samuel Ayisi - sayisi@leumassolutions.com

For hospitality businesses that strive to be top performers, a successful analytics strategy is absolutely critical.  One of the key requirements for analytics success is the analytics team.  To be considered successful, the analytics team needs to actually create business value.  This implies that not only do they have to deliver valuable and relevant insights to the appropriate audiences within the organization, they must also earn the trust of the businesspeople, understand and speak their language, and work with them to ensure that the delivered analytics insights produce business value. Building a successful analytics team is therefore about creating a multifunctional workforce that can work together to fully optimize the value of analytics to the organization.


Similar to most strategic initiatives, effective leadership is critical to the success of any analytics team.  Strong and effective leaders influence the strategic direction and culture of the team.  Members of the analytics team are likely to adopt the working habits, integrity and commitment of the team leader.  Thus, leaders whose actions and attitude leave a lot to be desired will most likely lead the team toward failure.  The leadership of the analytics team also influences who joins the team.  This means that the leadership must have a clear and concise vision of what the analytics team is required to achieve and the types of individuals needed to make the attainment of the goals possible.  In instances where change management is required as part of the analytics initiative, leaders must be passionate about leading the re-orientation and making a long-term commitment to success.  Analytics is a long journey that can easily go wrong without the long-term commitment of leadership.



After establishing the right leadership, focus should now be placed on acquiring, developing, managing and retaining talent for the analytics function.  The foundation of any successful analytics team is diversity, in terms of business and technical skills, knowledge, background and experience that when combined has the greatest positive impact.  Not to mention important personal attributes such as open-mindedness, effective communication skills, passion and integrity.  The key to success is focusing on hiring people with the right personal attributes rather than the right technical skills or educational qualifications. Personal attributes cannot be easily taught. However, people with the desired personal attributes can quickly learn technical skills such as programming, datamining, or a new analytics tool.

There are various schools of thought on whether to hire a team of superhumans who can do it all or create a team made of up of people with strong unique skill sets in their domain.  There is no right answer as each side of the discussion presents strong points.  Whichever direction you choose, the key factor is that each member of the analytics team should be a top performer and must bring something unique to the table, which will also enhance and complement the capabilities of the other team members.  For instance, someone skilled at data extraction might need the expertise of another team member with stronger business acumen to ensure that the data extracted accurately represents what it is supposed to be.  This doesn’t mean that you cannot have multiskilled people on the team.  Just don’t focus too much on building a team of super individuals.

During your search, don’t get hung up on fancy titles. This is because a “data scientist” in one context may be considered a “data analyst” in another context.  Focus on unique skills sets and experiences that provide the team with a winning combination. Consider bringing in people from other industries and functional sectors as they could infuse newer perspectives and fresh ideas.  Despite the pressures, don’t be in a rush to hire.  Take your time to assess the specific needs and then put together the right people to fill the defined roles.  Remember that your analytics team should be unique and must fit your analytics objectives and working environment.  Don’t copy exactly what someone else has done.  Rather than gathering people with a specific set of credentials, look for smart, innovative and curious people who will work in harmony to tackle your analytics challenges.

As part of the training and development of the analytics team, it is important to embed the team members with the business units they support.  This will enable the analytics team to better understand the analytics knowledge and perspectives of that particular business function and also master their nuances.  When the business function becomes more comfortable with the analytics team, they are more likely to openly exchange ideas and give the reasoning behind the various assumptions made during their decision-making process.

Investment in the right analytics tools and technology is an important part of the team’s ability to execute successfully. This doesn’t mean that you should break the bank or go beyond your comfort zone.  The focus should be on attaining the desired strategic outcomes rather than a specific vendor, tool or technology.



The roles and responsibilities for the analytics team must be clear and unambiguous.  The various roles created should cover the entire spectrum of the team’s responsibility and accountability.  Examples of roles include data integration, data and analytic modelling, reporting and dashboards, end-user training and support.

After the roles have been created, clear job descriptions (including responsibility and accountability) must be developed for each role.  It would be useful at this stage to create a responsibility and accountability matrix using tested models such as RACI (stands for Responsible, Accountable, Consulted and Informed) which is usually presented as a matrix or chart with the R-A-C-I defined for each role.  You do not need to adopt the RACI matrix as there are variations and alternatives that may better suit your analytics team.  An example of a simple RACI matrix is shown to the right.
Ultimately, the success of any analytics team depends on its ability to deliver business value and also fit in with the organization. The next part of this article (coming up in the Spring 2017 issue of Hospitality Upgrade) will focus on the organizational structure of the analytics team, cultural issues and establishing credibility.
SAMUEL AYISI is the head of analytics with leumas Solutions. He can be reached at sayisi@leumassolutions.com

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