Organizational Alignment Around Analytics – Part One

By Elias Hadaya, Arlene Kim, Angele Levesque and Evan Wood of CMA's Customer Insights & Analytics Council

As organizational focus shifts to customer centricity, data analytics is often at the heart of the transformation. However, many companies struggle with breaking down the silos and infusing analytics into their entire organization.

In part one of this two-part blog, we will share some of the key challenges and considerations faced by organizations embarking on this journey. Part two will feature a case study on an organization that has successfully tackled this challenge.

As we explore this topic, there are several themes that rise to the surface for companies who have either been through this transformation or who are contemplating it. So no matter what stage of the journey you are on, there are a few key considerations that will help you make the most of your data and remain competitive:

  1. C-Suite advocacy: More often than not, getting an organization aligned around analytics begins at the top and comes to life because of executive level sponsorship. Because this will impact almost all parts of the business it is important to have C-suite buy-in early on. This, coupled with a clear vision, will set the course for your organization and will allow you to get traction quickly.
  2. An appetite for investment: Any transformation of this magnitude will usually require new head count, resources and technology investments. At the beginning, it may be difficult to prove the business case for these investments.  A clear appetite for risk and vision for investing in the organization’s future is needed ahead of proving that it works. In return, it will be important to share any quick wins, and merchandise how you are making better decisions and improving the business.
  3. One view of the customer: As companies become more customer-centric, there is added pressure to have all data centralized to build one view of the customer. In order to achieve this, data integration will be key. This is not without its challenges as data is often is not integrated (e.g. online and offline data sets) and sometimes even controlled by different departments. In addition, data quality will take on greater importance. However, the end product will give you a powerful tool to improve the customer journey and drive business results.
  4. Breaking down the silos: Often when legacy team structures were designed to meet the historical needs of a business, integration was a not a priority. This can mean that teams work silos, which in turn means limited knowledge sharing and a data-analytics-as-a-service-provider mentality. While short-term friction can sometimes occur as a result of breaking down silos, it is outweighed in the long-term by increased insight and knowledge sharing.
  5. Evolving your team structure: As part of this transformation, the structure of your data team will change along with the type of resources that will be required to achieve your company’s vision. Often roles will change to differentiate between technical (data management/business intelligence) versus analytics (solving business problems). As a result, the analytics department moves from order taking to become a strategy business partner.
  6. Talent Management: As analytics takes on greater importance within your organization, there will be increased pressure to attract talent (i.e. the highly sought-after data scientists). This brings with it the need to implement strategies to nurture and retain talent by offering opportunities for growth within your organization or competitive training and development.

Organizational alignment around analytics is not for the faint for heart. But by getting buy-in at the executive level, defining a clear vision and maintaining an openness to learn from how others in a similar position have succeeded, your company will be able to reap the rewards of being a data-centric organization.

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Tags: organization, data analytics, c-suite, investment, talent, executive, team