For each level of maturity, the assessment needs to consider four key disciplines that an enterprise must focus on. The first is analytics vision and strategy. Enterprises need to have processes for developing analytic strategies by engaging strongly with the business stakeholders, defining the business outcomes, and measuring the results through benefits realization. At higher levels of maturity, the use of a product portfolio approach to managing change rather than traditional silos becomes important. Vision involves deciding on what you want analytics to do for your business, how you want your culture to evolve to take advantage of data, and even how you want to evolve your products and services in incorporating, taking advantage of, or being transformed or replaced using data and analytics. Vision is the what while strategy is the how. Strategy involves determining what the analytics roadmap looks like, and what resources must be marshalled to achieve it. Next is the value on outcome management. Analytics organizations need to have a sufficient record of where money is spent to accurately estimate work and budgeting for upcoming initiatives and programs. They also need to monitor the progress to plan and evaluate the business benefits accrued as a result of the work also to help improve the whole process. This is about tying reality to the vision. It involves determining what metrics will be used to gauge success, and about setting and communicating expectations, and about regularly communicating the progress to stakeholders. Next is a focus on people, skills, and organization. Enterprises have to have the people, skills, and structures in place for fostering and securing skills as well as developing their own internal capabilities. They have to anticipate upcoming needs and ensure that the proper skills, roles, and organizations exist are developed or can be sourced to support the work identified in the analytic strategy. Especially in the world of analytics, it's hard for many organizations to find, attract, and retain analytics talent, especially data scientists. This is why so many organizations are trying to build from within encouraging self-service analytics in the hopes that certain people will emerge as serious analytics talent and to obviate the need to hire as many from the outside. As we discussed, there are alternatives for contracting consultants to fill staffing gaps. But it's not all about people and skills, the right organizational structures need to be put into place to ensure the support and coordination for developing and deploying analytics tied to business outcomes. Finally, we have technology and solutions. Analytics organizations increasingly implement a variety of technologies from self-service analytics and data preparation tools to reporting packages to data science platforms to specialized business functions specific or industry specific analytic applications to analytic governance solutions to AI technologies. The list goes on and on. This all requires concentrated vendor management skills and managers who understand how and when to implement each kind of technology. It also demands implementation and training skills as we've discussed to optimize the usage of each technology and to integrate analytic results into existing or new business processes, products, and services