Creating an environment that drives organizational efficiency requires more than tools and technology. It requires definition, learning from others, mitigating risk, understanding what operational excellence looks like and practical application in business development.

Analytics is a broad discipline that can easily be interpreted differently by different people; for this reason it is important to define commonly used terms across the organization.

Broadly speaking, analytics is the process of discovering, interpreting and communicating meaningful patterns in data, and applying results towards effective action.

Artificial Intelligence describes machine programs that mimic cognitive functions, that people generally associate with human intelligence, such as decision making.

Machine Learning is the application of algorithms and statistical models that rely on patterns in data to perform specific tasks without specific instructions.


Analytics can be separated into three distinct categories which often represent an organizations data and analytics maturity.

  • Descriptive analytics interpret historical data to better understand what has happened, which is the domain of Business Intelligence (BI).
  • Predictive analytics uses numerous techniques to analyze data for the purpose of making future predictions and is categorized as advanced analytics.
  • Prescriptive analytics suggests the actions required to make future predictions happen. As organizations progress along the analytics maturity curve so does the complexity of projects, their cost and value returned to the organization.


What are some common characteristics of top performing organizations that succeed in gaining value from data analytics, and what can we learn from bottom performers? Top performers exhibit the following:

  • A very clear understanding of business requirements.
  • Key stakeholder buy-in maintains momentum.
  • Analytics and business goals are closely aligned.

Conversely, Bottom Performers Exhibit:

  • Poorly or inaccurately defined business requirements.
  • Apathetic stakeholders or little desire to gain momentum.
  • Inability to communicate analytics goals in simple business terms.

* NOTE: Many failed analytics efforts can be traced to the fundamental error of focusing on technology.


  1. Define Your Vision – Develop a clear vision of your future business and analytics state.
  2. Engage the Right People – Subject matter experts (Business, IT, Data, Analytics, Privacy, etc.)
  3. Never Stop Improving – Maintain focus on continual improvement and momentum.


Knowing your current capabilities will go a long way towards setting the right vision for how analytics will help the organization achieve objectives. Utilize the following checklist to guide conversations.

  • What value to we expect to derive from our analytics efforts?
  • How closely linked in analytics to our organizational strategy?
  • What is the demand for analytics in our organization?
  • Do we have the backing at all levels of leadership?
  • What is our motivation and ability to sustain efforts?
  • How complex and diverse are our analytics plans, including deploying results?
  • Are our data and infrastructure strategies aligned?
  • Have we identified the investment needed to meet our requirements?
  • What data privacy and security requirements do we need to be aware of? Note: 151 data privacy specific bills introduced in 21 states in 2019, CCPA goes into effect 1/20.


The following roles/skill sets are almost always present in highly successful analytics organizations.

  • Translator / Project Management Specialist
  • Database / Data Warehouse Specialist
  • Data Analyst / Data Visualization Specialist
  • IT / System Specialist
  • Data Security / Data Privacy Expert


The following foster an environment for analytics efforts to succeed. Creating a high-performing center of excellence improves performance far beyond the analytics function.

  • Engage Skilled Talent
  • Ensure good communication with all levels of leadership, encouraging collaboration.
  • Start with a real, actionable, costly business pain that is aligned to the organizations strategic plan.
  • Agree to measures of success before implementation.
  • Broadcast analytics success and continually promote capabilities across the organization.


Including all stakeholders in continual improvement helps achieve operational excellence and ensures analytics success.

  1. Clearly define short and long-term business vision, goals and objectives. * The clearer the better.
  2. Gain a data understanding, identify all potential data sources (internal, external, public & private) to ensure that data supports business goals.
  3. Define specific requirements (data preparation, transformation, feature generation, etc.) & select technical/modeling approach.
  4. Evaluate initial results to determine if defined requirements (business & technical) have been met.
  5. Develop an implementation plan for implementing results into production.
  6. Continually evaluate, improve and adopt a data first approach.