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Building a Rock Solid Data and Analytics Strategy

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Every organization’s analytics strategy should be connected to its data strategy; and every organization should have a data strategy that supports its business strategy

Like the lyrics in the children’s song Dem Bones, “the hip bone’s connected to the thigh bone”, your data strategy is connected to your analytics strategy – or it should be! Further, your analytics strategy should support your business strategy and be driven by it and your data strategy should support your analytics strategy. Both the data strategy and the analytics strategy must include executive governance and working committee participation to be successful.

Yet too often we see lots of tactical activity without much strategy resulting in disparate, ungoverned data marts that may meet initial needs but aren’t sustainable and don’t tie into a governed, sanctioned data environment.

Defining a data strategy is becoming even more complex and challenging in light of big data as the variety of data types and its volume grows. Part of an effective data strategy for the 21st Century is an analytics strategy, which states how the organization will address its decision-making and data-oriented, analytical needs.

Components of an Analytics Strategy

An analytics strategy must include 5 key areas: Objectives, Business Needs, Business Value, Technology and Organization.

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These elements must be defined and communicated within the organization so everyone is aware of the corporate charter for analytics, its scope, current vs. futures state, technology and tools and the governance processes. You probably have most of these elements defined but not made available outside a limited audience in IT or certain areas of the business.

Without the communication of this strategy and a voice in the analytics program governance, business units develop “shadow IT” organizations, increasing the overall total cost of analytics. Even worse, some potential best practices and valuable assets are locked in a single department instead of being shared. These elements should be contained in a document that is made available to everyone in the organization, ideally through an analytics collaboration intranet site or portal.

The following describes the components of each of these five key pillars.

  • Objectives should include the overall organization analytics mission statement and organizational structure, as well as background and purpose. The current state and history until now should also be documented. Lastly, the analytics objectives, scope, and the future state should be documented.
  • Business Needs should include a summary of all functional data and analytic business needs and gaps as well as the desired to-be state. Every business area believes it’s needs are the most important, so a method of aligning and prioritizing needs and a roadmap to deliver them across the organization is important. Executive governance and working committees should address alignment and prioritization.
  • Business Benefit or Value helps ensure ongoing data and analytics program funding. Most organizations have a threshold at which they require a formal business case, but every analytic request should have some expected business value in terms of revenue increases, decreased costs or improved efficiency. Larger projects shroud use clear business Key Performance Indicators (KPIs) to express business value. Ideally, an existing corporate KPI framework can be leveraged. A business value methodology should be in place, e.g., internal rate of return, etc. Lastly, after a key project or initiative has been completed, a post implementation value assessment should be conducted with results broadly communicated.
  • Technology includes your information categories or taxonomy, architecture and standards, and tools and applications. The information categories ad taxonomy should be part of your overall data strategy; semantic views to support the business should be developed. Architecture and standards are critical and are continuously updated to meet ongoing business needs and analytics maturity. The right tools and applications are critical, especially to support end user self-service. Analytics tools standardization and centralized licensing is highly desirable to reduce license, maintenance and administration costs. This area should also address various data types, metadata management, data security and technology platforms.
  • Organization is the people part of analytics, often the Achilles heel of most programs. It includes analytics leadership in the form of a chief analytics or data officer, the executive governance committee. It also addresses the working committee and program management capabilities. Key executive and working committee meeting notes should be published to the analytics portal. The Data Governance steering committee and working committee including data stewards are also critical. A short and long term strategy roadmap and milestones should exist and be continuously maintained and published to the portal. An analytics dashboard with analytics focused KPIs should also be published. An education and training program for both end users as well as IT is critical. Onboarding for new users as well as ongoing community of interest or practice communication helps ensure business and IT alignment as well as productivity. Finally, end user support should include levels for application vs. data /data base support.

Once you have defined your Data Strategy and Analytics Strategy, they must be operationalized. One of the most effective ways to do so is through a Business Analytics Competency Center (BACC) or Center of Excellence. Many BI IT teams have a shared services function or a program office. However, these areas focus more on the data, tools and technology elements of analytics and less so on the business alignment and organization elements. A full time, experienced BACC leader should be appointed. The leader organizes the strategy document which includes identifying the key people and helps ensures alignment between the business and IT on both the overall analytics strategy as well as the data strategy and governance, disseminates overall knowledge management, and fortifies best practice sharing.


Does developing a data strategy and analytics strategy sound overwhelming? It needn’t be. Start by documenting what you have and identifying the gaps. Then develop a plan to tackle the gaps. Remember to keep aligning it back to your business strategy; that will help prioritizing which gaps to address first.


Patricia L. Saporito

Patricia L. Saporito is an industry expert and thought leader with over 20 years in data warehousing and analytics. She helps companies assess and evolve their analytic maturity. Pat is an expert on insurance industry analytics and is author of the book, Applied Insurance Analytics, published by FT Press/Pearson. She has served on the advisory board for the Big Data & Analytics Master Program at Stevens Institute of Technology, and has been an advisor to several financial technology startup organizations.

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