A Share the Data client project plan

Stephen McDaniel, Chief Data Officer AdvisoryHere's an actual Share the Data project.  This is a simplification of a plan developed with a real client, with details changed to remove any identifying information.  While every project is unique, this is not an unusual engagement. Share the Data engagements range in length from 3 days to several weeks, depending on project complexity and client resource availability.


The client is approaching a key turning point in their operational history- going beyond traditional reporting with mainframe technologies to an advanced data warehouse with web-based reporting, dashboards, self-service analytics and even advanced analytics to optimize customer recruitment, operational management and improve the overall customer experience.

All of these areas are a dynamic, iterative undertaking with each area interlinked and interdependent.  It is critical that the management team at the client work to prioritize, fund and support each iterative phase of these projects.  Likewise, it is imperative that each phase of these projects show an acceptable rate of return (or greater!) for the time and money invested in these initiatives.  Better data and analytics are not an end in themselves, but rather a means to a more profitable and competitive company.


The objective of the contract is to augment the client team with experienced data warehousing and analytic professionals with clear, demonstrated success in implementing analytic tools and practices.  We will provide both strategic and tactical guidance via an analytic roadmap, hands-on data warehousing and analytic work and socialization of the tools, techniques and results of this work.  Ultimately, our objective is to create a self-sufficient culture at the client that will integrate the value of analytic in their tactical and strategic decision-making.


The initial scope of the project may include

1)     Roadmap of future data warehouse and analytic projects

  1. Built in collaboration with the management team
  2. Prioritized based on risk and reward estimates
  3. Scoped based on iterative implementation
  4. Foresee data needs that ultimately help answer business questions

2)     Auditing of migrated mainframe data

  1. Identify and optimize quality for key data fields
  2. Develop automation of data checking
  3. Work with business subject matter experts to ensure that data quality issues are researched, understood and addressed as practical
  4. Key data areas with critical need include customer address data, duplicate customer records and to be reconciled web and call center data sources


3)     Migration from mainframe reporting

  1. Some errors likely exist in the mainframe reports for a variety of reasons including old business rules / assumptions and infrequently encountered data errors

4)     Marketing opportunities

  1. A wide range of opportunities appear possible in this area including customer segmentation, marketing program valuation, customer valuation, analytic-based acquisition programs and many others to be identified in collaboration with the marketing director
  2. Gain a better understanding of the top customers, overall buying patterns and areas of their buying behavior that the client is not capturing but could or should be seeking
  3. Provide an exhaustive dashboard/dossier application for marketing and the sales force that will answer customer lifetime information questions


5)     Poorly defined data terms and metrics

  1. Develop a data dictionary that defines term and algorithms.

6)     Executive needs

  1. Executive-tailored dashboards allowing objective based monitoring of the business. This will be linked to the line of business dashboards so that drill-down to more detailed questions is possible
  2. Development of predictive infrastructure to help with strategic decision-making
  3. Forecasting capabilities to enhance planning and inform spending and capital investment decisions

7)     Various areas of the business

  1. Exhaustive dashboards with drill-down for division and department managers relative to their key objectives
  2. Accumulate data needs from business leaders and translate them back onto the strategic data warehouse and analytics road-map
  3. Infrastructure and tools suggestions to meet identified analytic needs
  4. Data warehouse and self-service analytics
    1. Provide standard data views that relate to common business questions
    2. Work with selected team members to train them in basic and advanced business analytic tools and techniques
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