Effective Solutions Through Partnership

How to Manage Data to Achieve Business Goals

Best Practices, Data Management, Project Management, Project Management Professional (PMP)

Data Management

By Tim Cleary

Businesses and regulators are continuously faced with a flood information that is out of control. It’s a veritable information firestorm in the complex data management environment. Even more, information has many different looks and packaging, making it is easy to get lost in the maze. Here’s just a small sampling of the types of data you may encounter:

  • Knowledge management
  • Business intelligence
  • Information management
  • Content management
  • Data warehousing
  • Data cubes
  • Data analytics
  • Key performance indicators
  • Data mining dashboards
  • Scorecards
  • Data marts
  • Decision support systems
  • Document management systems

So, what do you do with all that data? Data management programs are typically tied to organizational objectives and are intended to achieve specific outcomes. First you must decide on the outcome you would like to achieve. Some outcomes include: Effective analysis and decision-making, improved performance, competitive advantage, innovation, lessons learned, knowledge transfer, and the development of social networks, collaboration, and teaming.

To achieve your desired outcome, you must effectively manage the data by coming up with a formal process for determining what information you have and then devising ways to make it easily available. Key data management components include:

  • Awareness: (Knowing What You Know) – about people, skills, markets, competitors, customers, alliances, suppliers, the environment, regulation, legislation, and other factors that are key to organizational success
  • Sharing, Capturing, and Storing Data – electronically and/or through communities of practice, informal groups, knowledge sharing and best practice workshops, training, mentoring, and using collaboration tools and approaches
  • Using the Data in the Workplace – finding and applying the appropriate information through operational systems, having data embedded in processes, and encouraging the appropriate data sharing, capturing and transferring of behaviors through reward systems that recognize the use of data in decision-making and delivery
  • Defining Organizational Priorities – like customer satisfaction, improved processes, improved innovation, profits, and employee satisfaction

At the beginning, and throughout the development of a new data management and intelligence program, you should seek to envision a new model for workforce effectiveness. This vision should precede any system or process planning and define the end business state from a benefits and usage point of view.

While this vision is useful for identifying priorities and creating excitement for the program among managers, it will also be useful in communicating the new workday to staff. Delivering the vision to staff will require communication, training, incentives, and many other program attributes associated with the data management changes.

Data management doesn’t have to be daunting—simply keep your eye on what you want to achieve, put into place a data management plan that supports that outcome, and inform staff along the way so they too are aligned with your vision.

About the Author: Tim is an Executive Consultant with project management, consulting, business development, and sales experience spanning business transformation, technology adoption, change management, shared services, knowledge management, learning and development, and enterprise cost reduction. Tim has more than 30 years of business system development and implementation experience focused on Energy – Utilities/Oil & Gas, Technology, Media & Entertainment, Financial, Federal, State, and Life Sciences (Disability, Medicare Claims Management, and Conservation) organizations. Tim has lectured at the university level and at business conferences focusing on technology adoption, change/knowledge management, and business issues and solutions.

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