Information Life-cycle Management Strategy

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Information Life-cycle Management Strategy 2014-09-09T16:32:50+00:00

In today’s business, most if not all processes are run or assisted with technology. Each system and process generates its own data.  Therefore, to monitor or improve processes, the associated data must analyzed and insights drawn.  To ensure the data can accurately lead to insights, data quality, consistency and comparability must be created and maintained.  The processes and governance related to this comprises the Information Life-Cycle Management Strategy.

When a strategy is created and implemented, innovative collaboration between disparate parts of the organization can happen because data are comparable and consistent.  Key Performance Metrics (KPI’s) can be created to truly indicate good and poor performing areas of the organization.  Planning and forecasting also becomes more accurate resulting in a better performing organization overall.  While a Information Life-Cycle Management Strategy may not be at the top of every executives mind, it is a key to unlocking the organization’s potential.

netlogx Information Architects are experienced in managing data in large and small organizations.  We take the mystique out of this work and get to practical results.  By “doing our homework” to understand the business first, we begin with the end in mind.  Instead of just simply analyzing systems’ data to identify where improvements can be made, the netlogx team focus on the key data that drives the business and targets the areas where the most value can be gained.  Using a framework, such as the Method for an Integrated Knowledge Environment (MIKE2.0), a systematic and comprehensive approach can be developed.

The netlogx team works with the management to implement appropriate governance to control and guide new systems’ data and improve existing systems’ data.  We will then work with all levels of the organization to roll-out the management strategy and complementary governance structure.

Benefits

Increased productivity is realized in reduced time to gather and analyze the data.  Also, the time to insights in shortened due to not needing to heavily manipulate the data or validate it’s accuracy.