In the last three decades, there has been an tremendous growth in the area of information processing needs of data-driven businesses in government, science, and private industry. Businesses compete on capturing, integrating, and analyzing data to help knowledge workers make sound business decisions and create growth. Data integration across companies (legal entities) is one of the most challenging steps.
To date, three (3) key levels of data integration have been identified across enterprises. First is data access. During most merger & acquisition (M&A) activities, Corporate Development Officers settle for having access to data from each enterprise. All of the work goes into finding facets of data that relate to each other, supporting the same grain of analysis, and finding people knowledgeable enough to analyze each set. The manual and duplicative effort creates its own complications and delays to providing accurate insights.
Second, is pulling data into a common platform. Some companies are able to pull data into one of the platforms for subsequent analyses. While not integrated, this level of automation and access allows a single team to analyze data relevant to all companies in the portfolio with a common set of basic business rules.
The final model is nirvana, the consolidated data model. At this point, the data from each company augments each other. Executives can examine the impact of one company on another, time series analyses, forecasts, and pricing analytics. This level is rarely achieved early on in the lifecycle.
Each level of integration has its own set of complexities that requires a certain amount of time, budget, and resources to implement.
We created a methodology based on industry best practices to measure the readiness of an organization and its datasets against the different levels of data integration. Our Integration Level Model (ILM) and ILM tool are used to quantify an organization’s readiness to share data at a certain level of integration. It is based on the established and accepted framework provided in the Data Management Association (DAMA-DMBOK). It comprises several key data management functions and supporting activities, together with several environmental elements that describe and apply to each function. By scoring the maturity of a company’s systems and data in a pragmatic method, we can target specific areas of an integration to prioritize. This reduces the complexity, cost, and risk associated with multi-enterprise data integration, and accelerates the time-to-value of analytic insights.
https://www.researchgate.net/publication/269321702_A_qualitative_readiness-requirements_assessment_model_for_enterprise_big-data_infrastructure_investment
allen+cass consulting 