Continuing with the theme of business \/ IT partnerships, another example I\u2019ve been involved in is integrating business data into an IT managed environment \u2013 in this case, an enterprise data warehouse.\u00a0\n\n\nAs we all know, business agility very much depends on data agility. \u00a0\u00a0And, data agility often requires data integration to be truly robust.\u00a0 It\u2019s useful to do data discovery and analytics on a single source of data, but even more useful to follow up with analytics capabilities that leverage several sources that have some level of integration.\u00a0 According to Informatica, data integration projects are executed 5x faster in companies where the business and IT collaborate.\u00a0 If you add creative approaches into that mix, business agility can skyrocket!\n\n\nIn many companies, business areas are leveraging data in many ways without IT involvement, using a myriad of available tools.\u00a0 With so much data available, this approach makes sense for data discovery and insights.\u00a0\n\n\nHowever, I\u2019ve seen deepening concerns around data quality, data governance and master data as this trend continues.\u00a0 Without these, business insights can be useful, but it may be risky to make decisions based on them. \u00a0The consequences of less-than-optimal data quality can\u00a0be far-reaching and severe.\u00a0 A single mistake can cascade\u00a0throughout an organization.\u00a0 Questionable data can hinder performance and damage valuable customer relationships.\u00a0 It can\u00a0derail vital initiatives and put your entire organization at risk. \u00a0 According to a Pitney-Bowes white paper on The ROI of Data Quality, 81 percent of companies have difficulty generating\u00a0meaningful Business Intelligence. \u00a0Data inaccuracies are\u00a0largely to blame. \u00a077 percent of companies\u00a0believe they lose revenue\u00a0(12 percent, on average)\u00a0because of inaccurate and\u00a0incomplete contact data. \u00a0\n\n\nHowever, additional value (and confidence) can be gained by working with IT to integrate these ensuing business results into an enterprise data solution that can then incorporate data governance, data quality, and even master data as needed.\u00a0\n\n\nHere\u2019s a use case example: \u00a0data analysts at a prior company I worked with would query, explore and model various sources of data.\u00a0 Frequently, they would develop some extremely useful results, especially leveraging predictive analytics models.\u00a0 These results were interesting, and led to quite a bit of excitement from the business from an opportunity perspective.\u00a0\n\n\nBut where they really became useful was when they were integrated with additional standard data sources, already available in the enterprise data warehouse.\u00a0 We developed an architecture that allowed these business results (underwriting curves, customer scores, etc.) into the enterprise data warehouse, modeled in such a way that other relevant data could then be integrated along with these business results.\u00a0 The resulting integrated data was many times more useful in ensuing analytics, decision making, and operational integration.\u00a0 By taking this approach, the business data also became eligible for data governance oversight and data quality practices.\n\n\n\nThe benefits of this type of approach are numerous:\n\n\nEach success resulted in increased collaboration between business and IT\nThe solution allowed for business self-sufficiency in identifying opportunities\nThe business results that were identified as useful fell under the company\u2019s data governance program\nBecause the results were integrated into an IT supported solution, there was more organizational confidence in the resulting data\nThis solution encouraged the development of additional, more robust analytics capabilities and modeling\n\n\nThe next logical step - automate the use of the business results into analytics that feed operations, which will be my next blog topic\u2026\n\n\nThis is another real use case of ways that an organization can encourage a business \/ IT partnership.\u00a0 These partnerships will inevitably improve the balance between structure and flexibility that organizations struggle with today in the world of data and analytics.\n\n\nFor related articles in this series,click here.