Back in pre-silicon times, "data governance" meant cleaning old records out of the filing cabinets. Today, it's an infinitely thornier issue.[/caption] Data governance is one of those amorphous terms that businesses struggle to define, much less implement. In broad strokes, it involves the implementation of processes and methods that govern how data analysts and others within an organization can handle and process data. That sort of control—even in the name of regulations and quality—is liable to spark political infighting within even the most sedate organization. Does the need to quickly analyze data outweigh the risks of regulatory fines? Will the implementation of data security interfere with the efficiency of analysis? But with more and more regulations in place, business executives and IT departments have little choice but to wrestle with the issue. “The stakes are high when it comes to data-intensive projects, and having the right alignment between IT and the business is crucial,” Michele Goetz, an analyst for Forrester, wrote in an Oct. 4 corporate blog posting. “Data governance has been the gold standard to establish the right roles, responsibilities, processes, and procedures to deliver trusted secure data.” Policies and procedures can weed out bad data and faulty implementations, she added, making governance more crucial than ever. However, most governance is focused on risk avoidance and led by a company’s IT department, with the business side of things contributing relatively little to the discussion. That massive amount of management and process, in turn, “takes time and stifles experimentation and growth.” Yet companies need data analysis to happen in a speedy enough way to make said data actually useful to strategy; recall how Nucleus Research, in a study released over the summer, suggested that the average half-life of data for tactical decision-makers is 30 minutes or less, while strategically-oriented data tends to go stale after only a few days. As a result, days’ worth of check and balances can rapidly degrade the useful of data. “Data governance needs to evolve to develop policies that are not just about what you can’t do, but what you can do,” Goetz wrote. “If you really want your data governance program to mature and truly be business led, the greatest pivot will be for IT to give up control of the data and the facilitation of data governance.” In other words, give business control: “Have the business take over and define the amount of governance and control it wants over its use. Have the business create a framework that aligns trust in data with use.” Whether or not one agrees with Goetz that business needs more control over data governance, the fact remains that the increasing amount of data handled by organizations—and the increasing pressure to analyze it for insight—can lead to slowdowns and paralysis without a plan and structure. Some organizations are wrestling with this brave new world by hiring chief data officers to handle everything from data stewardship to communicating data schemas. Others are embracing self-service B.I. solutions that help automate and wrangle data without the need for quite so much active effort on employees’ part. Image: Everett Collection/Shutterstock.com Does Business or IT Drive Data Governance?
[caption id="attachment_5022" align="aligncenter" width="500"] Back in pre-silicon times, "data governance" meant cleaning old records out of the filing cabinets. Today, it's an infinitely thornier issue.[/caption] Data governance is one of those amorphous terms that businesses struggle to define, much less implement. In broad strokes, it involves the implementation of processes and methods that govern how data analysts and others within an organization can handle and process data. That sort of control—even in the name of regulations and quality—is liable to spark political infighting within even the most sedate organization. Does the need to quickly analyze data outweigh the risks of regulatory fines? Will the implementation of data security interfere with the efficiency of analysis? But with more and more regulations in place, business executives and IT departments have little choice but to wrestle with the issue. “The stakes are high when it comes to data-intensive projects, and having the right alignment between IT and the business is crucial,” Michele Goetz, an analyst for Forrester, wrote in an Oct. 4 corporate blog posting. “Data governance has been the gold standard to establish the right roles, responsibilities, processes, and procedures to deliver trusted secure data.” Policies and procedures can weed out bad data and faulty implementations, she added, making governance more crucial than ever. However, most governance is focused on risk avoidance and led by a company’s IT department, with the business side of things contributing relatively little to the discussion. That massive amount of management and process, in turn, “takes time and stifles experimentation and growth.” Yet companies need data analysis to happen in a speedy enough way to make said data actually useful to strategy; recall how Nucleus Research, in a study released over the summer, suggested that the average half-life of data for tactical decision-makers is 30 minutes or less, while strategically-oriented data tends to go stale after only a few days. As a result, days’ worth of check and balances can rapidly degrade the useful of data. “Data governance needs to evolve to develop policies that are not just about what you can’t do, but what you can do,” Goetz wrote. “If you really want your data governance program to mature and truly be business led, the greatest pivot will be for IT to give up control of the data and the facilitation of data governance.” In other words, give business control: “Have the business take over and define the amount of governance and control it wants over its use. Have the business create a framework that aligns trust in data with use.” Whether or not one agrees with Goetz that business needs more control over data governance, the fact remains that the increasing amount of data handled by organizations—and the increasing pressure to analyze it for insight—can lead to slowdowns and paralysis without a plan and structure. Some organizations are wrestling with this brave new world by hiring chief data officers to handle everything from data stewardship to communicating data schemas. Others are embracing self-service B.I. solutions that help automate and wrangle data without the need for quite so much active effort on employees’ part. Image: Everett Collection/Shutterstock.com
Back in pre-silicon times, "data governance" meant cleaning old records out of the filing cabinets. Today, it's an infinitely thornier issue.[/caption] Data governance is one of those amorphous terms that businesses struggle to define, much less implement. In broad strokes, it involves the implementation of processes and methods that govern how data analysts and others within an organization can handle and process data. That sort of control—even in the name of regulations and quality—is liable to spark political infighting within even the most sedate organization. Does the need to quickly analyze data outweigh the risks of regulatory fines? Will the implementation of data security interfere with the efficiency of analysis? But with more and more regulations in place, business executives and IT departments have little choice but to wrestle with the issue. “The stakes are high when it comes to data-intensive projects, and having the right alignment between IT and the business is crucial,” Michele Goetz, an analyst for Forrester, wrote in an Oct. 4 corporate blog posting. “Data governance has been the gold standard to establish the right roles, responsibilities, processes, and procedures to deliver trusted secure data.” Policies and procedures can weed out bad data and faulty implementations, she added, making governance more crucial than ever. However, most governance is focused on risk avoidance and led by a company’s IT department, with the business side of things contributing relatively little to the discussion. That massive amount of management and process, in turn, “takes time and stifles experimentation and growth.” Yet companies need data analysis to happen in a speedy enough way to make said data actually useful to strategy; recall how Nucleus Research, in a study released over the summer, suggested that the average half-life of data for tactical decision-makers is 30 minutes or less, while strategically-oriented data tends to go stale after only a few days. As a result, days’ worth of check and balances can rapidly degrade the useful of data. “Data governance needs to evolve to develop policies that are not just about what you can’t do, but what you can do,” Goetz wrote. “If you really want your data governance program to mature and truly be business led, the greatest pivot will be for IT to give up control of the data and the facilitation of data governance.” In other words, give business control: “Have the business take over and define the amount of governance and control it wants over its use. Have the business create a framework that aligns trust in data with use.” Whether or not one agrees with Goetz that business needs more control over data governance, the fact remains that the increasing amount of data handled by organizations—and the increasing pressure to analyze it for insight—can lead to slowdowns and paralysis without a plan and structure. Some organizations are wrestling with this brave new world by hiring chief data officers to handle everything from data stewardship to communicating data schemas. Others are embracing self-service B.I. solutions that help automate and wrangle data without the need for quite so much active effort on employees’ part. Image: Everett Collection/Shutterstock.com