There was once a time when everyone had a spreadsheet \u2014 or even the dreaded SpreadMart \u2014 and no one had the right answers to business questions. Wise leaders will avoid the pitfalls of bad business analytics<\/a> by not simply embracing new tools but by establishing a clear and strategic set of rules to govern them.<\/p>\n Note: If you don\u2019t know what a SpreadMart is (or you\u2019ve suppressed the memory), see the nightmare example below.<\/p>\n This image is an example of SpreadMart insanity. Each employee with 10 versions of the same budget \u2014 shifting rules, loose version control, general chaos. Yes, you may see SpreadMart-like practices come back in the form of flexible programs like DataBricks, Python, SQL, and you guessed it, ChatGPT. Still, our use of these programs doesn\u2019t have to be unruly or decentralized like the spreadsheet collections of yesteryear. Reject unruly data practices. Choose data governance strategy.<\/p>\n Data governance is not the same as data management, and every business leader should know the difference. Data governance strategy<\/a> refers to the decisions determining how a business stores, shares and uses data assets (like customer preferences, purchase patterns, and so on).<\/strong> Ideally, executives lead data governance with guidance from IT professionals in close collaboration with the people and teams that use data systems.<\/p>\n Examples can include shared or centralized:<\/p>\n Data management happens when the professionals who design and maintain data systems implement those strategies. For a long time, business leaders have relied heavily on IT teams (data managers) to set their company\u2019s data standards. These leaders may have incorrectly believed data governance strategy was beyond their understanding or control.<\/p>\n Neither business leaders nor IT professionals should blindly depend on one another. Why? Because each field brings a wealth of knowledge that is unique, important and cannot function properly without the other.<\/strong> When data scientists build systems without executive (and end-user) input \u2014 or when executives make assumptions about the quality of their data \u2014 even the most advanced tools can become misleading. Likewise, leaders should not blindly trust their teams to handle company data carefully and consistently.<\/p>\n A colleague recently shared a cringeworthy example from their top-ranked healthcare college: During a strategic summit, it became clear that each department had used a different definition of \u201cstudent\u201d in a pricey, year-long data collection initiative. It would be problematic for any organization to have inconsistent definitions of their customer.<\/p>\n But in the health sciences \u2014 with its countless specialties, certifications and academic pathways for \u201cstudents\u201d to pursue \u2014 the error rendered the year\u2019s data almost entirely unusable. To make matters worse, the discovery happened in front of the school\u2019s board and clinical partners, undermining their reputation.<\/p>\n How do your teams define customer, sale, retention and other key terms? How are these terms used in data systems? If you don\u2019t know, ask. Their answers may surprise you. Variability or bias in your teams\u2019 assumptions could introduce flaws to the data systems that shape your decisions and impact your revenue.<\/strong> These are the hidden cracks that cause good businesses to crumble.<\/p>\n Even if you know nothing (or very little) about data science, you need to play an active role in setting your company\u2019s data standards. How you approach data governance strategy will depend on your company\u2019s type, size, stakeholders and funding.<\/strong> Agile and entrepreneurial operating systems (EOS) are two popular change management<\/a> models to possibly adopt as you lead.<\/p>\n However, just because a model is popular doesn\u2019t mean it\u2019s right for your company. Think about how the pace, ethos, demands and rewards of these models would resonate with your teams. A third-party vendor specializing in data governance strategy can help you design a tailored approach.<\/p>\n Regardless of process, your teams will need you to:<\/p>\n Listening and learning are important, but what do you do with your collected knowledge? Some companies might require a third-party vendor to help turn insights into action plans. Whether you DIY or work with outside help, this webinar<\/a> offers tips on how to get started.<\/p>\n Just twenty years ago, a degree in data wasn\u2019t a thing. Today, junior executives are graduating with diverse specializations in business intelligence. They speak data natively and won\u2019t be constrained by any one tool your company might use. Give the cowboys a chance. Learn from them. Welcome their product demos. Offer them opportunities to learn from your knowledge and experience in return. Finally, partner with them to ensure your company can reach its full analytic potential.<\/p>\n\n ![\"SpreadMart](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2023\/04\/SpreadMart-Image-300x274.jpg\")
<\/a><\/p>\nWhat is Data Governance?<\/h2>\n
\n
Bad Data Hurts Good Companies<\/h2>\n
How to Protect Your Company\u2019s Future With a Data Governance Strategy<\/h2>\n
\n
Conclusion<\/h2>\n