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This is part of Solutions Review\u2019s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Mighty Canary<\/a> CEO Tabrez Syed outlines key data friction signs and tips to know and consider right now. At the end of the day, the business is what matters and reports should serve as supporting roles.<\/p>\n In a decision-making environment, analytics solutions<\/a><\/strong><\/span> are a window into what’s happening with the business. They should not be an end in themselves. Spending valuable decision time talking about the report is like talking about the glass at the zoo instead of the lion behind it.<\/p>\n Don\u2019t get me wrong, questioning and verifying that data is correct is a good thing. But superficial questions about a report, especially ones that get asked repeatedly, are a red flag. It could mean a few things. Maybe the data actually isn\u2019t right. Or maybe the data is right, but it isn\u2019t trusted due to previous reporting errors or mistakes made in the past. Or lastly, your colleagues want to avoid hard and meaningful conversations about the business. No matter the reason, all paths point to data friction.<\/p>\n Separating dashboard critiques<\/a><\/strong><\/span> from decision-making conversations is not only a good idea for the sake of efficiency, but it can also help you avoid the second symptom.<\/p>\n When questions and arguments are focused on the report and not the underlying problem, more reports and dashboards tend to follow. Many organizations tend to think that creating another report can resolve the question at hand.<\/p>\n The desire to add another report is often due to stakeholders not knowing how to articulate their criticism of the original report in a sufficiently nuanced way. So they say “we need a new report” rather than “this report doesn’t reflect my need, because of XYZ”.<\/p>\n Unfortunately, this approach leads to scattershot attempts at a solution instead of a clearly defined path to a specific destination.<\/p>\n It takes time for your stakeholders to learn how to parse through, consume, and interact with new reports. Time spent understanding a new report takes time away from making critical business decisions.<\/p>\n It’s also a compounding problem for your data team. If they’re spending all of their time supporting old reports and building a bunch of new ones, it\u2019s harder to be strategic in driving an effective analytics strategy to keep up with your dynamic business.<\/p>\n In an organization with a healthy understanding of data and analytics, reports are firmly grounded in a business need. Under these conditions, reports will typically evolve with the business rather than proliferate exponentially.<\/p>\n If your data team is too busy building new reports, you may be more susceptible to data quality issues, leading to constant questions like the one above. In addition to slowing down decision velocity, constant firefighting and double-checking the same numbers is likely to lead to burnout among your data team, compounding these problems.<\/p>\n In contrast, having deep trust in your data refocuses the conversation around what the data is saying rather than how it’s being presented, leading to a positive, upward spiral rather than a negative one.<\/p>\n Questioning data quality rather than listening to what the data is telling us is just another example of data friction inhibiting organizational shifts. Trust is built over time, and in the journey towards a culture of data-driven decision-making, one bad number can set you back months.<\/p>\n Even so, data quality issues<\/a><\/strong><\/span> are inevitable – having visibility into when and what these issues are affecting is crucial to building trust. How do you communicate these issues in a way that doesn’t discourage usage or trust? Keep reading for a few tips.<\/p>\n To support a positive, data-literate culture<\/a><\/strong><\/span> that enables meaningful, efficient conversations around data, you need the technical capabilities to provide visibility and trust. Utilize tools that help you signal the status of a dashboard, answer questions in one place, and automate your team\u2019s support functions.<\/p>\n It\u2019s crucial to ensure your data stack doesn\u2019t have any gaps. The proliferation of tools in today’s data environment means choosing the right set for the task at hand. There are a lot of great data and analytics tools<\/a><\/strong><\/span> out there. Make sure they are compatible and understandable to keep your data friction low.<\/p>\n Once you’ve built up more trust with your stakeholders and implemented the right tools, you can set expectations and a plan of action for your data. Your data consumers need to know when data will be updated, how reporting solutions will change over time and the proper channels for feedback and ad-hoc requests. This will enable more focused, actionable discussions when it’s time to get in a room and make a decision.<\/p>\n
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Data friction is a common ailment among even the most advanced companies. Overcoming data friction is crucial to data leadership and being truly data-driven<\/em>. Below we\u2019ll dive into the signs you have data friction in your organization and how to combat it.<\/p>\nWhat are the Tell-Tale Signs Your Organization is Dealing with Data Friction?<\/strong><\/h2>\n
1. You’re talking about the report – not the business<\/strong><\/h3>\n
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<\/a><\/p>\n<\/div>\n\t\t<\/div><\/div>\n2. The number of dashboards is growing, but not your decision velocity<\/strong><\/h3>\n
3. “Is that dashboard right?” de-rails meetings<\/strong><\/h3>\n
Tips to Combat Data Friction<\/strong><\/h2>\n
1. Mend the cycle of distrust<\/strong><\/h3>\n
2. Evaluate the tools in your data stack<\/strong><\/h3>\n
3. Set expectations and a plan of action for data processes<\/strong><\/h3>\n
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