
<p><em>Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. Our <strong>BI Best Practices</strong> demystify the analytics world and empower you with actionable how-to guidance.</em></p>



<h2 class="wp-block-heading">Building a Data-Driven Future</h2>



<p>It’s important for everyone at a company to have the data they need to make decisions. However, if they just work with their data team to retrieve specific metrics, they are missing out. Data teams can provide a lot more insights at a faster rate, but you will need to know how to work with them to make sure that everyone is set up for success.&nbsp;</p>



<p>Data teams can be thought of as experts at finding answers in data, but it’s important to understand how they do that. In order to get the most value out of your collaboration, you need to help them understand the questions that matter to you and your team and why those questions need to be answered. There are a lot of assumptions that get built into any analysis, so the more the data team knows about what you are looking for, the more knowledge they may find as they explore data to produce their analysis. Here are four tips to make more productive requests from members of your data team:&nbsp;</p>



<figure class="wp-block-image size-large"><a href="https://www.sisense.com/blog/how-to-reduce-insight-erosion-in-collaborative-data-analysis/" target="_blank" rel="noreferrer noopener"><img decoding="async" src="https://cdn.sisense.com/wp-content/uploads/sisense-blog-Reducing-Insight-Erosion-in-Collaborative-Data-Analysis-20191105-banner1.jpg" alt="" class="wp-image-90118"/></a></figure>



<h2 class="wp-block-heading">Approach data with an open mind</h2>



<p>It’s important to treat the data request process as an open-ended investigation, not a way to find data that proves a point. A lot of unexpected insights can be found along the way. Make your goal to ask questions and let your data team search for the answers. This approach will allow you to <a href="https://www.sisense.com/blog/real-difference-reporting-business-intelligence/" target="_blank" rel="noreferrer noopener" aria-label=" (opens in a new tab)">get the best insights</a>, the type of unknowns that could change your decision for the better. If you put limitations on what you’re asking the data, you’ll end up putting limitations on the insights you can get out of your inquiry.&nbsp;</p>



<p>To really dig into this, think about how questions are answered scientifically. Scientists treat any bias as an opportunity for the insight to be compromised. For example, let’s say you are looking to improve customer satisfaction with your product. Requesting a list of customers with the highest and lowest NPS scores will give you a list of people who are happiest or most frustrated, but it is not going to let you know how to improve customer satisfaction. This request puts too much attention on the outliers in your customer base rather than identifying the key pain points. That’s part of the picture, but not all of it. If you’re trying to create a program that targets your goal, let your data team know the goal, give them a few optional starting points, and see what they come back with. They might surprise you with some sophisticated analysis that provides more insight and helps you launch a fantastic program.&nbsp;</p>



<h2 class="wp-block-heading">Start with a conversation, not a checklist</h2>



<p>The single biggest mistake a line-of-business professional can make when requesting data is to present a data expert with a list of KPIs and tell the data team to just fill in the blanks. This approach misses so much of the value a data team can provide. Modern data teams have technology and abilities that allow them to go much further than just calculating numbers. They can guide analytical exploration through flexible, powerful tools to make sure you’re getting the most valuable insights out of your data.</p>



<p>Instead of a list of metrics, think about starting your data request as a meeting. You can provide the business context needed and a list of questions that you want answered. You can even present some initial hypotheses about what those numbers may look like and why they might move in one direction or another. This is a great way to kick off the conversation with your data counterpart. From there, you can benefit from their experience with data to start generating new and more informed questions from their initial inquiries. The data team’s job is to get you information that helps you be more informed, so give them as much context as possible and let them work as a problem solver to find data-driven recommendations.</p>



<h2 class="wp-block-heading">Data should recommend actions, not just build KPIs reports</h2>



<p>A lot of standard business KPIs measure the results of company efforts: revenue, lead conversion, user count, NPS, etc. These are important statistics to measure, but the people tracking them should be very clear that these numbers track <em>how</em> the company is moving, not <em>why</em> it is moving that way. To make these data points actionable, you need to take analysis further. Knowing that your NPS is going up or down is useless if it doesn’t inform a customer team about the next step to take.&nbsp;</p>



<p>A good data team will <a href="https://www.sisense.com/blog/knowledge-graphs-why-every-company-needs-them/" target="_blank" rel="noreferrer noopener" aria-label=" (opens in a new tab)">map important KPIs</a> to other data and find connections. They’ll comb through information to find the levers that are impacting those important KPIs the most, then make recommendations about how to achieve your goals. When you get a list of levers, make sure to understand the assumptions behind the recommendations and then take the right actions. You can always go back to those KPI reports to test if the levers are having the intended effect.</p>



<h2 class="wp-block-heading">Data requests are iterative, give the data person feedback</h2>



<p>Communication about data should not end when the data has been delivered to you. It’s important to dig into the analysis and see what you can learn. Instead of reporting that data or taking action on it right away, you should check with your dashboard creator to make sure that he or she can verify that you’re reading all of the data properly and that the next steps are clear. There are a lot of ways to misinterpret data; a good way to prevent mistakes is to continue communicating.</p>



<p>Even if you’ve gotten the right takeaways from the data, it’s still good to consult with your dashboard creator and go over your interpretation of the information so they know how you read data. You may need a follow-up meeting to restart with the overall question you want to answer, then see what additional data needs to be collected or what modifications are needed to make the report or <a href="https://www.sisense.com/blog/4-design-principles-creating-better-dashboards/" target="_blank" rel="noreferrer noopener" aria-label=" (opens in a new tab)">dashboard work best</a> for your intended use-case.</p>



<figure class="wp-block-image size-large"><a href="https://www.sisense.com/blog/how-to-reduce-insight-erosion-in-collaborative-data-analysis/" target="_blank" rel="noreferrer noopener"><img decoding="async" src="https://cdn.sisense.com/wp-content/uploads/sisense-blog-Reducing-Insight-Erosion-in-Collaborative-Data-Analysis-20191105-banner1.jpg" alt="sisense-blog-Reducing-Insight-Erosion-in-Collaborative-Data-Analysis" class="wp-image-90118"/></a></figure>



<p><em>Christine Quan is a seasoned data and analytics veteran, focused on data visualization theory and building tools to empower data teams. She&#8217;s an expert at constructing SQL queries and building visualizations in R, Python, or Javascript.</em></p>


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