{"id":7009,"date":"2024-01-19T00:00:00","date_gmt":"2024-01-19T05:00:00","guid":{"rendered":"https:\/\/www.sisense.com\/how-to-reduce-insight-erosion-in-collaborative-data-analysis\/"},"modified":"2024-09-23T15:43:55","modified_gmt":"2024-09-23T19:43:55","slug":"how-to-reduce-insight-erosion-in-collaborative-data-analysis","status":"publish","type":"post","link":"https:\/\/www.sisense.com\/blog\/how-to-reduce-insight-erosion-in-collaborative-data-analysis\/","title":{"rendered":"How to reduce insight erosion in collaborative Data Analysis"},"content":{"rendered":"

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

A shared goal<\/h2>\n

The goal of analyzing data is to translate insights into action, to combine the strengths of data experts and the line-of-business users in a synthesis that utilizes each person’s strengths. That all sounds great on paper, but in practice, it ends up producing a workflow with too much room for error, which we\u2019re calling the erosion of insights.<\/p>\n

In most companies, the process of going from data to actions is something like this: a business professional asks the data team to deliver information about a certain topic, the data experts collect that data into a dashboard and deliver it back to the business professional, who studies it and makes decisions using the insights from that dashboard. Information switches hands several times without any care taken to preserve the expertise of each party. The end result is a game of data telephone where insights are mangled or even lost before the decision-making stage.<\/p>\n

Dashboards are an easy opportunity to deliver analytics instead of insights. The creators and consumers of these dashboards both have responsibilities to improve the process described above and ensure that their expertise is preserved throughout it.\u00a0<\/p>\n

When do insights erode?<\/h2>\n

At the beginning of any dive into data, the intention is always to make great decisions based on the insights found there. For example, imagine that the leader of your customer success team asks the data team for information about churn. Deciding to investigate the data is a great first step in the process of minimizing that number.\u00a0<\/p>\n

Unfortunately, asking a data team to \u201cbuild a dashboard about churn\u201d is too general of a request to be useful. The value of a data team is that they can translate business questions into specific, quantifiable inquiries into a dataset. To remove their power to do this and instead allow the business expert to define what data is being collected for a dashboard will limit the potential of the inquiry.\u00a0<\/p>\n

When the dashboard is prepared and delivered by the data team, there are also plenty of opportunities for insights to be lost in translation. If the data team focuses on getting the most accurate data into the dashboard, they may create something that is analytically correct, but doesn\u2019t speak to the business user. Conversely, if they tailor the content to the user\u2019s initial question, they may miss opportunities to add their data expertise to the equation.<\/p>\n

\"\"<\/figure>\n

Any time information is passed from the business experts to the data experts or vice versa, there\u2019s an opportunity for insight erosion. The right approach isn\u2019t to minimize exchanges of information, it\u2019s to invest time and energy making those exchanges as robust as possible.<\/p>\n

Better insights through communication<\/h2>\n

What if that initial request for a dashboard was replaced by a conversation? Instead of a basic request for a dashboard about churn, try a conversation between the customer success team and the data team to nail down the data that\u2019s connected to churn. If the data team understands the bigger questions being asked, they\u2019ll be better able to translate that inquiry into the resulting dashboard.<\/p>\n

Every one of those exchanges is an opportunity for a conversation that can do a more complete job of educating both parties and preserving the value of the insights each has to offer. Investing in these conversations is a great way for everyone to use the process to gain a better understanding of each other\u2019s expertise. It\u2019s also a great way to introduce empathy into the process and develop an appreciation for the types of expectations each party has.<\/p>\n

Throughout the analysis process, chart creators and consumers need to stay focused on preserving analytical insights. Instead of prioritizing the creation of dashboards, the focus should be on finding the most accurate data to answer the root question and finding ways to ensure that knowledge doesn\u2019t erode before a decision is made. With this shared goal in mind and a commitment to a collaborative process, we\u2019re sure you\u2019ll be on your way to creating better dashboards, building stronger cross-team collaboration, and making smarter data-driven decisions.<\/p>\n","protected":false},"excerpt":{"rendered":"

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. Our BI Best Practices demystify the analytics world and empower you with actionable how-to guidance. A shared goal The…<\/p>\n","protected":false},"author":4,"featured_media":7205,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[50],"tags":[507,479,501,571,474],"application":[46,10],"buyer-role":[101,47],"buyer-stage":[],"department":[5,22,9,23,6,8,21,7],"industry":[177,417,25,418,423,11,471,424,420,431,425,426,427,421,428,13,38,429,430,12,422],"topic":[24,666,66],"class_list":["post-7009","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bi-best-practices","tag-dashboard-design","tag-data-analysis","tag-data-analyst","tag-data-analysts","tag-data-visualization","application-bi-analytics-teams","application-cloud-data-teams","buyer-role-business-user","buyer-role-it-rd","department-customer-service","department-executive","department-finance","department-hr","department-it","department-operations","department-product","department-sales-marketing","industry-digital-marketing","industry-education","industry-enterprise","industry-finance","industry-government","industry-healthcare","industry-industrial-manufacturing","industry-insurance","industry-logistics-transportation","industry-media-entertainment","industry-nonprofit","industry-online-gaming","industry-operations-logistics","industry-other","industry-pharma-life-sciences","industry-professional-services","industry-retail","industry-software-saas","industry-supply-chain","industry-technology","industry-travel-hospitality","topic-bi-best-practices","topic-dashboards","topic-visualizations"],"acf":[],"yoast_head":"\nHow to Reduce Insight Erosion in Collaborative Data Analysis l Sisense<\/title>\n<meta name=\"description\" content=\"When translating insights into action, to combine the strengths of data experts and the business users to get the best results.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.sisense.com\/blog\/how-to-reduce-insight-erosion-in-collaborative-data-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to reduce insight erosion in collaborative Data Analysis\" \/>\n<meta property=\"og:description\" content=\"Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. 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