{"id":9004111223080561,"date":"2022-02-03T13:48:55","date_gmt":"2022-02-03T17:48:55","guid":{"rendered":"https:\/\/www.pragmaticinstitute.com\/?p=9004111223016284"},"modified":"2024-02-05T15:35:42","modified_gmt":"2024-02-05T15:35:42","slug":"the-four-non-technical-elements-of-a-successful-data-project","status":"publish","type":"resources","link":"https:\/\/www.pragmaticinstitute.com\/resources\/articles\/data\/the-four-non-technical-elements-of-a-successful-data-project\/","title":{"rendered":"5 Elements of Successful Data Analytics Project"},"content":{"rendered":"

When teams start working with data, the earliest mistake they make is failing to\u00a0identify the right market problem<\/a>\u00a0or align the problem they are trying to solve with the broader organizational strategy.<\/p>\n

A truly effective data analytics project requires more than just technical skills and the right data models. With the non-technical elements of a successful data analytics project outlined below, your data team can uncover powerful insights that often go untapped.<\/p>\n

Here are 4 elements of successful data projects.<\/p>\n

#1 Deliberate Dialogue<\/h2>\n

It\u2019s important to have the right context for your analytics project. And that\u2019s true whether you are the data team or the product team. Whether you are on a data team or product team, all parties should commit to being open and listening actively.<\/p>\n

It\u2019s easy to quickly jump to conclusions and say, \u201cWe have analytics and insights.\u201d However, moving too fast might mean failing to address biases that cause us to look at the data in a specific way.<\/p>\n

Instead, we should carefully examine if any cognitive biases are impacting our insights and actively listen to our stakeholders.<\/p>\n

Most of us have debates in our organizations; debates aren\u2019t about listening. The goal of a debate is to get your point across no matter what. While someone else is talking, you might be thinking about how you\u2019re going to contradict their point to prove your own.\u00a0So, deliberate dialogue versus debate is a big step in the right direction.<\/p>\n

#2 Managing Uncertainty<\/h2>\n

We live in a volatile, complex, ambiguous, and uncertain world. That means our brains are navigating uncertainty. You need to educate leaders that data always has an element of uncertainty.<\/p>\n

We could run a model about the segmentation of our customers and whether they\u2019ll buy our product. The output might indicate a 95% confidence interval, which isn\u2019t certain. It\u2019s best if all the decision-makers understand that everything is a probability.<\/p>\n

There is no situation where we can say there is a 100% chance that investing in a specific demographic will increase our revenue.<\/p>\n

You should acknowledge that you will never have all the data, and you will never have all the answers. We don\u2019t have any data on what will happen in the future. It\u2019s not there.<\/p>\n

So, we have to make predictions using foresight and strategy.<\/p>\n

And, if you\u2019re data literate, you\u2019re more likely to know if your strategy is a good bet.<\/p>\n

Simply put, we manage uncertainty in data projects by seeing our efforts as reducing risk with data-driven insights.<\/p>\n

#3 Knowing the Audience<\/h2>\n

Understanding your audience, and tailoring your communication to fit their needs and knowledge, is an essential communication tactic for any data analytics project. Suppose you\u2019re an advanced analyst and do some complex modeling using advanced visualization (like a box plot).\u00a0Your audience may not be familiar with that level of advanced analytics and data visualization \u2013 this risks them misunderstanding your data, or tuning out completely.<\/p>\n

Studies show\u00a0people listen and attach emotionally to stories<\/a> instead of just numbers. The first thing is to pick the proper visualization and pick the correct terminology to map to your stakeholders. You must meet them at their level and communicate the stakes of the data problem at hand.<\/p>\n

Essentially, your job is to answer the question, \u201cSo what?\u201d If you\u2019re sharing numbers from the latest marketing campaign with the goal of increasing the marketing budget, the approach will be different from communicating with the creative team.<\/p>\n

It comes down to knowing the stakeholders, not simplifying, but prioritizing what you\u2019re going to show them. Everything else is just noise.<\/p>\n

#4 Systems Thinking<\/h2>\n

Which numbers matter? That question is hard to answer because it\u2019s not a simple one-to-one ratio. You can\u2019t say, \u201cThis number drives this number,\u201d and you\u2019re done.\u00a0Twenty other numbers are impacting an outcome. If you\u2019re lucky enough to have a data scientist, some algorithms help you understand which numbers have a higher correlation.<\/p>\n

However, if you don\u2019t, one of the non-technical elements of a successful data project you can employ is\u00a0systems thinking<\/a>.\u00a0The goal of systems thinking is to visualize a system map, which visualizes whether those variables are correlated (and if they are, how strongly). System maps don\u2019t share discrete numbers \u2013 they just visualize the relationship between variables. If you communicate those relationships well, you\u2019ll have a dialogue with the stakeholders.<\/p>\n

Here\u2019s an example of systems thinking:<\/h3>\n

In a business context, let\u2019s say sales revenue is down. So, you use active listening skills to uncover hypotheses from stakeholders as to why that might be the trend we\u2019re seeing.<\/p>\n

Here are some possible variables that would impact sales:<\/p>\n