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5 approaches for developing a data-driven mindset | Technology

5 approaches for developing a data-driven mindset | Technology
5 approaches for developing a data-driven mindset 

5 ways to develop a data-driven mindset

You don't have to be a data scientist to become data literate.

As data continues to grow in importance to organizations across industries, data literacy (one's proficiency in interpreting and communicating data and data-related activities) has become an increasingly crucial skill, even for those who are not data analysts, data engineers or data scientists

Developing such a competency in data interpretation and communication certainly takes time and effort, but a certain mindset for dealing with data and data-related issues also helps facilitate the development of data literacy.  Here are five ways anyone, even non-data professionals, can develop (and improve) a data-driven mindset.

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Strive to be an expert on your data

You probably work with at least some data in your current day job.  Maybe it's an Excel spreadsheet containing customer information, quarterly financial reports, or a list of transactions from a point-of-sale system.  Still, understanding the data that is relevant to your domain can give you an opportunity to be a valuable contributor to projects that use your data.

At a minimum, you need to know what measures (also known as columns, variables, and fields) are included in the data, the filters applied to the data (does a given dataset of interest include all relevant data points or just a subset of data points that meet some criteria?) and the mechanisms used to collect the data (is the data generated by online activity, questionnaires, store purchases, or some other process?).  Knowing these facets of your data helps you understand what questions can and cannot be addressed by that data.

This knowledge can also put you in a position to provide advice on what measures may be relevant to a particular project and what records, if any, should be included or excluded from the data for a given project, making you an invaluable resource.

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Understand what the data is for

It sounds trivial, but small startups and Fortune 500 companies often skip analysis before forming a core understanding of what they're looking for.  It's just sexier to talk about the insights you'll get from that fancy new machine learning tool than it is to do the hard work of critically asking yourself, "Why does it matter?"  Most of the time with this approach, you end up spending tons of time and resources analyzing all of your data before realizing that you have nothing to show and have to start over.  If you don't know what you're looking for, how will you know when you find it?

Before you begin, ask yourself the following questions:

  • What are we searching for?
  • What question are you looking to answer?
  • Why does that matter?
  • What impact does this information have?
  • What information do I need to answer that question?
  • Where do I find that information?

If you haven't asked yourself these questions, you should probably stop and start over.  You've done it all wrong.  Stop before you waste more of your company's time and money.

If you can start by asking a simple question (how can I get more users?), then break that down into what elements drive user growth in your company (such as what channels do they sign up on, who makes up a plurality of users, and so on) ), then identify what data you need to answer that question.  By going through this process, you've already accomplished two very important parts of setting up your data initiative for success.  First, you've reduced the amount of data you'll eventually need to analyze, since you know what you're looking for and what you don't.  Second, you've set an endpoint, making it easy to provide feedback to stakeholders on that specific question.

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Get ready to get dirty

At Promotable, we teach a variety of data-related topics, and in our classes we emphasize to students that much of the data they will work with in their careers will be messy.  To illustrate, let's say you're looking at a spreadsheet that shows the length of time each visitor spends on your company's website, and you see that some users have negative numbers reported in the duration column.  Or suppose you get a dataset of responses from a customer satisfaction survey, only to discover that a substantial portion of the responses to multiple questions are blank.

Data collection and processing errors can and do occur.  Unfortunately, missing data is commonplace in many data sets.  Understanding that real-world data is often not clean is important for two reasons.  First, awareness of the possibility of anomalous and/or missing data points is a crucial part of approaching data with a critical eye toward data quality.  Remember, the results mean little if the underlying data is unreliable;  garbage in garbage out.

Second, it is important to take into account the fact that data cleaning work typically needs to be done before any meaningful analysis or predictive modeling can be done to appreciate the true amount of time and effort required to bring it to fruition. a project.  To help ensure accuracy and completeness, budget time to analyze data and perform data cleansing tasks as needed.

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Apply a healthy skepticism to data-related decisions

While splitting a word with data (such as "data-driven") can be used to convey some authority to an outcome or process, the truth is that data alone doesn't lend credibility to anything.

Rather, data only becomes useful and valuable through various decisions made by humans.  From decisions about filtering (are data points with certain characteristics irrelevant to my analysis?) and descriptive statistics (should I use the mean or median to summarize this variable?), to decisions about modeling (should I use a tree of decision or a random forest?) and interpretation (is a model that makes a prediction with 95 percent accuracy good enough to put into production?), there are a number of stages in the life of a data-driven product, or data-inspired, or any type of product where human judgment is necessary.

Does this mean that we should automatically suspend trust in the products and insights that data enables?  Certainly not.  To the extent that data is the end result of processes that measure events, behaviors, and perceptions in the real world, the scientific use of data has the potential to provide predictions and insights derived from more than just the loudest voice in the world. someone's room or instinct. 

That said, it's important to note that anything with data in front of its name is the result of human activity and therefore subject to errors and inefficiencies.  In addition, critical examination of data collection procedures, data storage practices, statistical analysis, and predictive modeling is likely to motivate others, and/or yourself, to identify operational blind spots and assess whether an approach existing for a data-driven problem is really best practice.  compared to existing alternatives.

In turn, this critical thinking about data and data-driven outcomes has the potential to improve data quality, build stronger statistical and predictive models, and ensure data is collected and used ethically.

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Accept that the answers you get may not be the answers you want

One aspect of working with data that is both exciting and scary is that you don't know what the results will show.  You might have an idea of ​​what the results of a statistical analysis or machine learning model might show, sure, but until the data is actually analyzed, there's no way to be sure.  Sometimes, in fact, the data will even give you unexpected or even unwanted results.

For example, let's say you're running an experiment to test whether a new user interface (UI) feature in your company's mobile app generates more revenue compared to the current version of the app that doesn't include this feature.  After running the analysis of a carefully designed experiment, you find that the new UI feature does not make a significant difference to revenue. Does that mean the experiment was a failure? Absolutely not. In this case, you were able to reach a conclusion based on data and have more information than before the analysis.

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Foster a data-driven culture

You may be wondering why we put so much emphasis on employees having a data-driven mindset across the organization.  After all, aren't data professionals paid to handle and think about all things data?

It's true that an organization shouldn't expect its HR associates to build data pipelines or its accounting department to build machine learning models.  However, having people in various roles who can interpret and communicate data-related processes is a competitive advantage.  When decision makers can interpret different data visualizations and basic statistics, they can transform the work of data professionals into sound strategies more quickly and easily.  For example, when managers have a basic understanding of all the steps involved in creating a usable machine learning model, it enables better planning, helps set reasonable expectations, and ultimately leads to a better product and outcome.

In short, organizations that make a data-driven mindset a core part of their culture will be more successful in maximizing the value of their data than organizations that think they are only relevant to data professionals.

Source: iArtificial, Direct News 99