Four Simple Criteria for Choosing Your Next Advanced Analytics Project
Tags: datascience, analytics, beginners, computerscience
Published: 2021-11-12 01:56:00

There are several questions you can ask yourself when deciding which analytics project to take on next.

Four criteria have proven to be the most helpful in making this decision.

The first step is to determine whether or not your business has core competencies and goals that align with the analytics project at hand. If so, it may be worth exploring what tangible benefits that project will bring about for your company.

Second, if there is a clear goal or expected outcome of the analytics project, then it should be explored further because analytics projects without an end game rarely yield positive results.

Thirdly, if the analytics project will help build skills in an area that is not yet well-developed within your organization, then explore whether or not this would be beneficial for your business.

Finally, it is important to keep in mind that analytics projects should be strategically picked based on what will bring about the most value for your organization long-term rather than short-term gains.

## Does the project align with your company's core competencies and goals?

It is critical to ensure the [analytics efforts are relevant to your organization](https://mitsloan.mit.edu/ideas-made-to-matter/10-best-practices-analytics-success-including-3-you-cant-ignore) and keep you from spending valuable time, money, and effort on projects that may not pay off in the end.

Analytics projects that are an extension of your core competencies, business goals, and strategy will have a higher chance of success.

## Can you get quick results for this project?

It's often better to [fail fast and cheap](https://www.inc.com/jim-schleckser/how-to-fail-early-fail-cheap.html) than to wait on a project that will never produce results.

If your analytics project does not have a clear timeline associated with it, you may be wasting valuable time and resources on something that will never give any benefits.

Make sure the analytics team has an idea of how much [time is needed to collect data and start getting results](https://www.northeastern.edu/graduate/blog/data-analysis-project-lifecycle/) before starting work on anything. If it takes longer, it might not be the best analytics project to work on.

Related: [**_How to Do a Ton of Analysis in Python in the Blink of An Eye._**](https://www.the-analytics.club/python-eda-tools)

## Is there a clear goal or expected outcome of the project?

When all analytics projects are evaluated based on their ability to deliver tangible results, you can make better decisions about how many analytics projects to take on and which ones will have the most impact on your business.

If there is no clear [goal or expected outcome for the analytics project](https://www.informs.org/Explore/Building-Successful-O.R.-and-Analytics-Teams/Establishing-Goals-for-Analytics), it may be difficult to measure success and have a justification of why you are doing this analytics project in the first place.

Is the analytics solution long-term sustainable? Does it scale with growth? It's important that analytics projects can grow as your business grows to ensure analytics remains a priority within your organization.

If an [analytics solution is not scalable or sustainable](https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/breaking-away-the-secrets-to-scaling-analytics), it may be difficult for you as the company scales to keep up with analytics requirements. This could lead to analytics solutions that are incomplete and fail at giving any benefits from data analysis.

## Will this project help you build skills in an area that is not yet well-developed within your organization?

If the analytics project will help build new and necessary skills for your organization, then explore whether or not this would be [beneficial for you as an organization](https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Deloitte-Analytics/dttl-analytics-analytics-advantage-report-061913.pdf) long-term.

If so, choose that analytics project over others on the table.

What is the expected ROI of this analytics project? Does this analytics project's value outweigh its cost to implement? You can't afford to spend time or money on projects that will never yield any benefits for your business.

It's important that [analytics projects have a high ROI](https://www.idg.com/news/idc-study-finds-analytics-projects-yield-431-average-roi/) and bring about more value than they cost in order to be successful.

Look at the expected ROI of each analytics project before moving forward with development. If you can't do this, it might not be worth exploring further for your business!

Related: [**_How to Evaluate if Deep Learning Is Right For You?_**](https://www.the-analytics.club/is-deep-learning-right-for-you)

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