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4 Crucial Points to Consider When Choosing a Data Science Business Case

4 Crucial Points to Consider When Choosing a Data Science Business Case
Emails, social network posts, app logs, and photographs – given the volume and variety of data we generate on a daily basis, harnessing it seems like a natural solution to any business problem. With data science success stories aplenty, it's simple to understand this mindset:
Customers are matched with products they are most likely to buy through recommendation engines in ecommerce.

To detect scam attempts, financial services firms use fraud prevention algorithms.

Manufacturers can utilise predictive analytics to accurately adjust supply to demand expectations.

These are just a few instances of how data science services may help businesses. In actuality, data analytics has endless uses across all business sectors. However, while data-driven solutions can help businesses in any industry, their effectiveness should never be assumed.

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Only 20% of data science projects will produce commercial outcomes by 2022, according to Gartner. Most of them aren't even guaranteed to make it to the level of production. This demonstrates that putting data science to use is a promising but dangerous venture.

If a project isn't established on the appropriate premises, cost investments and technology alone won't be enough to turn data science discoveries into concrete commercial benefits. So, what exactly are these? Let's see what happens.

Why do so many data science projects fall short of their goals?

Although each project is unique, and failures are frequently the result of a series of blunders, some faults are widespread. Here are five things to consider before starting your data science project.

Pre-assessment was inadequate.

How long do you think the project should take? How much do you think it'll cost? Is the benefit greater than the risk? Unfortunately, when enticed by the promise of data science benefits, many business owners overlook these basic factors.

The wrong questions are being asked

You won't find the answers in data if you don't know what you're looking for or what problems you're trying to solve. Plunging into large amounts of data without a clear set of questions is a surefire way to kill your project from the start.

There is no obvious commercial goal.

The project's success will eventually be determined by the precise business outcomes it produces, regardless of how innovative it is. If you ignore business objectives, you may discover halfway through the project that your insights will have no impact on ROI.

Problems with data

Because the data you utilise is so important to the project, it can't be inconsistent, incomplete, low-quality, compartmentalised, or otherwise defective. Fixing these issues while the project is still in progress would inevitably stymie progress.

Stakeholder engagement is low.

The morale of your data science staff deteriorates when work drags on for weeks or months. The initial excitement wears off, stakeholders lose interest, and they move on to other initiatives. In the end, the project is abandoned due to a lack of support.

All of the failures listed above have one thing in common: they occur or emanate from the project's early stages, before any actual work is completed. So, let's look at how you might avoid problems with case selection and planning.

Begin by assessing the situation.

The easy part is coming up with fresh data science use cases; the difficult part is deciding which one will go into production. A thorough examination of the possibilities simplifies the decision-making process.

Begin by weighing the advantages of the many use cases you're investigating. Determine which ones will solve immediate company difficulties and which ones will promote growth. Be specific - if the project's goal is too broad, it'll almost certainly only appear nice on paper.

Then, for each situation, determine the level of work required. This is an excellent time to sketch out a preliminary project roadmap to gain a sense of its scope and duration.

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Another important factor to consider is potential dangers. Assume the worst-case scenario and select a use case that will not result in catastrophic outcomes if it fails.

Concentrate on important business issues.

Although acknowledging the project's commercial goals is part of the review stage, it merits its own section.

In some way, the utilisation of data must result in a favourable business consequence. You're searching for goals like lowering user churn, improving the customer journey, or just replacing inefficient procedures with more efficient ones. Consider whether data can help you reach your goals in a realistic way.

A collection of well-defined KPIs is required to achieve the goal. Discuss what indicators you may use to track progress with other stakeholders during the planning stage.

It takes a team effort to turn knowledge into profit. You'll need data scientists for analytics and algorithm training, an IT staff to keep the data flowing, and marketing experts to get the project out to the public if it's a consumer-facing effort. By involving everyone from the start, you'll have a wider range of perspectives to choose from, boosting your chances of picking the best use case.

Keep in mind that with so many stakeholders, you'll need someone to oversee the process. Appoint a project owner who will be in charge of the project and guarantee that it moves forward.

Define and protect the data that is required.

Different sorts of data are required for different use cases. Are you looking for something external or internal, structured or unstructured, owned or acquired? Answer these questions as soon as possible to see what you have and what you'll need.

Limiting oneself to existing datasets or certain data sources will limit your inventiveness. Rather, concentrate on broad topic fields that match your interests and expertise while also aligning with the company's objectives.

Another issue that should be addressed early is data governance. Ascertain that everyone has the necessary access privileges and that the data quality is adequate. Identify any potential issues with respect to data privacy, security, and ethics.

Begin small and work your way up.

If you can't pick between a highly impactful but difficult use case and one that is less profitable but more actionable, go with the latter. This method provides a number of advantages.

Projects with a small scope can produce results in weeks rather than months. Apart from the financial benefits, this will improve your team's morale and involvement in the future.

Small initiatives, on the other hand, are safer because they involve fewer people and workloads. They're also simpler, which means there are less things that can go wrong.

Choosing the low-hanging fruit, on the other hand, does not imply abandoning more ambitious use cases. On the contrary, your team will get the experience needed to take on larger tasks this way. Small project insights might also help with work or even create fresh and intriguing ideas.

In conclusion

Finding the correct use case for data science can be tough, despite the quantity of viable applications. There is no one-size-fits-all technique for ensuring the success of any data science project, which is why many firms use outside experts to help them. Whether you work with a team or go it alone, one thing to keep in mind is that in data science, educated decisions make all the difference.

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