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4 Vital Tips for Choosing a Business Use Case for Data Science

According to Gartner, only 20% of data science projects bring business outcomes by 2022. So, applying data science is a promising but…

Photo by Myriam Jessier on Unsplash
Photo by Myriam Jessier on Unsplash

Emails, social media content, app logs, images – given the loads and variety of data we generate every day, harnessing it naturally seems like a recipe for any Business challenge. It’s easy to understand this mindset with data science success stories everywhere:

● In ecommerce, recommendation engines match customers with products they’re most likely to buy.

● Financial services providers leverage fraud prevention algorithms to detect scam attempts.

● Manufacturers can accurately adjust supply to demand forecasts with the use of predictive analytics.

These are only a few examples of how data science services benefit various industries. In reality, the applications of Data analytics across all business verticals are limitless. But although data-driven solutions can empower companies in any industry, their effectiveness should never be taken for granted.

According to Gartner, only 20% of data science projects will bring business outcomes by 2022. Most aren’t even guaranteed to make it to the production stage. This shows that applying data science is a promising but risky endeavor.

Cost investments and technology alone won’t suffice to translate Data Science findings into tangible business benefits if a project isn’t built on the right premises. So what are these? Let’s see.

Why do so many data science projects fail?

Although every project is different, and failures are usually a sum of many mistakes, some errors are commonplace. Here are five that you should know before you launch your data science project.

Poor pre-assessment

How long should the project take? What is the estimated cost? Does the impact outweigh the risks? Unfortunately, many business owners forget about these rudimentary considerations when tempted by the prospects of data science benefits.

Asking the wrong questions

Data may hold all the answers, but you won’t find them if you don’t know what you’re looking for or what problems you intend to solve. Plunging into volumes of data without clearly defined questions is a recipe for slogging your project right at the start.

No clear business purpose

No matter how innovative the project is, its success will ultimately depend on the specific business outcomes it delivers. Forget about business goals and you may find out halfway through the project that your insights won’t contribute much to ROI.

Data issues

Being central to the project, the data you use cannot be inconsistent, incomplete, low-quality, siloed, or otherwise flawed. Fixing these problems when the project is already underway is bound to hinder work severely.

Low stakeholder engagement

As work drags on for weeks and months, the morale of your data science team diminishes. The initial enthusiasm wanes, stakeholders lose interest, and they gradually move on to other projects. In effect, the project lacks support and eventually becomes abandoned.

All the above failures have one thing in common: they occur or stem from the earliest stages of the project, before any actual work gets done. So let’s see how you can prevent difficulties with planning and selecting the right case.

Start with evaluation

Coming up with new use cases for data science is the easy part; the struggle starts when choosing the one that will go into production. A thorough evaluation of the options makes the selection much easier.

● Start by comparing the benefits of the use cases you’re considering. Identify those that answer urgent business problems and the ones that will stimulate growth. Be specific – if the project’s purpose is too abstract, there’s a high chance that it only looks good on paper.

● Then, estimate the level of effort required for each case. It’s a perfect moment to draw up a rough project roadmap to get an idea of its scale and timeline.

● Potential risks are another essential consideration. Assume the worst-case scenario and choose the use case that won’t lead to disastrous consequences if it fails.

Focus on key business questions

Acknowledging the project’s business goals is a part of the evaluation stage, but it deserves a separate section on this list.

● The use of data must bring a positive business outcome in one form or another. Reducing user churn, streamlining the customer journey, or simply replacing existing processes with more efficient ones – these are the kind of objectives you’re looking for. Ask yourself if data can realistically contribute to meeting them.

● To achieve the goal, a set of well-defined KPIs is necessary. During the planning stage, discuss with other stakeholders what metrics you can use to track the progress.

● Turning insight into profit is a multidisciplinary effort. You’ll need data scientists for analytics and algorithm training, the IT team to maintain the data flow, and marketing specialists to deliver the project to the market in case it’s consumer-facing. Involving everyone from the beginning gives you a range of perspectives, increasing the odds of choosing the right use case.

· Remember that with such a variety of stakeholders, you’ll need someone to coordinate the process. Appoint a project owner who’ll be responsible for it and ensure that things are moving forward.

Specify and secure necessary data

Different use cases require different types of data. External or internal, structured or unstructured, owned or acquired? Answer these questions early to see what you can work with and what you’ll need.

● Don’t restrain your creativity by limiting yourself to the existing datasets or specific data sources. Instead, focus on broad thematic fields that match your interest and expertise while corresponding with company goals.

● Data governance is another matter that’s better taken care of early. Ensure that everyone has the appropriate access rights and that data quality is high enough. Identify any possible issues regarding privacy, security, and ethics of data usage.

Start small to grow over time

If you can’t decide between a largely impactful but challenging use case and a less profitable but more actionable one, you may want to go with the latter. This approach has several advantages.

● Small-scope projects can bring results within weeks rather than months. Aside from the business results, this will boost your team’s morale and increase its engagement in the future.

● For the same reason, small projects are safer since they involve fewer people and workloads. They’re also less complicated, hence fewer things that can go wrong.

● However, choosing the low-hanging fruits doesn’t mean completely giving up on more ambitious use cases. On the contrary, this way, your team will gain the expertise necessary to tackle larger projects. Plus, the insights from small projects can facilitate work or even spark new exciting ideas.

Bottom line

Despite the abundance of possible applications, finding the right use case for data science can be difficult. There’s no one-size-fits-all method that would guarantee the success of any data science project, which is why many companies entrust external consultants with their projects. Whether you team up with specialists or go solo, there’s one thing you need to remember: in data science, informed choices make all the difference.


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