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Data Science Consulting Overrated

November 17, 2022
Posted in: Consulting, Data, Innovation

Every company works with data in some way and needs to use it in every way. It’s been a dedicated field of expertise for a long time, and companies have started to notice its immense value in the last decade. Data science gets a lot of hype and is considered one of the most in demand jobs in the market today.

Many companies hire data science consulting services to help with their business problems. Most of the time, that makes sense, and the demand for data science has created new opportunities for many data science consulting companies to flourish. But business intelligence has inherent limitations, which is why it hasn’t been widely used. 

The use of data science is the only way to effectively incorporate past trends and patterns into accurate forecasts for the future. The tech experts at RTS Labs believe that when you feel like you’re being buried in a sea of data, an experienced data scientist can help you make sense of it all. Use this to pick up on details that you and your coworkers would otherwise overlook. It will bolster your case while trying to persuade others to support your idea.

However, a data science consulting partnership may not always be a seamless experience, and there are a few pitfalls that companies can end up in if they don’t understand how data science works. After all, you will likely be spending large sums of money on consulting services, and if your problem is not fixed, you’ve just wasted your time and resources.

Here are Some Reasons Why Data Science Consulting is Overrated

Is Data Science Consulting Overrated?

You won’t understand the technical details

Data science is a complex topic to understand for lay people or anyone unfamiliar with advanced statistics and mathematical modeling. Many managers today understand how to work with basic spreadsheet tools like MS Excel, which they can use to come up with some rudimentary analyses of their own. 

But the range of tools, applications, and techniques used in data science is much broader and deeper, and it may sound totally strange to anyone outside this field. If you or your team is looking to understand the work that goes behind data science, prepare to spend time reading about data science. A lot of time! 

To add to this problem, techniques like Machine Learning (a staple of data science) have many intricacies that you need to be aware of, and your consultant will try to inform you about them. And suddenly, you might find yourself in a situation where you have to deal with too many details. There will be an urge to ask the consultants to get to the point.

Naturally, moments like these can be a source of friction between members on both sides.

Some consultancies hire client servicing or account managers to bridge the relationship between the data scientists and the client. That means that the final decisions can be made – or broken down into smaller decisions – based on a simple PowerPoint presentation!

The consultant might sell you a model that does not work for you

When working with data science consultants, you will often be presented with a range of products and methodologies that look flashy, but they may not be designed to look for the problem you are looking for. For example, a machine learning model may not make a significant difference in your sales forecasting, and it will not help you identify the subjective context in a given scenario (like what consumers feel when they see different product screenshots on your e-commerce store).

For data science consultants, clients represent a sales opportunity, and like any other business, their team members will be keen to sell you one of their products. That sounds crazy when you think that in cases like Machine Learning, there’s a high failure rate. However, if a data model ends up working for you, one of your consultants could get a promotion. The gamble is in the consultant’s favor, as there are getting paid for their job. RTS Labs suggests that you focus on your specific business requirements when reviewing data science proposals.

You might not have enough data

A lack of data is a common problem in the business world. Experts at RTS Labs bet you have wished to have a bigger data set to work with at least once on your job. Some data science problems also require a large volume of data. For example, if you are working on a predictive algorithm, then you will usually need a large data set for training and cross-validation.

Collecting additional data takes time and financial resources. That’s the opposite outcome of going to a consultant hired to optimize your resources. The realization that your company cannot collect additional data also opens up a lot of questions for management that can jeopardize a project. So you are faced with the dilemma of an imperfect solution to your problem versus the pressure of cutting the company’s bottom line.

Let’s face it: you’d never want to be in that situation!

Sometimes your problem is too complex, or too simple

The problem of not having enough data we mentioned above is an example of a complex problem. You could also be in a situation where you have enough data to train a model, but the model still fails to accurately predict an outcome. Then there are adverse events like COVID-19, that further complicate matters and make operations or predictions harder. These are situations where no data science consultant can save you.

On the flip side, there’s also the chance that you just have a simple problem that your team has already figured out. Consider this example of a data scientist using a clustering algorithm to advise a bank on key market segments (Hint: the bankers had already figured out the top 2 segments identified by the algorithm).

While we never advocate using gut feelings to solve problems, it’s still a common practice in the business world. According to one survey of nearly 700 respondents, over 50% still base their decisions on gut feeling. That’s a major bias in the decision-making process. And if you go with just your instincts, you will likely ask a data science consultant to confirm your biases. That means you will look for those “I knew it!” moments instead of looking into the finer details of a data plot. 

An in-house data science department can bring synergies

Hiring a data scientist or a data science team can have many benefits that can synergize with your organization. Firstly, you will avoid the high consulting fees charged by external firms. There will be no retainer or hidden fees to pay.

In-house employees will also be a potentially good cultural fit within your company. By being a part of your company, professionals like those at RTS Labs are in a better position to understand your business processes, your business needs, and your working style. If you have a cross-functional team with both technical and functional experts, you can create in-house solutions that perfectly fit your business processes and organizational requirements. Understanding business processes is, therefore, crucial to figuring out the data engineering pipeline to create a data-driven solution for each stage. Anyone working in the company is at an advantage in this matter.

Finally, you will be able to minimize scheduling conflicts often faced when working with external consultants.

When does it make sense to work with a data science consulting firm?

Is Data Science Consulting Overrated?

Despite the potential pitfalls of working with data science consulting services, it’s not all bleak for businesses. We know there are genuine reasons to refer to data science consultants. Here are some situations where hiring a data science consultant will benefit your company.

No budget for an in-house team

If you need the services of skilled professionals outside your organization to handle data-related problems, hiring a helping hand from outside will help immensely. You can even opt for small-scale project teams.

Not using proprietary data

Some projects require common or public data which consultants have already worked on. These situations can speed up your workflows, and possibly yield improved results.

No viable solutions available in the market

If you have checked all off-the-shelf solutions for your data use case, and cannot find an option having your desired features, a data science consultant can provide a solution for you. 

Working with non-sensitive information

When not working with sensitive information such as medical records, personal profiles, or with data protected by laws (such as GDPR and CCPA), you can share the data with an external agent to help you.

Skill gaps and professional guidance

In cases where you have identified skill gaps in handling data, or when your leadership needs professional help to work on business strategy and process implementation, a data science consultant will be invaluable.

We’ve covered the value of data science consulting in a previous article, which you can read here.

The Bottom Line

Is Data Science Consulting Overrated?

Data science problems in business can be very challenging to tackle on your own. But they also give opportunities for professionals to show their creativity when designing solutions. In our experience, data science is a multidisciplinary domain that involves people with different skill sets. Whether you choose to have an in-house expert or work with a consultant, you need teamwork to succeed.  

Looking for the right software development partner? RTS Labs has helped hundreds of businesses of all sizes successfully develop the right outsourced custom software for many business needs.

Get in touch with RTS today to learn more about how we can help.