What Is Data Science Consulting?

It is easy to get lost in the world of data science, so in this article, we will show you why data science consulting is the ultimate solution to your business problems.

Many companies out there claim to specialize in data science solutions, deep learning, algorithms, and AI software development, however, AI is still an emerging field.

As in any emerging field, it lacks maturity and proper B2B solutions even for common use cases. Investment in AI can be costly and time-consuming. If your company is looking for an industry-specific AI solution, you need to assess your vendors carefully or opt for hiring an in-house team.

This article aims to give an introduction to a holistic data science solution that will allow your business to leverage the power of AI and AI business process optimization with the least risks. Choose your direction after weighing the pros and cons to avoid pitfalls and extra costs.

Why Are AI and Data Science Important for Your Business?

The benefits of AI adoption in business are countless. 54% of business executives say that AI tools have boosted productivity:

Businesses gain a competitive advantage by making data-driven decisions. Advanced analytics powered by AI help predict market trends and develop innovative products.

Research by Infosys and analysis confirms the impact of AI and Machine learning on revenue and profitability. For over 1200 businesses surveyed in this research, AI contributed a 39% average increase in revenue.

Among other advantages of leveraging AI are automation, cost-cutting, and streamlining business processes. One of our case studies shows how AI helped save 6x costs on manual labor for our customer.

In this article, we will go through three most popular ways for businesses to adopt AI into their daily operations: an in-house data science team, conventional outsourcing, and hiring a data consulting firm.

Pros and Cons of an In-House Data Science Team


  • Having your in-house data science team offers flexibility to make quick changes and customization. Your company is self-reliant and independent. You don’t have to deal with different time zones and overcome language barriers.
  • Your in-house team will be familiar with your business domain and industry specifics, processes, and systems. With an in-house data science team, you can focus on your resources to respond to changing market demands.


  • On average, it can take over a year and cost 10 thousand dollars to hire a data scientist. Considering that most of them stay at their jobs for two years or less, you may end up spending a fortune to replace them.
  • If you are not a data scientist, assessing skills and interviewing can be another challenge. You could hire a consultant - but here is where you resort to the external sources, anyway.
  • In-house teams are more established in their comfort zone and have less sense of urgency to deliver the results. It can cause late deliveries and lower quality of work.

Pros and Cons of Conventional AI Outsourcing Companies


  • Access to a variety of talent in data science. An increase in demand for data scientists causes a scarcity of talent. So outsourcing offers a quick and easy solution to find resources for your project.
  • Cost-effectiveness. You can pick experts according to their hourly rates and skill set. You save on hiring full-time employees and can swiftly scale up or down the level of data science tasks to meet your changing needs.


  • Traditional data science and AI outsourcing companies offer many benefits. But outsourcing scattered talent can pose extra risks that companies struggle to identify and mitigate.
  • Among them are cultural, language, and communication barriers. They result in poor collaboration between data scientists and the client.
  • Many data scientists that work for traditional outsourcing firms have a limited formal theoretical base. They fail at executing complex projects requiring in-depth analysis.
  • High personnel turnover is another problem with conventional AI outsourcing companies. As a result, they carry over one project from one data scientist to another, often with no proper handover and lack consistency. It makes it even more challenging if data scientists work from various parts of the world and have significant time differences.

Data Consulting Companies can offer you the best out of both worlds

As with conventional outsourcing, you get access to an extensive talent pool. The difference is that in most cases, data scientists work full time for the consulting companies and form stable teams. They go through a rigorous interview process before being hired.

Reputed data consulting companies check data scientist’s education, credentials, and background. For example, at BroutonLab, we conduct research, review scientific papers, and write for academic journals.

Data science consulting firm performs a comprehensive assessment of your project. It provides actionable insights into each step before getting to work. Consultants analyze your business processes and spot problems that you need to solve. To find the best solutions, they brainstorm and examine them with you. Then they make sure that everyone involved in the project understands and agrees on the next steps.

Data consultants help you save costs and mitigate risks. For an average annual data scientist salary of $120 000 in the US (excluding 401K and insurance), you get access to an entire company’s expertise. But you don’t even need to spend this much! When hiring a data science consultancy firm, you pay only for what you need.

How to Choose an AI & Data Science Consulting Company?

It is essential to choose the right crew if you want to get the best results. Considering that AI & Data Science are still an emerging field, it can be a challenge identifying the right people at first glance. So you need to pay extra attention to the process of hiring specialists.

How to avoid failure?

We have summarized the steps you need to follow before hiring an AI & Data Science Consultant:

Step 1. Identify your needs and summarize them adequately.

Understand how you want to apply data science in your business and what type of product you would like to deploy.

Here are the questions you need to ask yourself:

  • Are you looking for data analytics to gain insights into your business and enhance your operations? Or to analyze how your business is doing?
  • Do you want to take your customer service to the next level with AI-based applications?

If you are unsure which direction to go, here are the key areas of the work of data science consulting firms:

AI-based customer-facing apps:

  • Voice and text assistants
  • Sound recognition and analysis applications
  • Price prediction engines
  • NLP-based apps for conversion of natural speech into text
  • e-Commerce recommendation engines
  • Image editing applications
  • Image recognition apps and features
  • Object detection apps
  • Visual and voice translators
  • Facts extraction, text analytics, and document classification apps
  • AI Trading automation

AI-powered business analytics:

  • Big data analytics
  • Data management
  • Market predictive analytics
  • Customer lifetime value prediction
  • Price, sales, or market demand forecasting
  • Customer data analytics
  • Market and customer segmentation
  • Sentiment analysis on social media and review sites
  • Risk analysis
  • Upsell opportunity analysis, etc.

Step 2. Consider off-the-shelf products

Study off-the-shelf products before looking for a data science consulting firm. Beware of possible pitfalls that come with ready-made solutions. They include costly customization, lack of support, business, and legal risks.

Step 3. Go through online listings

Several websites can help you find the right match. On Designdrizzle, you can look through annual top AI and Data Science companies listings. Freelance sites, like Upwork.com, feature some data science companies. It is easy to see their portfolio of projects and customer reviews, together with guideline pricing per hour.

Step 4. Study Data Science’s firm portfolio and team

Here is what to look for:

  • Is the data science firm transparent about their team?
  • Are their names, titles, and bios visible on the website?
  • Do you know who will handle your project?
  • Can you speak to data scientists without mediators?
  • Can you see the case studies with precise technical details and the results they delivered for their past customers?

Data scientists with solid academic backgrounds and experience in R&D will have publications available online. Any reputable data science firm will be proud to feature their names and achievements on the website. To go through their research papers, you can do a simple Google search. Here is the example of the one we did for our CTO Michail Yurushkin:

Case studies will not always mention the name of the customer as many prefer to stay confidential. Description of the case studies can tell you about the specialization of the data science firm. So you can understand whether they handled projects like yours.

To Wrap It All Up

Finding skilled data scientists that also have business-intelligence can be challenging, especially for a small business. The demand for machine learning, learning algorithms, and AI experts exceeds supply. So, data scientists with a specific set of skills ask for compensations that defy imagination.

Data Science Consulting firms offer turnkey AI and Machine Learning solutions to businesses. They lead data science projects from planning to execution and drive digital transformation in many industries especially computer science. In times of crisis, businesses are looking to cut operational costs and save on recruitment and payroll. Right data science consulting companies have skilled teams with experience in specific domains.

Reputable data science consultants don’t just execute projects. They work with their clients to educate them on delivering end-to-end data science solutions. So you will walk away with a much greater understanding of the data science process. They can tailor cost-efficient solutions to the most crucial business problems. As a result, implementing machine learning and AI technology becomes efficient and hassle-free.

Questions? Ask Us Now!

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