How to Choose Data Science Consultants in 2024
According to the U.S Bureau of Labor Statistics (BLS), data science is among the 10 fastest-growing jobs of the next decade and the expected growth rate through 2030 is 31%. Yet, data science talent is still scarce. That’s why businesses that lack data science talent may need to rely on data science consulting companies.
In this article, we explain how, when and why to choose a data science consultant.
What is data science consulting?
Data science consulting is the activity to effect change by building up the client’s analytics skills, developing competencies, and understanding of the inner workings of their business.
Data science consulting firms provide 4 services to companies. These services are:
- Strategy building
- Validation of strategy
- Model development
- Employee training
Strategy
The strategy part of the consulting explores what’s possible with data and aims to create a plan. This part requires extensive knowledge regarding the use cases. Depending on the client’s industry, the data collection method, regulation, and objectives can be completely different.
For one case, the objective can be optimizing the energy consumption of a plant, which can be achieved through collecting the data through machinery and getting the necessary paperwork from the business owner itself. Whereas, for an FMCG firm, trying to create a data pipeline to maximize the sales, the data collection can be limited by red tape, consumer protection and personal data protection requires considering the legal side of the work.
Collaboration between different departments is the key to success. The nature of data science makes the process more interdisciplinary and interdepartmental.
The strategy usually answers the following questions:
- What to do?
- What to collect?
- How to collect it?
- Where to store it?
- How to protect it?
- How to implement the solution?
Validation
The validation step is necessary to validate the identified strategy. While creating the strategy can be completed in hours in urgent cases, implementation can take months. Therefore, it is important to validate the strategy.
Validation is a natural step in finalizing the strategy. However, this may cause a conflict of interest if the validity of the strategy is evaluated by the same people providing the consultation.
In most consulting projects, in the interest of time, the same team builds and validates the strategy. Having another team for validation would require them to start the analysis from almost scratch, creating significant inefficiencies. Separation of strategy and its validation makes it easier to find and spot the problems in the strategy and clarify how the validation step improved the strategy.
Validation includes answering these questions:
- What is the insight behind this strategy?
- What is a low-cost way to test this strategy without fully implementing its findings?
- What do tests tell about the validity of the strategy?
Development
Development is the activity of designing and building a modern data product or internal tool. This is more like the IT part of data science consulting. Custom-tailored solutions for specific problems require a heavy emphasis on the development process.
Training
Training provided by consultants boosts the data literacy of your teams. Continuous training ensures that your teams are aware of the data science development process built by consultants. This also ensures that internal teams capture the main points and provide a meaningful contribution to the continuous improvement of the entire data science process.
Recommendations for end users:
- Ensure the data science consultancy team follows collaboration best practices and a process that is interdisciplinary and interdepartmental.
- Choose a data science consultancy that separates strategy and its validation. This makes it easier to find and spot the problems in the strategy and clarify how the validation step improved the strategy.
- Check developers’ domain expertise by interviewing and asking them domain specific questions.
- Reach out to customer references of consultants and check the success of continuous improvement of data science initiatives started by consultants.
How do data science consultants work?
Top management consultants like McKinsey have been putting significant effort into modernizing their data science project management approaches. Their frameworks are similar to the ones we outlined above, but it would be good to look at the areas they emphasize.
Below, you can see how McKinsey approaches advanced analytics/data science consulting:
Source of Value
Everything starts with the problem definition. The problem of most data science projects is finding a new opportunity that will enforce revenue growth and performance improvement. Consultants can also help in this step by identifying key value creation opportunities powered by analytics/data science. The most common use cases are improving customer-facing activities, optimizing internal processes with data-driven insights, and expanding clients’ portfolio of offerings.
Data Ecosystem
Consultants look for data sources to use in the project to unlock the value of data sources.
Data sources that data science consultants can use are:
- existing data sources such as organizations’ CRM systems
- 3rd party sources from data marketplaces or other data providers
- Raw data sources from IoT devices and sensors
Modeling Insights
Data science consultants either build new data models or select from existing models specific to the client’s problem. These models are tested on the client’s data to uncover insights. They can use tools such as AutoML to increase the efficiency in the modeling process.
Turning Insights into Actions
With their models’ results, consultants create a feasible action plan that will include both process and technology changes. These steps can also include rolling out models built during the project to empower operational decisions.
Adoption of Technology
Data science consultants should know that their clients may not have a data-driven culture and be ready to adapt to new data science tools. Consultants spend time on training clients’ employees, ensuring implementation of the prescribed actions, and enabling an effective change management.
Optimization of Organization and Governance
Lastly, consultants help build data governance and IT infrastructure to ensure that organizations can have lasting performance improvement. Performance improvements that do not address governance aspects of change tend to be short-lived.
Necessary Skills for Data Science Consultants
Below image from AltexSoft highlights what skills are required to be a data scientist consultant. Required and preferred skills can be categorized as follows:
Required skills:
- Coding languages
- Data management skills
- Knowledge of pre-existing ML algorithms and models
- Business acumen and collaboration
Preferred skills:
- Knowledge of frameworks and libraries
- PyTorch
- TensorFlow for neural networks
- Skicit-learn for machine learning
- Experience in the industry
- Enthusiasm for problem-solving
Cases where hiring a data science consulting agency is a better option
Data science projects can be handled via the following approaches:
- hiring consulting companies
- developing solutions with an in-house team
- crowdsourcing model development through data science competitions
- hiring freelance developers
Companies can choose either option, yet, each approach has pros and cons depending on the business’ industry, objectives, and budget.
Data science consulting is more advantageous than other approaches when
- There is no suitable off-the-shelf solution for your use case: If companies have specific needs and existing off-the-shelf solutions do not meet those expectations, consulting companies can help build customized products so that businesses eliminate or minimize off-the-shelf solution risks such as costly customization projects.
- Budget is not enough to build an in-house team: A data science team includes roles such as Chief Data Officer, data analyst, business analyst, data scientist, data architect, data engineer, etc. Building such a team is an expensive approach considering an average salary of a single data scientist working in-house is $94,000.
- Data science projects don’t require unique proprietary data: If your case and data are not unique, then consultants probably worked with similar data before. Their experience can help accelerate your projects faster.
- Data set does not contain sensitive information: Companies must be careful before sharing data with third parties due to data privacy regulations. Methods such as synthetic data generation and data masking can help companies make their data ready for sharing.
- Your company needs guidance on identifying the business aspects of data science projects: This is why consulting firms are still popular. Most companies are specialized in the market, and their knowledge of strategy and implementation of projects is limited. Consultants help identify business processes where data science projects can be implemented.
For more information on model development approaches, please check our guide on the ideal way to build AI projects.
Data Science Consulting Industry
The industry players can be categorized into four types. These are
- MBB,
- Historical Tech Companies,
- Start-ups,
- Big-Data-Big-Companies
For more on specific industry players, you can check our article on AI consulting landscape.
3 Factors to Consider When Choosing a Data Science Consultant
3 criteria can help choose the right data consulting partner:
- Experience
- Analytics knowledge
- Duration of service they offer
Here are the questions you should be asking:
Do they have enough domain and field experience?
It is important to see that the consultants experienced a project in a similar setting. This shows that the consultant can put meaningful insight and knows the practices in the specific industry. Organizations need to examine consultants’ previous projects to see that they have expertise in the following approaches:
- Technical
- Process-specific
- Industry-specific
Do they have analytics translators on the team?
A data scientist’s technical capabilities are important for consultants as long as they can turn insights into actionable decisions. Analytics translators work with the data science team and combine their findings with the business domain expertise to create actionable decisions.
Translators should be able to interpret and translate analytics insights into business benefits and guide the analytics work. These consultants should have domain knowledge, technical fluency, project management skills, and an entrepreneurial spirit to achieve this goal.
Can they provide a long-term plan?
You need to make sure that the consultant’s plan is viable and can be upgraded regularly. Data science is a field experiencing constant improvement, so it would be important to see its potential. Think about it as a long-term investment, you may need consulting again, and updates so make sure they can provide the greater planning horizon.
Salaries of data science consultants
Salaries of data science consultants vary based on experience and location. According to Neuvoo, these are the average data science consultant salaries by country. The top and bottom end of the ranges can help you understand how experience impacts salary:
Country | Median Salary (per year) | Lowest Salary (per year) | Highest Salary (per year) |
---|---|---|---|
United States | $122,850 | $50,000 | $183,300 |
United Kingdom | £65,000 | £21,100 | £90,000 |
Germany | €80,000 | €27,384 | €95,000 |
France | €31,992 | €20,640 | €62,740 |
India | ₹ 1,287,500 | ₹ 216,000 | ₹ 1,750,000 |
If you have access to data which you would like to use to build a machine learning model:
If you are looking for a consultant for your data science project, feel free to check our regularly updated list of data science consultants or our list of AI consultants. We can also help you find data science consultants even if you haven’t identified your machine learning problem yet:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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