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.
Data science services help companies run experiments on their data in search of business insights. ScienceSoft renders data science consulting leveraging Machine Learning, Artificial Intelligence, and Deep Learning technologies to meet our clients’ most deliberate analytics needs.
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?
It’s become a universal truth that modern businesses are awash with data. Last year, it was McKinsey who estimated that big data initiatives in the US healthcare system “could account for $300 billion to $450 billion in reduced healthcare spending or 12 to 17 percent of the $2.6 trillion baselines in US healthcare costs”. On the other hand, though, bad data is estimated to be costing the US roughly $3.1 trillion a year.
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.
It is estimated that we create roughly 2.5 billion GBs of data per day. With this ever-growing amount of data, collecting what’s important for your customers and your business can be a struggle. Every piece of data that your company collects from your customers – whether it be social media likes, website visits, or email surveys – contains data that can be analyzed to understand your customers more effectively.
By using data science with the information your customer provides, you can combine data points to generate insights to target your audience more effectively. This means you can tailor services and products to particular groups. Finding correlations between age and income, for example, can help your company create new promotions or offers for groups that may not have been accessible before. Implementing data science methodology throughout your business can add value in a variety of ways across decision making, recruiting, training, marketing, and more. Data analysis can lead to making well-informed decisions that allow your company to grow in smart, strategic ways. Taking the time to use data science and discover the evidence behind your performance is a tool that every business should find valuable.
Pros and Cons of an In-House Data Science Team
An experienced data scientist is likely to be a trusted advisor and strategic partner to the organization’s upper management by ensuring that the staff maximizes their analytics capabilities. A data scientist communicates and demonstrates the value of the institution’s data to facilitate improved decision-making processes across the entire organization, through measuring, tracking, and recording performance metrics and other information.
- 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
With the development of Big Data, new businesses have also been created. We then speak of "consultant data scientist" or simply "consultant big data". They are professionals who have specifically trained in interconnected technologies and data management projects.
They have therefore acquired invaluable skills and experience to help you implement your digital transformation strategy. These data consulting experts are dedicated to meeting the needs and requirements of today's market. They also take into account those consumers who are more than ever looking for personalized offers.
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?
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
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.
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
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
Data science can add value to any business who can use their data well. From statistics and insights across workflows and hiring new candidates, to helping senior staff make better-informed decisions, data science is valuable to any company in any industry.
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.
Data science solutions are finally accessible to companies and enterprises of all sizes. Today, any organization that wants to take advantage of the data it possesses, can now leverage data science solutions to extract hidden insights. Data science consulting offers a unique opportunity to your team and business – a source of single, accurate, high-quality information.
Data science apps enable business leaders to make informed decisions based on forecasts, predictions and optimizations, so their competitive advantage increases.