Data science outsourcing is the fastest, most reliable, and most affordable way to get maximum value from your data.
Today’s most successful companies use data to make better decisions, improve their customer experience, and get ahead of their competitors. By outsourcing data science, your company can do the same without the cost or time of building an in-house data science team.
At Vidi Corp, we have delivered data analytics consulting projects for 600+ clients, including Google, the UK Ministry of Defence and Heineken. As a result, we have extensive experience in ensuring the successful delivery of outsourced data science projects.
This article explains everything you need to know for your company to outsource data science successfully.
Data science outsourcing is strategically hiring an external, ready-made team of data professionals to handle some or all of your business’s data-related tasks, such as data cleaning, analysis, visualisation, and predictive modelling.
By outsourcing data science, you gain access to highly specialised skills, advanced tools, and the latest technology, all without the high cost of building and training an in-house team.
This lets your team stay focused on strategy and growth, not wrangling data all day.
You don’t pay salaries, training, benefits, and all the other costs of having a full-time in-house team; you only pay for the work you outsource. As a result, you usually pay a bit more for your project during the development phase, and then the cost goes down to 0.
You get help from experienced data professionals who have worked across industries and are skilled in niche technologies. Outsourced data science teams usually have separate experts specializing in different areas of data science including data engineering, data visualization, etc. Nurturing high-levels of these skills in-house takes a long time and usually requires hiring multiple people.
Outsourcing data science speeds up development because you skip the long process of hiring, training, and trial-and-error. Instead you work with experts who already know how to handle data pipelines, AI models, and analysis.
Whether your data needs grow or shrink, outsourcing lets you scale up or down without hiring or layoffs. As a result, data science outsourcing is a perfect choice for companies with occasional data analysis needs.
Data science is a broad field that includes many specializations from data engineering to machine learning and data visualization. Depending on your needs, your outsourced data science project may require skills in one or more of these areas.
Data engineering involves building (and maintaining) the infrastructure for automatically extracting, storing, and transforming data so that it’s ready for further analysis.
A data engineer in the outsourced team will take care of this by helping you set up robust Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) pipelines that will ensure your data is always accurate, consistent, and easy to access when needed.
If your business wants to integrate multiple data sources or automate data extraction and transformation, this service is for you.
At Vidi Corp we provide data engineering consulting services by using Azure and Google Cloud Platform technologies. We have experience with extracting data from APIs and on-premise data sources as well as transforming it in SQL and scheduling automatic refresh.
Business intelligence refers to visualizing the data in a series of graphs and combining them in a dashboard. These dashboards are usually produced in ad-hoc reporting tools like Power BI, Tableau or Looker Studio.
BI consultants would usually work on descriptive analytics and management reporting tasks to generate automated and real-time reports.
Vidi Corp is the #1 business intelligence consultancy in the world according to G2. We have delivered 1,000+ dashboards for 600+ clients like Google, Teleperformance and Heineken.
Predictive analytics involves building statistical and machine learning models around your data to forecast future business outcomes. The use cases of predictive analytics vary from time-series analysis to predicting the likelihood of a certain event, like customer churn.
Predictive analytics consultants would rely on technologies like Python, R, SPSS Modeller, and SAS to build their models.
Predictive analytics services are useful for companies that want to predict future demand forecasts, which customers have a high risk of churning.
Computer vision refers to analyzing images and video files to automatically detect an event or classify an image.
It is used in sports for detecting when a ball crosses a line or in biotech to analyze medical scans and classify them.
Computer vision is also often used for optical character recognition, where algorithms are used for extracting handwritten data at scale. Some companies use computer vision for extracting data from large PDF files (invoices, team schedules, etc) for subsequent analytics.
Natural language processing refers to analyzing text data at scale to identify trends and patterns. Data scientists would usually rely on R and Python skills to clean and analyze text data.
Common NLP analysis tasks include word frequency analysis and sentiment analysis. The main benefits of NLP is identifying what people frequently talk about and how they feel towards something.
This service is suitable to companies who want to analyze social media posts, customer product reviews or in-house surveys.
These will help you choose a data science outsourcing partner and also have a successful project with them:
As you can see, data science has many areas and each one of them requires a different skillset.
The first step to successfully outsourcing data science projects is to identify which type of data scientist you will need. Use the section above to decide which skills are essential to your project. After this you can search for data analytics outsourcing companies that specialize in the area of analytics that is relevant to you.
Client reviews can give you a pretty good idea of how a vendor measures up. If you see a lot of verified reviews over a long period of time, this is usually a sign that a data analytics outsourcing provider consistently delivers high quality solutions.
For example at Vidi Corp we have 200+ verified reviews on Clutch, G2, Gartner and other platforms.
The best way to evaluate the technical capability of your data science outsourcing partners is to see their case studies. Ideally, they would have experience working with a type of data similar to yours and solving similar problems for companies in your industry.
For example, at Vidi Corp, we have dozens of business intelligence case studies available to see publicly. The show the analytics reports that we have created from different sources like Hubspot, QuickBooks Online and many more.
It is usually a good practice to start your data analytics project with a proof-of-concept phase. This is the best way to test the solutions that your data science outsourcing partner can deliver for you without risking your entire budget.
The scope of your POC project should test all the needed skills for the successful delivery of the entire project. For example, you can get your outsourced data analysts to calculate 10 KPIs out of 100 and visualize them on the dashboard.
If you are happy with the results of your POC, you can continue development and invest more into the project.
Once your POC is delivered, you should scale development and deliver the remaining data analytics requirements. This step is pretty self-explanatory, but it requires proper project management.
Discuss timelines and schedule regular checkups to monitor the project’s progress and ensure that it is moving in the right direction.
When working with a third party, you should be aware of some challenges that may spring up and, even more, how to bypass them.
Vidi Corp has successfully helped several hundred businesses make better use of their data through services such as data engineering, predictive analytics, and business intelligence reporting, among others.
Large firms like Google and the UK Ministry of Defence, as well as SMBs across 14 industries and departments, including manufacturing, education and retail, have partnered with us and seen great results.
Our clients rave about our speed, expertise, and business value in their reviews. In a recent review, our client said our automated BI solution, which we built for them, saves their team 5 hours a month. If you want similar results, we’re just a message away.
Yes, you can outsource data science to external teams that have the skills, tools and experience your business needs but doesn’t have internally.
Data engineering, business intelligence, predictive modelling, and AI development.
Look for a vendor with proven expertise, good client reviews, and transparent processes for handling your data securely.
