In-House vs. Outsource Data Analytics: What to Bet On in a Crisis?

Extreme times lead to extreme (and often non-conventional) solutions for resource optimization. Data analytics, which may seem like an optional add-on rather than a vital part of your business’s well-being, can become such a tool. Nonetheless, business owners and CEOs keep questioning its efficiency and necessity, especially in times of crisis. 

Why is data analytics important? Can you cut costs by omitting data science? Should it be in-house or outsourced? These are some typical questions that business owners ask themselves. In this article, we will elaborate on each to find out if data analytics is important in crises and which is better: in-house or outsourced data analytics. 

Crises and Analytics: Can the Latter Help Withstand the Former?

Suppose you caught the flu and need to monitor your well-being. The most common tool is a thermometer, which allows you to understand if you are getting better or if it’s time to go to the hospital. That’s exactly how data analytics helps businesses in crises. 

In times of uncertainty and economic disbalances, businesses undergo a lot of unexpected changes, from mandatory cost reduction to altered market sentiments. Data analytics allows you to collect valuable data and interpret it to get meaningful insights. You can easily track the quality of transformations and assess the results. They, in turn, form the assessment metrics basis and give you a broader picture of your current business state. If we are to continue the analogy of the thermometer, data analytics allows you to analyze your business health and call an ambulance (i.e., launch anti-crisis management) if there’s an emergency.  

While some entrepreneurs may still be skeptical, most enterprises (99%) largely invest in Big Data and AI to get superior data-driven business decisions, according to the 2021 NewVantage Partners Big Data and AI Executive Survey. Almost the same number (96%) claim that such investments improved their competitive edge and allowed them to achieve measurable business outcomes.

The outcomes vary from business to business. Yet, they all drive significant cost reductions: for example, Netflix saves around $1 billion annually on customer retention with Big Data. This technology helps the streaming service decrease subscription cancellation rates via enhanced personalization.

In-House vs. Outsource Data Analytics

With 65% of enterprises heftily investing in Big Data, alongside Chief Data Officers who appoint AI, one may believe that it’s a sure way to leverage piles of data into actionable business outcomes. The truth is that in-house and outsourced data analytics are good for different business tasks.

Suppose you haven’t dealt with data at all. In this case, you are to proceed with complex groundwork. After all, data processing and analytics are only the tip of the iceberg. The preliminary stage includes the following initiatives:

• Launching data collection processes
• Data collection
• Running databases
• Preparing the data transformation process so that data analytics are in the required format

If you hire the wrong analytics specialists, their lack of experience will result in a waste of time and money, as they may not be able to perform your needed tasks or meet business objectives. Such an analyst may be good at dealing with specific tasks like creating and exporting Excel reports but might fail to set up a systematic and complex approach to business analytics. At this stage, businesses should look for a data engineer rather than an analyst. 

In order to reduce the amount of effort required and minimize mistakes, it’s best to outsource data analytics. In this scenario, you will have an experienced team of experts that analyze the input data, help you come up with business goals, and find ways to achieve them. 

Let’s break down the pros and cons of outsourced vs. in-house data analytics.

To summarize, outsourced data analysts are experienced data science experts; they can fulfill a wide variety of analytics tasks and streamline processes. However, they often lack the ultimate in-house analyst benefit – a deep understanding of your business context and processes. If you are willing to devote a little more time for them to investigate your business from the inside out, you should get yourself a superior team with the right expertise, unlimited resources, and a thorough understanding of your business goals.  

Let’s Speak Money: The Profitability of Outsourced Data Analysts vs. In-House Ones

Here are the key criteria to take into account to find out which option is more affordable:

1. Timeframe. If you need to build data analytics processes from scratch, opt for an agency that will closely work with you for a maximum of six months. If you want to embed data analysis into your core business and perform it regularly, most likely, an in-house data analyst is a better option.
2. Management. An in-house specialist should be recruited, onboarded, and trained. Besides, you or another stakeholder needs to understand how to source the right talent and set up the department’s workflow to meet business objectives. Outsourcing allows you to buy hands-on expertise and achieve business goals faster. 
3. Corporate data maturity. A full-time employee can deliver if the data collection processes are already set up. Building the processes from scratch is often more efficient with an outsourced data analytics agency.

Opt for an in-house data analyst if you have data analytics processes, and business goals set, plan to regularly work with data to gain a competitive edge with data-driven decisions, and are ready to manage a new department. If you are too busy to engage in a brand-new IT niche, aren’t data mature, or plan to rely on data for a short-term project, outsourcing a data analytics vendor is a cheaper and easier solution.

Final Thoughts

A crisis implies significant and often negative changes that happen suddenly. That’s why you may need to ramp up data analytics overnight. If you lack any previous experience in the field, outsourcing data analysts is the best option. You avoid losing time and resources on hiring, onboarding, and training. Once all the data-related processes are set, you can invest in an in-house data analyst that will deeply understand your business context and monitor all the corporate changes. This allows you to keep a close eye on transformations and promptly address them.

You need this guide, because it includes:

• evolution of data stack
• reasons why many well-known tools do not meet the challenges of the new world
• vital structure of modern data stack
• the newest tools for every part of modern data stack

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