25.11.2022 | Nikolay Valiotti
Top 5 Data Visualization Tools in 2023
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.
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.
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:
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.
Here are the key criteria to take into account to find out which option is more affordable:
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.
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.
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