Outsourced Data Analytics As a Business Driver in Times of Uncertainty

09.12.2022 | Nikolay Valiotti

Outsourced Data Analytics As a Business Driver in Times of Uncertainty

Simple Mobile Analytical Stack: Firebase + BigQuery

If information is the oil of the 21st century, then analytics is the combustion engine, as stated by the Senior Vice President and Global Head of Research at Gartner. Think about it—today, every business, big or small, is bound to overcome a number of challenges: inflation and economic downturn, accelerated digital transformation, security, the intensified war for talents, and increasing customer expectations are some of them. It’s data that can help an organization overcome them and keep afloat.

Analytics solutions allow for data-driven insights and informed decision-making, which lead to resource and cost optimization, together with enhanced efficiency. Why has data analysis become a business driver, and which approach to data management should you choose—in-house data scientists or third-party service providers? Let’s find out in this article.

Why Do Data Investments Keep Increasing?

According to the annual New Vantage survey, 97% of the polled organizations were investing in data initiatives in 2022, which means that businesses had acknowledged the importance of data maturity. The majority of respondents (92%) cited measurable business benefits as the output of their data analytics investments. It’s a significant increase, compared to 70% in 2020 and only 48% in 2017, among those who realized data-driven business benefits.

The same trend has been detected in terms of revenue from Big Data and business analytics worldwide. Statista forecasted it to reach $274 billion in 2022, which is almost $100 billion more than in 2018.

While some may believe that such results are achieved because of advanced analytics, there is more to unveil. The key element to insightful reporting and predictive analytics is an executor or a skillful data analyst who can investigate your business landscape, finding proper ways to collect and process data so that the outcomes facilitate the achievement of corporate objectives.

5 Key Reasons to Outsource Data Analytics

Both in-house data scientists and third-party sourcing providers can help you reap vivid data-driven benefits. However, the latter—data analytics outsourcing—is becoming a more popular option. Let’s speak numbers:

According to Market Research Engine, the global Data Analytics Outsourcing market is expected to grow by $7.5 billion by 2025 at a CAGR of 29%.
According to Tecgnavio, the global Data Analytics market will surpass $172 billion by 2025 at a CAGR of almost 17%.

The data analytics outsourcing market is developing at a faster pace, which indicates that more and more organizations delegate their Big Data analytics to a reliable outsourcing partner. Valiotti Analytics has come up with 5 key reasons why companies abandon in-house data analytics in favor of third-party analytics services. Here’s why you should start outsourcing data analytics.

1. Source-Lacked Competencies

Depending on your corporate data maturity, you may have different needs. Suppose, you are a beginner in terms of data analysis. Then, your HR may spend tons of work/hours and money to recruit data analysts while, in reality, you need a data scientist. All this is because of a wrong understanding of the required hard and soft skills. At the same time, if you manage to specify the role correctly, you may fail to retain a specialist because of:

Misaligned objectives, like when you need to cover basic data analytics tasks but an employee wants to conquer new heights.
Poor onboarding, which results in a data specialist’s complete misunderstanding of your business’s specifics and goals.

What is more, you may not even understand how to benefit from data collection and analytics. This applies to you if:

Excel tables are used to store data, not to make conclusions and visualize them.
You use the average instead of the median when estimating important metrics.
“Sanity check” has no meaning to you, and you fall for the first “insights” without double-checking them.

If you already have a data analyst who manages to set up efficient and relevant data collection, you are moving to the next level: amateur. At this stage, you already realize why data analytics matter, but you can’t figure out how to connect the dots or build an informative report. As a result, you waste resources by trying to manually merge Excel data, dealing with duplications and consequential errors, and exporting data to irrelevant tools.

A professional data analytics outsourcing agency is a one-stop-shop to nail data management and avoid costly mistakes. A trustworthy service provider analyzes your business goals, drafts a map to their achievement, and comes up with several implementation options. As a result, you get professional help and extensive expertise across a variety of fields, BI tools, machine learning, data analytics, data science, etc., while you can focus on your core business.

2. Improve Data Accuracy and Decision Making

Outsourcing data analytics for the sake of enhanced accuracy can be broken down into three vivid outcomes:

No more managerial failures because of discrepancies and wrong conclusions. Before, you could have used incorrect data analysis approaches or formulas to calculate LTV, CAC, and Retention rates, as well as relied on average figures. Besides, many people even make fatal decisions based on poor-quality data, which results in extra expenses or even reputation loss.
No marketing budget waste. Many companies keep investing in digital ads without even looking through reports. Moreover, they may not calculate the cost of every lead depending on the channel nor analyze their LTV. Those who aren’t lucky are likely to throw away marketing budgets with minimum ROI, while a decent data analysis can foster correct budget allocation and maximize returns.
No misleading future plans. Suppose you conduct a historical analysis to forecast the future and at least try to curb uncertainty. If you rely on average figures instead of median ones—and neglect seasonal trends and other factors in terms of business growth—we have bad news for you. Most likely, your forecasts won’t come true, and you’re wasting resources, preparing for some fictional future.

To sum up, data analytics outsourcing is your chance to make correct informed decisions and minimize mistakes. As a result, you won’t only save on extra costs (that accompany wrong decisions) but will also harness the power of data for the sake of improved sales and revenue.

3. Speed Up Your Goal-Achieving Marathon

Every business has a so-called goal timeline. A small investment company dreams of becoming a local leader, and a medium-size FinTech solution seeks country-wide adoption, while some large-scale enterprises with thousands of users across one continent dream of conquering the world. Regardless of your goal, analyzing large amounts of data can unveil new opportunities, solutions, and challenges. However, it’s the right data and analysis method that matter.

An outsourcing partner helps you:

Learn how to use data to spot growth points. It’s best to rely on recent dynamics rather than long-term ones. Remember, the world changes, and historical data may not be accurate enough.
Use your clients’ data to create efficient marketing and product development strategies. By outsourcing data analytics, you won’t mix up B2B and B2C clients, get distracted by unverified clients, or forgetfully combine all the user data into a single profile, even if they access your site from various devices.
Acknowledge the efficiency of each stage of the sales funnel. A professional service provider knows that every sales funnel is unique, and a user may go through several funnel stages without proceeding to the “checkout.”

In the end, you receive informative reports that align with your business objectives and provide a comprehensive outlook on a specific topic. This allows you to make a prompt yet robust data-based decision to foster business growth.

4. Successfully Copy Competitors to Maintain a Competitive Advantage

Although 47% of New Vantage respondents were competing on data and analytics, almost 40% were managing data as a business asset; barely over a quarter created a data-driven organization, and just 19.3% established a data culture. It means that the majority of the business sector invests in data analytics yet doesn’t utilize all the possibilities.

Benefiting from a blend of Big Data and AI technologies, managing unstructured data, and coming up with data-based valuable insights and solutions are some steps on the way to a competitive advantage. If data-mature businesses prosper because of data analytics, why can’t you?

Instead of reinventing a wheel and burdening yourself with complicated data analytics processes, consider a reliable outsourcing partner. Service providers don’t only have a good grasp of technology and hands-on expertise—they are well aware of best practices that promote your competitor’s business growth.

Data analytics outsourcing allows you to delegate data collection and analysis to a professional vendor who:

Sets correct tasks to leverage data for the sake of improved revenue.
Engages the right team of experts to quickly deliver value.
Has a good understanding of various technologies, approaches, frameworks, cloud tools, etc.

5. Break Down and Understand Data Management Processes to Run It Yourself

Over the past decade, 53% more companies adopted a Chief Data Officer (CDO) role: from 12% in 2012 to 65% in 2021. However, almost half of all CDOs have primary responsibilities for data within their companies—meaning that businesses fail to set up correct data analytics processes, and, therefore, aren’t leveraging any data-driven insights. In 2022, even more respondents (almost 60%) reported the CDO to be a nascent, evolving, or subject-to-turnover role.

Instead of outsourcing data analytics, businesses often set up no or wrong KPIs for a data analytics department – for example, revenue, which hardly assesses the team’s performance. Usually, it’s a secondary KPI, which derives from the primary ones like an increased number of leads, retention rates, etc. (depending on your current business goals).

Data analytics outsourcing can guide you into data-related processes and best practices. Think of it as a win-win situation: you get instant professional support, for example, with Big Data analytics, while your team is trained to keep delivering quality analytics when a service agreement finishes.

By the way, a data analytics outsourcing vendor can even provide consulting services. They will analyze your industry, business processes, objectives, etc., and set up a team for you. You will get not only a team with the right expertise but also a set of managerial guidelines: an outsourcing partner usually sets up accurate KPIs and works on the culture and internal Agile processes.

Data Analytics Outsourcing vs. an In-House Department: The Verdict

While there is no ultimate solution for everyone, you can surely rely on data analytics outsourcing if:

You lack the relevant competencies and don’t want to spend extra time and money on recruitment.
You need urgent data analytics team mobilization to fulfill the tasks.
You have no idea how to work with data analytics and why you should but want to reap a competitive advantage in a data-driven way.
You want to leverage data to promptly get from point A to point B (in terms of business growth).
You lack accuracy and don’t want to rely on misleading insights because of poor analytics.
You want to optimize the costs of marketing, HR, etc.
You aim to adopt data analytics best practices but need assistance.
You want to buy consulting services to manage your data analytics department yourself.

Looking for a reliable vendor with hands-on expertise to outsource data analytics services? Don’t hesitate to contact Valiotti Analytics! We are here to design and implement a data strategy in accordance with your business needs.

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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