What are the key features of Analyst 2.0?


The brand-new community of analysts is ready to answer the market challenges in any industry.

Due to the emergent changes in data analytics, including the volume of raw data to be investigated, the approaches to data analysis have drastically changed. Nowadays, analysts are expected to use AI technologies to structure figures, catch similarities and trends, and provide answers about market changes to business owners with cosmic velocity.

Analysts’ major roles in the modern community

Today’s picture of the analyst’s occupation comes from the Age of Actionable Insights. The primary approach to transferring information to the client was adequately encapsulating data into easy-to-read views, such as charts, tables, and graphs. Analysts were responsible for manual data structuring and statistical analysis. Advanced researchers used to work with R, MATLAB, or SQL. Everything has changed with the introduction of AI technologies. Machine learning and neural networks let specialists train models and automate the analysis. Old-school analysts have become less popular in the field. The focus on positions requiring narrow areas of expertise has significantly weakened the demand for such data experts.

When I served in data analytics at a leading organization, I felt useless until we gained the status of data scientists, supported by the relevant training in AI technologies. The prejudices we faced stepped over the bounds of teamwork. According to statistics, data science specialists or data engineers have earned twice as much as analysts in the last few years.

In this situation, analysts are caught between taking the lead as gurus in their field or staring into space and doing nothing.

What’s put at risk?

It’s necessary to understand the root of the matter. On the one hand, access to data on the stack is effective if the provided information is intelligible, straightforward, and focused on actionable analytics. On the other hand, professional data analysts are experts in understanding, consuming, and managing the information required by the company.

Even if we have developed efficient data models and updated databases or applications, it’s up to the analyst to foster the integration of the produced information into the client’s organization.

Less analyst involvement may result in a lack of structured data or trends vision, necessary for the decision-making for the top management.

Final Thoughts

We require a more powerful position for an analyst in the organization. To achieve this trust, the analyst must gain new knowledge of ML approaches, methodologies, and principles of big data structuring that are in high demand for this profession.

It’s critical for skillful analysts to obtain the essential qualities of an AI expert, understand the ML and NN opportunities, and cleverly compare the tools necessary for their task implementation.

Meet an Analyst 2.0

The qualities possessed by modern analysts are insufficient for the successful realization of tasks. Perfect mathematical reasoning and data communication abilities benefit analysts to a great extent. Nevertheless, some new skills are needed for adaptation in the contemporary context:

  1. Focus on translation

Any successful analyst should be well-trained to assist with connecting data to the set organizational goals. Reading charts, tables, and graphs should reflect the company’s needs. If an analyst can translate the obtained data, the organization’s problems are easy to understand. The major role of an analyst is to see the key segments, trends, and deviations from the organization’s strategic plan and let the company arrive at adequate business decisions based upon the data.

  1. Simplification of decision-making

In order to make complicated decisions, we require more information in an easy-to-evaluate format. It is an analyst who can help team member’s structure and classify the available data and visualize it for better decision-making. Having made things as clear as possible, an analyst contributes to the achievement of the company’s strategic goals.

It’s great, but what further steps should we take?

Analysts should gain new knowledge and study AI technologies for more effective task performance. They should see the opportunities provided by frameworks and custom AI software to achieve the required analysis according to the increasing and ever-changing demands of the global market.

Kevin Lindsey

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