Discovery

The key data analyst skills for the future

As you can see, companies nowadays have to deal with large amounts of data due to the really fast improvements in data ingestion, unification and storage. This trend has led to a lot of chances for companies to advance both strategies and operations. For instance, thanks to using customer data, they are able to deliver more customized and suitable experiences for clients. The data revolution also creates much pressure which forces companies to keep up with their rivals by taking advantage of new and revolutionary ways.

Nevertheless, even a large amount of data has little vale for decision makers. Thus, there is always a need of specialists that can support managers or executives to learn what all of this data means for their companies.

One of those specialists are data analyst. Data analysts are able to sift through data to look for the patterns that make sense and show the actionable and helpful insights to decision makers.

Such work allows companies to make their own choices on objective knowledge. In terms of technically advanced level, data analysts may become true data scientists, dealing with large amount of data and handling complicated issues such as developing new algorithms for machine learning technology.

In the following post, you are going to read about what skills that a data analyst should have when the busines word is developing towards data.

  1. Critical thinking and problem solving

High – level data analysts may understand how to carry out experiments, test hypotheses and create causal inferences from the data. Also, they are able to consider different problems in a more creative manner. For instance, they know how to translate the business-related enquires of decision makers to helpful questions about data. When software needs to do an increasing number of different tasks, the value of data analysts will be up to their capability of implementing human judgement to business difficulties. Machine learning and artificial intelligence technologies may turn critical thinking and problem-solving skills into even more important parts in the upcoming time.

  1. Statistics

Data analytics is something around the statistical analysis of data. Therefore, strong quantitative skills are an important part of the tools that a data analyst owns, in spite of the fact that various jobs ask for various levels of mathematical understanding. The professionals working in this field at least need to have a solid knowledge of basic figures. Senior data analysts should be skillful in such techniques as predictive modeling, trend analyzing and so on. Analysts should also know how to use those skills in order to satisfy the practical business questions instead of just knowing theories.

  1. Data management, querying and analysis

To simply understand, data analysts should be able to work with data in a comfortable manner. In other words, they should be clear about collecting, arranging and manipulating huge amounts of data by utilizing databases as well as other technologies. In other words, they should make sure that they know how to look for and extract the particular data required to carry out their analyses. SQL, which stands for Structured Query Language, is a universal demand. This language enables analysts to code their own personalized queries and pull huge amount of detailed data from relational databases. In order to work with huge amounts of datasets making use of such frameworks as Hadoop, analysts need to learn an extra query language like HiveQL.

  1. Programming

It is also highly recommended to establish a strong knowledge of programming because analysts will need to handle many problems that can not be solved by software lacking power and flexibility. In these cases, they should write their own code, customized to their particular datasets and business demands. R and Python are among the most popular languages for analytics. R is aimed at developing programs for statistical analysis while Python is necessary for automating repeated tasks and generating visualizations of data. Other programming languages like MATLAB may also be required to solve some particular difficulties.

  1. Business Acumen

A good data analyst will know to combine statistical skills and technical ones together with the capability of understanding the particular difficulties that their companies and decision makers are facing. As a result, data analysts will get enough knowledge about their particular industries as well as the business operations that they serve inside the organization. For example, a data analyst who takes responsibility of offering suggestions to marketing managers of a company needs to have a strong knowledge of marketing strategy.

  1. Visualization and communication

In order to assist decision makers, data analysts are required to tell stories with data and transfer their findings in an accessible and understandable manner. Thus, they should be able to make ideal visual aids like graphs, diagrams and dashboards. This may need programming and business intelligence solutions. In addition, being a data analyst, you must be good at written and verbal communication. Because of the fact that data is now becoming more and more important to making decisions across the companies, data analysts will need to work with personnel under different roles. As a result, the capability to speak and communicate with various people tend to be a must.

  1. Machine Learning and Artificial Intelligence

With the improvements in machine learning, more and more analytic activities will be delivered to smart systems which are able to recognize patterns in data, and learn the experience to advance the overall performance. For instance, if you utilize such an enterprise customer data platform as Arm Treasure Data, you can identify valuable customer segments and start to use predictive analytics by evaluating the potential of future events. This trend can alter the role of a data analyst without any predictions. What is more, analysts may also have to understand how to implement artificial intelligence tools and accesses to practical issues and machines can take over the more repetitive tasks related to data analysis. As a result, this can help gain the whole productivity for the operation of a company.

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