The primary responsibility of a data analyst is to gather, refine and evaluate data to generate valuable insights regarding an organisation and its challenges.
Apart from comprehending structured and unstructured information, data analysts also assist companies in comprehending their current position.
Data analysts are leveraging their proficiencies in computer science, mathematics, and statistics to scrutinise data and unearth meaningful revelations that might enhance the organisation’s financial performance.
Significant Responsibilities
The major responsibilities of a data analyst comprise:
- Identifying data requirements and meeting them through primary and secondary research
- Refining and refining data for analysis by sifting and scrubbing it
- Employ statistical techniques and software to scrutinise the data.
- Incorporating key stakeholders in the exercise of identifying business requirements
- Analysing and reporting the discoveries
- The aptitude to discern patterns and trends in expansive datasets
- Formulating data storage systems and applying database development methodologies
What makes data analysts indispensable?
Most businesses are encountering a dip in profit margins, an increase in expenses, and an expansion of commodification. This has induced big corporations to look for alternate means of generating revenue and attaining a competitive edge.
The enormous amount of data companies possess is exceedingly advantageous when it comes to generating revenue. Regrettably, numerous organisations lack the skill, knowledge and tactics essential to utilise their data effectively.
Employing data analysts with the appropriate amalgamation of skills and disposition can empower them to harness their data to their advantage and unlock novel revenue streams.
Proficiency in Technology
Cleansing and Preparing Data
Crowdflower discovered that 19% of data scientists’ time is spent on data collection, while another 60% is allocated to data cleansing and organisation.
Your data analyst will spend more than 80% of their time (on average) getting the data ready for analysis, hence it is imperative that they excel at it!
Data is acquired from a myriad of sources (such as databases, software, surveys, and third-party data sets), followed by a process to eliminate any duplicates, incorrect values, or other maladies. To ensure compatibility of datasets, certain modifications are frequently required.
We are in search of a candidate with an established history of working with both structured and unstructured data, who can exhibit a proficient understanding of Extract, Transform, Load (ETL) tools utilised for data cleansing and conversion.
Data Analysis
Data analysis is an essential expertise post data preparation. Here, unprocessed data is converted into practical business intelligence.
Proficiency in mathematics and statistics, combined with professed knowledge of prevalent data analysis platforms like Tableau, Jupyter Notebook, and Microsoft Power BI is essential for thorough data analysis.
Familiarity with avant-garde computational methods for data analysis confers an advantage. Knowledge graph, a tried and tested technique for integrating data from diverse sources and obtaining insights from their associations, is one such example. Additionally, data analysis may include artificial intelligence and machine learning (see below!).
Data Visualisation
The appraised value of data to the organisation only materialises upon evaluation and communication of the consequent insights to the relevant business stakeholders. It is at this juncture that the data becomes genuinely valuable.
The ability to envision data through graphical representation is indispensable. It demands the capacity to decipher intricate data and insights, then convey them in a manner that viewers can easily comprehend, using visual aids like charts, graphs, and so forth.
A combination of skills is necessary to comprehend the implications of data and construct a narrative around it that can be comprehended by the audience. These skills include expertise in data analytics as well as storytelling and presentation ability.
Possessing the proficiency to employ potent intelligence tools like Tableau and Power BI can prove to be highly advantageous, as these applications enable users to capture data intelligence and insights through effective visualisation.
Metrics and Reporting
A crucial aspect of the job profile of a data analyst is to ensure that pertinent information and insights are readily accessible to other departments.
Therefore, it may be incumbent on you to construct anything from a rudimentary data report to a sophisticated dashboard, which consolidates data from numerous sources.
The ideal candidate ought to possess adept knowledge and experience in employing data tools (spanning from Excel to Tableau) for generating user-friendly dashboards that enable employees to access crucial information swiftly and competently. This facilitates them in achieving their highest potential.
SQL
Structured Query Language (SQL) is the most extensively employed language for querying relational databases.
It is widely accepted that Structured Query Language (SQL) is the most potent approach to examine and manoeuvre data in relational databases. According to a recent survey, SQL was identified as the most in-demand skill for data scientists.
Given its ubiquitous significance, possessing a specialised understanding of data analysis is an indispensable prerequisite for any job seeker. This cannot be disregarded.
The perfect candidate should possess a comprehensive understanding of SQL syntax and conditional filters, as well as a familiarity with relational databases, tables and indexes. Moreover, it would be advantageous if they have intermediate proficiencies such as Joins, subqueries, along with experience in programming languages like PHP, and databases such as MySQL and PostgreSQL.
NoSQL
NoSQL, also known as ‘not only SQL’, is a type of data storage that concentrates on formats other than the conventional relational tables. The freedom that NoSQL databases provide, can be beneficial to developers as they enable storage of vast quantities of unstructured data.
The triumphant data analyst should possess a thorough understanding of the four principal categories of NoSQL databases: key-value, document, graph, and column family stores. Moreover, they should have knowledge about frequently used NoSQL databases and related technologies such as MongoDB, Cassandra, Redis, and Neo4J.
Statistical Programming Languages (R or Python)
Statisticians rely on computer languages that are intended for intricate studies and predictive analysis of substantial datasets to expedite the analysis process. Without such programming languages, data analysts would have to rely on spreadsheets and SQL, which would be a more time-consuming process.
Candidates should display advanced expertise in either R or Python. These are two of the most frequently employed programming languages, and both are open-source. Python is a universal language while R is primarily used in the field of statistics.
Python is renowned for its potential to manoeuvre and scrutinize data, along with its application in machine learning. Whereas, R is particularly tailored to functions such as cleaning, preparation and visualisation of data.
Teaching Machines to Learn (ML)
Numerous businesses regard ML as a leading investment and research topic due to its potential to extract valuable insights and provide precise forecasts.
It is a technique for handling information that enables the automated and large-scale creation of analytical models.
Data analysts can save time and acquire more insights from vast datasets by utilising ML to automate a significant portion of their customary data analysis process.
An expert data analyst should exhibit a broad understanding of Machine Learning (ML) methodologies, data modelling, and neural networks. Additionally, familiarity with MLOps – a framework employed for automating the deployment of ML models to production – is extremely advantageous. MLOps is one of the numerous methods for AI/ML operations that facilitate the implementation of Machine Learning at a large scale.
Proficiency in Business
Concentration on the Data
An extremely skilled data analyst will exhibit an extensive understanding of data and its pertinence to the organisation, empowering them to tackle business issues with an informed, data-driven strategy.
They acknowledge the enormous potential of data to facilitate various advantageous transformations, such as enhanced efficiency, stimulated innovation driven by data, upgraded products, and a more gratifying customer experience.
Their approach towards their role and the goals they establish will manifest an understanding of the potential of data to revolutionise businesses.
Ethics of Data
Concerning the management of sensitive information or consumer data, working with data presents several significant ethical concerns (PII).
Particularly, one should be watchful of the following:
- Title (Have you obtained consent to preserve this information?)
- Do individuals trust your intentions when it comes to you sharing their data?
- Privacy (How easily can personally identifiable information be disclosed?)
- Purpose (What is the reason behind conducting this study?)
- Significant outcomes (What is the objective behind collecting this information?)
As a data analyst, it is essential to be cognizant of ethical considerations and to possess a comprehensive comprehension of the possible ethical concerns that may emerge while working with data.
There are numerous potential challenges that may appear, such as avoiding racial or gender bias in machine learning algorithms, following data protection laws when sharing personal information, and ascertaining the duration for which personal data needs to be preserved.
Critical Thinking
Each corporation has its own established beliefs, doctrines, and traditional understanding.
Data analysts possessing exceptional analytical abilities are capable of delivering creative, unique resolutions to commercial predicaments by providing a distinct outlook on the issue.
Data analysts need to utilise their critical thinking abilities to ascertain the nature of the corporate issue, the data needed for analysis, the best possible way to conduct the analysis, and the most relevant inquiries to pose. However, relying solely on automated data analysis technologies is inadequate.
Final Words
The capability to scrutinise and construe data is increasing in significance since it has become the key driver of modernisation and transformation.
Acquiring the services of a proficient data analyst is imperative for the success of any data-oriented endeavour within a corporation. By investing in a team of data specialists, companies can uncover an array of advantages.
Explore our database consisting of meticulously screened technical experts from various parts of the world who can effortlessly integrate into your team in case you need highly qualified data analysts.
