Overview Data Analytics?
As the world becomes more data-driven, storytelling through data analysis is becoming a more vital component and aspect of businesses large and small and is the reason organizations continue to hire data analysts.
Data-driven businesses make decisions based on the story their data tells, and data is not being used to its full potential, a challenge that most business face today. Data analysis is and should be a critical aspect of all organizations to determine the impact to their business, including customer sentiment, market and product research, identifying trends, or any other data insights.core components of analytics are divided in the following categories:
Descriptive analytics
Descriptive analytics helps answer questions about what has happened, based on historical data. Descriptive analytics techniques summarize large datasets to describe outcomes to stakeholders By developing KPIs (Key Performance Indicators), Specialized metrics are developed to track performance in specific industries.
Examples of descriptive analytics include generating reports to provide a view of an organization's sales and financial data
Diagnostic analytics
Diagnostic analytics helps answer questions about why things happened. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse. This generally occurs in three steps:
- Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.
- Collect data that are related to these anomalies.
- Use statistical techniques to discover relationships and trends that explain these anomalies.
Prescriptive analytics
Prescriptive analytics helps answer questions about what actions should be taken to achieve a goal or target. By using insights from predictive analytics, data-driven decisions can be made. This technique allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies to find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated.
Cognitive analytics
Cognitive analytics attempts to draw inferences from existing data and patterns, derive conclusions based on existing knowledge bases, and then add these findings back into the knowledge base for future inferences, a self-learning feedback loop. Cognitive analytics helps you to learn what might happen if circumstances change, and how you might handle these situations.
Inferences aren't structured queries based on a rules database, rather they're unstructured hypotheses gathered from several sources and expressed with varying degrees of confidence. Effective cognitive analytics depends on machine learning algorithms. It uses several NLP (Natural Language Processing) concepts to make sense of previously untapped data sources, such as call center conversation logs and product reviews.
Roles in data
The following highlights the different roles in data and the specific responsibility in the overall, end-to-end spectrum of data discovery and understanding.
Business Analyst
While there are some similarities between a data analyst and business analyst, the key differentiator between the two roles is what they do with data. A business analyst is closer to the business itself and is a specialist on interpreting the data that comes from the visualization. Often the data analyst and business analyst could be the accountability of a single person
Data Analyst
A data analyst enables businesses to maximize the value of their data assets through visualization and reporting tools such as Power BI. Data analysts are responsible for profiling, cleaning, and transforming data, designing, and building scalable and performant data models, and enabling and implementing the advanced analytics capabilities into reports for analysis. They work with the appropriate stakeholders to identify appropriate and necessary data and reporting requirements and then are tasked with turning raw data into relevant and meaningful insights.
A data analyst is also responsible for the management of Power BI assets, including reports, dashboards, workspaces, and the underlying datasets used in the reports. They are tasked with implementing and configuring proper security procedures, in conjunction with stakeholder requirements, to ensure the safekeeping of all Power BI assets and their data.
Data analysts work with data engineers to determine and locate appropriate data sources that meet stakeholder requirements and work with both the data engineer and database administrator to ensure the data analyst has proper access to the needed data sources. The data analyst also works with the data engineer to identify new processes or improve existing processes for collecting data for analysis.
Data Engineer
The primary responsibilities of data engineers include the use of on-premises and cloud data services and tools to ingest, egress, and transform data from multiple sources. Data engineers collaborate with business stakeholders to identify and meet data requirements. They design and implement solutions.
A data engineer adds tremendous value to both business intelligence and data science projects. When the data engineer brings data together, often described as data wrangling, projects move more quickly because data scientists can focus on their own areas of work.
Both database administrators and business intelligence professionals can easily transition to a data engineer role. They just need to learn the tools and technology that are used to process large amounts of data.
Data Scientist
Data scientists perform advanced analytics to extract value from data. Their work can vary from descriptive analytics to predictive analytics. Descriptive analytics evaluate data through a process known as exploratory data analysis (EDA). Predictive analytics are used in machine learning to apply modeling techniques that can detect anomalies or patterns. These are an important part of forecast models.
Descriptive and predictive analytics are just one aspect of data scientists' work. Some data scientists might even work in the realms of deep learning, iteratively experimenting to solve a complex data problem by using customized algorithms.
Database Administrator
A database administrator implements and manages the operational aspects of cloud-native and hybrid data platform solutions built on Microsoft Azure data services and Microsoft SQL Server. They are responsible for the overall availability and consistent performance and optimizations of the database solutions. They work with stakeholders to identify and implement the policies, tools, and processes for data backup and recovery plans.
The role of a database administrator is different from the role of a data engineer. A database administrator monitors and manages the overall health of a database and the hardware it resides on, whereas a data engineer is involved in the process of data wrangling, i.e., ingesting, transforming, validating, and cleaning data to meet business needs and requirements.
The database administrator is also responsible for managing the overall security of the data, granting and restricting user access and privileges to the data as determined by business needs and requirements.
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