Question

• Describe the characteristics and common uses of structured data. Provide examples as necessary.
• Describe the characteristics of unstructured data (e.g., big data) and where these data elements may be found. Provide examples as necessary.
• Discuss the common business analysis role and the types of data that may be used. Provide examples as necessary.

Discuss the use of analytics and the types of data that may be used. Provide examples as necessary

Answer 1

Structured Data:

Structured data is comprised of clearly defined data types whose pattern makes them easily searchable.
Structured data concerns all data which can be stored in database SQL in table with rows and columns. They have relational key and can be easily mapped into pre-designed fields.

Structured data is highly organized information that uploads neatly into a relational database

Structured data is relatively simple to enter, store, query, and analyze, but it must be strictly defined in terms of field name and type
Structured data usually resides in relational databases (RDBMS). Fields store phone numbers, Social Security numbers, or ZIP codes. Even text strings of variable length like names are contained in records, making it a simple matter to search. Data may be human- or machine-generated as long as the data is created within an RDBMS structure. This format is eminently searchable both with human generated queries and via algorithms using type of data and field names, such as alphabetical or numeric, currency or date.

Common relational database applications with structured data include airline reservation systems, inventory control, sales transactions, and ATM activity. Structured Query Language (SQL) enables queries on this type of structured data within relational databases.

Unstructured Data:

Unstructured data may have its own internal structure, but does not conform neatly into a spreadsheet or database.

Most business interactions, in fact, are unstructured in nature.

Today more than 80% of the data generated is unstructured.

The fundamental challenge of unstructured data sources is that they are difficult for nontechnical business users and data analysts alike to unbox, understand, and prepare for analytic use.

it refers to any kind of data that carries unknown form or structure.

It cannot be stored, obviously, in the way structured data can be stored in spreadsheets.

Examples :

Media ( MP3, digital photos, audio and video files )

Text files (Word processing, spreadsheets, presentations etc. )

Social Media (Data from Facebook, Twitter, LinkedIn)

Business analysis :

Business analysis includes the activities to help managers make strategic decisions, achieve major goals and solve complex problems, by collecting, analyzing and reporting the most useful information relevant to managers' needs. Information could be about the causes of the current situation, the most likely trends to occur, and what should be done as a result.

Activities can include identifying and verifying potential strategies and solutions, and testing the feasibility of the most favored solutions. Analysis is based, as much as possible, on relevant, accurate and reliable information, often involving interactive and automated statistical analysis -- or data analysis. This analysis in business is often referred to as business analytics(BA).

BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.

Data Analytics:

We live in a world where the amount of data collected is rising every second. When such a high volume of data is being generated, it’s only natural to have tools that will help us handle all of this information. Raw data is often a pile of unstructured information. Data analysts use their expertise to derive statistically significant information from the data. This is where different types of data analytics come into play. Data-driven insights play an integral role in helping businesses form new initiatives.

The usefulness of any data type or data source depends on the type of analytics being performed. For some businesses, data analysis functions as a tool of real-time intelligence gathering and performance measurement. Another business might use purely descriptive analytics that focus on profiling, segmentation and consumer identification. A more ambitious version of data analytics is concerned with transforming data into predictions.

four types of data analysis are:

• Descriptive analysis
• Diagnostic analysis
• Predictive analysis
• Prescriptive analysis