1. Data refers to information that has been converted into a digital format that is efficient for transmission and processing. It can refer to either a single or plural subject.
2. Data preparation involves gathering, structuring, and organizing data from various sources so it can be used for business intelligence, analytics, and visualization. This includes collecting, profiling, cleansing, structuring, transforming, validating, and publishing the data.
3. There are four main types of analytics: descriptive analytics which answers what happened; diagnostic analytics which explains why something happened; predictive analytics which predicts what will happen; and prescriptive analytics which prescribes actions to address future problems or opportunities.
1. Data refers to information that has been converted into a digital format that is efficient for transmission and processing. It can refer to either a single or plural subject.
2. Data preparation involves gathering, structuring, and organizing data from various sources so it can be used for business intelligence, analytics, and visualization. This includes collecting, profiling, cleansing, structuring, transforming, validating, and publishing the data.
3. There are four main types of analytics: descriptive analytics which answers what happened; diagnostic analytics which explains why something happened; predictive analytics which predicts what will happen; and prescriptive analytics which prescribes actions to address future problems or opportunities.
1. Data refers to information that has been converted into a digital format that is efficient for transmission and processing. It can refer to either a single or plural subject.
2. Data preparation involves gathering, structuring, and organizing data from various sources so it can be used for business intelligence, analytics, and visualization. This includes collecting, profiling, cleansing, structuring, transforming, validating, and publishing the data.
3. There are four main types of analytics: descriptive analytics which answers what happened; diagnostic analytics which explains why something happened; predictive analytics which predicts what will happen; and prescriptive analytics which prescribes actions to address future problems or opportunities.
1. Data refers to information that has been converted into a digital format that is efficient for transmission and processing. It can refer to either a single or plural subject.
2. Data preparation involves gathering, structuring, and organizing data from various sources so it can be used for business intelligence, analytics, and visualization. This includes collecting, profiling, cleansing, structuring, transforming, validating, and publishing the data.
3. There are four main types of analytics: descriptive analytics which answers what happened; diagnostic analytics which explains why something happened; predictive analytics which predicts what will happen; and prescriptive analytics which prescribes actions to address future problems or opportunities.
Data In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject.
1: factual information (as measurements or statistics) used as a basis for reasoning,
discussion, or calculation 2: information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must processed to be meaningful 3: information in numerical form that can be digitally transmitted or processed.
data preparation Data preparation is the process of gathering, combining, structuring and organizing data so it can be used in business intelligence (BI), analytics and data visualization applications.
Steps in the data preparation process
The process of preparing data includes several distinct steps. There are variations in the steps listed by different data preparation vendors and data professionals, but the process typically involves the following tasks:
1 Data collection. Relevant data is gathered from operational systems, data warehouses and other data sources. During this step, members of the BI team, other data professionals and end users gathering data themselves should confirm that the data is a good fit for the objectives of the planned applications.
2 Data discovery and profiling:The next step is to explore the collected data to better understand what it contains and what needs to be done to prepare it for the intended uses. Data profiling helps identify patterns, inconsistencies, anomalies, missing data, and other attributes and issues in data sets so problems can be addressed.
3 Data cleansing: In this step, the identified data errors are corrected to create complete and accurate data sets that are ready to be processed and analyzed. For example, faulty data is removed or fixed, missing values are filled in and inconsistent entries are harmonized.
4 Data structuring:. At this point, the data needs to be structured, modeled and organized into a unified format that will meet the requirements of the planned analytics uses.
5 Data transformation and enrichment: In connection with structuring data, it often must be transformed to make it consistent and turn it into usable information. Data enrichment and optimization further enhance data sets as needed to produce the desired business insights. 6 Data validation and publishin :. To complete the preparation process, automated routines are run against the data to validate its consistency, completeness and accuracy. The prepared data is then stored in a data warehouse or other repository and made available for use.
Types of data analytics
There are 4 different types of analytics. Here, we start with the simplest one and go further to the more sophisticated types. As it happens, the more complex an analysis is, the more value it brings.
1 Descriptive analytics Descriptive analytics answers the question of what happened. Let us bring an example from ScienceSoft’s practice: having analyzed monthly revenue and income per product group, and the total quantity of metal parts produced per month, a manufacturer was able to answer a series of ‘what happened’ questions and decide on focus product categories.
Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, our data consultants don’t recommend highly data-driven companies to settle for descriptive analytics only, they’d rather combine it with other types of data analytics.
2 Diagnostic analytics At this stage, historical data can be measured against other data to answer the question of why something happened. For example, you can check ScienceSoft’s BI demo to see how a retailer can drill the sales and gross profit down to categories to find out why they missed their net profit target. Another flashback to our data analytics projects: in the healthcare industry, customer segmentation coupled with several filters applied (like diagnoses and prescribed medications) allowed identifying the influence of medications.3 Predictive analytics Predictive analytics tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Check ScienceSoft’s case study to get details on how advanced data analytics allowed a leading FMCG company to predict what they could expect after changing brand positioning.
Diagnostic analytics gives in-depth insights into a particular problem. At the same time, a company should have detailed information at their disposal, otherwise, data collection may turn out to be individual for every issue and time-consuming
3 Prescriptive analytics The purpose of prescriptive analytics is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. An example of prescriptive analytics from our project portfolio: a multinational company was able to identify opportunities for repeat purchases based on customer analytics and sales history.
4 Predictive analytics Predictive analytics tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Check ScienceSoft’s case study to get details on how advanced data analytics allowed a leading FMCG company to predict what they could expect after changing brand positioning.
Difference between Structured and Unstructured data
1 Structured data – Structured data is data whose elements are addressable for effective analysis. It has been organized into a formatted repository that is typically a database. It concerns all data which can be stored in database SQL in a table with rows and columns. They have relational keys and can easily be mapped into pre-designed fields. Today, those data are most processed in the development and simplest way to manage information. Example: Relational data.
2 Unstructured data – Unstructured data is a data which is not organized in a predefined manner or does not have a predefined data model, thus it is not a good fit for a mainstream relational database. So for Unstructured data, there are alternative platforms for storing and managing, it is increasingly prevalent in IT systems and is used by organizations in a variety of business intelligence and analytics applications. Example: Word, PDF, Text, Media logs..
