the most relevant attributes to classify data
000; 150-word summary
One of the most relevant attributes to classify data is internal or external. On most occasions, organizations tend to emphasize internal data as they are cheap and available. Use of market, competitors, prospects, and customers data, end users forum, blogs, and tweets together lead to the production of a clear, useful picture and precise overall data than how an internal data would. On public data, the competitors have a similar leverage opportunity to analyze improvement, and the difference is created is on the decisions made. Knowing the competitor’s ignorance can be an advantage as it will lead to better decisions. On the other hand, private data has side effects such as having to pay for them; the availability in the future is never assured; they are more involved with the maintenance and updating. It brings in data privacy problems if not privacy is not carefully maintained.
000;300 Word Summary
Call center analytics to assist big and small enterprises in measuring performance and providing ways to improve their agents.
- Call center speech analytics.
Recorded calls are the primary source of data for speech analytics. The software automatically recognizes the emotions of everyday customer’s problems. Thus, the data is focused on. Though data analyzation shortcomings of the current scripts are identified and updated.
- Call center desktop analytics.
Regarding security improvement and obtain feedback opportunities for performance by agent, the call center desktop analytics are combined with monitoring of real-time calls. Together, through the call center, desktop analytics optimization of customer and agent experience can be improved.
- Self-service analytics.
The realization of self-service benefits by customers from older demographics is quick despite their resistance. The use of self-service analytics helps reduce costs and leads to both customer satisfaction and more engagement of employees.
- Predictive analytics.
Predictive analytics offers a tool for contact centers vital to tracking and filling the call volume, service level, and wait time for customers. Predictive analytics assist customer care departments in solving problems through the use of historical data.
5.Text analytics.
Text analytics main focus is on written communication. The mining functions of data then identify the relationship and patterns in the data sets. Thus, the data in use to showcase the sent messages are from the company’s customers bring out any issues that the customers might have.
Understanding your metrics
Through data measurement of the call center in real-time, areas that require attention are brought out and pinning down the cause root of the customer care problems helps give customers the best experience
Measuring agent performance
It is important to use call center analytics on performance monitoring in real-time. To create the best customer experience using advanced performance analytics takes a lot of variables. The use of both advanced, as stated, determines the language and behavior assisting agents achieve their objectives and Key Performance Indicators. This assists in decreasing the average handle time, costs of call center operations, and increase of first contact resolution.
111; 300Word Summary
change how storage is deployed and specified by an overview of technologies and critical applications
The key driver
The key driver explains why we need solutions while technology provides the answers. Heavy and trending industries require to keep up with technology changes and thus have to collect and analyze streaming time-series data to get the current direction of their markets.
Storage management
It is challenging to discover who is using storage and the reason behind it with developers spinning up terabytes in hundreds for testing software and with cloud gateways involved in enterprise storage arrays.
Large memory servers
Non-Volatile, Random Access Memories (NVRAM) retain data through power cycles when there are no batteries. It can be accessed by memory bytes giving system architects the flexibility to configure systems to perform to its maximum.
Scale-out storage
Scale-out architectures protect data. All cloud vendors use scalable storage that’s high to store exabytes for data.
Highly resilient storage.
To increase data density in disk drivers that are in for of RAID for storage arrays, erasure codes that were used in the past decades.
Data security in the next few years changes on data security that relates to availability and focuses on keep data off the wrong hands will occur.
Rack scale design
Rack scale design is a solution to the difference in rates in technological advances such as storage, networks, and CPUs.
Storage-based processing
The processing of data now moves to storage.
High-capacity disk drivers
Disks are of low cost and have random storage access, and with the renaissance of technology, the amount of space is doubling. Technologies leading to high-capacity disk drives are the helium, shingled magnetic recording, and HAMR.
Conclusion.
Data is a weapon increasing competition. Data that is stored well even when old provides value due to the current tools of analytics. The storage of information is practical on cost and trending.
222 300 Word summary
What ETL tools are
ETL tools are open-source and commercial means that add value and accomplish duties during the ETL process through ETL testing and linking business intelligence tools.
Importance of ETL tools to Data Analysts
The use of the best ETL tools gives benefits such as scalability and complexities added to new data means. The ETL tools include;
1.Stitch
Stitch is a cloud-first powerful, and focused developer for rapidly moving data. It provides ETL self-service solution for data and replicates data from all sources while handling significant data updates. It also supports the integration of data from many sources and warehouses and is a tool for analysis.
2.Blendo
Blendo is a tool with no ETL scripts, coding, and maintenance in minutes that can enable one to integrate data. Blendo ensures data for analysis is available by optimizing data per warehouse data. It also lets one choose the times to get data from the source of your choice and monitors the usage.
3.Fivetran
Fivetran is a tool assist without maintenance, data pipelines, and configuration to replicate the business data of your warehouse data faster. It ensures no data is lost even after stopping to use source applications.
4.Matillion
It is an ETL tool built for Google BigQuery and Amazon Redshift use only. It permits the integration of various sources.
5.Panoply
Panoply is more than an ETL tool. Professionals build panoply for professional analytics, as it is an autonomous data warehouse. This tool provides all that is required with a data warehouse of a smart cloud that makes the collection, scaling, and modeling of any data automatic. The tool enables the collection of data with no coding, including data from all types of sources. Inside panoply, the data is modeled automatically and adds data to the cloud data warehouse instantly. When in need of a certain BI panoply seamlessly connects.
333; 300 Word summary.
Online Analytical Processing
Online analytical processing is a technology that supports complex analysis and organizes large database business. It performs a review that is complex without affecting systems of transactional negatively. Online Transaction Processing (OLTP) databases are used by the company to store all their records and transactions. The database records are entered on at a time as they contain valuable information for the organization. The databases were not designed for analysis but are used for OLTP; thus, making retrieving of answers to be time-consuming and requires a lot of effort. The OLAP databases were optimized for low write workloads and heavy read; hence their systems were designed to assist in business intelligence information extraction from data on a high rate of performance.
The differences of OLAP from OLTP are as stated below;
1.OLAP application.
The management uses OLAP for information used in making decisions while the OLTP application is operational, and its users are the employees.
2.OLAP outlook.
OLAP is based on a historical and long-term strategy rather than a few months or weeks. On this OLTP horizon, operational information will have significant effects in some years’ time.
3.OLAP storage
In consideration of the number of users that approach similar data but have different objectives and directions for their analysis, OLAP data is stored in a multi-dimensional database, which is the data attribute. The data users may search for a similar set of data, but depending on their goals, focus on different data attributes.
4.OLAP emphasis
OLAP emphasis leads to OLAP being refreshed for cleaning and collecting data for analysis on a specific frequency as it depends on information retrieval used to make decisions. Unlike OLTP, which is not performed on a rate as their emphasis are instantly
444;250 Word Summary
Exploratory Data Analysis
Exploratory data analysis is one of the significant steps of the data analysis process as it manipulates the data in hand to making sense. It is a crucial step before modeling data as it provides the context required in interpreting correct results and developing the right model. Exploratory data analysis gives much information that is critical and can be missed to help study from framing questions on the provided results.
Exploratory Data Analysis
Tools and Techniques.
- Programming languages of R and S-plus.
The R and S-plus statistical programming languages are very significant in performing exploratory data analysis. The languages have a plethora of tools that assist in conducting functions such as; classification to different group sets of databases with a similar variable together. This dataset is multi-dimensional and is challenging to carry out classification on.
- PCA and LDA techniques
The dimensional decrease of PCA and LDA techniques is done to decrease the dataset dimensionality and securing valuable information from the data.
3.Data connectors
Several data connectors help to install exploratory data analysis into the intelligence software of business. Data connectors also permit one to set up data in the opposite direction through building and running models that are statistical in R using BI and updates automatically upon the new flow of information into the model.
Exploratory data analysis is about obtaining knowledge and understanding of data before deciding on the next direction of the data mining, assisting in escaping making accurate models on wrong data.
555; 300Word summary
Data obtained from the analysis of a job assists in providing information that’s essential to the employee’s life cycle. The collection of information can be done through the following ways;
1.Gathering archival data
It is essential to collect information that exists, such as descriptions of a job found on the list of job requirements. It can involve criteria and forms performance evaluation, thus benchmarking the employee performance and also include the current and existing competency models providing the significance for a competition that is leading to organizational success.
2.Job observation
This type of method for collecting data allows observation of happenings and activities taking place in the workplace. In this method, better judgment is highly needed.
- Focus groups with job content experts
In this method, the analyst meets with experts of the work done. The goal of the analyst is to learn what it takes for the job roles to come out successfully, the activities carried out, and the type of skills demonstrated by the successful employees.
4.Surveys
Practically every incumbent in the role is not to be interviewed but to obtain information on a larger population of employees with positions. Having additional incumbents to complete a survey is excellent in cases where they give ratings of the significance of the identified competencies as essential for achievement in other steps analysis of the job. This step is vital as it enables more quantitative information to be obtained for objective analysis.
- Meeting with stakeholders
In job analysis, it is essential; to discuss with the stakeholders as the analyst gains their insight concerning their positions in an organization. The meetings provide a forum for debating the organization’s goals and determining if there are any concerns about the legality of the job.