Data analytics is the science of analyzing raw data to make conclusions about that information. Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system. For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity.
Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.
Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better products and services.
Data analytics is broken down into four basic types.
- Descriptive analytics: This describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last? Examples of descriptive analytics,
- The sales team can learn which customer segments generated the highest dollar amount in sales last year.
- The marketing team can uncover which social media platforms delivered the best return on advertising investment last quarter.
- The finance team can track month-over-month and year-over-year revenue growth or decline.
- Operations can track demand for SKUs across geographic locations throughout the past year.
- Diagnostic analytics: This focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales? Examples of diagnostic analytics are
- The sales team can identify shared characteristics and behaviors of profitable customer segments that may explain why they’re spending more.
- The marketing team can look at unique characteristics of high-performing social media ads compared to more poorly-performing ones to identify the reasons for performance differences.
- The finance team can compare the timing of key initiatives to month-over-month and year-over-year revenue growth or decline to help determine correlations.
- Operations can look at regional weather patterns to see if they’re contributing to demand for particular SKUs across geographic locations.
- Predictive analytics: This moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year? Examples of predictive analytics
- The sales team can learn the revenue potential of a particular customer segment.
- The marketing team can predict how much revenue they’re likely to generate with an upcoming campaign.
- The finance team can create more accurate projections for the next fiscal year.
- The operations team can better predict demand for various products in different regions at specific points in the upcoming year.
- Prescriptive analytics: This suggests a course of action. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output. Examples of prescriptive analytics
- How the sales team can improve the sales process for each target vertical.
- Helping the marketing team determine what product to promote next quarter.
- Ways the finance team can optimize risk management.
- Help the operations team determine how to optimize warehousing.
Intelligent Data Analysis (IDA) is an interdisciplinary study that is concerned with the extraction of useful knowledge from data, drawing techniques from a variety of fields, such as artificial intelligence, high-performance computing, pattern recognition, and statistics. Data intelligence platforms and data intelligence solutions are available from data intelligence companies such as Data Visualization Intelligence, Strategic Data Intelligence, Global Data Intelligence.
Intelligent data analysis refers to the use of analysis, classification, conversion, extraction organization, and reasoning methods to extract useful knowledge from data. This data analytics intelligence process generally consists of
- Data preparation stage: Data preparation involves the integration of required data into a dataset that will be used for data mining
- Data mining stage: Data mining involves examining large databases in order to generate new information;
- Result validation: result validation involves the verification of patterns produced by data mining algorithms
- Explanation stage: result explanation involves the intuitive communication of results.
Business Intelligence: A Business Intelligence Analyst is responsible for taking the data that a company holds and mining it to achieve valuable insights. These insights are then used to inform critical business decisions. The insights play a crucial role in shaping the company's future and the way it operates.
Big Data Intelligence involves the use of Artificial Intelligence and Machine Learning to make big data analytics actionable and transform big data into insights, and provides engagement capabilities for data scientists, enterprise analytics strategists, data intelligence warehouse architects, and implementation and development experts. Enterprise data intelligence is used in business intelligence operations, analyzing sales, evaluating inventories, and building customer data intelligence.
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