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OLAP Software or Online analytical processing is a software whose ability to create actionable business intelligence from a company’s available data is beyond talks. It is done by empowering analysts for navigating hierarchical relationships between categories and levels of detail in the data called as dimensions. The whole power of OLAP lies in the ability to identify and anticipating trends—Outcomes which are central to every business intelligence initiatives. It ids key to business that companies looking for business intelligence (BI) tools be familiar with OLAP. Both end-to-end BI platforms and modern, self-service BI tools offer traditional OLAP or equivalent capabilities for multidimensional analysis.
The basic characteristic of OLAP is that it’s multidimensional. Dimensions of data have geographic categories such as country, city, state, temporal categories such as year, month, day, levels of aggregation with total sales, sales by dept., sales by store, etc.
OLAP permits analysts to navigate easily between levels in the hierarchies to understand business problems. For example, to understand why total sales rose in a given quarter, it may be essential to go deep down to a more detailed level: sales by shops and store, and compare that category with data on the types of items sold.
OLAP solutions are used for historical analysis aimed at deriving insights for trends affecting the problems, business, opportunities for growth etc. Human user guides the analysis in most contexts. This is opposed to operational analytics, or analytics offers to process data used in the business’s operations in real time or approximately real time.
Monitors and records ongoing business transactions, like that of purchases and sales. Searches patterns which can help explain issues. Used to guide future plans and strategies. OLAP servers are generally used in data warehousing operations and data mining.
An OLTP database can be represented as a spreadsheet or simple table. This is easy to do as OLTP databases have a limited number of variables and these are directly related to one another.
The department store’s head office “stacks” the individual transactional databases from the six branches into one single dataset. It creates a data “cube”—the format used in OLAP systems. It is also known as multidimensional cubes or hypercubes.
The data cube above looks impressive,and astute readers may have noticed: Despite the added dimension, it can be read like an normal table or spreadsheet. Arranging datasets into cubes only makes it possible to start the analysis.
The actual processing is where the value of OLAP lies. It relies on three general functionalities, everything made possible by the flexibility of the database which is a non-flexible.
Drill-down is used to present more granular detail about a given variable. The company may wish to focus in on sales of a typical brand’s products. They could de-aggregate the sales-per-brand totals above to learn using a drill-down function, that items from an individual brand have sold in how much quantities.
Slice-and-dice lets users look at the business’s datasets from various perspectives and angles. Tthe department store might want to correlate sales to a variable other than the individual salesperson or selection of brands. They may wish to know how the number of salespeople working in a single shift across all branches affects sales of one selected brand. The slice-and-dice functionality of OLAP tools makes that possible.
Roll-up is the opposite of drill-down, and the two are often used in conjunction. Decreasing the level of detail, roll-up combines data into broader categories. In the slice-and-dice, the store might roll-up the per-branch sales information before further analysis. It is given that they’re not concerned with that variable for the analysis.
OLAP arranges data: It provides benefits that other methods
The wide variety of variables permitted and the ability to slice and dice them any which way offers companies new opportunities to find value in the existing company data.
The OLAP tool tries many options to “predict” : OLAP helps in predicting how the number of salespeople might impact sales. The store can then weigh this information against the cost of staffing and determine the ideal number of employees for per day.
OLAP uncover patterns and relationships which have not been previously considered. This is important for problem solving. The department store above is having a difficulty with sales of a particular brand’s products, but only in two of its six stores. OLAP analysis helps in revealing the root cause as an inexperienced manager that works in both of the problem stores.