1C: Analytics | 1C: Through the Looking Glass

05.03.2020

This text is an announcement of recent performance.
It’s not really useful to make use of the content material of this text to grasp the brand new performance.
A full description of the brand new performance shall be supplied within the documentation for the corresponding model.
An entire checklist of modifications within the new model is supplied within the v8Update.htm file.

Carried out in model 8.3.17.1496

As a part of the 1C: Enterprise 8.3.17 platform, a brand new 1C: Analytics element will seem. It’s an interface for administration and accounting techniques that simplifies the method of rapidly constructing analytical stories and interactive knowledge evaluation on the fly.

When working with knowledge within the accounting system, analytical processing of information is commonly clearly or not clearly distinguished. When analyzing data, you must have a look at the information in an aggregated type: what number of orders had been obtained in the course of the interval, for what quantity, what number of of them had been shipped to prospects. On the similar time, to be able to receive the mandatory data on this analytical type, the consumer is thinking about:

  • The supply of data the place the information for evaluation comes from. For instance, the information is taken from the buildup register Gross sales.
  • Filters what data is used to mixture knowledge. For instance, for what interval are gross sales knowledge taken, for which departments and for which objects?
  • What knowledge and the way are offered in aggregated type. For instance, we want a report on how a lot items had been bought, how a lot VAT on them, the common variety of items in an order.
  • What sections / classes will we need to view the information on. For instance, I want to get gross sales knowledge for this 12 months within the context of areas and product classes.

The required report could be constructed within the type of an embedded or exterior report of the 1C: Enterprise platform, however when viewing such aggregated analytical knowledge, there’s at all times a want to attempt to perceive why they turned out precisely as they’re? Why is that this 12 months’s gross sales up or down in comparison with final 12 months? Which division made the most important impression on the gross sales change? And so forth … Having obtained a “view from a top” on the knowledge mendacity in your accounting system, you’ll instantly need to have a look at the identical knowledge on the fly from different sections and in different views to be able to higher perceive each the image of what’s occurring within the firm as a complete and the explanation why it seems like that.

To unravel this drawback, 1C: Analytics is meant, which works as an integral a part of the platform and permits:

  • Create analytical charts from scratch primarily based on knowledge contained in the 1C: Enterprise platform
  • Work with charts and dashboards already created in it
  • On the fly, edit the information composition and filters for a deeper dive into the analyzed knowledge

1C: Analytics is primarily meant for workers of firms concerned within the evaluation of gross sales, profitability, items turnover and different key efficiency indicators of firms. Ready diagrams and dashboards could be transferred to firm administration for viewing indicators and additional knowledge evaluation.

Examples of 1C: Analytics screens:

Supply of 1C: Analytics and its work at the side of mechanisms Accelerator date and database copies

Server 1C: Analytics is equipped as a separate distribution equipment. The capabilities of the 1C: Analytics server embrace the work of the system’s net interface, wherein customers create and execute their analytical stories. In the middle of 1C: Analytics work, it generates requests to obtain knowledge and transfers it to a cluster of 1C servers, on which the 1C configuration is deployed. Which means the execution of queries for these analytical stories imposes a further load on the computing energy of the 1C server cluster.

To make sure quick and cozy work of analytical stories for the consumer, in addition to to share the load on the 1C server from common customers of the system and from 1C: Analysts, it’s vital to make use of 1C: Analytics along with mechanisms Accelerator date and Database copies… This strategy lets you rapidly generate generated analytical stories with out extra load on the principle 1C system, and never intrude with the work of different customers of the accounting system.

The information accelerator is a specialised DBMS for accelerating the processing of analytical queries, that’s, queries that course of massive quantities of information and return a comparatively small variety of data because of this. The information accelerator combines a number of applied sciences. That is the storage and processing of information straight in RAM (in-memory DB) and using particular knowledge constructions that may considerably scale back the processing time of an analytical question. Date accelerator is designed to work with hundreds of thousands and tens of hundreds of thousands of data. Knowledge processing for 1C: Analytics on the Knowledge Accelerator as a substitute of the principle DBMS lets you put together knowledge and show it interactively, because the consumer prepares and modifications the diagram.

The Date accelerator works at the side of the database copy mechanism, which lets you configure what knowledge shall be positioned within the Date accelerator for quick processing of requests, and likewise displays the relevance of this knowledge. When establishing the mechanism of copies of the database and the Date of the accelerator, analytical queries is not going to load the principle DBMS the place the information of the accounting system is situated, and can have an effect on the work of strange 1C customers.

Publication of 1C: Analysts and its integration with the 1C configuration

1C: Analytics implements work with analytical stories by a separate net interface, which can be utilized independently, or be built-in with 1C configuration net publication.

After configuring the combination of the 1C: Analytics server with 1C configuration net publication, the 1C: Analytics net interface shall be out there on the primary net publication deal with with the “/ ans” postfix added. So, if the configuration is printed at https://analytics.demo.1c.ru/analytics/, then the interface for working with 1C: Analytics shall be out there at https://analytics.demo.1c.ru/analytics/ans… Additionally, to go to the 1C: Analytics interface, you need to use the command within the system configuration menu.

Ideas of constructing stories in 1C: Analytics

The 1C: Analytics interface has three primary modes of operation:

  • Desktop for looking and viewing out there charts and dashboards, in addition to creating them.
  • View and edit the diagram. Means that you can view the present knowledge within the diagram, open knowledge on new sections, edit filters and the composition of the output knowledge and the kind of graphs for displaying data.
  • Viewing and modifying the dashboard. Means that you can choose the composition of diagrams on the dashboard, design parts, change normal knowledge filters all through the dashboard, open particular person diagrams which can be a part of the dashboard.

The principle mode of operation is viewing and modifying the diagram in 1C: Analytics. When making a chart, the consumer chooses which knowledge supply from the out there ones shall be used. The information supply for the diagram could be any metadata object from the 1C configuration. So, the consumer can construct a diagram primarily based on the buildup register, data register, reference books, paperwork or their tabular sections. When working with these metadata objects, the consumer receives knowledge within the diagram straight primarily based on the precise data contained within the 1C configuration. As a foundation for constructing a diagram, you need to use 1C queries that mix a number of metadata objects. Such digital sources should be specifically ready by the system administrator or implementing accomplice.

Usually, making a diagram consists of the next sequence of steps:

  • Choice of displayed knowledge fields (dimensions and details). The distinction between dimensions and details lies in the truth that in keeping with the chosen dimensions, the obtained knowledge shall be grouped, and inside this grouping, the chosen knowledge aggregation mode shall be carried out for every of the details. So, for instance, if a diagram is constructed utilizing the Gross sales accumulation register and the size Division, Nomenclature Sort and the very fact Revenue with aggregation by quantity are chosen, then all gross sales knowledge shall be obtained for the diagram from the 1C infobase. Then the obtained knowledge shall be grouped in keeping with the out there Departments and Nomenclature Varieties. For every dimension worth, the full sum of the Revenue values ​​shall be proven.
  • Deciding on a filter by knowledge. By default, the system queries and shows all knowledge that has not been filtered. Subsequently, after selecting the required dimensions and details, a outcome shall be obtained for all out there knowledge within the system. For actual charts, it’s often essential to slim down the quantity of analyzed knowledge, for instance, point out for what interval the knowledge is required, for what forms of items, warehouses or companions we need to view the information. That is executed by making use of filters to the specified dimensions or details.
  • Establishing the sequence of displayed fields, sorting, displaying subtotals for particular person dimensions within the diagram.
  • If vital, create a grouping of information.
  • Customise the looks of the chart and choices for his or her show.

The 1C: Analytics interface is designed for interactive work as a lot as potential, due to this fact, each time the chart parameters are modified, it’s promptly redrawn, displaying its present view on the display screen. Modifying the chart is carried out in visible mode when the consumer drags the specified dimensions and details into the chart content material. Within the filter editor, the consumer selects the values ​​he wants from the checklist and adjusts the mandatory chart parameters by the menu. All these actions are translated inside 1C: Analytics into the formulation of the service language for describing the diagram. For superior customers, it’s potential to modify the editor to edit mode straight for the diagram presentation formulation, which lets you fine-tune the filters and output knowledge.

Tags: 1C: Analytics

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