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Jobs For Supporting Data Analysis

Informer 5 Job Feature

Wouldn’t it be nice if you could email one filtered data view to a coworker and a different filtered view to another, save a data view to your file system, or even FTP it? And, do this all on a schedule! Well, Informer 5 Jobs enables you to do just that.

An Informer Job is a task or collection of tasks to be executed against data in your system. These tasks can be automated using a recurrence schedule, or simply saved in the system to be manually triggered when needed. The three main components of creating a Job are:

  • Identifying the data that the Job is going to be using
  • Selecting the actions that the Job will be executing based off the data
  • and, scheduling the Job

Jobs are tightly governed, and the individual or Team that creates a Job owns that particular Job and only certain roles are allowed to manage those Jobs.

Setting Up The Data

After you define the data to use from one or more Informer Dataset(s) or Ad hoc Queries, select whether you want the data to refresh for your data analysis whenever the Job is run.

For example, you can bring in the Dataset that contains all your organization’s sales across the country and then filter the Dataset per geographical region, resulting in four data components – Northern Sales, Southern Sales, Eastern Sales, and Western Sales. See Figure 1.

Setting Up The Actions

Define the set of tasks you would like executed against the data. For example, perhaps you would like to send an email to the Director of Sales – Northern Division with Visuals from the filtered Dataset Northern Sales, and send similar emails to the South, East, and West Directors of Sales. And, then store the filtered data components to the file system to perform data analysis. This can all be accomplished within the same Job. See Figure 2.

There are four options for Actions on a job and custom Actions specific to your organization can be easily introduced via a plugin:

  • Send an email – Use a rich email editor to send an email using content sourced from the Data components of the Job. You can also add file attachments to the email. For example: a new HTML file, text file, zip file, a file from the file system, or the Data components of the Job in a certain file format.
  • Send an email burst – Use a rich email editor to send emails to different individuals using specific content per email sourced from the Data components of the Job. You can also attach files to burst emails.
  • Send to file system – Save files to the file system of your Informer instance. Choose the files to send. For example: an HTML file, text file, zip file, upload files, or send from the Data components of the Job.
  • Send to FTP – Establish and send files via an FTP connection. Choose the files to send. For example: an HTML file, text file, zip file, upload files, or send from the Data components of the Job.

All the Actions above can be selected to ‘Run on a Condition’, where the Action is only run if there is a configurable threshold number of records in the data components.

Setting Up The Schedule

A Job can be run on a defined schedule or manually at any time. See Figure 3.

Informer provides tremendous flexibility in setting the interval of the schedule:

  • Minutes – You can designate a job to run every X minutes, starting at a designated date and time. For example: Run every 5 minutes.
  • Hourly – You can designate a job to run every X hours, starting at a designated date and time. For example: Run every 6 hours.
  • Daily – You can designate a job to run either every X day at a designated time or to run every weekday at a designated time. For example: Run every third day.
  • Weekly – You can designate a job to run on specific days of the week at a designated time. For example: Run every Monday, Wednesday, and Friday.
  • Monthly – You can designate a job to run on a specific date of a specific month or to run on the first/second/third/fourth occurrence of a given day of the week every X months. For example: Run on the third Wednesday of every second month.
  • Yearly – You can designate a job to run once a year on a specific date of a specific month or to run on the first/second/third/fourth occurrence of a given day of the week for a specific month.
  • Custom – You can also specify a cron expression for very granular recurrence schedules that are not covered above.

Jobs List Page & History of Jobs

All Jobs are listed on the Jobs List Page where you can see a snapshot of the Job by status. Details show the Owner, the Schedule for the Job, last run time, the next run time, status of the Job, when it was created, when it was last modified, and more. The History page lists all the recent fire times of the Job, who ran the Job, the duration of the Job, and the result of the Job, i.e., success, failed, or warning. See Figure 4.

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Data Visualization with Discover

Informer Discover Makes Visualizing Data Easy

With Discover, Informer 5 does a lot of the thinking for you by automatically serving-up interesting business intelligence Visuals based on the Filters and type of Fields you select. This is a huge timesaver!

In Discover, you simply click on the filter icon to identify the criteria you want to use to visualize and explore data on a macro level in your Dataset. See Figure 1.

You can also apply data filters to your individual charts within a multi-chart view without affecting the other visuals. Just select a field you want to analyze without being concerned with how you want to present it. Then quickly change the charts and other information.

Sort & Group Data Instantly

Discover groups all your Dataset column fields by data type (i.e., text, date, Boolean). This enables you to make quick selections and see resulting data visualization displays based on the type of field you choose. By selecting Boolean fields (Yes/No-type information) the resulting visual quickly shows you how many of the characteristic you have, or don’t have. See Figure 2.

Date fields generate trend analysis charts. Location fields generate geographic maps (e.g. heat maps, Google Maps, state maps, county maps, etc.). You can also combine multiple selection fields to aggregate information into a singular chart, for example: revenue, gender, age, location. The Leaderboard visual provides a ranking based on values associated with what you are aggregating.

Visualize Data in Multiple Ways

Visual options vary depending on the type of data visualization you are configuring:

  • A pop-up list enables you to change the chart type. You can also change the title and description for the Visual.
  • For graphical Visuals such as pie or column charts, you can change the chart type, Y-axis value, primary and secondary grouping value, and maximum number of items to show.
  • If the selected Field is a date, you have the option to change the Interval (Year, Month, Day, Month of Year, etc.).
  • For Leaderboards, you can change the Field to display the aggregate value used for ranking See Figure 3.
  • Pivot tables allow you to change the Fields used for rows and columns, as well as the aggregate value used in the body of the table.

Filtering on Areas of Interest

Filters are a very powerful data analytics productivity tool in Informer. They enable you to easily narrow down your data to focus on your area of interest. There are two types of data Filters within Informer – Dataset Filters and Visuals Filters.

Visual Filters – In addition to the overall Dataset Filters, Visuals have their own Visual Filters that apply to a specific Visual. Discover displays all the selectable fields within its Visual Card view, as shown in Figure 3. When you select different combinations of these fields, you will see how easy and fun it is for you to uncover new insights into your data from all the Visuals served up. To narrow down your Dataset even further, simply continue to add more Filters. See Figure 4. You can merge multiple filters together or save a specific filter. You can also save the filters for your Visual in your Datasets.

Dataset Filters – Enable you to narrow the scope of Visuals so you can quickly glean business intelligence insight into your area of interest without needing to configure charts or graphs. For example, if you select a location based field, Discover will automatically present heat maps highlighting the data behind the location field, (e.g., a Visual of Total Student Enrollment across the United States). By selecting a combination of fields, you automatically see Visuals like Trend Charts, Leaderboards, Averages, Totals, and Statistics.

Discover Reports

There are two types of Reports used in Discover: Dashboard Reports and Comparison Board. After choosing the Dataset you would like to visualize and selecting a field to group your data, you will be presented with several different Visual options (Date Fields, Text Fields, and Numeric Fields).

  • Date Fields display Visuals such as trend charts, pivot tables or statistics. (See Figure 5).
  • Text Fields – For text fields such as country, state, or city, using maps and possible leaderboards or other similar charts would best complement your data visualization. To generate new Visuals from text fields, choose your desired values underneath the fields indicated by green quotation mark icons. (See Figure 6).
  • Numeric Fields – Create statistics, total boxes, leaderboards, and similar charts. (See Figure 7).
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Datasets: The Foundation For Successful Data Analytics

Create a Single Source of Truth

Data exists everywhere throughout your organization: in databases, on shared network servers, in the cloud, on countless desktops. Data also exists in various formats: as records in multiple types of databases, as structured text files, as proprietary spreadsheet documents, as streams from REST calls to a 3rd party API. Informer enables you to discover business intelligence from all these disparate silos but first you’ll want to get your data into the form of an Informer Dataset.

A Dataset is the elementary building block for Informer content to perform data analytics. A Dataset is a set of indexed Records and a Record is a collection of Fields. A Field can be a simple value such as a number or text, an object, or an array of objects.

Creating Datasets From Structured Files

You can create Datasets from existing documents by simply dragging a document with a supported file format from your desktop onto any location in Informer. See Figure 1. Informer will create a new Dataset from the file content. It’s that easy! Currently supported formats include Comma Separated Values (.csv), JSON (.json), and Excel documents (.xls, .xlsx). As with supported Datasource types, Informer will support more file format types over time.

Informer Workspace

While dragging in an individual static document file to create a Dataset is ideal for doing quick one-off analytics, creating an Informer Workspace provides more flexible and governed access to the set of data. A Workspace is a collection of associated source documents which Informer treats as a virtual database. It enables you to create mapping associations and other standard Datasource functions just like a traditional database. Workspaces are ideal for importing multiple files for business intelligence.

Dataset Designer

The Dataset Designer makes it easy for you to create a Dataset visually by using Mappings. You do not need to be an expert! It is especially useful for those who don’t know the Datasource’s query language and/or are not intimately familiar with the schema and structure of the underlying Datasource.

After choosing a Datasource and Mapping for your query within Dataset Designer, just select Fields and Criteria — the two primary components of a Query:

  • Fields define what details you want to see about the records you’ll be retrieving from a Datasource. Datasets can be customized to show only certain Fields by adding the desired Fields within your Query. Add as many Fields as you want. You can also add Fields from other Mappings. See Figure 2.
  • Criteria enables you to pare down which records are retrieved.

For example, let’s say you have an Orders table with historical details of orders including product details, shipping details, billing details, etc. The Fields you choose for the Dataset determine what you ultimately view from the selected records. If you want a Dataset to include only those orders containing a specific product, you would add that restriction within your Criteria.

Add more Mappings if would like to. You can add a link or an SQL link:

  • Add Link – Pair up Fields between Mappings. Add as many new Fields as you’d like.
  • Add SQL Link – Join tables to create a joint Mapping.

Add Criteria To the Query

Criteria defines which records are retrieved from your Dataset for your data analytics. You can include Criteria within a Dataset or Ad-hoc Query Report. You can also choose whether you want a Criteria to be based off a Field or Value. See Figure 3.

  • You can select the condition for the Criteria, for example “exactly matches”. Select either a Value, Field, or Input to compare the first part of the condition.

Flows

For more in-depth business intelligence, Flows enables you to augment your Dataset records with additional data, metadata, or logic as your Dataset records are indexed. You can easily derive new values from your existing data as well as cleanse bad data from your Datasource. Within the Dataset, you have the options to:

  • Create new Fields that perform certain functions against one or more selected Fields from the Dataset or Fields from a different Dataset. See Figure 4.
  • Configure the Fields that you’ve added by adjusting data types, replacing Field values, removing duplicates, and splitting Fields with multiple values. See Figure 5.
  • Include advanced power scripting which adds more advanced programming capabilities to the Flow step process to further derive new values or cleanse existing data.
  • Remove Fields that you’ve added.

Refreshing Your Dataset

There may be occasions where you want to periodically refresh one of your Datasets. Refreshing enables you to update the data in your Dataset with current data from your Datasource, manually or automatically via an Informer Job. See Figure 6.

An Informer Job is a task or collection of tasks to be executed using data in your system. These tasks can be automated using a recurrence schedule, or simply saved in the system to be manually triggered when needed.

Alternatively, you can easily append data to a Dataset without replacing all your current data. See Figure 7.

Native SQL

If you have an existing query statement, or if you are comfortable with authoring one and simply don’t want or need to use the Dataset Designer… Native SQL is for you! Just choose a Dataset Name and a Datasource against which to execute your query. Click Query and begin typing. You’ll notice the rich editor offers autocomplete and color coding for both SQL syntax and schema as shown in Figure 8.

Upon execution of your query, Informer provides progress updates and forwards you to the Dataset Explorer page containing your query result, once the results are available. You now have a Dataset that’s ready for data analytics!

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Extensibility with Plugins and APIs

An Open Architecture

By employing an open architecture based on end-to-end 100% JavaScript, AngularJS and plugins, Informer 5 is designed to be highly adaptable, flexible and scalable. Informer makes it easy to integrate with other modern JavaScript libraries and to write, test, share, and deploy plugin code. This architecture offers many benefits to you for data analysis.

For example, you can bring in:

  • Unique customizations
  • Input forms
  • Authentication schemes
  • Datasources
  • Datasets
  • Flow steps
  • Mappings
  • Visuals
  • and other functionality into the system.

Plugins

Plugins enable the system to be safely customized (by customers, system integrator partners, and our own services team) to meet customer requirements without the need for a new software release. These customizations can also be easily shared between users and organizations via Github or the NPM Central Repository.

The possibilities with plugins are really limitless. New functionality or customized features can be easily added to the system to support your data analysis. You can have a plugin that automatically provides links between tables in your Datasource, or a customized formatted output, for example: invoice, or a new visual type, or a new logo or color scheme, etc.

Interacting with APIs

Informer 5 offers a rich, flexible, and easy-to-understand REST API for external programs to interact with. We decided early on in the development process that almost all of the interaction between our UI and our server would be done via this API – thereby ensuring it is robust and usable for customers who want to tightly integrate Informer 5 with their other applications for improved data analysis.

Overview of REST

APIs which use standard http(s) interactions as their method of interaction are referred to as RESTful. You can use a REST client of your choice. Interaction with Informer 5’s API is accomplished by issuing carefully crafted, PUT, POST, GET, and DELETE HTTP commands to specific URLs on the Informer server, and then interpreting the response the server gives. In this way, you can interact directly with Informer 5’s server outside of the UI.

All REST requests are authenticated and authorized by the Informer server. Informer supports three authentication methods:

  • Session – Uses a session cookie set after a successful login and carried with each REST request.
  • Token – A query parameter attached to a request URL that allows access to a set of routes relevant to accessing the asset for which the token was created.
  • Basic – HTTP Basic authentication method where username and password are communicated using the HTTP Authentication Header.
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Recorded Webinar: Migrating from Elasticsearch 5 to Elasticsearch 8

Navigate the complexities of upgrading from Elasticsearch 5 to 8 with our detailed webinar. Gain insights into the migration process, key changes, and enhancements to optimize your search and data analytics capabilities. Ideal for developers and IT professionals looking to leverage the latest Elasticsearch features for improved performance and scalability.

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Data Governance and Security

Security and Governance by Design

Data governance and security lie at the heart of Informer 5 by incorporating organizational governance policies at the data level, content level, and functional level security. Informer strikes a balance between enabling self-service analysis and protecting your organization’s sensitive business intelligence information. And, it provides transparency and traceability of data, while maintaining data integrity and data quality.

With data governance and security functionality designed into the system from inception, Informer answers questions like:

  • ‘How can I trust the data?’
  • ‘How can I ensure that only authorized persons can see the data, or parts of it?’
  • ‘How can I have traceability of key business decisions?’
  • ‘How do I keep my data from getting unwieldy and uncontrollable?’

Informer 5 utilizes a Teams security model to specifically support real world business operations and security needs. A Team within Informer is defined as a group of Users that comprise a logical business unit within an organization. The Roles within a Team are determined by one’s business role. Your organization’s logical groups and security rights for your employees map easily into Informer’s Teams and Roles.

Teams provide identity access management via an intuitive model whereby you can secure your data and access to it. You can easily determine why a specific User or Team has the rights to perform a specific action or access specific content. As a result, it is easy to audit usage of the system and content and provide users with a single source of truth for content.

The Teams model ensures that:

  • Access to sensitive data is secured
  • The data used for reporting has not been doctored
  • Shared data is current and accurate
  • Users interact with and access the data based on determined security settings
  • Only those with determined access rights can alter the query behind the data.

Privilege Access Management with Users

Users are assigned certain access rights within Informer based on their role within your organization and business intelligence needs determined by their system administrators. In addition to the typical username, password, and email, a user must be defined as either a Normal User or a Super User. See Figure 1.

  • Normal Users have no special privileges outside of Team-based Roles and Security.
  • Super Users have full access rights to the entire system, superseding any Team-based Role assignment. A Super User can view all content within the system, including all fields within a given Datasource, and can modify any Datasource, Dataset, Report, or Job. Only a Super User can define another User as a Super User.

Identity Access Management with Teams

While Users are the individuals with access to Informer, typical business intelligence activities within an organization are completed by groups of individuals comprising a department or logical business unit within a department. See Figure 2. By nature, different groups have different access rights to business data. And, since members of a group have different functional roles, their access rights within the group differ as well. For example, some members of a department create data content for an organization, while others simply use the data to build business insights.

Every Team has a Team Landing Page for identity access management. From the Team Page, you can view and manage the list of all Users and their Roles, the list of Datasets, Reports, and Datasources owned by the Team, and the list of Datasets, Reports, and Datasources shared to the Team.

Defining an individual’s role within the Team is an important aspect of identity access management and adding a Member to a Team. Informer 5 provides comprehensive pre-defined role types for Team members with sensible access rights. See Table 1. These role types map easily to your organization’s security permissions for your employees.

Role Name Rights
Member View anything Owned by the Team
Designer All Member rights
Create content from Datasets available to the Team.
Upload spreadsheets into new Datasets.
Create Reports from Datasources available to the Team.
Data Wizard All Designer rights
Create Workspaces
Create Datasets from Datasources available to the Team.
Edit Team-owned Datasets.
Publisher All Data Wizard rights
Share Team-owned Datasets and Reports to other Teams.
Admin All Publisher rights
Manage members
Add a Datasource to the Team
Share a Team-owned Datasource to other Teams

Table 1: Team Roles

As Teams model logical business units within an organization, Users can be Members of more than one Team. Their role within a specific Team is determined by their business role with respect to that Team. For example, the Manager of the Graduate Students Division of the Registrar’s office in a University may be the Admin for the Graduate Student Team as well as a Data Wizard for the larger Registrar’s Team. In the scenario where an individual has different roles in different Teams with access to the same content, the highest role permissions will prevail. For example, in the case above, the Manager would have Admin privileges for any content that is available to both the Graduate Student Team and the Registrar’s Team.

As with Users, you can also source Teams from a third-party repository using Informer’s Plugin Architecture. For example, Teams can be retrieved for use in Informer by referencing Divisions within your organizational chart and applying those Users and Teams to Informer together with the appropriate Roles. As a User is introduced into Informer, they are assigned to a default Team determined by the Informer system administrators. For example, connect Informer via LDAP and assign to an umbrella Team for the organization named All Employees.

Ownership of Content for Strong Data Governance

Ownership of content (Datasource, Dataset, Report, and Job) is a powerful concept in Informer 5. It implies quality and confidence in the content. All content in the system has a single Owner. Ownership can be by an individual User or a Team, with the typical scenario being a User owning the content while creating and iterating on it, and eventually passing on Ownership to the Team once finalized. When content is owned by a Team, it represents a credible single source of truth providing for Data Governance.

Ownership is an important part of privilege access management as it implies privileges on the content and determines who can modify an entity. As a result, the content quality is preserved and once shared with others, holds credibility. If the Owner is a User, then the User is considered an Admin for the content with all functional privileges – create, edit, copy, delete. If the Owner is a Team, then the Role within the Team determines the privileges on the content. See Table 1: Team Roles for role definitions and privileges.

Best Practices Data Governance Example for Creating & Ownership of a Dataset

Bob is a member of the Human Resources Team with a Data Wizard role. He creates a Company Attrition Rate Dataset by pulling in appropriate data and creating Data Flows. He is the Owner of the Dataset and thus considered the Administrator of the Dataset. This is now his personal workspace to iterate on the creation of the Dataset.

After he is satisfied with the Dataset, he changes Ownership from himself to the Human Resources Team. The Team can then iterate on the Dataset in a collaborative fashion. Once the Dataset is finalized, the Publisher role within the Human Resources Team can then decide to which other Teams the Dataset should be shared. The Dataset now holds credibility as it is owned and shared by the Human Resources Team within the organization. This example of privilege access management applies to all content – Datasets, Reports, and Datasources – and provides Data Governance.

Sharing Content Confidently

Organizations rely on departments to share content between each other reliably and confidently. Sharing content while tracking edits and editors enables Teams to create a library of curated content and provides for Data Governance. With Informer, providing content access to members outside of your Team is tightly controlled and monitored.

In Informer 5, Datasets and Reports are either shared across Teams in full (though read-only), regardless of a User’s role within the shared Team, or not shared at all. Users with the appropriate Role within the owning Team have edit capability.

Datasource Sharing

Role Name Rights
No Access Default – Datasource does not appear
Limited Access Only the Query Designer may be used to create Datasets.
No Restricted Fields
Full Access Datasource can be queried without any restrictions
Custom Access Only the Query Designer may be used to create Datasets.
Selected Mapping Sets only (choose whether to allow Restricted Fields)

Table 2: Datasource Access Roles

Dataset & Report Sharing

Sharing a Dataset provides read-only access to the selected Teams while the Dataset Owner retains editing access to the Dataset. Privilege access management determines when sharing a Dataset, the Sharing Team must select the level of access being given to the selected Team (No Access, Full Access, or Custom Access) See Figure 3.

Custom Access gives only a Filtered view of the Dataset to the selected Team filtered out of the view. This is a way to also achieve row level security.

Sharing a Dataset does not include sharing associated Reports — those must be shared explicitly. Even though sharing a Report implies access to underlying Datasets for the purposes of the Report, Users can only filter. The underlying Datasets are not available as source for other content and will not display as an available Dataset outside the scope of the shared Report.

Datasource & Report Sharing

Sharing a Datasource provides Teams with query access to the Datasource as specified on an individual Team basis:

  • Limited Access
  • Full Access
  • Custom Access
  • or No Access

Selecting a level of access for the Shared Team involves choosing an access level for their:

  • Data Wizard
  • Publisher
  • and Administrator

The available levels of privileged access management and their respective rights are detailed in Table 2.

Role Rights
No Access Default – Datasource does not appear
Limited Access Only the Query Designer may be used to create Datasets.
No Restricted Fields
Full Access Datasource can be queried without any restrictions
Custom Access Only the Query Designer may be used to create Datasets.
Selected Mapping Sets only (choose whether to allow Restricted Fields)

Table 2: Datasource Access Roles providing privilege access management

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Informer Shines in the BARC BI & Analytics Survey 23 

Entrinsik is thrilled to share that Informer is top ranked in the BARC BI & Analytics Survey 23 for the third straight year. This is the world’s largest and most comprehensive survey of business intelligence end-users, based on feedback from a diverse group of 1,900+ where 34 key performance indicators (KPIs) were evaluated for 24 products.

Regarding KPI results, Informer scored 35 top rankings and 46 leading positions in 4 different peer groups, those being Embedded Analytics Focus, Report & Dashboard Focus, BI & Analytics Specialists, and Midsize/Departmental Implementations. A top ranking indicates winning the first position out of the entire peer group.

Interestingly, Entrinsik has the highest proportion of users from the IT department (83 percent) in this year’s survey. Informer achieved perfect scores for price to value, vendor support, distribution of reports, ease of use, sales experience, data preparation, and operational BI.

One user commented: “It‘s an excellent product at an excellent price – among the best price-to-value ratios of all the software we use. Plus, support is amazing and everyone we‘ve had contact with (including sales and training) have been great to work with.”

The following provides a deeper dive into our top rankings:

Analyses & Ad hoc Query – All Informer users surveyed claimed to be satisfied with our product’s support for Ad hoc query and analysis capabilities. Our clean drag-and-drop interfaces with good contextual menus make life easy for business users.

Business Value – ‘Improved operational efficiency’ was achieved by a higher proportion of Informer customers than any other vendor in this year’s survey.

Customer Experience – According to BARC, Informer offers the right software and the ease of use required to empower business users in their daily work.

Customer Satisfaction – With special customer support teams, tailored services, pleasant employees, a focus on certain industries, and a product that fulfills user needs, customers could hardly be served better, according to BARC.

Data Preparation – Top rankings in 3 peer groups and perfect scores of 10/10 in each – An unusually high proportion of business users prepare data with Informer, and data transformation and query features are key to customers.

Distribution of Reports – Top rankings in all 4 peer groups and perfect scores of 10/10 in each – Informer reports can be sent to other users, scheduled, or embedded in other applications, fulfilling all customer needs in this area.

Ease of Use – Top rankings in all 4 peer groups and perfect scores of 10/10 in each – This KPI is the main reason why customers choose to buy Informer. 73 percent (well above the average of 28 percent) purchased the product for its ‘ease of use for report designers’.

Functionality – Informer was built for MultiValue databases and enhanced with connectivity to other relational databases and APIs, bringing high value to users of these databases.

Implementer Support – Informer is ranked top of the Embedded Analytics Focus peer group for provided implementation services.

Operational BI – Top rankings in all 4 peer groups and perfect scores of 10/10 in each – 84 percent of Informer respondents reported having used the product in the area of operational BI, the highest rate of all products.

Price to Value – Top rankings in all 4 peer groups and perfect scores of 10/10 in each – For the third year in a row, Informer has achieved top marks for price to value.

Product Satisfaction – The highest rating out of all products for ‘no significant problems’ encountered with the software belongs to Informer.

Recommendation – Our agility in querying data along with our flexible, easy to use solution for discovering insights has contributed to Informer’s top ranking.

Sales Experience – Top rankings in all 4 peer groups and perfect scores of 10/10 in each – Informer received the top rating out of all vendors for industry-specific knowledge, general conduct, and pricing and contract flexibility.

Vendor Support – Top rankings in all 4 peer groups and perfect scores of 10/10 in each – Our vendor support is rated better than all other products featured in The BI & Analytics Survey 23.

Click here to access the full report.

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Teams For Identity & Access Management

The Informer 5 Teams Framework

Informer 5 employs a Teams framework to model the needs of real world business operations and security.

The Teams model ensures:

  • Shared data is current and accurate
  • Data used for reporting has not been doctored
  • Access to sensitive data is secured
  • Users interact with and access the data based on determined security settings
  • Only individuals with determined access rights can alter the query behind the data

See Data Governance & Security for more on the Teams security model.

An Informer Team is defined as a group of Users that comprise a logical business unit within an organization. Privileged access management enables Roles within a Team to be determined by one’s business role. Your organization’s logical groups and security rights for your employees easily map into Informer’s Teams and Roles. See Figure 1.

Identity access management is determined by your system administrators whereby Informer users are assigned certain access rights based on their business role and needs for data analytics. They can also be authenticated through a third-party application user database.

While individual users have access to Informer, typical identity and access management is based on groups of individuals within a department or a logical business unit within a department. These different groups have different access rights to their organization’s data. And, as members of a group, individuals typically have different functional roles that require their access rights to differ within the group.

Role Name Rights
Member View anything Owned by the Team
Designer All Member rights
Create content from Datasets available to the Team
Upload spreadsheets into new Datasets
Create Reports from Datasources available to the Team
Data Wizard All Designer rights
Create Workspaces
Create Datasets from Datasources available to the Team
Edit Team-owned Datasets
Publisher All Data Wizard rights
Share Team-owned Datasets and Reports to other Teams
Admin All Publisher rights
Manage members
Add a Datasource to the Team
Share a Team-owned Datasource to other Teams

Table 1: Team Roles

Some members of a department might create data content for their organization, while others simply use data analytics to build business insights. For example, the Registrar’s office within a University might have a manager of the Graduate Students Division and a manager of the Undergraduate Students Division creating content based on student data, while division members use this content to create annual reports for the University.

An important step in privileged access management is adding a Member to a Team and defining their role within the Team. Informer 5 provides comprehensive pre-defined role types for Team members. These role types define sensible access rights and map easily to your organization’s security permissions for your employees. See Table 1.

Although Teams model logical business units within an organization, users can be Members of more than one Team. From an identity and access management perspective, their role within a specific Team is determined by their business role in that Team. For example, the Manager of the Registrar’s office within the University’s Graduate Students Division may be the Administrator for the Graduate Student Team as well as being a Data Wizard for the larger Registrar’s Team.

You can also source both Users and Teams information from a third-party repository using Informer’s Plugin Architecture. For example, Teams can be retrieved for use in Informer by referencing divisions within your organizational chart and applying those Users and Teams to Informer together with the appropriate Roles.

Privilege Access Management Through Ownership

Ownership of content (Datasource, Dataset, Report, and Job) is a powerful concept in Informer 5 and reinforces quality and confidence in your organization’s content. With ownership comes specific privileges on who can modify the way Informer handles data. In this way, the content quality is preserved and holds credibility when shared with others.

All content within the system has a single Owner. Ownership can consist of an individual User or a Team. However only those with an appropriate Role within the Team owning the content can have edit capability. A typical scenario is that a User creates, modifies, and owns the content and eventually passes Ownership to the Team once finalized. When content is owned by a Team, it supports Data Governance by providing a credible single source of truth. For example, a Dataset of Financial Data that is owned by the Finance Team holds more credibility than a Dataset owned by Bob Smith from the Finance Team.

Identity Access Management with Sharing

Departments within organizations need to share content, reliably and confidently without concern for source edits. Shareable objects within Informer (Datasources, Datasets, and Reports) are shared across Teams as read-only content, regardless of a User’s role within the shared Team. Through identity access management, you can provide access to your content to members outside of your Team by explicitly choosing to Share that content and by selecting the specific Teams that are allowed access. This enables Teams to create a library of curated content while adhering to strong Data Governance.

When Sharing a Dataset, the Sharing Team selects the level of access provided to the selected Team through privilege access management:

  • No Access
  • Full Access
  • or Custom Access via a Saved Filter. See Figure 2.

Custom Access gives only a Filtered view of the Dataset to the selected Team as rows are filtered out of the view. This is a way to also achieve row level security.

As a result, the Teams receiving the shared Dataset have full confidence in using it to build Reports or include it as part of a scheduled Job because they understand the Dataset Owning Team has full responsibility for maintaining it. For example, the Registrar’s Office in a University creates a Dataset of Student Enrollment that is then shared among different departments. These departments can now build content confidently from the Student Enrollment Dataset.

Sharing a Dataset does not include sharing associated Reports. Those must be shared explicitly.

Sharing a Report implies access to underlying Datasets for the purposes of the Report. However, the underlying Datasets are not available as source for other content and will not display as an available Dataset outside the scope of the shared Report.

Datasource Sharing

Sharing a Datasource provides Teams with query access to the Datasource as specified on an individual Team basis:

  • Limited Access
  • Full Access
  • Custom Access
  • or No Access

Selecting a level of access for the Shared Team involves choosing an access level for their:

  • Data Wizard
  • Publisher
  • and Administrator

The available levels of privileged access management and their respective rights are detailed in Table 2.

Role Rights
No Access Default – Datasource does not appear
Limited Access Only the Query Designer may be used to create Datasets.
No Restricted Fields
Full Access Datasource can be queried without any restrictions
Custom Access Only the Query Designer may be used to create Datasets.
Selected Mapping Sets only (choose whether to allow Restricted Fields)

Table 2: Datasource Access Roles providing privilege access management

Facilitate Team Interaction Through Collaboration

In typical organizations, coworkers share ideas, and iterate on projects. Through Informer 5’s comment feature within the Teams Collaboration function, Informer encourages and facilitates Team interactions associated with Informer content.

For example, Members within a Team might engage in Team discussions that include Datasources, Datasets, Reports, Jobs, content creation, gleaning business insights from Dashboards. Team members can collaborate on the relevant sales data to extract for the creation of a Dataset, discuss and iterate on fields and Visuals to hone in on to create a Dashboard, discuss business trends and course of action for their next sales quarter, etc.

Identity Access Management at the Team Homepage

Informer provides a Team Landing Page for every Team to access content and view activities relevant to them and manage Members. This enables you to view and manage:

  • The list of all Members and their Roles
  • The list of Datasets, Reports, and Datasources owned by the Team
  • and, the list of Datasets, Reports, and Datasources shared to the Team.

The Activity feed function on the Team Landing Page helps Team members keep abreast of events that they would be interested in monitoring, and see a preview of the respective content. An Activity feed consists of comments and system events that pertain to the Team. For example:

  • A new Report has been shared with the Team
  • A new Member has been added to the Team
  • A Member has commented on a Dataset owned by the Team, etc.
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