Informer Shines in the BARC BI & Analytics Survey 23

Kyle Watson Oct 27, 2022

Informer Shines in the BARC BI & Analytics Survey 23

Blog,Informer

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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Informer 5 Voluntary Product Accessibility Template (VPAT)

Kyle Watson Oct 24, 2022

Informer 5 Voluntary Product Accessibility Template (VPAT)

Blog,Informer

Informer 5 Voluntary Product Accessibility Template (VPAT)

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Teams For Identity & Access Management

Betsy Ricotta Oct 19, 2022

Teams For Identity & Access Management

Blog,Informer

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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Informer Reports & Visuals

Betsy Ricotta Oct 04, 2022

Informer Reports & Visuals

Blog,Informer

Visualize Data in New Ways

To ensure your competitive position remains solid, it’s critical to optimize performance by having access to the data you need for business intelligence and being able to easily analyze that data from different perspectives. Informer makes this easy by providing a variety of different interactive data analysis report types that are also convenient to embed in external applications and websites for collaboration. For example:

Dashboard
Comparison Board
Data View
Ad-hoc Query

Dashboard Reporting Made Easy

Informer Dashboards make it easy for everyone in your organization to make data-driven decisions, optimize efficiency, and accelerate bottom-line results by quickly and easily delivering the right information to the right users at the right time. Now you can view, interact with, and personalize your data visualization software to analyze information coming from multiple sources and improve decision-making across your organization.

With Informer’s intuitive and interactive interface, non-technical users can easily explore data visually and conduct data analysis by creating customized Dashboard Reports on their own. Because Informer Dashboards are dynamic in nature, you can quickly change your filter criteria, chart type, intervals, and drill down into the details for a deeper understanding. Maybe you want to see a map of all your sales orders to identify strong and weak markets with supporting visuals on one screen.

See Figure 1 for a classic Dashboard Report.

Choose the design and layout of your Dashboard Reports. Simply drag and drop the Informer Visuals and resize them to highlight particular insights from the data. And, you can always go back to resize your Visuals, change the position of your Visuals, or even add another Dataset.

Informer Dashboards provide data visualization using real-time information and integrating data from multiple databases, data warehouses, and spreadsheets, whether on-premise or in the cloud, to display all KPIs and metrics from a single web-based interface. Informer’s advanced built-in security framework also supports your data governance parameters by including role-based user permissions and granular data access control.

Informer Dashboard Features Include:

Create Dashboard Reports that integrate data from multiple data sources including:
- Data warehouses
- Microsoft® SQL Server
- Informix®
- Oracle®
- IBM DB2®
- MySQL
- U2™
- Google Docs
- Excel, and more
Develop Dashboard Reporting using an intuitive drag and drop web-based interface
Data visualization with multiple Reports on one screen to provide data context and deeper understanding
Drill down into the details of marketing Dashboards to answer specific questions
Specify filter parameters and KPIs on the fly using run-time variables
Dashboard visuals refresh automatically with real-time data rendering
Available library of data visuals or customize your own

There are Six Different Visual Options You Can Choose from:

Discover – Informer automatically creates different Visuals based on the Field types you select
Saved Visuals – Choose from a list of saved Visuals created in your Dataset
Charts – Chart Visuals are one of the Visual options for Datasets, Dashboard Reports, and Comparison Board Reports. Charts take data from a Dataset and create graph-like Visuals that you can customize. You can drill down on visuals to create a pivot view for any piece of the visual.
- Choose from a list of different Chart types that can all be used to show multiple aggregates in the same chart, for example: Trend, Pie, Column, Bar, Area, Line, Spline, Area Spline, Scatter, Scatter Plot
- Grouped pie charts can be created by specifying a “split by” field
- Donut charts can be created by specifying a donut radius on any pie chart
KPI – This displays one large aggregate value of your choice
Maps – Based on what you are selecting to show, Informer can automatically show heat maps and geographic maps including country maps and state maps down to the county level. Informer 5 uses Google Maps to aid in presenting location data. This enhances Informer’s ability to provide useful and easily understandable data concerning geographic regions across the globe
Tables – Displays your data in a table format where you can specifically customize the values and limits of rows and columns

Comparison Boards for Faster Data Analysis

A Comparison Board is a collection of Visuals comparing segments of data against others or against the whole. These are very helpful for data analysis where you want to compare multiple options and make complex decisions simpler. Sourced from one or more Datasets, a Comparison Board typically presents Visuals of easily segmented data in a vertically organized, side-by-side display, such as Year-over-Year Sales by Salesperson.

Supporting a multiple column format, to compare left-column Visuals with right-column Visuals, Informer will present a list of Field types from your Dataset. After you choose a Field and a condition to compare Visuals (distinct values, like, enter value, is empty, is not empty, etc.), you’ll see your Visuals screen immediately display a comparison. See Figure 2: Comparison Dashboard.

Data Views for Summary & Pivot Style Analytics

Data Views are used to create cross-tab summary data including aggregates and drill-down details. Sourced from a single Dataset, Data Views provide record-level detail in a familiar spreadsheet type format. You can quickly Filter the Dataset and select specific Fields for Column display and custom formatting. You can create:

Summary Tables and Pivot Tables
On-the-fly groups
Sorts
Aggregates
Formatting

Summary Tables and Pivot Tables provide great ways for you to visualize data you might not typically see – leading you to uncover valuable data stories that include exposing operational issues and highlighting opportunities.

With just a few clicks, Informer’s Pivot Tables and Summary Tables make it extremely quick and easy for you to aggregate and summarize information from a large Dataset to get answers to questions.

You don’t need knowledge of Excel. Just select what you want for rows and columns from the list of available Dataset fields and you’ll immediately see your aggregated results. Then have fun further exploring by selecting other field combinations and see the immediate results. See Figures 3 and 4.

To ensure data governance, Data View users only access the resulting grid and Pivot Table. They do not have direct access to the Dataset driving a Data View.

Ad hoc Query for Monitoring Data at the Datasource

Although Datasets are the preferred method for working with your data, there may be times you need temporary query results or near-real-time data. In those situations, it may be advantageous for you to tie your query directly to the Datasource. For example, if you are processing a payroll run and you need to see the data as it is right then to make sure everything is in balance. Or, you might want to monitor your data for certain conditions and email an alert to someone if an error occurs. In those cases, an Ad-hoc Query Report can come in handy.

Ad-hoc Queries have:

Field definitions
Criteria that limit the results
Flow Steps defined
Results with a short life-span
Results that are only available to the user who ran the Query
Results that can only be used to generate Pivot Tables

An Ad-hoc Query can be created using either the Query Designer UI or Native SQL statements in the case of SQL-based Datasources. After running the Ad-hoc Query, Informer creates a temporary Result Set that is visible only to the user who ran the Query. You can then interact with the Result Set in the same way you do with Datasets – filter, sort, group, create Pivot Tables, etc.

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Informer for MultiValue Databases

Betsy Ricotta Sep 18, 2022

Informer for MultiValue Databases

Blog,Informer

Why lose the investment you’ve made in your business application built on MultiValue? Instead let us help you modernize your application to grow your business, save money, gain flexibility, improve performance, and stay competitive.

Entrinsik has been working with MultiValue databases including UniData® and UniVerse® for decades. Informer 5 represents a new generation of business intelligence and data analytics for the MultiValue market, simplifying the process of accessing, cleansing, blending, and analyzing data from multiple sources.

Aggregate all your disparate data, including spreadsheets, using a single platform to create a cohesive, curated, governed data hub for self-service data visualization and analysis across your organization.

Identify patterns and trends within disparate data.
Curate datasets of relevant structured and unstructured data.
Blend data from multiple sources all on one dashboard.
Empower business users to drill into data and explore trends.

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Informer Datasources & Datahub

Betsy Ricotta Sep 11, 2022

Informer Datasources & Datahub

Blog,Informer

Connect To All Your Data

Datasources are how Informer connects to your databases for data analysis. Anything that produces a JSON data stream — structured or unstructured — into Informer is a potential Informer Datasource.
There are so many possibilities! In Informer 5, a Datasource does not have to comply with a rigid definition such as SQL, TCL/ECL, or other standards. Any ‘source’ can serve as a Datasource, including a traditional physical database, a REST API endpoint, a spreadsheet on your desktop, a proprietary data feed, or even a Twitter feed!

Datasource Drivers

An Informer Datasource is ultimately responsible for populating Informer Datasets. A Dataset is the elementary building block for Informer content and consists of a set of indexed Records. If you have a Datasource defined, you can create Datasets in one of two ways using: Informer Query Designer or Native SQL statements.

Connecting to your Datasource to create curated Datasets is accomplished using a Node.js-based driver which is published for most modern database platforms and proprietary data stores. In the absence of such a vendor-supplied driver, Informer developers can create one. Out of the box, Informer provides drivers for several types of databases.

Drivers are released for different types of databases over time and can easily be added by customers via plugins.

Adding and Editing a Datasource

Once connected, Informer scans your Datasource for tables and properties. For SQL-based databases, once the database is scanned, you can hide certain Mappings that have no relevance to data analysis or reporting. Hidden Mappings do not appear in the list of Available Mappings when creating Datasets or Queries.

Change Owner – The Datasource can be owned by individuals or a Team. Access to Team-owned Datasources is governed by the roles within the Team. Change the owner of the Datasource to another user or team.

Delete – Delete the Datasource
Test Connection – This tests the connection between Informer and the database
Scan – This rescans the database so if there is something new added or if you have unhidden some mappings/fields, they will show up after the rescan
Export – This creates a package of the mappings, fields, and links
Import Schema – This imports any packages already created
Rename – Allows you to rename the Datasource
Edit Connection – This screen allows you to edit the connection details

Workspace Datasources

Many times, organizations have different Comma Separated Value (CSV) files that hold pieces of the overall puzzle, but they find it challenging to create a single view to extract meaningful value. For example, there may be separate CSV files for each sales department region and you want to bring them together into one Workspace for analysis.

Workspaces allow collections of one or more related Comma Separated Value (CSV) files into an Informer Datasource which can be linked, queried, typed, etc. all the same as any other Datasource.

As you import a CSV file into a Workspace, Informer:

Scans for columns headers
Intelligently guesses at data type for each column
Provides the ability for the user to reconfigure the types
Creates a PostgreSQL table per the document structure

Once a CSV successfully imports into a Workspace, it exists not as a file but as an actual table in the local Informer PostgreSQL database for data analysis. In this way, it is manipulated as any other Datasource. So, as you add associated CSV files into the same workspace, you can create links provided the files contain logical associations.

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Data Flows For Data Transformation

Betsy Ricotta Sep 04, 2022

Data Flows For Data Transformation

Blog,Informer

Review, Cleanse, and Transform Your Data

In many cases, Datasets don’t contain all desired information when originally pulled from their source. Or, the data within the Dataset may not be formatted in the desired fashion. For example, each record in a Dataset may contain two text fields but not the concatenation of the two. Or, a Dataset could contain two numeric fields but not the product of the two.

Informer’s unique Data Flows data transformation feature makes it easy for you to review, cleanse, and prepare your data as it comes into the system. With Flows, you can intercept Dataset records as they are being indexed (e.g. a database, network document, or API call result) and augment the records with additional data, metadata, or logic, such as the concatenated text and multiplied numeric fields example above. As a result, you can perform data analysis and use Informer Dashboards and Ad-hoc Reports to track and analyze key performance metrics based on one version of the truth.

Data Flows enable you to easily transform data however you choose, for example:

Join data from disparate sources and append incomplete entries
Scrub duplicates and normalize inconsistent fields
Author custom scripts to transform data into exactly how you want it to appear

Data Flows can be much more than simple data transformation expressions for data analysis. For example, consider a Dataset that contains a street address for each record. You could apply a flow to these records which:

Authenticates against the Google Maps API
Returns latitude and longitude values
Adds those coordinates as a new field in your Dataset

You can then add a heat map as a visual to the Dataset or use Informer Discover to create a heat map for you.

Another common usage is two-pass evaluation. You may have a numeric field and want to calculate its percentage of the total of all records in the Dataset. This requires retrieving the grand total by first processing all records, then passing through your records again to calculate the per record percent of the total. You can do this using a Data Flow.

Data Transformation Using Standard Data Flows

Data Flow steps include several options for you to choose from: Add Field, Transform, Remove, and Advanced:

• Add Field – Enables you to create new Fields that provide stock functionalities for one or more selected Fields from the Dataset or Fields from a different Dataset.
• Transform – Enables you to configure the Fields that you’ve added by adjusting datatypes, replacing Field values, removing duplicates, and splitting Fields with multiple values.

Data Transformation Using Custom Data Flows

You may have the need for a type of Data Flow that is uniquely specific to your organization or industry. Or, you may have a commonly used calculation and want to stop authoring the same script calculation repeatedly. In this scenario, you would simply author an Informer Plugin to register your desired functionality.

Let’s say you manage the Order Processing Department and your users commonly work with result sets that contain an ‘Order Quantity’ Field, a ‘Unit Price’ Field, and a ‘Zip Code’ field. Your processors then calculate an order sub-total from the Order Quantity and Unit Price Fields, a tax amount based on Zip Code, a Shipping & Handling Fee, an estimated delivery date based on Order Quantity and Zip Code, and a total amount from the whole lot.

In this scenario you have a choice:

rely on your processors to calculate each of these correctly for every order that comes through the system
or, you could author an Informer Plugin as part of your Flow that prompts for the required input fields from the existing results and amends each record with the appropriate values, all before the user retrieves the Dataset.

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Creating A Single Source of Truth for Higher Education

Pam Barber Aug 08, 2022

Creating A Single Source of Truth for Higher Education

Blog,Informer

There are stories that we are told time and time again by colleges and universities regarding reporting and data analysis. One common comment we often hear is how difficult it can be to connect disparate data to see the bigger picture. For example, you could be interested in evaluating general student success, but grades over the course of the semester are in an LMS while student information is on-premises. You may find yourself asking how to pull all that information together into one comprehensive report.

Picture a situation where you are in the same meeting as your colleague presenting data on the same topic. Your numbers are not lining up with one another. How do you know who is right? Imagine being able to eliminate the guesswork with data you know is correct. Informer gives you that single source of truth with perfectly curated Datasets based on your specific needs.

Data exists throughout the organization in multiple formats. A Dataset aggregates all formats into a single location within Informer. The Dataset creates a single source of truth that can then be curated through filters and Jobs to provide individualized data views to those within the organization. Implementing a single source provides data that you can trust, eliminating questions about which data is correct or trying to make sense of duplicate data.

We know that every school is unique, but everyone shares the need of connecting data to see the bigger picture, getting the data you need when you need it, data transparency, and having secure data for everyone. Informer is here to help you put the right information in the right hands, quickly and efficiently.

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Craft Brewers Conference 2022 – Key Trends for 2022 and Beyond

Kyle Watson Aug 08, 2022

Craft Brewers Conference 2022 – Key Trends for 2022 and Beyond

Blog,Informer

Craft Brewers Conference 2022 has come to a close, and while many of us participated last year in Denver, the pre-pandemic energy and excitement truly returned this year in Minneapolis. Like in past years, there were plenty of opportunities to try the newest NH Hazy hop combination and notice how West Coast IPAs are on the rebound. But more than ever before, the prevailing theme of the conference was how brewers can master their supply chains to run more efficiently. In 2022, the burden of inflationary pressures and an inconsistent supply chain necessitated that brewers think differently as they plan for the future. These challenges and related solutions were present in several of the key operational trends at the Craft Brewers Conference.

Trend #1 – Eliminating Stockouts

Stockouts can be devastating to a growing brand. Customers interested in a product are unable to buy, resulting in lower revenue as well as consumer dissatisfaction. Without the ability to buy your product, customers will seek out other product options, causing you to lose out to other brands. The team at GP Analytics is using data to simplify the supply chain with the right product allocation. By measuring the sales data, they can accurately forecast future demand and automatically communicate the results to wholesalers, eliminating the stockout before it happens.

Trend #2 – Brewers Battling Keg Loss

The Brewers Association estimates that 5% of a brewer’s kegs will be lost each year. Steel is even more expensive and the impact on the bottom line can be dramatic. In the past, brewers have relied on barcode tracking and deposits to help curb keg loss. Katch Assets has taken keg tracking to an entirely new level with their Internet of things powered location intelligence. A beacon is secured to each keg, utilizing event triggers to send messages via a worldwide infrastructure. In addition to securing kegs from loss, brewers also receive key information on cycle time and temperature performance.

Trend #3 – Making Data-Driven Decisions

While brewing remains an art, business and operational sides of breweries have fully embraced that better performance can be achieved through science. Over the course of the week, we had several conversations with individuals that collected data but had no way to extract actionable insights. From taproom POS to inventory to flow meters, users needed a way to visualize their data. With Informer brewers can simplify the process of accessing, cleansing, blending, and analyzing data. Informer combines data into a single platform with an intuitive, web-based interface so that users can make data driven decisions.