Christian Fuchs

Selecting the Right Software with BARC Scores

BARC analyst Christian Fuchs and Regina Schmidt offer up some advice on how to use BARC Score in your financial performance management and integrated planning & analytics software selection projects.

Christian Fuchs

Sneak Preview: The 2022 BARC Score Financial Performance Management

A preview of BARC Score Financial Performance Management 2022, BARC’s overview of the market for Financial Performance Management (FPM) software tools.

Christian Fuchs

A Sneak Peek at the 2022 BARC Score Integrated Planning & Analytics (IP&A)

A preview of BARC Score Integrated Planning & Analytics 2022, BARC’s overview of the market for Integrated Planning & Analytics (IP&A) software tools.

Christian Fuchs

BARC Scores: Supporting You in the Evaluation and Classification of Software Markets

We explain the methodology behind our BARC Score software vendor evaluations.

Christian Fuchs

One Size Fits All? How to Find the Right BARC Score Segment for Your Shortlist

BARC analysts Christian Fuchs and Nina Lorenz offer up some advice on how to use BARC Score in your financial performance management and integrated planning & analytics software selection projects.

Christian Fuchs

Sneak Preview: The 2021 BARC Score Financial Performance Management

A preview of BARC Score Financial Performance Management 2021, BARC’s overview of the market for Financial Performance Management (FPM) software tools.

Christian Fuchs

A Sneak Peek at the 2021 BARC Score Integrated Planning & Analytics (IP&A)

A preview of BARC Score Integrated Planning & Analytics 2021, BARC’s overview of the market for Integrated Planning & Analytics (IP&A) software tools.

Christian Fuchs

How BARC Scores Help Companies to Assess and Classify Business Software Markets

The assessment and classification of business software and its suppliers, for example in the software selection process, can often be a complicated task for companies. BARC Scores are designed to demystify this process.

BARC analyst Timm Grosser

The Data Catalog – The “Yellow Pages” for Business-Relevant Data

BARC analyst Timm GrosserAn overview of data catalogs by BARC Analyst Timm Grosser, including tips on how to select the right data cataloging solution for your organization.

Data is essential for companies to keep up with the digital age. Everyone knows that by now. But it’s not so easy to extract the desired value from data and shine with innovative, data-driven business applications. Instead, we often see data chaos that has been growing for years in the form of fragmented data landscapes and distributed expert knowledge.

A hotly discussed technological approach to make knowledge of distributed data available is the data catalog, the “Yellow Pages” for business-relevant data. It stores information about data in the form of metadata and structures, and makes it searchable.

A data catalog tool achieves its usefulness primarily through three essential points:

  1. covering information needs quickly and easily
  2. capturing and curating metadata (knowledge) as efficiently (automated) as possible
  3. providing a platform for the exchange of knowledge for “all”

In addition, functions for data governance and/or data access are valuable.

Finding the right tool can be more complicated than you might expect. The market for data catalogs is anything but transparent. As with other trending areas, the range of products is exploding and we are now aware of more than 90 solutions with data cataloging functions operating worldwide. But not all data cataloging is the same. These offerings vary in focus, content, features and supported use cases. The following table provides an overview of the basic tool types for data cataloging. Basically, there are options for specific use cases (as part of a BI or analytics user tool, as part of an environment) and offerings that provide a comprehensive, independent solution (specialists, as part of a data governance (DG)/data management (DM) platform).

Pay particular attention to interfaces and transparent, open metadata models for metadata exchange with other catalogs and systems when selecting a data catalog. This offers you a number of advantages:

– You avoid vendor lock-in and can use the tool’s capabilities in a targeted manner

– You can more easily transfer catalogs from different areas or environments to a parent catalog

– It allows easier migration or integration with more powerful tools or tools with a different focus

Catalog scenario Characteristics Tool examples
…homemade Rudimentary catalog functions Excel, Confluence, Wiki, …
…as part of a BI/analytics tool Catalog functions related to the data/artifacts in the environment Alteryx, Qlik, Tableau, …
…as part of an environment Catalog functions related to technical data/artifacts in the environment Amazon, Cloudera, Google, …
…as specialist Comprehensive catalog functions related to data and partly artifacts from different tools/environments, added functionality such as data governance Alation, Waterline, Zeenea, …
…as part of a data governance/DM platform Comprehensive catalog functions related to data and partly artifacts from different tools/environments. Additional functionality from the portfolio (e.g., workflows, data quality, etc.) Collibra, Infogix, Informatica, SAP, …

Table 1: Data Cataloging tool types

When selecting a data catalog, its functions should be carefully checked. A checklist should normally include:

– Adapters and functions for metadata integration and exchange

– Supported content (e.g., supported metadata types, openness and extensibility of the metadata model)

– Functions and machine support for the maintenance (curation) of metadata

– Functions and machine support for catalog use and search/navigation/analysis of metadata

– Ease of use

– Support for collaboration

– Further data management functions (e.g., for data governance, data preparation, data quality and data protection)

We are also happy to support you directly – with our best practice experience, established process models and numerous templates – through the entire selection process from requirements gathering to the creation of a shortlist, proof of concept support and deciding which tool to use. This gives you greater decision security, saves you time and resources and provides you with a partner who can help to create a data cataloging roadmap which is both transparent and acceptable to management and relevant stakeholders.

Self-service BI and the DIY trap

Here is a common scenario, seen in organizations all over the planet. I’m talking about self-service, which is meant to be a good idea.

DIYFor quite a while now, self-service BI has been all the rage. Understandably, business users are often discouraged, disappointed or outright annoyed by the lack of support for BI they get from their local IT departments, and so they often decided to take matters into their own hands. In most cases, Microsoft Excel comes to the rescue, but when that approach doesn’t scale or is otherwise not manageable anymore, those functional departments are looking for a more pragmatic solution. They called it self-service BI, but it was really more self-help. This is where companies such as Qlik or Tableau have seen huge success from generating demand and adoption through grassroots movements, which often happened behind IT’s back. After a while, those DIY implementations became a little more complex and the newly generated shadow IT groups needed help.

What usually happens next?

After IT’s aggravation has subsided, and the rogue projects have been reigned in again, Big IT decides to give the business what they say they want and sets out to enable their users through controlled self-service BI. Or so they thought.  Because it is not about the tool. Now, the functional departments are creating reports, dashboards, and are analyzing data to their hearts’ content, and all with IT’s blessing, yet they still don’t seem to be happy. Why? Because they often rely on self-service definitions (for lack of a better description). Only those organizations that base their BI infrastructure on a commonly agreed data model and semantic layer, the confusion over what means revenue, how many customers have churned, or the profitability of a certain product, can be kept to a minimum. Many others look at their beautiful self-serviced dashboards, nicely rendered on the latest mobile gadget, and have the same funny feeling as before that the figures they are looking at do not represent the truth. So, be careful what you wish for when you say you want “self-service” as there are still a few traps along the way. Some infrastructure guardrails are necessary.