THE LINK BETWEEN ESG AND DATA: TRANSPARANCY FUELED BY MEASUREMENT & REPORTING

In recent years, Environmental, Social, and Governance (ESG) has become a buzzword in the corporate world. Investors and stakeholders are increasingly concerned with a company’s commitment to sustainability, social responsibility, and ethical practices. As a result, many companies are now incorporating ESG factors into their decision-making processes. But what is the link between ESG and data? In this blog, we explore how data plays a critical role in ESG transparancy.

Tracking ESG Factors with Data

Firstly, it’s essential to understand that ESG encompasses a wide range of factors, from environmental impact to labor practices to corporate governance. Companies must be able to track and measure these factors accurately to report on their ESG performance. This is where data comes in. By collecting and analyzing data, companies can gain insights into their ESG performance and identify areas for improvement.

Using Data to Report on ESG Performance

For example, an organization may collect data on its carbon emissions, water usage, and waste generation to assess its environmental impact. It may also collect data on employee turnover, diversity, and working conditions to assess its social impact. Finally, it may collect data on board composition, executive compensation, and shareholder rights to assess its governance practices. Once a company has collected this data, it can use it to report on its ESG performance.

The Importance of Data for Benchmarking ESG Performance

Reporting is an essential part of ESG because it allows investors and stakeholders to evaluate a company’s ESG practices and make informed decisions. ESG reporting typically involves disclosing data on a range of metrics, such as carbon emissions, employee diversity, and board diversity. Data is also crucial for benchmarking ESG performance. Companies can compare their performance against industry peers and ESG standards to identify areas for improvement.

Benchmark Data for ESG Investing

This benchmarking process often involves the use of ESG ratings and rankings, which are based on data collected from multiple sources. By using these ratings, companies can identify areas where they may be falling behind their peers and take steps to improve their ESG practices. Finally, data is critical for ESG investing. ESG investors use data to identify companies that are committed to sustainability, social responsibility, and ethical practices. They often look for companies with strong ESG ratings, which are based on data collected from multiple sources. By using data to identify these companies, ESG investors can make informed investment decisions that align with their values.

Conclusion

In conclusion, data plays a critical role in ESG. Companies must collect and analyze data to measure and report on their ESG performance accurately. Data is also crucial for benchmarking ESG performance and for ESG investing. As ESG continues to grow in importance, companies that prioritize data collection and analysis will be better equipped to meet investor and stakeholder expectations.



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THE VITAL ROLE OF DATA SHARING AGREEMENTS AND CONTRACTS IN ENSURING SAFE & RESPONSIBLE DATA EXCHANGE

What Are Data Sharing Agreements & Contracts?

Data sharing agreements and contracts are essentially documents that outline the terms and conditions of sharing data between two or more parties. These agreements are important to ensure that data is shared in a safe and responsible manner, and that all parties involved understand their rights and obligations.


Key Elements



Data sharing agreements typically include the following elements:

  • Purpose of data sharing: The reason why the data is being shared and how it will be used.
  • Data to be shared: The type of data that will be shared, including any restrictions or limitations.
  • Data security and privacy: The measures that will be taken to protect the data and ensure its privacy.
  • Data ownership and control: The ownership and control of the data, including any intellectual property rights.
  • Data retention and disposal: The length of time that the data will be retained and how it will be disposed of.
  • Liability and indemnification: The responsibilities and liabilities of each party involved in the data sharing, and any indemnification clauses.
  • Dispute resolution: The process for resolving any disputes that may arise during the data sharing process.

To Conclude

Data sharing agreements and contracts are important to ensure that data is shared in a responsible and safe manner, and that all parties involved understand their rights and obligations. They help to establish trust and transparency between parties, and can help to prevent legal and financial consequences that may arise from data breaches or misuse.

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SAP DATASPHERE: GAME-CHANGING LEAP WITH COLLIBRA, CONFLUENT, DATABRICKS & DATAROBOT PARTNERSHIPS

What is the SAP Datasphere announcement all about?

SAP has unveiled the SAP Datasphere solution, the latest iteration of its data management portfolio that simplifies customer access to business-ready data across their data landscape. In addition, SAP has formed strategic partnerships with leading data and AI companies such as Collibra, Confluent, Databricks and DataRobot to enrich the SAP Datasphere and help organizations develop a unified data architecture that securely combines SAP and non-SAP data.


What is SAP Datasphere?

SAP Datasphere is a comprehensive data service that delivers seamless and scalable access to mission-critical business data and is in essence the next generation of SAP Data Warehouse Cloud. SAP has kept all the capabilities of SAP Data Warehouse Cloud and added newly available data integration, data cataloging, and semantic modeling features, which we will continue to build on in the future. More info on the official SAP Datasphere solution page.



Why does this matter to you?

The announcement is significant because it eliminates the complexity associated with accessing and using data from disparate systems and locations, spanning cloud providers, data vendors, and on-premise systems. Customers have traditionally had to extract data from original sources and export it to a central location, losing critical business context along the way and needing dedicated IT projects and manual effort to recapture it. With SAP Datasphere, customers can create a business data fabric architecture that quickly delivers meaningful data with the business context and logic intact, thereby eliminating the hidden data tax.

As a solution partner in this ecosystem, we are excited about the collaboration and the added value it provides:

  • With Collibra SAP customers can deliver an end-to-end view of a modern data stack across both SAP and non-SAP systems, enabling them to deliver accurate and trusted data for every use, every user, and across every source.
  • Confluent and SAP are working together to make it easier than ever to connect SAP software data to external data with Confluent in real-time to power meaningful customer experiences and business operations.
  • Databricks and SAP share a vision to simplify analytics and AI with a unified data lakehouse, enabling them to share data while preserving critical business context.
  • DataRobot and SAP’s joint customers can now also leverage machine learning models trained on their business data with speed and scale to see value faster, using the SAP Datasphere as the foundation layer.

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TO CURE OR TO OBSERVE? HOW DATA OBSERVABILITY DIFFERS FROM DATA CURATION

In the world of data management, there are many terms and concepts that can be confusing. Two such concepts are data observability and data curation. While both are important for ensuring data accuracy and reliability, they have distinct differences. In this article, we will explore the key differences between data observability and data curation.

What is Data Observability?

Data observability refers to the ability to monitor and understand the behavior of data in real-time. It is the process of tracking, collecting, and analyzing data to identify any anomalies or issues. Data observability is often used in the context of monitoring data pipelines, where it can be used to identify issues such as data loss, data corruption, or unexpected changes in data patterns.

Data observability relies on metrics, logs, and other data sources to provide visibility into the behavior of data. By analyzing this data, it is possible to identify patterns and trends that can be used to optimize data pipelines and improve data quality.

What is Data Curation?

Data curation, on the other hand, refers to the process of managing and maintaining data over its entire lifecycle. It is the process of collecting, organizing, and managing data to ensure its accuracy, completeness, and reliability. Data curation involves tasks such as data cleaning, data validation, and data enrichment.

Data curation is essential for ensuring that data is accurate and reliable. It involves the use of automated tools and manual processes to ensure that data is properly labeled, formatted, and stored. Data curation is particularly important for organizations that rely heavily on data analytics, as inaccurate or incomplete data can lead to faulty insights and poor decision-making.

Key Differences Between Data Observability and Data Curation

While data observability and data curation share some similarities, there are key differences between the two concepts. The main differences are as follows:

  • Focus: Data observability focuses on monitoring data in real-time, while data curation focuses on managing data over its entire lifecycle.

  • Purpose: Data observability is used to identify and troubleshoot issues in data pipelines, while data curation is used to ensure data accuracy and reliability.

  • Approach: Data observability relies on monitoring tools and real-time analysis, while data curation relies on automated tools and manual processes.

Conclusion

In summary, data observability and data curation are two important concepts in the world of data management. While they share some similarities, they have distinct differences. Data observability is focused on real-time monitoring and troubleshooting, while data curation is focused on ensuring data accuracy and reliability over its entire lifecycle. Both concepts are important for ensuring that data is accurate, reliable, and useful for making informed decisions.

COLLIBRA DATA CITIZENS 22 – INNOVATIONS TO SIMPLIFY AND SCALE DATA INTELLIGENCE ACROSS ORGANIZATIONS WITH RICH USER EXPERIENCES

Collibra has introduced a range of new innovations at the Data Citizens ’22 conference, aimed at making data intelligence easier and more accessible to users.

Collibra Data Intelligence Cloud has introduced various advancements to improve search, collaboration, business process automation, and analytics capabilities. Additionally, it has also launched new products to provide data access governance and enhance data quality and observability in the cloud. Collibra Data Intelligence Cloud merges an enterprise-level data catalog, data lineage, adaptable governance, uninterrupted quality, and in-built data privacy to deliver a comprehensive solution.

Let’s have a look at the new announced functionality:

Simple and Rich Experience is the key message

Marketplace

Frequently, teams face difficulty in locating dependable data for their use. With the introduction of the Collibra Data Marketplace, this task has become simpler and quicker than ever before. Teams can now access pre-selected and sanctioned data through this platform, enabling them to make informed decisions with greater confidence and reliability. By leveraging the capabilities of the Collibra metadata graph, the Data Marketplace facilitates the swift and effortless search, comprehension, and collaboration with data within the Collibra Data Catalog, akin to performing a speedy Google search.

Usage analytics

To encourage data literacy and encourage user engagement, it’s important to have a clear understanding of user behavior within any data intelligence platform. The Usage Analytics dashboard is a new feature that offers organizations real-time, useful insights into which domains, communities, and assets are being used most frequently by users, allowing teams to monitor adoption rates and take steps to optimize their data intelligence investments.

Homepage

Creating a user-friendly experience that allows users to quickly and easily find what they need is crucial. The revamped Collibra homepage offers a streamlined and personalized experience, featuring insights, links, widgets, and recommended datasets based on a user’s browsing history or popular items. This consistent and intuitive design ensures that users can navigate the platform seamlessly, providing a hassle-free experience every time they log into Collibra Data Intelligence Cloud.

Workflow designer

Data teams often find manual rules and processes to be challenging and prone to errors. Collibra Data Intelligence Cloud’s Workflow Designer, which is now in beta, addresses this issue by enabling teams to work together to develop and utilize new workflows to automate business processes. The Workflow Designer can be accessed within the Collibra Data Intelligence Cloud and now has a new App Model view, allowing users to quickly define, validate, and deploy a set of processes or forms to simplify tasks.

 

Improved performance, scalability, and security

Collibra Protect

Collibra Protect is a solution that offers smart data controls, allowing organizations to efficiently identify, describe, and safeguard data across various cloud platforms. Collibra has collaborated with Snowflake, the Data Cloud company, to offer this new integration that enables data stewards to define and execute data protection policies without any coding in just a matter of minutes. By using Collibra Protect, organizations gain greater visibility into the usage of sensitive and protected data, and when paired with data classification, it helps them protect data and comply with regulations at scale.

Data Quality & Observability in the Cloud

Collibra’s latest version of Data Quality & Observability provides enhanced scalability, agility, and security to streamline data quality operations across multiple cloud platforms. With the flexibility to deploy this solution in any cloud environment, organizations can reduce their IT overhead, receive real-time updates, and easily adjust their scaling to align with business requirements.

Data Quality Pushdown for Snowflake 

The new feature of Data Quality Pushdown for Snowflake empowers organizations to execute data quality operations within Snowflake. With this offering, organizations can leverage the advantages of cloud-based data quality management without the added concern of egress charges and reliance on Spark compute.

New Integrations

Nowadays, almost 77% of organizations are integrating up to five diverse types of data in pipelines, and up to 10 different types of data storage or management technologies. Collibra is pleased to collaborate with top technology organizations worldwide to provide reliable data across a larger number of sources for all users. With new integrations currently in beta, mutual Collibra customers utilizing Snowflake, Azure Data Factory, and Google Cloud Storage can acquire complete visibility into cloud data assets from source to destination and offer trustworthy data to all users throughout the organization.

 

Some of this functionality was announced as beta and is available to a number of existing customers for testing purposes.



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THE MARKETING DATA JUNGLE

Customer & household profiling, personalization, journey analysis, segmentation, funnel analytics, acquisition & conversion metrics, predictive analytics & forecasting, …  The marketing goal to deliver a trustworthy and complete insight in the customer across different channels can be quiet difficult to accomplish.

A substantial amount of marketing departments have chosen to rely on a mix of platforms going from CEM/CXM, CDP, CRM, eCommerce, Customer Service, Contact Center, Marketing Automation to Marketing Analytics. A lot of these platforms are best of breed and come from a diverse number of vendors who are leader in their specific market segment. Internal custom build solutions (Microsoft Excel, homebrew data environments, …) always complete this type of setup.

78% According to a Forrester study, although 78% of marketers claim that a data-driven marketing strategy is crucial, as many as 70% of them admit they have poor quality and inconsistent data.


The challenges

Creating a 360° customer view across this diverse landscape is not a walk in the park. All of these marketing platforms do provide added value but are basically separate silos. All of these environments use different data and the data that they have in common, is typically used in a different way. If you need to join all these pieces together, you need some magical super glue.  Reality is that none of the marketing platform vendors actually have this in house.

Another point of attention is your data scope. We don’t need to explain you that customer experience is the hot thing in marketing nowadays. However marketeers need to do much more than just analyze customer experience data in order to create real customer insight.

Creating insight also requires that the data that you analyze goes beyond the traditional customer data domain. Combining customer data with i.e. the proper product/service, supplier, financial, … data is rather fundamental for this type of exercises. This type of extended data domains is usually lacking or the required detail level is not present in one particular platform.

38% Recent research from  KPMG and Forrester Consulting shows that 38% of marketers claimed they have a high level of confidence in their data and analytics that drives their customer insights. That’s said, only a third of them seem to trust the analytics they generate from their business operations.


The foundations

Regardless of the mix of marketing platforms, many marketing leaders don’t succeed in taking full advantage of all their data. As a logical result they also fail to make a real impact with their data driven marketing initiatives. The underlying reason for this issue is that many marketing organizations lack a number of crucial data management building blocks that allow them to break out of these typical martech silos. The most important data capabilities that you should take into account are:

 

Capability

Description

Master Data Management (aka MDM)

Creating a single view or so called golden record is the essence of Master Data Management. This allows you to make sure that a customer, product, etc is consistent across different applications.

 

Business Glossary

Having the correct terms & definitions might seem trivial but reality is that in the majority of the organizations noise on the line is reality. However having crystal clear terms and definitions is a basic requirement to have all stakeholders manage the data in the same way and prevent conflicts and waste down the data supply chain.

 

Data Catalog

Imagine Google-like functionality to search through your data assets. Find out what data you have, what’s the origin, how and where it is being used.

 

Data Quality

The why of proper data quality is obvious for any data consuming organization. If you have disconnected data landscape, data quality is even more important because it also facilitates the automatic match & merge glue exercise that you put in place to come to a common view on your data assets.

 

Data Virtualization

Getting real-time access to your data in an ad hoc and dynamic way is one of the missing pieces to get to your 360° view in time and budget. Forgot about traditional consumer headaches such as long waiting times, misunderstood requests, lack of agility, etc.

 

 

We intentionally use the term capability because this isn’t a IT story. All of these capabilities have a people, process and technology aspect and all of them should be driven by the business stakeholders. IT and technology is facilitating.


The results

If you manage to put in place the described data management capabilities you basically get in control. Your organization can find, understand and make data useful. You improve the efficiency of your people and processes, and reduce your data compliance risks. The benefits in a nutshell:

  1. Get full visibility of your data landscape by making data available and easily accessible across your organization. Deliver trusted data with documented definitions and certified data assets, so users feel confident using the data. Take back control using an approach that delivers everything you need to ensure data is accurate, consistent, complete and discoverable.
  2. Increase efficiency of your people and processes. Improve data transparency by establishing one enterprise-wide repository of assets, so every user can easily understand and discover data relevant to them. Increase efficiency using workflows to automate processes, helping improve collaboration and speed of task completion. Quickly understand your data’s history with automated business and technical lineage that help you clearly see how data transforms and flows from system to system and source to report.
  3. Reduce data and compliance risks. Mitigate compliance risk setting up data policies to control data retention and usage that can be applied across the organization, helping you meet your data compliance requirements. Reduce data risk by building and maintaining a business glossary of approved terms and definitions, helping ensure clarity and consistency of data assets for all users.

42% of data-driven marketers say their current technology in place is out of date and insufficient to help them do their jobs. Walker Sands Communications State of Marketing Technology report.



Conclusion

The data you need to be successful with your marketing efforts is there. You just have to transform it into useable data so that you can get accurate insights to make better decisions. The key in all of this is getting rid of your marketing platform silos by making sure that you have the proper data foundations in place. The data foundations to speed up and extend the capabilities of your datadriven marketing initiatives.


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CHANGE & DATA GOVERNANCE – TAKE A LEAP FORWARD

A successful data governance initiative is based on properly managing the People, Process, Data & Technology square. The most important element of these four is undoubtedly People. The reason for that is that at the end it boils down to people in your organization to act in a new business environment. This always implies change so make sure that you have an enabling framework for managing also the people side of change. Prepare, support and equip individuals at different levels in your organization to drive change and data governance success.

Change & the critical ingredient for data governance success.


Change is crucial in the success or failure of a data governance initiative for two reasons:

1First of all you should realize that with data governance you are going to tilt an organization. What we mean by this is that the situation before data governance is usually a silo-oriented organization. Individual employees, teams, departments, etc are the exclusive owner of their systems and associated data. With the implementation of data governance you will tilt that typical vertical data approach and align data flows with business processes that also run horizontally through an entire organization. This means that you need to help the organization to arrive at an environment where the data sharing & collaboration concept  is the new normal.

2The second important reason is the so-called data governance heartbeat. What we see in many organizations is that there is a lot of enthusiasm at the start of a program. However, without the necessary framework, read also a change management plan, you run the fundamental risk that such an initiative will eventually die a silent death. People lose interest, no longer feel involved, no longer see the point of it. From that perspective, it is necessary to create a framework that keeps data governance’s heart beating.

How to approach change?


Change goes beyond training & communication. To facilitate the necessary changes, ChangeLab and Datalumen designed the ADKAR-based LEAP approach. LEAP is an acronym that stands for Learn, Envision, Apply & Poll. Each of these important steps help realize successful and lasting change.


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CALCULATING DATA GOVERNANCE ROI

TOP 5 DATA GOVERNANCE MISTAKES & HOW TO AVOID THEM

The importance of data in a digital transformation context is known to everyone. Actually getting control and properly governing this new oil does not happen automatically. In this article we have summarized the top 5 Data Governance mistakes and also give you a number of tips on how to avoid them.

1. Data Governance is not business driven

Who is leading your Data Governance effort? If your initiative is driven by IT, you dramatically limit your chance of success. A Data Governance approach is a company-wide initiative and needs business & it support. It also needs support from the different organizational levels. Your executive level needs to openly express support in different ways (sponsorship but also communication). However this shouldn’t be a top down initiative and all other involved levels will also need to be on board. Keep in mind that they will make your data organization really happen.

2. Data Maturity level of your organization is unknown or too low

Being aware of the need for Data Governance is one thing. Being ready for Data Governance is a different story. In that sense it is crucial to understand the data maturity level of your organization.  

There are several models to determine your data maturity level, but one of the most commonly used is the Gartner model. Surveys reveal that 60% of organizations rank themselves in the lowest 3 levels. Referring to this model, your organization should be close (or beyond) the systematic maturity level. If you are not, make sure to first fix this before taking next steps in your initiative. You need to have these basics properly in place. Without this minimum level of maturity, it doesn’t really makes sense to take the next steps. You don’t build a house without the necessary foundations. 
3. A Data Governance Project rather than Program approach

A substantial amount of companies tend to start a Data Governance initiative as a traditional project. Think about a well-defined structure, the effort and duration is well known, the benefits have been defined, … When you think about Data Governance or data in general, you know that’s not the case. Data is dynamic, ever changing and it has far more touch points. Because of this, a Data Governance initiative doesn’t fit a traditional focused project management approach. What does fit is a higher level program approach in which you could have defined a number of project streams that focus on one particular area. Some of these streams can have a defined duration (i.e. implementation of a business glossary). Others (i.e. change management) can have a more ongoing character. 

4. Big Bang vs Quick Win approach

Regardless of the fact that you have a proper company-wide program in place, you have to make sure that you focus on the proper quick wins to inspire buy-in and help build momentum. Your motto should not be Big Bang but rather Big Vision & Quick Wins.

Data Governance requires involvement from all levels of stakeholders. As a result you need to make everyone clear what your strategy & roadmap looks like.

With this type of programs you need to have the required enthusiasm when you take your first steps. It is key that you keep this heart beat in your program and for that reason you need to deliver quick wins. If you don’t do that, you strongly risk losing traction. Successfully delivering quick wins helps you gain credit and support with future steps.

5. No 3P mix approach

Data Governance has important People, Process and Platform dimensions. It’s never just one of these and requires that you pay the necessary attention to all of them.

  • When you implement Data Governance, people will almost certainly need to start working in a different way. They potentially may need to give up exclusive data ownership … All elements that require strong change management.
  • When you implement Data Governance you tilt your organization from a system silo point of view approach to a data process perspective. The ownership of your customer data is no longer just the CRM or a Marketing Manager but all the key stakeholders involved in customer related business processes.
  • When you want to make Data Governance a success you need to make it as efficient and easy as possible for every stakeholder. This implies that you should also thoroughly think about how you can facilitate them in the best possible way. Typically, this implies looking beyond traditional Excel, Sharepoint, Wiki type solutions and looking into implementing platforms that support your complete Data Governance community.



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THE GDPR BUSINESS VALUE ROADMAP

Getting a good understanding of the requirements but also the opportunities and business value is not easy. We designed a GDPR business value roadmap to help you with this and also make you understand what capabilities you need to get the job done.  


1
2
3
4
1

  • How will you understand what in-scope data is used for, for what purpose and by whom?
  • How will you demonstrate how you’re aligning to the principles?
  • Is your approach mostly manual, using interviews, questionnaires & static documentation?
  • Is your approach inaccurate, time consuming, resource consuming, out-of-date –or all of the these?


2

  • Do you understand where in-scope data is across your organisation and how it is shared?
  • How will you demonstrate you understand the size & shape of the data problem across domains and data subjects?
  • Is your approach mostly manual, using interviews, questionnaires & static documentation?
  • Is this approach inaccurate, time consuming, resource consuming, out-of-date –or all of the these?

3

  • How will you capture, manage and distribute consents across channels and business units?
  • How will you demonstrate you have captured the lawfulness of processing across all in-scope data sources?
  • Do you have anything in place already? Or are you planning on extending existing preferences capabilities?

4

  • How will you put protections and controls around identified in-scope data?
  • Can you demonstrate you have relevant control over the relevant in-scope data?
  • Are you planning to manually apply controls? Or apply masking, deletion & archiving solutions as required?
  • Will this approach give you a holistic view around the protections & controls you have in place?





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