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

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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.



Want to Accelerate your Collibra time to value and increase adoption?

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HOW A BELGO-GLOBAL BIOPHARMACEUTICAL COMPANY ACCELERATES LIFE-SAVING DRUG DISCOVERIES BY REDUCING QUERY LATENCY 30X

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Discovering new medical therapies and drugs is a complex and time-consuming process. High-performance computing and interactive data analytics can make a substantial difference and  accelerate discovery and time-to-market.

UCB  is a multinational biopharmaceutical company headquartered in Brussels. UCB focuses primarily on research and development, specifically involving medications centered on epilepsy, Parkinson’s disease, and Crohn’s disease. The company’s efforts are focused on treatments for severe diseases treated by specialists, particularly in the fields of central nervous system disorders (including epilepsy), inflammatory disorders (including allergy) and oncology.

Challenges

The company’s data backbone built on Oracle database technology was struggling to meet the new data platform’s performance demands. Next to performance scalelability was also a point of attention as future data volumes would make the current system unstable.
UCB wanted to build a data platform for early stage drug discovery enabling scientists to quickly access research data via self-service and as a result accelerate the early discovery process. Its goals included:
  • Decreasing the time scientists spend waiting for data. Previously, scientists had to wait for days or weeks for the data to be made available for analysis by IT.
  • Helping scientists reach answers faster. A faster and easier-to-use system would allow scientists to test hypotheses quickly.
  • A user-friendly self-service interface. Scientists would be able to use this system without needing assistance from IT. They could easily pick and choose the right data for their experiments, prepared in the way that makes the most sense for the success of the experiment.

 

30X Speed improvements enabling real-time dashboards 
48X batch data refresh ops improvement 

Requirements

The new data platform needed to deliver faster query performance, while being able to easily scale to handle the large, complex queries scientists used to gather research data. The new solution had to accommodate massive data sets and growth so the company wouldn’t need to transition to another database in the future. 

More importantly, UCB’s new early stage discovery data platform needed to be built around the FAIR principle: Findable, Accessible, Interoperable, and Reusable. The chosen database technology would need to fit this approach and easily connect with the rest of UCB’s data stack. 

Platform choice

Up to 800 scientists worldwide rely on UCB’s data platform every day to power their essential research. “We chose SingleStore because it offered a modern database that can accelerate time to insight with ultra-fast ingestion, super-low latency, high-performance queries, and high concurrency,” said Frédéric Vanclef, Senior IT Expert, UCB. SingleStore offers parallel, high-scale streaming data ingest that can handle trillions of events per second for immediate availability and concurrency for tens of thousands of users, which supports UCB’s current needs and positions it for future growth.


Outcomes

With SingleStore, UCB is now giving scientists what they need in real time to get the answers they need faster to drive their research:

  • Empowering scientists with a high-performance self-service solution 
    UCB scientists can now collect and prepare their data themselves, getting exactly what they need for their experiments. The SingleStore-powered data backbone gathers all of the research and referential data into a single source of truth, allowing users to select from a wide range of high-quality data. Now, scientists can ask more questions during early stage drug discovery. “Thanks to SingleStore, we can do more and do it faster, which is invaluable to our research,” said Vanclef.
  • Elevating the role of IT from tactical to highly strategic 
    UCB has a dedicated IT support team to assist its scientists. Before the rollout of the early stage discovery data platform, this team was responsible for handling data requests from scientists. Unfortunately, this scenario meant scientists experienced long delays between when they asked for the data and actually received it. With the new self-service solution, the IT team was able to transform its mission and actions from tactical to highly strategic.
  • Accelerating drug discovery
    UCB’s new data backbone has massive improvements in query speed and latency. Instead of being forced to wait up to 20 minutes for query results, scientists now have access to real-time data and query results in 20 seconds, reducing query latency by more than 30X. They can check on available data sets in real time, eliminating delays between data publication and availability in the data mart. The platform’s batch data refresh operation run times dropped by 48X: from 4 hours to 5 minutes(!).
  • Providing scientists with analytical flexibility
    Each data type has its own optimal analytics approach, requiring different tools to handle the wide range of information that UCB scientists work with. SingleStore supports multiple popular analytics solutions so that scientists can work with their preferred applications for a particular experiment.

Source: SingleStore UCB casestory. Full document can be downloaded here.


Also in need for a data acceleration solution to boost your data?

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THE MARKETING DATA JUNGLE

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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.


Need help unlocking your marketing data?

Would you like to find out how Datalumen can also help you with your marketing & data initiatives?  Contact us and start our data conversation.

CHANGE & DATA GOVERNANCE – TAKE A LEAP FORWARD

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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.

• Organizational culture and risk mitigation culture; “Readiness” for change

• Scope and type of the (sub)projects

• Timing and path for the change

• Set up the change team

• Stakeholder assessment & impact analysis (personnel analysis)

• Sponsorship analysis

• Risk assessment



Need help covering change in the context of your data initiatives?

Would you like to find out how Datalumen can also help you with your Data Governance initiative?  Contact us and start our data conversation.




CALCULATING DATA GOVERNANCE ROI

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TOP 5 DATA GOVERNANCE MISTAKES & HOW TO AVOID THEM

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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.



Also in need for data governance?

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DATA VIRTUALIZATION: TOP USE CASES THAT MAKE A DIFFERENCE

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Data Virtualization is definitely on the rise. At its Data and Analytics Summit in London, Gartner was projecting accelerated data virtualization adoption for both first-time and expanded deployments. Besides market analysts, we also see high demand and can confirm this being one of the hottest data solutions. But what are the top uses cases for data virtualization?


Traditional data warehouses entered on repositories are no longer sufficient to support today’s complex data and analytics landscape. The logical data warehouse (LDW) combines the strengths of traditional repository warehouses with alternative data management and access strategies to improve your agility, accelerate innovation, and respond more efficiently to changing business requirements.

Challenges include:

  • A data services approach that separates data access from processing, processing from transformation, and transformation from delivery
  • Diverse analytic tools and users
  • Diverse data types and sources including traditional data repositories, distributed processing (big data), virtualized sources, and analytic sandboxes
  • Unified business ontologies that resolve diverse IT taxonomies via common semantics
  • Unified information governance including data quality, master data management, security, and more
  • Service level agreement (SLA) driven operationalization

Data Virtualization provides a virtualization-centric LDW architecture solution. 

With  Data Virtualization you can:

  • Access any source including traditional data repositories, distributed processing (big data), virtualized sources, and analytic sandboxes, both onpremises and in the cloud
  • Model and transform data services quickly, in conformance with semantic standards
  • Deliver data in support of a wide range of use cases via industry-standard APIs including ODBC, JDBC, SOAP, REST, and more
  • Share and reuse data services across many applications
  • Automatically allocate workloads to match SLA requirements
  • Align data access and use with enterprise security and governance requirements

With Data Virtualization you get:

  • One logical place to go for analytic datasets regardless of source or application
  • Better analysis from broader data access and more complex transformations
  • Faster analysis time-to-solution via agile data service development and reuse
  • Higher quality analysis via consistent, well-understood data
  • Higher SLAs via loose-coupling and optimization of access, processing and transformation
  • Flexibility to add or change data sources or application consumers as required
  • More complete and consistent enterprise data security and governance

 


Interested in Data Virtualization?

Would you like to know how Datalumen can also help you understand how your organization can benefit from using Data Virtualization?  Contact us and start our data conversation.