Manta Business

MANTA Welcomes Two New Top Executives

October 14, 2019 by

NEW YORK, October 14, 2019 (Newswire.com) – Tech company MANTA is proud to announce two new top-level executives: Ernie Ostic as SVP of Products and Reagan Evans as SVP of Revenue. MANTA provides top US banks and other Fortune 1000 companies with a unified platform to comprehend and leverage the flow of information (lineage) in their IT systems. Both Ernie and Reagan are joining the management team to accelerate MANTA’s expansion in the US and worldwide.

NEW YORK, October 14, 2019 (Newswire.com) – Tech company MANTA is proud to announce two new top-level executives: Ernie Ostic as SVP of Products and Reagan Evans as SVP of Revenue. MANTA provides top US banks and other Fortune 1000 companies with a unified platform to comprehend and leverage the flow of information (lineage) in their IT systems. Both Ernie and Reagan are joining the management team to accelerate MANTA’s expansion in the US and worldwide.

Ernie Ostic is joining as Senior Vice President of Products after more than 14 years with IBM working in various roles in product management and technical sales. Ernie brings 30+ years of data integration and lineage background to MANTA with a simple goal: to provide guidance and infuse experience into MANTA’s rapidly developing platform.

“MANTA is pushing a new vision of how to understand data and its movement through information systems,” says Ernie Ostic, the new SVP of Products. “For me, it’s a great opportunity to put my experience in enterprise data integration to use in support of this growing discipline.”

Reagan Evans is starting at MANTA after more than eight years at another SaaS company, leaving the position of Regional Sales Vice President. His new role at MANTA is Senior Vice President of Revenue, and his primary objective is to enlarge the sales and pre-sales teams and secure MANTA’s position as the lineage market leader in the metadata management space.

“I am thrilled to join MANTA at this key phase of its growth,” adds Reagan Evans, the new SVP of Revenue. “My primary focus is to expand the outbound sales organization and build on the solid base of existing enterprise customers.”

MANTA originally started as a niche data lineage solution built by a few engineers in Prague, Czech Republic, and eventually moved its headquarters to New York. Five years later, after successful investment rounds, MANTA has grown to be the central hub of all data flows, enabling digital transformation and saving significant resources in the process.

About MANTA: A Unified Platform for Lineage

Every organization uses data to stay relevant and competitive as it undergoes a constant digital transformation process. This process creates bigger and more complex systems with millions of data flows, where trust in data is undermined, operational costs skyrocket, and every change has unpredictable consequences.

MANTA is the core of all dataflows in the organization, and with its lineage capabilities, it enables digital transformation. The self-service platform demonstrates the data journey in a way that is clear and easily understandable to those at all levels of the organization. MANTA lineage delivers actionable intelligence to boost governance efforts, accelerate development, shorten time-to-market, speed up the modernization process, ensure data quality, and enforce data security.

Original Source: www.newswire.com

Deep Dive into MANTA 3.26: IT and Business Are Now Closer Than Ever (And More!)

September 30, 2019 by

MANTA has introduced a new update, 3.26, and it’s a game-changer yet again. Business users are getting more insight into IT, EDC users have the full package of lineage, and an experimental Java scanner is now available.

MANTA has introduced a new update, 3.26, and it’s a game-changer yet again. Business users are getting more insight into IT, EDC users have the full package of lineage, and an experimental Java scanner is now available.

Business and IT Collaboration Just Got Easier

The wide adoption of a data lineage platform is crucial for any organization to reach its true potential, but people without a technical background have difficulties understanding technical descriptions of dataflows. After all, your job is to deliver business results, not to swim in the ocean of complexity.

Creepy naming, complex flows with a lot of calculations, and transformations make lineage hard to work with. That is why we at MANTA continuously invest in features that facilitate easier understanding for everyone in the company. One of the key issues, typically, is how to do mappings between physical and conceptual terms so MANTA can use them to simplify lineage diagrams.

Nowadays, people do it manually or wish for AI to do it for them. There is one great source of mappings already available though—every organization has tons of existing architecture diagrams and models that can be used by MANTA to simplify lineage for less technical users.

MANTA already supported Informatica PowerDesigner and ER/Studio, and now we are on board with ErWin as well. That means MANTA now supports three of the most popular modeling tools with over 60-70% market share. Sharing information between IT and business has never been easier.

Deeper ETL Understanding with IBM DataStage Scanner

ETL is another critical component of any data pipeline today. But there are so many flavors of it and so many different technologies one can use. MANTA already supported 3 of 6 market leaders, according to Gartner 2019 Magic Quadrant for Data Integration Tools. And now, we have one more!

(source: Gartner)

Now that the IBM DataStage scanner has been released, coverage of the most popular ETLs is almost complete. You can enjoy DataStage ETL support, parallel jobs, and more.

The Java Way

Have you ever asked yourself what the most frequently used programming language in your company is? The TOP3 languages are Java, SQL, and Javascript. That simply means that the majority of your data pipeline is somehow hidden inside tons of SQL and Java code. With our great support for many many different flavors of SQL, our next big thing is to help our customers with the Java part.

MANTA’s Java scanner is available in experimental mode with support for programs with multiple entry points, using Spring framework with a basic Spring Bean configuration. We worked hard to deliver as quickly as possible because this is going to change the way we all think about metadata and how it can be used not only for governance but for quality, security, and engineering.

Full Lineage in the Package for Informatica EDC

The great news for all our customers is that we push harder with our native user interface. On top of that, our goal is to deliver the same or similar experience via supported third-party tools, if possible. This release brings new features for Informatica Enterprise Data Catalog—users will benefit from improved integration with MANTA.

Lineage, no matter how complex, is first visualized simplified to the column-to-column level without ETL, SQL, or other transformations visible. But with just one click, all tech-savvy EDC users can access all the additional details available thanks to MANTA.

Extended Cognos Support

Another very important area that helps with wider adoption of lineage by less technical users and supports data governance initiatives is a great understanding of the existing reporting layer. In the end, reports are THE place where most business people spend their time when working with data.

Even if more than 70% of companies still use Microsoft Excel, there are other very popular reporting solutions, and one of them is IBM Cognos, which has been supported by MANTA since release 3.25. The great news for every Cognos fan and user is that with this release, MANTA significantly extends the scope of supported Cognos features like data modules, uploaded files as sources of data, data sets, and reports with embedded SQL statements.

A Little Something for the Admins as Well

MANTA is all about automation, and the installation and update process is no exception. We have implemented a lot of changes and improvements over the past year and a half, but it is still not enough for us. We want to make it even easier for our customers to install and use MANTA. With this release, the install and update processes were unified into one single simple workflow. No more unnecessary steps, it’s automated as much as possible.

Ready to take a deeper look at MANTA? Just let us know and we’ll show you how it’s done.

MANTA 3.26: IBM DataStage, erwin, and Detailed Lineage in EDC!

Leaves are gently falling to the ground in front of MANTA’s engineering office here in Prague as autumn arrives together with Software Release 3.26. What masterpiece have our developers put together this time? Read all about it in our release blog post.

Leaves are gently falling to the ground in front of MANTA’s engineering office here in Prague as autumn arrives together with Software Release 3.26. What masterpiece have our developers put together this time? Read all about it in our release blog post.

Not Just Any Ol’ Scanners

As always, we bring you the new and greatly improved as well as the old BUT tuned and tweaked. In this release, we have added two new scanners. The first one is an experimental version of the ETL tool from the IBM InfoSphere family, IBM InfoSphere DataStage. Experimental means that we are looking for pioneers for testing. But you had better be quick because the waiting list for this scanner was quite long, and so is the list of prospective testers!

MANTA analyzes DataStage parallel jobs provided by users, then creates a detailed visualization of the data lineage that can be pushed into any third-party metadata management solution or viewed in MANTA’s native visualization. This allows you to plug information such as data validation rules and other data from separate databases into DataStage, all visualized in the lineage showing the complete end-to-end journey of your data.

The second scanner we’ve added is erwin Data Modeler. MANTA can scan erwin and automatically pull physical and logical models that can then be added to your data lineage. This can come in extremely handy when it comes to your database architecture projects, as you can add complex data models from erwin to MANTA and use them to accurately project a situation—an enormously valuable asset for your database engineers.

Up a Notch

We have worked hard to perfect our Cognos scanner. MANTA’s scanner now processes Framework Manager report specifications retrieved from Cognos’ report storage (Content Store). Using the extracted information and other scanners, MANTA can create complete end-to-end data lineage (from database data sources through analytical models to reports in Cognos Analytics).

We are also happy to inform you that we have stepped our Informatica EDC integration up a notch. We have added more filtering options to detailed lineage in EDC, allowing you to see both the big picture as well as the finest details all the way down to the column level, and you can filter dataflows based on what information you want in your final lineage map. Now, MANTA helps you see not only complex SQL code in your EDC Catalog but also information from all the other scanners and integrations that MANTA supports—all ETL, analytics, and reporting tools. This causes user productivity and workflow efficiency to skyrocket!

Last but Not Least

Last but not least, we have made some improvements to the install and update process. Now, the magic happens automatically. When you download a new update packet and the installer notices that you need to update MANTA, it can automatically run your updater when needed, saving you a wrinkle or two in the process!

That’s all for MANTA 3.26. Any questions? Want to become a pioneer? Write to us at manta@getmanta.com.

Mess in, Mess Out: How Low Quality Data Ruins Your Analytics

September 9, 2019 by

A few days ago, I found an interesting article published by Moshe Kranc, CTO at Ness Digital Engineering, on Aug 1, 2019, at InformationWeek. He did an excellent job of reminding us all that with low-quality data the only result anyone can expect from analytics is a mess (mess in, mess out).

A few days ago, I found an interesting article published by Moshe Kranc, CTO at Ness Digital Engineering, on Aug 1, 2019, at InformationWeek. He did an excellent job of reminding us all that with low-quality data the only result anyone can expect from analytics is a mess (mess in, mess out).

I do not agree with his terminology when he says that “data is clean by instituting a set of capabilities and processes known collectively as data governance.” This contributes to the never-ending terminology war between what data governance really is and what the traditional term data management means. But it is a very minor issue.

What is more interesting is another sentence at the beginning of Moshe’s post addressing one of the benefits of data lineage: “standardization of the business vocabulary and terminology, which facilitates clear communication across business units.”

Lineage or Business Glossary—Which Goes First?

While almost every DG vendor tells you that data lineage is something too technical for the initial phases of your data governance program and that you should start with more “fundamental” tasks (like building your business glossary, for example), Moshe says something different—in order to understand your business terms and how they relate to each other, data lineage is essential. Because it shows you your data’s real journey; it helps you understand how different KPIs or critical data elements in your reports and dashboards are calculated and using what data. This is necessary to eliminate misunderstandings between different teams and units and build a high-quality business catalog.

We very often see teams starting with a DG tool, trying to implement basic processes and build a business vocabulary, manually analyzing lineage to decipher relationships among different terms, real data elements duplicated and distributed across so many different domains and systems.

Three Types of Lineage

The main part of the article is all about different types of lineage. Moshe sees three of them: decoded lineage (getting metadata from the code manipulating data), data similarity lineage (getting lineage by examining data and schemas), and manual lineage mapping. We wrote a similar article a year ago with one more type of lineage—check it out here. Moshe builds a list of pros and cons for each approach, a very good one. But I feel it is important to share a slightly different opinion.

Decoded Lineage as a Holy Grail (Or Not?)

Let’s start with decoded lineage first. One objection is that developing scanners for all technologies is impossible (very hard). That is true. But there are different approaches, and a solid data lineage platform should support them all, if possible. Besides traditional scanning, it is also possible to extend the existing code base with extra logging or to use a specialized library to monitor your calls and transformations in the code so you can see useful information. It is never as detailed as true decoded lineage, but it is still good enough. (We call it execution lineage.)

Another objection is that code versions change over time, so your analysis of the current code’s data flow may miss an important flow that has since been superseded. True again, but that is why every data lineage platform should support versioning so you can see changes in your lineage over time, compare lineage, and do other fun things for DevOps (or DataDevOps? or DataOps? [I am confused with so many new buzzwords]), security, or compliance reporting.

Dynamic code is a traditional objection that has already been resolved. What is more interesting are commands directly executed by DBA (to fix something, for example). But these are also easily resolved with execution lineage using specialized logs, for example (see above). Basic processes are necessary to make it possible, but nothing dramatic.

I was a bit confused by another objection—a decoded lineage tool will faithfully capture what the code does without raising a red flag (if the code violates GDPR rules, for example). I do not understand why this is wrong. I believe it is the only right thing to do—to reveal what is really happening so it can be detected and fixed. Typically, with the manual approach, we too often see people creating so-called make-believe lineage (lineage as they think it is instead of true lineage). And yes, the really important issue is how to use lineage information to detect possible violations of company policy. That is why a data lineage platform should offer you a way to build quality rules or a way to build them in your DQ tool and execute them against your data lineage platform.

Another confusing part is about duplicates in data. Two different processes work with the same data element and create a duplicate (for example, two duplicate data elements used in different reports instead of only one used by all reports). Yes, that happens very often, but it is not a reason to stop using the decoded lineage approach there. And even better—it is possible to detect duplicates by comparing processes/workflows and associated sources of data. MANTA is also able to understand calculations you have in the code (see our post about transformation logic), so comparing different processes for duplications is getting even easier.

Data Similarity Lineage

I agree with everything in that part. A lot of vendors try to trick customers by using this approach. The results can only be good in a very limited environment without too much complexity or logic. Another major issue is that there are no details about the transformations and calculations used to move and change data, which is very limiting for all super interesting use cases like impact and root-cause analyses, incident management, DevOps, and migration projects.

My opinion is that this type of lineage is satisfactory for basic governance use cases, but no real automation can be achieved if this is the major approach. On the other hand, we see this approach as complementary to decoded lineage for scenarios where no code (in any form) is available—for example, when sending data via email or copying data manually.

Manual Lineage Mapping

Manual is always make-believe lineage—how people see it, and not what the lineage really is. You cannot mix design (where manual is the right way) and reality. Lineage that differs from reality is of no help if you want to use it every day to increase your productivity and reduce risks. Another very important aspect, very often ignored by data officers, is that people do not enjoy doing boring very routine work like manually analyzing the environment and getting lineage from there. Manual is always part of the picture (to fix things that are simply not supported by the lineage platform), but it should be as tiny as possible.

One Ring to Rule Them All?

The result of Moshe’s analysis is inevitable—any really good solution should and needs to combine all the mentioned approaches (and a few more tricks based on our experience). I do not want to argue about the healthy division between decoded, similarity, and manual, but my opinion is very clear: decoded as much as possible, similarity only if there is no (like really) code/logic manipulating data, and manual never or, more precisely, only if there is no other option.

But there is one even more important insight, something I have learned over the past 20 years in data management. We created a concept of metadata that is too complex and vague (check this excellent 2016 article: Time To Kill Metadata). Then we replaced metadata management with data governance without understanding what it really means. And in the meantime, we introduced tons of new complexity—the cloud, big data, self-service, analytics, data privacy regulations, agile, etc.

Don’t get me wrong—all the concepts of unified metadata management are extremely interesting, but the whole problem is so broad that trying to build one unified solution is simply nonsense. Data lineage is an extremely important area connecting business, technology, and operations, independent from data governance, data quality, master data management, data security, or development but yet integrated with all of them. That is why we believe so much in the open ecosystem with a focus on powerful APIs. But more about that next time.

Master Data Management Joins Forces with MANTA to Provide Unique Data Lineage Solution

August 26, 2019 by

New reseller partnership provides complete, accurate technical metadata quickly and cost effectively to reduce data risks, increase agility and trust, and ensure compliance.

New reseller partnership provides complete, accurate technical metadata quickly and cost effectively to reduce data risks, increase agility and trust, and ensure compliance.

Master Data Management (MDM) and MANTA today announced a partnership agreement. The new reseller partnership allows MDM to resell, implement and support MANTA’s software to automatically scan, document and visualise data lineage across many different technologies – including databases, ETL tools, reporting and analytics platforms, and modelling tools.

Data lineage tracks the origins of data, how it moves through the enterprise, and how it changes over time.  This is essential to understand the impact of changes to systems, deliver trusted reporting and to meet compliance objectives.

MANTA improves the accuracy, completeness, effectiveness, and automation of most data governance solutions. MANTA scans databases, ETL scripts and reporting platforms to automatically and quickly gather metadata and extract data processing logic. This technical metadata is then visualised in MANTA Flow, or pushed to a third-party data governance tool such as Collibra.

“The partnership with MANTA extends our value proposition to support the automated harvesting of hard to reach technical metadata” says Gary Allemann, MD at MDM. “We have been impressed by how quickly MANTA can be deployed and start to deliver value. At one mutual client we were able to connect to multiple sources including Microsoft SSIS and Microsoft SSRS within a few days and deliver technical metadata into Collibra.”

This ability to automatically harvest metadata saves the customer hundreds of hours of manual effort, and ensures that technical lineage that they need for compliance stays up to date.

“We are specialising in helping our customers manage their data flows properly and making sure their digital transformation and modernisation projects go smoothly and efficiently,” says Tomas Kratky, CEO of MANTA. “Our partnership allows us to provide Master Data Management’s customers with hard-to-get, yet critical metadata in an automated way. MDM is our first partner in South Africa and we are grateful for the opportunity to serve new customers under our new partner’s wing.”

Going forward MANTA’s technology, will enable MDM’s customers to consolidate technical metadata and deliver trusted reporting; will provide comprehensive insights on the impact of alterations and updating of IT systems; and will help clients to meet their compliance goals.

For a list of all supported sources and details of information provided please visit https://www.masterdata.co.za/index.php/data-governance-solutions/manta-technical-lineage

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