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It has been a long time since we swore our 3-new-supported-technologies-every-release oath, and we have kept it every new software release since. And this version of MANTA is no different. In Release 3.25, we bring you support for Microsoft Excel (yes, Excel!), IBM Cognos, and SAP (Sybase) PowerDesigner. Read more in our regular blog post.

No time to read? Watch a video or dip into the details below:

Data Lineage for Excel? From Excel? With Excel? YES

No, this is not a drill. We have just released support for Microsoft Office’s Excel. The reason for this, which may seem like a weird flex to some, is that many of our customers still have disturbing amounts of data in Excel databases, which was more challenging for our customers to include in their data lineage.

MANTA now tracks data lineage in Excel, being able to read multiple sheets, pivot tables, slides, and graphs. We also have a detection algorithm with which MANTA can automatically reveal aggregated lists and tables that have formula relations among each other. MANTA is able to track data lineage from database tables and CSV files through tables and graphs among multiple Excel workbooks and push the whole picture into MANTA’s native visualization, as well as into third-party solutions.

Reporting Tools Are Our Friends…

The next connector that MANTA added to its technology portfolio is Cognos, a reporting tool from IBM. MANTA is able to create complete end-to-end data lineage from the database data sources, analytical models, and reports in Cognos Analytics by scanning:

• Reports (including queries)
• Interactive reports
• Framework Manager models
• Database connections

MANTA then pushes them into its own data visualization as well as into third-party solutions. (Visit our Tech Hub to learn more.)

Modeling Tools Too!

The third connector we have added is the modeling tool SAP (Sybase) PowerDesigner, our second after adding ER/Studio in Release 3.24. MANTA can scan PowerDesigner and automatically pull physical, logical, and conceptual models that can then be added to your data lineage to create end-to-end logical data lineage.

Behind the Scenes

Besides broadening MANTA’s Tech Hub, we have also made some improvements to existing supported technologies. The most work was probably done on SSAS, where we have added support to the newer version (level 1200) of the tabular models.

We also detect a connection between SSAS and SSRS from our Microsoft family, and these technologies are currently sharp crisp alfa versions. MANTA is also able to export more and more technologies into third-party solutions, including transformation logic.

Have you ever wondered how it all started back in the day? Well, as another part of our MANTA Cases series, we’ll introduce to you the very first way MANTA was ever used, “Patient Zero”.

Patient Zero of MANTA was a member of the Société Générale Group, the Czech bank Komercni Banka. Back then, MANTA was no more than an internal tool of a Czech consultancy company, Profinit, which was hired to solve a data quality issue within Komercni Banka.

The problem was that some parts of Komercni Banka’s data warehouse had different coding because of special Czech diacritics that were more space-consuming than regular letters and symbols. This was creating a lot of mess throughout the piles of custom code. The customer was using a Teradata database, whose price varies according to the amount of data stored. As Komercni Banka had to store a phenomenal amount of customer data, they were trying to store it as compactly as possible to save money on database space.

In a Teradata database, you can store data in Unicode or in ASCII. In Unicode, you can store Czech letters like “š” or “ů” or any other language-specific symbols, as it has a larger format. ASCII is able to store only classic letters and numbers, but therefore takes up less space on the database. As not all Czech words and names have diacritics, this was an opportunity to store some data in a more compact format. (This use case could work with any language that has language-specific symbols, not only Czech.)

Komercni Banka first looked into the source systems to identify all source columns in the database that might have contained Unicode and those that contained only ASCII, and with the help of MANTA, they identified where the Unicode data was propagated and where it was clear that there was only ASCII data. After that, the company used MANTA Checker, a tool that MANTA originally developed to quickly fix errors in code, to perform an automatic add conversion function to the data that could be converted from Unicode to ASCII.

The bank has estimated that thanks to the use of both MANTA and MANTA Checker, they have saved quite a lot of money on storage fees, freed up several FTEs in the organization, and have been able to both monitor their environment and develop new code more efficiently than ever before.

Want more information about MANTA or MANTA Checker? Don’t hesitate to sign up for a free trial and schedule a call using the form on the right, or directly at manta@getmanta.com. 

After introducing “MANTA 4 Finance”, “MANTA 4 Healthcare”, and “MANTA 4 Telco”, we present the next part of our “MANTA 4 Industries” series. Read about the issues MANTA helps insurance customers solve in the article below.

Regulatory Compliance and GDPR

It can be said that the regulatory requirements in terms of data protection for this industry are even more strict than in other industries. The companies’ systems contain large amounts of sensitive personal data that, as we now all know, has to be safeguarded with extra care. The General Data Protection Regulation (GDPR) threatens companies with astronomical penalties for noncompliance.

Big problems can also arise from an inability to accommodate the fundamental rights of data subjects (right to access, right to erasure, right to restrict data processing, etc.).  In these cases, the company has to show the supervisory authority exactly how it secures its data. 

In the worst-case scenario, if there would happen to be a data breach, the insurance provider would have to prove that it did everything humanly possible to stop it! Data lineage is a way of showing that, by drawing out an end-to-end map of the dataflows and all their movements within the BI and analytics environment.

Dynamic Insurance

One of the problems that the health insurance industry is facing, which most people outside of it fail to see, is the dynamic pace at which the industry is currently moving. It is not just about long-term insurance plans or car insurance. As people tend to engage in last minute and other forms of impulse travel, together with a growing demand for short-term nonstandard insurance such as for adrenaline sports and other activities, insurance companies have to be able to give their customers real-time access to their services.

If you take into consideration the amount of already-existing data that is needed to meet industry demands for fast analytics and calculation capabilities, this is almost impossible to achieve manually. Tools like MANTA come in as a must to allow companies to keep up with such a fast pace.

IoT & Insurance

With IoT slowly creeping into every industry, the insurance industry is no exception. Networks of connected sensors among household appliances and devices would be able to regulate the amount of electricity that is being used in the home, autonomously control a building or home’s energy consumption, and optimize energy intake. On a bigger scale, this can be used not only for utility companies but also to calculate and adjust house and property insurance needs.

Safe Zone

Insurance companies are all about keeping you safe and giving you assurance. What MANTA does for insurance companies is assures them that their data is being used to the fullest, and it effectively protects them in case of compliance needs or data breaches.

Do you have some questions about how to use MANTA in another industry? Book a call with one of our pre-sales experts using our bot or write us directly at manta@getmanta.com. 

You can look forward to meeting MANTA as a sponsor at Informatica World 2019 in Las Vegas! To warm you up a bit and give you some topics to discuss with us, we have prepared a special blog on how MANTA integrates with Informatica. Read more below!

How does MANTA work with Informatica?

MANTA together with Informatica purely automates the data-lineage gathering process. Informatica Enterprise Data Catalog, with strong machine learning abilities, provides automatic mechanisms for data discovery across multiple internal company systems. What MANTA can then do is complete Enterprise Data Catalog by handling some really tough programming code. How exactly does that work?

After introducing “MANTA 4 Finance” and “MANTA 4 Healthcare” in our new “MANTA 4 Industries” series, we are now moving on to another industry that MANTA is very familiar with. Read about the issues MANTA helps telecommunications customers solve in the article below.

Data is Gold

For most companies in telecommunications (before and after GDPR), data is a huge part of their business. But usually not such personal data as with healthcare patients, rather data about past and current subscriptions, tariffs, and packages that clients have been enjoying as well as phone numbers—both as a product and as a way to engage with customers for advertising purposes.

Apparently, business mergers and acquisitions, as well as database acquisitions, happen quite often in this industry. As a result, several of MANTA’s customers from the telco industry have sought MANTA’s help after running into problems caused by having one too many databases on hand.

Prior to such mergers, each company has its own DWH filled with unique and valuable data. Therefore, as part of their growth and data migration plans, it is necessary to consolidate. Other data governance solutions often cannot handle such large databases or automatically read them in a reasonable amount of time. That is why customers implement MANTA to map the part of their environment that is invisible to their data governance solution as well as to make the entire process of gathering data lineage faster and more effective.

Thanks to MANTA, the entire process of data migration and consolidation happens much faster than it normally would and with complete control over all the data being transferred, which prevents any errors or problems from occurring. And as a bonus, the next time the customer makes an acquisition, it will be much easier for it to merge the other company’s data into its system.

Data Anonymization

However, the data the companies store may still be subject to regulations, at least to GDPR. That is why many companies need to proceed with data anonymization. The subject of data anonymization, personally identifiable information (PII), consists of data elements that alone or in combination can directly or indirectly lead to the identification of a specific individual.

Companies must identify the various locations where sensitive or noncompliant data is being stored as well as discover the relationships between this data. Not all records are equally sensitive; not all need to be anonymized. Sometimes, only parts of the data need to be re-written. (For example, a name and a country code in the same table will most likely not lead to the identification of a customer, but adding a city name could end up leading to quite precise identification.)

Using MANTA, you can construct and analyze metadata models that will identify PII in any component of your BI and analytics solution.

Do you have some ideas about how you could use MANTA in your industry but want to discuss your case with one of our pre-sales experts? Don’t hesitate to book a call using our bot or write to us directly at manta@getmanta.com.