Tag Archive for: AI

Using Dynamic Data Labelling to Drive Business Value

Dynamic Data Labelling with Ixivault

Before deriving any value from data, you need to find and retrieve relevant data. Search allows you to achieve that goal. However, for ‘search’ to work, we need two things: A search term needs to be defined by humans; data must be indexed for the computer to find it with cost and speed efficiency and to keep the user engaged. But search efficiency breaks under the sheer scale of all-available data and the presence of dark data (with no indexes or labels attached), when considering either finance or response time points of view.

Technologies like enterprise search never took off for this exact reason. Without labels, it’s ineffective to ask a system to select results from the data. At the very moment of creating the data the creator knows exactly what a file contains. But as time passes our memories fail, and other people might be tasked with finding and retrieving data long after we’ve moved on. Searching data in enterprise applications often means painstakingly looking up each subject or object recorded. For end-user applications like MS Office, we lack even that possibility. Without good labels search and retrieval options are near impossible.  And, while the people who create data know exactly what’s in it, the people who come after, and the programs we create to manage that data, cannot perform the same mental hat trick of pulling meaning from unsorted data.

At SynerScope we offer a solution easily recover data that was either lost over time or vaguely defined from the start.  We first lift such ‘unknown’ data into an automated, AI-based, sorting machine. Once sorted, we involve a human data specialist, who can then work with sub-groups of data rather than individual files. Again, unsupervised, our solution presents the user with the discerning words that represent each sub-group in relation to each other. In essence, the AI presents the prime label options for files and content in each subgroup, no matter what the size in number of files, pages, or paragraphs. The human reviewer only has to select and verify a label option, rather than taking on the heavy lifting task of generating labels.

Thus labeled, the data is ready for established processes for enterprise data. Cataloging, access management, analysis, AI, machine learning, and remediation are common end goals for data after Synerscope Ixivault generates metadata and labels.

SynerScope also allows for ongoing, dynamic relabeling of data as new needs appear. That’s important in this age of fast digital growth, with a constant barrage of new questions and digital needs. Ixivault’s analysis and information extraction capabilities can evolve and adapt to future requirements with ease, speed, and accuracy.

How Does Unlabeled Data Come about?

Data is constantly created and collected. When employees capture or create data, they are adding to files and logs. Humans are also very good at mentally categorizing data – we can navigate with ease through most recent data, unsorted and all. Whether that means navigating a stack of papers or nested folders – our associative brain can remember the general idea of what is in each pile of data – so long as that data doesn’t move. But we’re very limited by the scale we can handle. We have mental pictures of scholars and professors working in rooms where data is piled to the ceiling everywhere, but where little cleaning was ever allowed. This paradigm doesn’t hold for digital data in enterprises. Collaboration, analysis, AI needs and regulations always put too much pressure on knowing where data is.

Catalogs and classification solutions can help, but automation levels for filling process are too low. That leads to gaps and arrears in labeling data. The AI for fully automatic labeling isn’t there yet. Cataloging and classifying business documentation is even harder than classifying digital images and video footage.

Digital Twinning and Delivering Value with Data

Before broadband, there was no such thing as a digital twin for people, man-made objects, or natural objects. Only necessary information was stored in application-based data silos. By 2007, the arrival of the iPhone and its revolution in mobile and mobile devices changed that. Everyone and everything were online, all the time, and constantly generating data. The digital twin, a collection of data representing a real person or a natural or man-made object was born.

In most organizations, these digital twins remain mostly in the dark. Most organizations collect vast quantities of data on clients, customer cases, accounts, and projects. It stays in the dark because it’s compiled, stored, and used in silos. When the people who created the data retire or move to another company, its meaning and content fade quickly – because no one else knows what’s there or why. And, without proper labels your systems will have a hard time handling any of it.

GDPR, HIPPA, CCPA etc. forces organizations to understand what data they have regarding real people, and they demand the same for any historic data stored from the days before those regulations existed.

Regulations evolve, technologies evolve, markets evolve, and your business evolves, all driving highly dynamic changes to what you need to know from your data. If you want to keep up, ensuring that you can use that data to drive business value – while avoiding undue risks from business regulations, data privacy and security regulation – you must be able to search your data. Failing this, you could get caught in a chaotic remediation procedure, accompanied by unsorted data that doesn’t reduce the turmoil, but adds to the chaos.

Dynamic Data Labelling with Ixivault

Ixivault helps you to match data to new realities in a flexible, efficient way, with a dynamic, weakly-supervised system for data labeling. The application installs in your own secure Microsoft Azure client-tenant, using the very data stores you set up and control, so all data always remains securely under your governance. Our solution, and its data sorting power, helps your entire workforce – from LOB to IT –  to categorize, classify, and label data by content – essentially lifting it out of the dark.

Your data is then accessible for all your digital processes.  Ixivault shows situations and objects grouped by similarity of documentation and image recordings and allows you to compare groups for differences in the content.  This simplifies and speeds the tasks of assigning labels to the data. Any activity that requires comparison between cases, objects, situations, data, or a check against set standards is made simple.  Ixivault also improves the quality of data selection, which helps in a range of applications ranging from Know Your Customer and Customer Due Diligence to analytics and AI based predictions using historical data.

For example, insurance companies can use that data to find comparable cases, match them to risks and premium rates, and thereby identify outliers – allowing the company to act in pricing, underwriting or binding or all of them.

SynerScope’s type of dynamic labelling creates opportunities to match any data, fast and flexible. As perception and the cultural applications of data change over time, you can also match data with the evolving needs for information extraction, change labels as data contexts change, and to continue driving value from the data you have at your disposal.

If you want to know more about Ixivault or its dynamic matching capabilities in your organization, contact us for personalized information.

Moving to the Azure cloud: unpacking dark data

Moving to the Azure cloud?

Today, more and more businesses are moving to the cloud – to automate and take advantage of AI and scalable storage, and to reduce costs over existing legacy infrastructure. In fact, in 2021, an estimated 19.2% of large organizations made the move to the cloud. And Microsoft Azure is close to leading that shift – with a 60% market adoption.

Often organizations focus on selected applications during a cloud transition. However, existing data might actually present the bigger complexity.  A majority of organizations use less than 50% of the data they own. At the same time, there is no oversight of data that is owned. This unused, unclassified, and unlabeled data is otherwise known as “dark data”, because it remains in the shade until abundant time is allocated to sort, label, and classify it.

Moving to the Azure Cloud is Like Moving House

We believe there is merit to comparing moving to the Azure cloud and moving house. You decide where to move, you choose your new infrastructure, and you get everything ready to move in. Then, you pack up your old belongings and move it with you. The problem is you likely already have plenty of boxes lying around. Think about your attic, your basement, and storage. Things from earlier relocations. You might have lost all knowledge of what’s in there. The same holds true when your organization’s applications and data must move house. But this time you also have to deal with ‘boxes’ of data left unlabeled by people leaving the organization, data left unused for a longer time, and data left behind from already obsolete applications. Moving this and other less well-known data may create bigger issues in the future.

  • Data is accumulating faster than it ever did before. You’ll have more of it tomorrow. Therefore now is the best time to go through data and categorize it
  • Proper governance of data is impossible without knowing its contents first. Older data collected from before GDPR regulations is still there. Compliance and Risk officers and CISOs dread this unknown data and fear it may fall out of compliance regulations.
  • It can be difficult to pass regulatory compliance audits with dark data ar If you can’t open a ‘box’ of data to show auditors what’s inside, you can’t prove you’re compliant.
  • You’re also not allowed to simply delete data. Industries and governments must comply with laws and regulations on archiving and maintaining open data.
  • When you know what data you have you can strategize and move towards controlled decisions on cold/warm/hot storage to optimize both costs and access. Moving data that is still dark may bring about irreversible data loss or at least expensive repairs in the future
  • Locating and accessing data requires the kind of information best-captured in classifications and labels, historical data analysis needs this metadata.
  • The parts of data that make up dark data leaves organizations vulnerable as it makes designing and taking security precautions extra hard.
  • Sometimes you can or must delete information. However, you can only do so if you know its contents beforehand and can determine regulatory compliance and have the foresight for future valuable analytics.

How can you optimize accessing this data? When one of our clients, the Drents Overijsselse Delta Waterschappen, looked at archiving and storing its past project documentation in the cloud, it found the necessary manual labeling a daunting task. The massive time-investment needed is very similar for other organizations making a cloud transition. Manually reviewing data is simply too labor-intensive for most organizations to undertake within a feasible timeframe.

Unpacking Data with Synerscope’s Ixivault

With Synerscope, you can achieve the data clarity you need. As a weakly supervised AI system, our solutions are built to perform where standard AI approaches would fail. Synerscope’s Ixivault implements onto your Azure Tenant – with no backend of its own. This means that all data stays inside your tenant, which is a big plus for all matters and concerns regarding security, governance, and compliance. Our friction-less implementation then allows you to open up, categorize, and label dark data using a combination of machine learning with manual review to speed up the full process by an average of 70%.

Ixivault analyzes your full data pool of structured and unstructured data, creating categories based on data similarities, pulling keywords and distinctive terms, and generating images of those data stacks – which your domain expert can then sit down to quickly label. Most importantly, Ixivault has built-in learning capabilities, meaning that it gets better at categorizing and labeling your specific data as you use it.

All this makes Ixivault the perfect tool to help you move – by unpacking boxes of data as you move them to the cloud. You can then choose appropriate storage, governance and access controls, even if you need or don’t need to keep the data. For the first time you can have a near edge-to-edge overview of all your data with zoom in options to very granular levels so you can make the best choice what to do next with this newly discovered data. Having new information about your data can make you money and save you money all at the same time.

If you need help with unboxing your dark data as you move, contact us for more information about how Synerscope can help. You may also purchase the Ixivault app directly at Microsoft’s Azure Marketplace.

Ixivault Helps Labeling and Categorizing Dark Data in the Azure Cloud

Ixivault, a managed app on Microsoft Azure

Your organization’s dark data presents challenges when you move to the cloud. Yet, leaving it in a current location is also not the solution.

Dark data includes digital data which is stored but never mobilized for analysis or to deliver information. If you have dark data, your organization is already missing opportunities to derive value from it. However, if you don’t take dark data with you to the cloud, it drifts even further from your other data assets. Meanwhile, the flexible computation and memory infrastructure of the cloud offers a very cost-effective solution to mobilizing that data. Most importantly, it does so at any scale your organization needs.

However, there are still challenges here. For example, overcoming the risks of governance and compliance, increased storage costs, and storage tiering choices. Do you choose to store data in close proximity to synchronize with other data – but at a higher storage cost?

Migrating Dark Data to the Azure Cloud

For most organizations, failure to create and execute a dark data plan as part of the cloud transition is undesirable at best and breaching data compliance at worst. Synerscope delivers the tools to analyze and “unlock” that data during the transition, making efficient use of cloud computing, while keeping data in your full control. This means no additional risks arise for compliance, security, etc.

Synerscope also helps you mobilize dark data, using a combination of machine learning, AI, and human expertise. Unlocking dark data is essential for most organizations. That remains true whether you’re shifting from legacy systems to Azure, are reducing your governance footprint, or are pressed into unlocking data for compliance or a regulatory audit. Synerscope’s Ixivault comes into play at any point where you need detailed and broad overviews of complex data. This is achieved through sorting, categorizing, and revealing patterns and giving domain experts the tools to label categories at speed, with high accuracy.

Your Data, Your Azure Tenant

Ixivault is a managed app on Microsoft Azure. When you deploy the tool, it installs on top of your Azure Blob or ADLS where the data stays in your control. We power Ixivault on Azure computing, meaning that it dynamically scales up computing power to meet the size and complexity of the data you direct to it for scanning and computation. At no point does the data leave your Azure tenant or any assigned secured storage used before separating sensitive data out. SynerScope’s design suits the most stringent demands for compliance and governance. Our Ixivault feels and operates like a SaaS but does so in your tenant, without any proprietary back-end for storing your data assets. Therefore, Synerscope allows you to categorize, sort, and label your dark data without introducing additional regulatory complexities. Your data stays in your cloud, the process is fully transparent, and you control and monitor your tenant for all matters related to data sovereignty.

That applies whether you’re importing data to Azure for the first time to inspect before deciding where to store it or already have data in a Blob or ADLS and must inspect it or want to open data on legacy infrastructure.

Sorting and Categorizing Dark Data

Ixivault leverages AI and machine learning for sorting and text extraction. Here, visual displays offer domain experts rich and discerning context from which to choose the most suitable labels of descriptive metadata. Our technology is a weak supervised system, first unsupervised computing handles the data in bulk, followed by a human operator to validate labels and bulk sorted data categories. The system works on raw data inputs directly, without training. Using raw data sets with human validation to add labels means we can make the system smarter over time. Future raw data sets are automatically checked for similarities with previously processed data sets. So, high value can be achieved from day one, but the system learns over time. .

Ixivault abstracts data to hypervectors – comparing the similarity between data algorithmically. Using algorithms, the AI can accurately sort data into “Stacks” of similar files. Format, lay-out and content of documents are all used by the algorithms to separate common business documents e.g., contracts, letters, offers, invoices, emails, brochures, claims, and different tables. And our algorithms separate sub-groups according to actual content within each of these. Our language extraction presents distinctive groups of words from each “Stack”, allowing humans to select the most appropriate labels. The same extracted words can also be matched to business glossaries and data catalogs already available to your organization. Hypervectors allow our algorithms to detect similarities across documents ‘holistically’, at a scale beyond unaided human capacity. The resulting merge of rich ontologies and semantic knowledge are re-usable throughout the organization and the many applications it runs.

Machine Learning with Human Context

Ixivault creates outputs that allow your data experts to step in at maximum velocity and scale. The application displays a dashboard showing the stack of data, visual imaging of what’s in this stack, and keywords or tags pulled from that data and metadata. Where descriptive metadata is lacking or absent, our system presents new candidates for labels. The system supports users in running fast and powerful data discovery cycles, which link search, sorting, natural language programming, and labeling. The output is knowledge about your organization’s dark data which can be used and reused by other users and software systems.

This approach allows data experts to look at files and keywords and very quickly add tags. More importantly, it creates room for human expertise, to recognize when data is outside of the norm – e.g., files are related to a special circumstance, which machines simply cannot reliably do. The result is a powerful, fast and flexible system, usable with a variety of data.

Once you select the machine proposed labels, you only have to individually inspect a small number of the actual files to confirm the labeling for an entire group of sorted files.

Unlocking Dark Data as You Move to the Cloud

Moving to Azure forces most organizations to do something with, or certainly think about, their dark data. You can’t move untold amounts of data to the cloud without knowing what’s in it. You would not be able to extract enough additional value from such a blind move. Directing data to the right storage solutions for easy governance, compliance, and management demands knowledge of its content. E.g., so you can prioritize data for further processing and computation, or save on storage for less value-added content. Data intelligence can mostly be paid for by decreasing ‘dark storage’. Meanwhile, your organization can improve its governance footprint and ensure compliance.

Synerscope can deliver the potential value in dark data by increasing knowledge, helping with retention, access management, discovery, data cleansing efforts, data privacy protection measures, and compliance. Most importantly, dark data mining gives organizations the information needed to make business as well as IT and compliance decisions with that data – because Data intersects between the three.

To learn more about Synerscope’s software and our approach, contact us to schedule a demo and see the software in action.

Real-time Insight in All Data, Present and Past

The promise of data-driven working is great: risk-based inspection, finding new revenue models, reducing costs and delivering better products and services. Every company wants this, but it often fails.
The most important business data cannot be fully unlocked by traditional analysis tools. Why does it go wrong and how do you manage to convert all data into insight?

The more you know, the more efficient and better you can make your products and services. That is why data-driven working is high on the agenda of many organizations. But an Artificial Intelligence or BI tools deliver, only partially or not at all, on the promise of data-driven work. That’s because they can only analyze part of the entire mountain of data. And what is an analysis worth if you can only examine half or a quarter of the data you have.

Insights are hidden in unstructured data

Many organizations started measuring processes in ERP and CRM systems over the past 20 years. They store financial data, machine data and all kinds of sensor data. These measurement data are easy to analyze, but do not tell where things go wrong during the entire operation.
This so-called structured data provides only partial insight, while you look for answers in the analysis of all data. It is estimated that 80% to 90% of all data from organizations is unstructured: we are talking about uncategorized data that stored in systems, notes, e-mails, handwritten notes on work-drawings and all kinds of documents across the organization. This valuable resource remains unexplored.

The unexplored gold mine of unstructured data

Organizations have terabytes of it: project information, notes, invoices, tenders, photos and films that together can yield an enormous amount of insights. This fast-growing gold mine is more of a data maze. Over the years, digitization took place step-by-step, process-by-process and department-by-department. During this digitization in slow motion, no one thought that it was useful to coordinate all information in such a way that you can easily analyze it later.

Artificial Intelligence and BI tools get lost

Departments of factories, offices or government agencies created their own data world through this so called ‘island automation’. Separate silos of application data, process data such as spreadsheets, presentations, invoices, tenders, and texts in all kinds of file formats. Moreover, departments and people all categorize information differently, and not structured like a computer would. Not everyone administers equally neatly, or categories are missing, so that colleagues simply write a lot of data away in the “other” field. The problem is that BI and AI tools cannot properly look into this essential and unstructured information. They lack signage, so they get lost in the maze of unstructured data.

Turning archives into accessible knowledge (and skills)

For many companies the future lies in the past. Because most organizations have boxes full of archived material from the pre-digital era, they are now digitizing at a rapid pace. Decades of acquired knowledge and experience are stored but hidden in these archives. Because, like many digital files, these are not well structured. Who categorized their project notes or files neatly into different categories, if they were available at all? If you want to use this unstructured data now, it will take you hundreds of hours of manual work to analyze. SynerScope’s technology searches terabytes or petabytes of data within 72 hours and provides immediate answers from all data.

Unstructured data harbor new revenue models

How that works? A non-life insurer did not know exactly where 25% of their insurance payments went. That is why SynerScope automatically examined the raw texts of millions of damage claims of the last 20 years. The word broken screen came up immediately for claims above 100 euros. The graph showed that screen breakage was rare until 2010, but then grew explosively. What happened? The insurer had never made the category of smartphones or tablets. As a result, they missed a major cost item, or to put it positively: for years they overlooked a new revenue model.


Turn data into progress

Thanks to the power of cloud computing in Azure, SynerScope is able to analyze large amounts of data in real time. And it doesn’t matter what kind of data it is. Spreadsheets, meeting minutes, drone images, filing cabinets full of invoices, you name it! Do you have hundreds of terabytes or even petabytes of satellite or drone data? Then it will be in the model tomorrow! Thanks to the analysis of the present and the past, organizations with SynerScope’s software live up to the promise of data-driven working. Leading companies such as Achmea, ExxonMobil, Stedin, VIVAT and De Volksbank are converting their data into progress with the solution from SynerScope.
Do you also want insight into your present & past to get a grip on the future?
Then request a demo!

The Art of Adding Common Sense To Data And knowing When To Dismiss It…

SynerScope

At Synerscope, helping people and notably domain experts, making sense of data is our core business. We bring both structured and unstructured data to use and we focus on combining the most advanced and innovative technologies with the knowledge and brainpower of the people in our customers’ business. Our specially developed visual interface allows domain experts to be directly plugged into the AI analysis process, so they can truly operate side-by-side with data scientists to drive results.

Thinking of the first needs in business continuity following on from the corona outbreak, we recently developed a GDPR compliant Covid-19 radar platform (https://www.synerscope.com/). Our Covid-19 radar supports the tracking and tracing of your personnel and provides HR specialists with ways for rapid and targeted intervention in the event an outbreak should hit your company after having come out of lockdown. Returning to work safely requires your HR departments to be equipped with more information to act upon. We can deliver these insights through our novel ways of handling all the data at hand.

Data, Artificial Intelligence & Common Sense

Due to Covid-19, data and the insights it provides have become an absolute must, as organizations base their decisions on data & analytics strategies in order to survive the outfall of the pandemic.

Making sense of the enormous influx of corona related data that is coming our way, we need Artificial Intelligence and Machine Learning to help master this. However, human common sense & creativity are equally needed in order to teach AI its tricks, as data becomes only meaningful when there is context.

It will be extra impactful as the call to rescue the economy and open up companies sooner rather than later is getting stronger. We have to take into account that we need to track, trace and isolate any cluster of direct contacts around new corona cases as quickly as possible, as not to hinder any progress made while also adhering to the prevalent GDPR rulings in Europe.

 AI Wizardry: it all stems from common sense

For any automated AI to deliver its best value, the models need training and the data input needs to be well selected. Training AI and selecting data are both dependent on human sense making, thus are best applied early on in the process. The quality and speed of human information discovery and knowledge generation depends on both the expertise and the richness of context in the data. Luckily that same AI can help experts to digest a much wider context faster than ever before. When you couple large diversity of expertise with a wider context of data the outcome of the analytical process clearly wins.

When and how do you best involve those people that bring context and common sense to the table? They will add the most value when locating useful data resources and validating the outcomes of algorithmic operations. Leveraging AI should therefore be an ongoing process: with the help of automated unsupervised AI domain experts a data scientist can identify data sources, amass and sort the data, then have data scientists add some more AI and ML wizardry. The outcomes will be presented to the people with the best common sense and with plenty of domain experience. Based on their judgment the algorithms can be optimized and repeated. Following this approach, companies are able to accelerate their progress in AI and learn and improve faster. Equipped with the right tooling and a small helping hand from data scientists, the domain experts will for sure find their way in and around the data themselves: we believe these so called citizen data scientists have a big future role to play in gaining more insight from data!

Reasoning by association

People with business sense help to continuously improve AI by injecting their common sense in the AI system. And what’s more: they add a key substance that typical AI is still missing, which is the capability of causal reasoning and reasoning by association. Enhancing AI with associative and creative thinking is groundbreaking. That’s exactly where our SynerScope technology sets itself apart.

We shouldn’t view AI as a black box, it is too important for that. In our vision, humans and technology are complementary: humans should have the overall lead and control but during the analytics process we should recognize for which steps we give the controls to our computer and let technology guide us through the data. Think of the self-driving car for a minute; while its ABS is fully automatic we still keep a finger at the steering wheel.

Unknown unknowns

As always, there is another side to the value of human contribution in AI. People tend to stick to what they already know, to relate to the context they are familiar with, i.e. to keep a steady course. But we want to ‘think out of the box’ and expect AI to help us with that.
Genuine paradigm shifting AI should help us master the ‘unknown unknowns’. Present us hidden insights and business opportunities that traditional ways of thinking will never unearth, like the cure for a disease that nobody ever thought of, or get the best scenario out of a number of variables too large to be handled by the human brain alone.

To select from the patterns of data that AI helps reveal, again you will need people, but a different kind: people that are fresh in the field, without being primed by too much knowledge, and are not encapsulated in corporate frameworks. And with the right technology solutions to help them find their way in the data, they are able to thrive and your company along with them.

Enabling humans to master all data

Synerscope creates and uses technology to augment intelligence on its way to a data-driven universe. We have developed groundbreaking inventions, some of which are patented. Our products & solutions are in use with some of the world’s Fortune 1000 companies. We are happy to partner with the leading Cloud providers of this world. SynerScope’s solutions provide Intelligence Augmentation (IA) to domain expert users that will make them 20-50x faster at extracting information from raw data. We focus on situations and use cases where insight goals and data inputs are by default not well predefined. And in situations where reaching full-context knowledge requires linking data from text, numbers, maps, networked interactions, transactions, sensors and digital images, in short we combine structured data with unstructured data and everything in between.

If you would like to listen & learn more about our view of the data & analytics market and our solutions, please click here to watch a short video (video SynerScope The Movie: a futuristic vision) or you can contact us by email: info@synerscope.com or call us on +31-88-ALLDATA.

SynerScope addresses your “white space” of unknown big data in your data-lake

The Netherlands, April 4, 2017 – As every organization is fast becoming a digital organization, powerful platforms that extend the use of data are imperative to use in the enterprize world.

By implementing SynerScope on top of your Hadoop, you are able to solve the whitespace of unknown data, due to the tight integration between the scalability of Hadoop with the best of SynerScope’s Artificial Intelligence (AI) including deep learning.  The result is a reduced Total Cost of Ownership, working with Big Data and it also creates, extremely fast, great value out of your data-lake.

As data science developments happen in your data lake, you currently encounter data latency problems. Hortonworks covers the lifecycle of data: data moving, data at rest and data analytics as a core infrastructure play.

Ixiwa, SynerScope’s backend product, will support and orchestrate data access layers and it will also make your whole data-lake span multiple services.

Hadoop is a platform for distributed storage and processing.  Place SynerScope on top of Hadoop and you gain advantage from deep learning intelligence, through SynerScope’ s Iximeer. It will bootstrap AI projects by providing out of the box interaction between domain expert, analysts and the data itself.

“AI needs good people and good data to get somewhere, so we basically help AI to make the best decision in parallel with first insight, then basic rules, then tuning”, says CTO Jorik Blaas.

We are proud to announce that as of today Hortonworks has awarded SynerScope all 4 (Operations, Governance, YARN and Security) certifications for our platform, which is a first in the history of their technology partners. 

For more info about the awarded badges go to https://hortonworks.com/partner/synerscope/

If you are interested and want to know more about us, there is the opportunity to visit us at the DataWorks Summit in Munich, April 5-6. We like to welcome you at our booth 1001 as well as at the IBM booth 704 and we will be presenting at the breakout session: “A Complete Solution for Cognitive Business” 12:20pm, room 5.

About SynerScope:

Synerscope enable users to analyze large amounts of structured and unstructured data. Iximeer is a visual analysis platform that represents big insights arising from analyzing AI data into a uniform contextual environment that links together various data sources: numbers, text, sensors, media/video and networks. Users can identify hidden patterns across data without specialized skills.

It supports collaborative data discovery, thereby reducing the efforts required for cleaning and modelling data. Ixiwa ingests data, generates metadata from both structured and unstructured files, and loads data into an in-memory database for fast interactive analysis. The solutions are delivered as an appliance or in the cloud. SynerScope can work with a range of databases, including SAP HANA as well as a number of NoSQL and Hadoop sources.

SynerScope operates in the following sectors: Banking, Insurance, Critical Infrastructure, and Cyber Security. Learn more at Synerscope.com.

SynerScope has strategic partnerships with Hortonworks, IBM, NVIDIA, SAP, Dell.