Tag Archive for: Dark Data

Artificial Intelligence is Only as Good as Data Labeling

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Data Labeling with SynerScope

Recent events in my home country inspired me to write this blog. Every day we hear stories about businesses and government organizations struggling to sufficiently understand individual files or cases. Knowledge gaps and lack of access to good information hurts individual-and-organizational well-being. Sometimes, the prosperity of society itself is affected. For example, with large-scale financial crime and remediation cases in banking, insurance, government, and pandemics.

We simply have little understanding of the data which means AI and analytics are set up to fail. In addition, it’s difficult to see what data we can or may collect to run human-computer processes of extracting relevant information to solve those issues.

Unlimited Data with No Application

The COVID19 pandemic shows not only how difficult it is to generate the right data, but also how difficult it is to use existing data. Therefore, data-driven decision-making often shows gaps in understanding data.

Banks spend billions on technology and people in KYC, AML, and customer remediation processes. Yet, they’re still not fully meeting desired regulatory goals.

Governments also show signs of having difficulties with data. For example, recent scandals in the Dutch tax office, such as the Toeslagenaffaire, show how difficult it is to handle tens of thousands of cases in need of remediation. And the Dutch Ministry of Economic Affairs is struggling to determine individual compensation in Groningen, where earthquakes caused by gas extraction have damaged homes.

Today, the world is digitized to an unbelievable extent. So, society, from citizens to the press to politicians and the legal system, overestimate the capabilities of organizations to get the right information from the data which is so plenty available.

After all, those organizations, their data scientists, IT teams, cloud vendors, and scholars have promised a world of well-being and benevolence based on data and AI. Yet, their failure to deliver on those promises is certainly not a sign that conspiracy theories are true. Rather, it shows the limits of AI in a world where organizations understand less than half of the data they have when it is not in a machine processing ready state. After all, if you don’t know what you have, you can’t tell what data you’re missing.

Half of All Data is Dark Data

Gartner coined the term “Dark Data” to refer to that half of all data that we know nothing about. And, if Dark Matter influences so much in our universe, could Dark Data not have a similar impact on our ability to extract information and knowledge from the data?

We have come to believe in the dream of AI too much, because what if dark data behaves as dark matter? By overestimating what is possible with data-driven decision making, people may believe that the powers that be are manipulating this data.

SynerScope’s driving concept is based on our technology to assess Dark Data within organizations. By better understanding our dark data, we can better understand our world, get better results from human and computer intelligence (AI) combined.

Algorithms Rely on Labeled Datasets

Today’s AI, DL (Deep Learning, and ML (Machine learning) need data to learn – and lots of it. Data bias is a real problem for that process. The better training data is, the better the model performs. So, the quality and quantity of training data has as much impact on the success of an AI project as the algorithms themselves.

Unfortunately, unstructured data and even some well-structured data, is not labeled in a way that makes it suitable as a training set for models. For example, sentiment analysis requires slang and sarcasm labels. Chatbots require entity extraction and careful syntactic analysis, not just raw language. An AI designed for autonomous driving requires street images labeled with pedestrians, cyclists, street signs, etc.

Great models require solid data as a strong foundation. But how do we label the data that could help us improve that foundation. For chatbots, for self-driving vehicles, and for the mechanisms behind customer remediation, fraud prevention, government support programs, pandemics, and accounting under IFRS?

Regulation and pandemics appear in the same sentence because, from a data perspective, they’re similar. They both represent a sudden or undetected arrival that requires us to extract new information from existing data. Extracting that new information is only manageable for AI if training data has been labeled with that goal in mind.

Let me explain with an easy example of self-driving vehicles. Today, training data is labelled for pedestrians, bicycles, cars, trucks, road signs, prams, etc. What if, tomorrow, we decide that the AI also must adapt to the higher speed of electric bikes? You will need a massive operation of collecting new data and re-training of that data, as the current models would be unlikely to perform well for this new demand.

Companies using software systems with pre-existing meta data models or business glossaries have the same boundaries. They work by selecting and applying labels without deriving any label from the content – otherwise they must label by hand, which is labor and time intensive – and often too much so to allow for doing this under the pressure of large-scale scandals and crises.

Automatic Data Labeling and SynerScope

The need to adapt data for sudden crises does not allow for manual labeling. Instead, automatic labeling is a better choice. But, as we know from failures by organizations and by government, AI alone is not accurate enough to take individual content into account.

For SynerScope, content itself should always drive descriptive labeling. Labeling methodology should always evolve with the content. That’s why we use a combination of algorithm automation and human supervision, to bring the best of both worlds together – for fast and efficient data labeling.

If you want to learn more about how our labelling works, feel free to contact us at info@synerscope.com

Using Dynamic Data Labelling to Drive Business Value

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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

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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.

Is Your Organization Prepared to Manage Dark Data?

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The Business Value of Mining Dark Data in Azure Infrastructure

As organizations accelerate the pace of digital transformations, most are moving to the cloud. In 2019, 91% of organizations had at least one cloud service. But, 98% of organizations still maintain on-premises servers, often on legacy infrastructure and systems. At the same time, moving to the cloud is a given for organizations wanting to take advantage of new tools, dashboards, and data management. The global pandemic has created a prime opportunity for many to make that shift. That also means shifting data from old infrastructure to new. For most, it means analyzing, processing, and dealing with massive quantities of “Dark data”.

Most importantly, that dark data is considerable. In 2019, Satya Nadella discussed Microsoft’s shift towards a new, future-friendly Microsoft Azure. In it, he explained that 90% of all data had been created in the last 2 years.  Yet, more than 73% of total data had not yet been analyzed. This includes data collected from customers as well as that generated by knowledge workers with EUC (End-user computing, such as MSFT Office, email, and a host of other applications. As a result, the process of big data creation has only accelerated and (unfortunately) more dark data exists now than ever before.

As organizations make the shift to the cloud, move away from legacy infrastructure and towards microservices with Azure, now is the time to unpack dark data.

Satya Nadella discusses Microsoft’s shift towards a new, future-friendly Microsoft Azure

Dealing with (Dark) Data

The specter of dark data has haunted large organizations for more than a decade. The simple fact of having websites, self-service, online tooling, and digital logs means data accumulates. Whether that’s automatically collected from analytics and programs, stored by employees who then leave the company, or part of valuable business assets that are tucked away as they are replaced – dark data exists. Most companies have no real way of knowing what they have, whether it’s valuable, or even whether they’re legally allowed to delete it. Retaining dark data is primarily about compliance. Yet, storing data for compliance-only purposes means incurring expenses and risks without deriving any real value. And simply shifting dark data to cloud storage means incurring huge costs for the future organization – when dark data will have grown to even more unmanageable proportions.

Driving Value with Dark Data

Dark data is expensive, difficult to store, and difficult to migrate as you move from on-premises to cloud-hosted infrastructure. But it doesn’t have to be that way. If you know what data you have, you can set it into scope, delete data you no longer need, and properly manage what you do need. While you’ll never use dark data on a daily, weekly, or even monthly basis – it can drive considerable value, while preventing regulatory issues that might arise if you fail to unlock that data.

  • Large-scale asset replacement can result in requiring decades-old data stored on legacy systems.
  • GDPR and other regulations may require showing total data assets, which means unlocking dark data to pass compliance requirements
  • Performing proper trend analysis means utilizing the full extent of past data alongside present data and future predictions.

Dark Data is a Business Problem

As your organization shifts to the cloud, it can be tempting to leave the “problem” of dark data to IT staff. Here, the choice will often be to discard or shift it to storage without analysis. But dark data is not an IT problem (although IT should have a stake in determining storage and risk management). Instead, dark data represents real business opportunities, risks, and regulatory compliance. It influences trend and performance analysis, it influences business operations, and it can represent significant value.

For example, when Stedin, a Dutch utility company serving more than 2 million homes, was obligated to install 2 million smart meters within 36 months, they turned to dark data. Their existing system, which utilized current asset records in an ERP was only enabling 85% accuracy on “first time right” quotes for engineer visits. The result was millions in avoidable resource costs and significant customer dissatisfaction. With Synerscope’s help, Stedin was able to analyze historical data from 11 different sources – creating a complete picture of resources and creating a situational dashboard covering more than 70% of target clients. The result was an increase to a 99.8% first time right quote – saving millions and helping Stedin to complete the project within deadline.

Synerscope delivers the tools and expertise to assess, archive, and tag archived data – transforming dark data from across siloed back-ends and applications into manageable and useable assets in the Azure cloud. This, in turn, gives business managers the tools to decide which data is relevant and valuable, which can be discarded, and which must be retained for compliance purposes.

If you’d like to know more, feel free to contact us to start a discussion around your dark data.

 

How to manage End User Computing and avoid GDPR or IFRS fines

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Author: Jan-Kees Buenen

I’ve long said that End User Computing (EUC) is here to stay, whether we like it or not.

EUC applications such as spreadsheets and database tools can provide a significant benefit to companies by allowing humans to directly manage and manipulate data. Unlike rigid systems like ERP, EUC offers flexibility to businesses and users to quickly deploy initiatives in response to market and economic needs.

However, EUC has become the villain in the big data story. EUC flexibility and speed often lacks lineage, logs and audit capabilities.

The risks of the incomplete governance and compliance mechanisms of EUC are not new. Organizations are pretty aware of the accidents they cause: financial errors, data breaches, audit findings. In the context of increasing data regulation (like GDPR and IFRS) companies struggle to embed EUC in a safe way in their information chains.

GDPR and the impact of EUC

GDPR (General Data Protection Regulation) was enforced on May 25, 2018. It is a legal framework that requires businesses to protect the personal data and privacy of European Union citizens.

Article 32 of the GDPR addresses the security of the processing of personal data. These requirements for data apply to EUC as well.

Article 17 provides the right to be “forgotten” for any individual. Companies have to precisely control data so there is no leftover data lying in unmonitored applications if the user decides to be deleted from all the systems.

The recent financial penalty of 53 Million euro against Google is a concrete example of what may happen to other companies. In accordance with GDPR, Google was fined for lack of transparency, inadequate information and lack of valid consent regarding the ads personalization.

The challenge of EUC applications: they generate data that largely remain in silos, also known as dark data.

IFRS and the impact of EUC

IFRS (International Financial Reporting Standards) aims at bringing transparency, accountability and efficiency to financial markets around the world.

The new compliance requirements, like the new IFRS9 and IFR17, include data at much more defined levels than ever before. Data that currently flows to and from EUC has to be traced, linked and precisely controlled by knowing its content.

Having a higher emphasis on the control environment, workflow and ability to adjust at a very detailed level is key as disclosure and reporting requirements increase.

Using SynerScope to manage the data linked to End User Computing

Organizations have to recognize that EUC falls under the purview of data governance. Any organization that deals with data – basically every organization – has to manage and control such apps so they are able to act immediately to ensure compliance.

SynerScope solutions offer 2 key ways to reclaim management and control over data:

1. Single Pane of Glass

The first solution to reclaim control is to gather the company’s entire data footprint together. Both structured and unstructured data in one unique space: a single pane of glass.

SynerScope offers an advanced analytical approach to include and converge unstructured and semi-structured data sources. All applications from different back-ends are gathered in a unique space. A single, powerful platform for operational analytics that replaces disjointed and disparate data processing silos.

2. Data protection within EUC

The second approach to reclaim control over EUCs is to track and trace all applications, their data and the respective users.

Synerscope combines a top-down overview with all the underlying data records, making it easy to investigate why a certain business metric is off, and where the changes came from. It fluently analyzes textual documents and contracts to help spot the differences between tons of thousands of documents in the blink of an eye.

Furthermore, an extra layer on the top of all data to control outcomes and keep data to check for governance and compliance.

Two powerful tools to get control and insight into End User Computing Data

SynerScope Ixiwa provides a more effective approach to data catalogue and data lake management for business users. Ixiwa is a data lake (Hadoop and Spark-based) management product that ingests data automatically, collects metadata about the ingested data (automatically) and classifies that data for the company. While Ixiwa will often be deployed as a stand-alone solution, it can also be viewed as complementary to third party data cataloguing tools, which tend to focus on structured data only and/or have only limited unstructured capability.

SynerScope Iximeer complements Ixiwa. It is a visual analysis tool that has the ability to apply on-demand analytics against large volumes of data, for real-time decision-making.

Figure 1: SynerScope Ixiwa and Iximeer provide a more efficient and visual approach to data management and analytics

What to do next?

If your organization is concerned about the new IFRS or GDPR regulations and you are searching for solutions to ensure compliance, please contact us to learn more.

 

IBM’s Power8 & Synerscope join forces

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Discover smart insights of all data-types with next generation data analysis to benefit your business, fast!

With the enormous computing power of the Power8 and the next generation analysis of SynerScope combined, we are able to process structured, unstructured and Dark Data fast.

  • Does your business have high transactional volumes
  • Do you work with Sensor signals (IoT)
  • Do you work with digital photo and video
  • Do you want to know large scale network behavior
  • Do you have two or more factors at play in your business?

Watch what we can do

SynerScope illuminates Dark Data

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Author: Stef van den Elzen

Nearly every company is collecting and storing large amounts of data. One of the main reasons for this is because data storage has become very cheap. However, storage may be cheap, the data also needs to be protected and managed which is often not done very well. Obviously, not protecting the data puts your company at a risk. More surprisingly, not managing the data brings an even higher risk. If the data is not carefully indexed and stored, it becomes invisible, underutilized, and eventually is lost in the dark. As a consequence the data cannot be used to the companies advantage to improve the business value. This is what is called dark data, “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.” — Gartner.

The potential of dark data is unimagined; performing active exploration and analytics enables companies to implement data-driven decision-making, strategy development, and unlock hidden business value. However, there are two main challenges companies are facing: discovery and analysis.

Discovery

Not only is the dark data invisible, it is often stored in separate data silos; all isolated and separated per process, department, or application, and all are treated the same, despite the widespread variation in value. There is no overview of all data sources or how they are linked and related to each other. Also, because all silos are detached and data is stored for business purposes it lacks structure or metadata that hinders the determination of its original purpose. As a consequence there exists no navigation mechanism to effectively search, explore, and select this wealth of data for further analysis.

Analysis

A large portion, roughly 80-90%, of this dark data is unstructured. So in contrast to numbers it consists of text, images, video, etc. Companies lack the infrastructure and tools to analyze this unstructured data. Business users are not able to directly ask questions to the data but need the help of data scientists. Furthermore, it is important not only to analyze one data source in isolation, as currently occurs with specialized applications, but to link multiple heterogeneous data sources (reports, sensor, geospatial, time-series, images, and numbers) in one unified framework for a better context understanding and multiple perspectives on the data.

Enlightenment

The SynerScope solution helps companies overcome the challenges of discovery and analysis and simultaneously helps customers with infrastructure and architecture.

SynerScope serves as a data lake and provides a world map of the diverse and scattered data landscape. It shows all data sources, the linkage between them, similarity, data quality, and key statistics. Furthermore, it provides navigation mechanisms and full text search for effortless discovery of potential valuable data. In addition, this platform enables collaboration, data provenance, and makes it easy to augment data. Once interesting data is discovered and quality is assessed it is selected for analysis.

With SynerScope all types of data types such as numbers, text, images, network, geospatial and sensor-data can be analyzed all in one unified framework. Questions to the data can be answered instantly while they are formed using intuitive query and navigation interaction mechanisms. Our solution bridges the gap between data scientist and business users and engages a new class of business users to illuminate the dark data silos for a truly data-driven organization. At SynerScope we believe in data as a means, not an end.

Example SynerScope Marcato multi-coordinated visualization setup for rich heterogeneous data analysis; numbers, images, text, geospatial, dynamic network, all linked and interactive.

 

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