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RPA and GDPR: Security Governance in the Automation Era

RPA and GDPR: Security Governance in the Automation Era

The data on security breaches is overwhelming on many fronts. Over a billion records of consumers have been compromised since 2005. The total number of breaches in the period is threatening at around 8000. As late as 2017, big companies like Target, Equifax, and Neiman Marcus could not shield themselves from data breach attempts. Mind you, one of these is a top national credit reporting agency.

Noted analyst Avivah Litan predicts the following instances of misuse for the stolen data:

  • The data can get tossed around in an endless sell-and-resell loop of underground data piracy
  • Sensitive data can be used to steal bank accounts from customers
  • Identity thieves can use the data to update their existing records of targeted individuals
  • Adversarial nation states can use the data to disrupt peace or launder money out of the US

None of these constitutes stray casualty. The cumulative implications of the breaches are beyond grave. In fact, it is very difficult to quantify the damage dealt by these breaches to the society at large. That is where the General Data Protection Regulation (GDPR) swings into action. It gives consumers greater control over their own data while making corporates bite the bullet on their data processing practices.

What is the GDPR?

The GDPR is a regulation adopted by the European Union. It lays out the norms for data protection and privacy for the individuals that live in the European Union. It is one among the series of regulations that have helped formalize governance around security concerns of the average consumer.

In addition to strengthening consumer rights, GDPR aims at formalizing security standards that companies must establish to protect the data of their consumers.

Every organization functioning out of Europe and non-European organizations that collect the data of European citizens are expected to comply with the GDPR. The latest GDPR guidelines regulate how personal data is used, processed, stored, and deleted.

The GDPR also lays out that data subjects can request for both access and real-time usage information from organizations. If there’s any breach involving the personal data of users, it must be reported to the appropriate authority that oversees the regulation.

Security Governance: The Onus is on the Enterprise

At the crux of the GDPR is the impetus the regulation puts on enterprises to do all things necessary to protect consumer information. This has forced every enterprise software vendor to re-evaluate their policies regarding storage and management of sensitive user data.

This is where Robotic Process Automation (RPA) is impacting the industry in a big way. RPA platforms like Automation Anywhere are instrumental in offering comprehensive features in security and reliability. Starting with automation at once promises the following benefits for organizations:

  • Data encryption at all levels – when the data is in memory, in motion, or at rest.
  • A robust security framework (either built-in or third party) that guarantees security in the management and storage of user information. As a default practice, machine that store user credentials meant for critical purposes and the machines that run the software should always be exclusive.
  • Analysis of codes on both static and dynamic parameters, including manual pen testing for unbreakable application security.
  • Seamless enterprise based authentication system integration
  • Expansive logs of audits to support forensic analyses and audit processes
  • Secure operations that that make sure data is not exposed to business process threats during standard execution of processes

RPA platforms work with many ERP tools and in effect touch extensive sets of data within your organization. In case you are already using an RPA platform, make sure to check with them on GDPR compliance and the security measures they follow to ensure compliance.

How is RPA Easing up GDPR Implementation?

The first and absolutely unavoidable threat with manual processing of customer data is the guarantee of human errors. It does not really matter what level of security you follow. Even the slightest margin of error means that the organization is at the risk of non-compliance.

With RPA, you can automate the process defined by the legal and business teams to become GDPR compliant. Here is a collection of ways in which bots are helping enterprises with GDPR compliance:

Audit Logs

Enterprise RPA platforms are loaded with audit logs which monitor every operational process, creating logs for users and events at every stage of a given process. When there’s a data breach, audit logs swing into action with recurring spells of root cause analysis. What follows is routine forensic analysis to recognize and thereafter report the breach.

Content that relates to specific internal or external events can be gathered concurrently in real time. This comes in especially handy in case an organization is attempting to decode a fraudulent activity.

Documentation of data

There’s a lot of data pouring in from devices, sensors, and systems at the office. From the organizations perspective, it must be able to document all the data that is held in its directory, along with the source of its origin. The organization must be able to submit updated reports to the authorities in charge of data protection. GDPR mandates companies to purge personal data once it has crossed the holding period.

This is another area where RPA can help organizations by using bots that automate the process of masking PII data that is identifiable across applications. For the PII data that does not adhere to established policy, Natural Language Processing (NLP) lets bots recognize such data and generate alerts that help in intercepting the issue.

Data Breaches

GDPR makes it mandatory that subjects affected by data breaches be informed about it within 72 hours. For data breaches of a magnanimous nature, sending out information to everyone involved within 72 hours can become almost impossible. Imagine the case of Equifax, where 143 million users directly affected by the breach.

On the flip side, it is way easier to automate software bots to perform the job. In most instances, it does not even take 72 hours and makes sure the security governance timeframe is met.

Right to Access Information

European customers can request to access their information and know how an organization stores and uses the information. GDPR guarantees this right to all European consumers. If an organization wants to do this manually, it would need a dedicated team of individuals. Plus, every individual on the team must have access to such information.

It is way easier for bots to navigate through different systems and pull out data relevant to every user in question.

Right to Information Deletion

If a user requests an organization to dispossess their personal information, GDPR mandates the organization to delete such information promptly. Consider there is no automated process to do this. An employee or a team will have to access the information and then delete it from dozens of applications. Bots can not only pull out the relevant information on users but also email the report back to the concerned customers.

Some Data Cannot be Seen

There are legacy systems hiding data more than a decade old. Data can be accessed from these systems when needed. However, it’s never been as important to uncover sketchy data as it is now. RPA is the most convenient way to integrate the current technology platforms with legacy systems. Automation is also perhaps the only way to document and recognize available data that might be the cause of non-compliance.

Most companies are still taking their own sweet time understanding and dissecting the General Data Policy Regulation. At this, there is the threat of flooding of requests by consumers. Adhering to these requests will be compulsory. Doing it manually will mount up heavy costs on the administration. But the fact is responding to such requests might only be subject to a few well-defined requests. That makes it a great process for RPA to flex muscles.

The crux of it is organizations will have a hard time maintaining GDPR compliance in the absence of RPA. RPA solves security governance through GDPR wholly with the promise of zero errors.

Seven Hottest Analytics And Big Data Trends For 2019

The Big data is the vast volumes of data generated from a number of industry domains. Big data generally comprises data collection, data analysis and data implementation processes. Through the years, there’s been a change in the big data analytics trends – businesses have swapped the tedious departmental approach with data approach. This has seen greater use of agile technologies along with heightened demand for advanced analytics. Staying ahead of the competition now requires businesses to deploy advanced data-driven analytics.

When it first came into the picture, big data was essentially deployed by bigger companies that could afford the technology when it was expensive. At present, the scope of big data has changed to the extent that enterprises both small and large rely on big data for intelligent analytics and business insights. This has resulted in the evolution of big data sciences at a really fast pace. The most pertinent example of this growth is the cloud which has let even small businesses take advantage of the latest technology.

The modern business is floating on a stream of never-ending information. However, most businesses face the challenge of extracting actionable insights from vast pools of unstructured data. Despite these roadblocks, businesses are deriving from the tremendous opportunities for growth presented by big data. Here is all that would count as the hottest big data analytics trends of 2019.

Booming IoT Networks

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Like it’s been through 2018, Internet of Things (IoT) will continue to trend through 2019, with annual revenues reaching way beyond $300 billion by 2020. The latest research reports indicate that the IoT market will grow at a 28.5% CAGR. Organizations will depend on more structured data points to gather information and gain sharper business insights.

Quantum Computing

 

Industry insiders believe that the future of tech belongs to the company that builds the first quantum computer. No surprise that every tech giant including Microsoft, Intel, Google and IBM are racing for the top spot in quantum computing. So, what’s the big draw with quantum computing? It allows seamless encryption of data, weather prediction, solutions to long-standing medical problems and then some more. Quantum computing allows real conversations between customers and organizations. There’s also the promise of revamped financial modeling that helps organizations develop quantum computing components along with applications and algorithms.

Analytics based on Superior Predictive Capacity

 

More and more organizations are using predictive analysis to offer better and more customized insights. This, in turn, generates new responses from customers and promotes cross-selling opportunities. Predictive analysis helps technology seamlessly integrate into variegated domains like healthcare, finance, aerospace, hospitality, retailing, manufacturing and pharmaceuticals.

Edge Computing

 

The concept of edge computing among other big data trends did not just evolve yesterday. Network performance streaming makes use of edge computing pretty regularly even today. To save data on the local server close to the data source, we depend on the network bandwidth. That’s made possible with edge computing. Edge computing stores data nearer to the end users and farther from the silo setup with the processing happening either in the device or in the data center. Naturally, the entire procedure will see an organic growth in 2019.

Unstructured or Dark Data

 

Dark data refers to any data that is essentially not a part of business analysis. These packets of data come from a multitude of digital network operations which are not used to gather insights or make decisions. Since data and analytics are increasingly becoming larger parts of the daily aspects of our organizations, there’s something that we all must understand. Losing an opportunity to study unexplored data is a big-time potential security risk.

More Chief Data Officers

 

The latest trendy job role on the market is that of a Chief Data Officer. Top-tier human resource professionals are looking for competent industry professionals to fill this spot. While the demand is quite high, the concept and value of a CDO are largely still undefined. Ideally, organizations are preferring professional with knowledge in data analysis, data cleaning, intelligent insights and visualization.

Another Big Year for Open Sourcing

 

Individual micro-niche developers will invariably step up their game in 2019. That means we will see more and more software tools and free data become available on the cloud. This will hugely benefit small organizations and startups in 2019. More languages and platforms like the GNU project, R, will hog the tech limelight in the year to come. The open source wave will definitely help small organizations cut down on expensive custom development.

Making of a Storm: What Happens to Dark Data in Analytics and Big Data?

Making of a Storm: What Happens to Dark Data in Analytics and Big Data?

Dark data is the kind of data that does not become a part of the decision making for organizations. This is generally the data from logs and sensors and other kinds of transactional records which are available but generally ignored. The largest portion of the yearly big data collected by organizations is also dark data.    

Dark data does not usually play a vital role in analytics because:

  1. Companies do not want to use their bandwidth on additional data processing
  2. There’s a lack of technical resources
  3. Organizations do not believe dark data adds any value to their analytics

All of these are valid reasons for the data taking the back seat. But today we have a string of data-centric technological advances. Together, they present a heightened ability to ingest, source, analyze, and store large volumes of data. With that, it becomes important for organizations to recognize this largely untapped volume of data.   

The conventional way to use this data would be to systematically drain all of it into a waterhouse of data. This is followed by the identification, reconciliation, and rationalization of the data. The reporting follows soon after. While the process is pretty methodical, there might not be as many projects that truly call for such a need.   

The Immense Volume of Dark Data in Enterprise

Dark Data Big Data Analytics 2

At the moment, we have solid  evidence to suggest that as much as 90% of all data used in enterprises could be dark. Since industries are now storing large data volumes in the ‘lake’, it should be natural to tag the data appropriately as it gets stored. Perhaps the key is to extract the metadata out of this data and then storing it. 

Profiling and exploring the data can be done using one or a combination of tools that are already available in the market. Cognitive computing and machine learning can further increase processing power and open up possibilities of making intelligent use of dark data.  

Dark data may or may not have an identifiable structure. For example, most contacts and reports in organizations are structured. But over the course of time, they add up to the pile of dark data. Unstructured data can be small bits of personally identifiable info like birth dates and billing details. In the very recent past, this type of data would remain dark.

Machine learning can help organize this data in an automated manner. It can then be connected to other attributes of data to generate the complete view of the data. Using geolocation data is slightly trickier though. While it is extremely valuable, the lifespan is rather short. A collection of historical geolocation data sets can be further leveraged using machine learning to aid in predictive analysis of data.    

Recognition of regular data as dark data

Other sets of data often considered “dark” in the past include data from sensors, logs, emails, and even voice transcripts. The longest stretch they would get in terms of application would be vested in troubleshooting purposes. Not many would look to make such data a part of actual decision making. Now that we can convert voice or text (and vice versa) and use the data to gather intelligence, there are many use cases that draw advantage of data traditionally considered dark.    

An IDC estimate suggests that the total volume of data could be somewhere close to 44ZB (zettabytes) in 2020. This data explosion will be influenced by many new data generators like the Internet of Things. And unless we light up this data with new technology and processes, a large volume of it will continue to stay dark.  

The first and obvious step will be to make all the dark data available for exploration. The second step is to categorize the data, scrape out the metadata and do a quality check for all the extracted data. Modern tools for data management and data visualization provide the ability to explore the data visually. This determines whether or not the data can be illuminated to remove the visual noise.      

The myriad advances in Artificial Intelligence (AI) will definitely aid in uncovering the secrets of the oft-ignored “dark data”. However, the trick is still in using the data prudently. Wrong use of data will inadvertently result in incorrect predictions and may invite regulatory sanctions.

The vastness of dark data demands handling by Big Data and AI experts. In addition, there needs to be a clear plan about the application of the data once it is sorted. At Futran Solutions, we work with a pool of incredibly talented Big Data and Artificial Intelligence experts who can help your organization make the most of dark data. Contact us today to talk solutions in big data and artificial intelligence.

Sports, Analytics and Their Manifold Permutations

We are bam in the middle of 2018 and technology is in every tissue and cell of the things we see around us. We all understand and appreciate this, don’t we? But besides technology, there’s another important entity that we often fail to notice. That’s analytics. Analytics empower technology and inform the development of newer products.

We might not always make a conscious observation about it but analytics plays a major role in every industry, including the sports industry. Everything about professional sports is heavily dependent on analytics. Football, baseball, basketball, soccer, cricket – all modern sports benefit greatly from analytics.  

Sports Analytics: Data Lives Around Us and Vice Versa

There’s no denying that analytics form a huge part of the sports ecosystem around us. But how much of a vital factor analytics really is in sports? Are we using it for just the heck of it? Or is there some categorical productivity that we draw from analysis and data in sports? Let’s try and solve this bit first.    

Besides the numbers that are flashed on the scoreboard, every match produces a ton of data. These sets of data relate to team detailing, player information, performance insight, fitness scores, injury charts, and what have you. Each section of the team management and members of the support staff work with their individual sets of data.

For example, the physio would work with the data set that relates to injuries and healing. Each row and column in their sheet represent a number that has something to do with keeping the team fit and advising players on their weaknesses in terms of physicality. From there, this data goes on to power a lot of the individual nutrition management charts of sportspersons.     

Market Analytics and Hype Generation

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Another set of analytics pertains directly to the fan following of players and teams in the market. These data sets help in the tracking and analysis of places, prices, people, promotions, and products. The learning from these charts of data translates into higher merchandise sales, greater team popularity, and regular updates for the followers at large.  

Toward the collection, segregation, and selection of all the data involved in the extended analysis, there is a bunch of analytical platforms that come to aid. As and when these platforms are used efficiently by the management, the market value of teams goes up.

Above and beyond teams and players, fans of different sports also create a whole lot of data every day. To facilitate this, we can make extended use of blockchain technology. The energy you use at your home, the hours you spend watching sports, the extra gadgets you buy for sports alone – everything generates important data. If all these data packets are stored securely over a distributed ledger, you will have a large computer file of information per follower of the sport.   

Perhaps the most crucial part that analytics plays in sports is in luring in new investors. Seasoned investors work with a team of analysts who do extensive studies of teams and find out the new scope of investments. In effect, this allows investors to pick the right teams at right times.  

FIFA is Riding High on AI Based Sports Analytics

As we are grinding through a crunch FIFA World Cup 2018, there’s some pro-grade data analytics playing alongside the sportspersons. These analyses bear significant implications on in-game strategy, team and player evaluation, sports science, and even opposition scouting.

Add to this the German team’s partnership with SAP in the 2014 FIFA World Cup to create insights that helped create strategies and understand opponents. Moreover, they even used data analytics to time and evaluate practice sessions of their own players.

That was four years back. In FIFA World Cup Russia 2018, we are actually looking at a wholesome measure of AI and machine learning partnering with data analytics to create an immersive fan experience. The New Zealand cricket team uses SAS while all kinds of sports analyses make use of data mining software. 

Furthermore, technologies that find abundant utility in sports data analysis include SQL technologies, Data Mining, and Machine Learning.

Inferential statistics are getting bigger and we continue to improve upon computing of all types. As this happens, especially relevant devices are being employed to seize on player detailing. Hence, it is important that cloud services, powerful processors, and embedded technology must work together to make this happen.     

Today, the demand for professionals with skill and experience in data analytics and particularly analytics for sports. At Futran Solutions, we work with a pool of talented professionals with interdisciplinary experience in data analytics, machine learning, and data mining software. Contact us today to know more about our solutions for data analytics and sports data analytics.