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#GDPR – Reform of EU Data Protection: 5 months left to be Fully Prepared

#GDPR - Reform of EU Data Protection- 5 months left to be Fully Prepared

Companies only have a few months left to prepare for the new European #DataProtection Regulation. On 25 May 2018, all companies managing personal data of citizens of the European Union will be required to comply with the new regulations and requirements of the General Data Protection Regulation (GDPR).

This regulation will impose significant new obligations on companies that manage personal data, as well as severe penalties for those who’ll violate these rules, including fines of up to 4% of global turnover or € 20 million highest amount being withheld.

Few months left before the entry into force of the Regulation, yet many companies have not started preparations and will have to develop and implement a compliance strategy. To facilitate their journey, we’ve listed, here below, eight rules to follow.

 

Understand your Data

 

The first step to comply with the GDPR is to understand how personal data is stored, processed, shared and used within the company. Through careful auditing, you will need to compare existing practices with the requirements of the new regulations and identify the changes needed to ensure your business in the way that best suits you. Remember that the obligations of the GDPR do not only apply to the strategies and measures put in place by your company but also extend to the providers who process personal data on your behalf.

 

Determine who is responsible for data protection

 

If some companies will have to appoint a data protection officer, everyone working within the company will have to adopt a data protection compliance program. Data protection officer may need to strengthen his strategies in this area and train his staff.

Please note that not all companies will necessarily have to appoint a Data Protection Officer, but good practice suggests that such a delegate is essential for companies that engage in two types of activities: large-scale processing of specific categories of data and large-scale monitoring of data, such as behavioral advertising targeting.

 

Ensure a legal basis for Data processing

 

Your company will want to examine the legal basis on which your strategy for handling various types of personal data is based. If it is based on consent, you will need to identify the method used to obtain that consent and will have to clearly demonstrate how and when that consent is given. Relying on consent means that data subject can withdraw his/her consent at any time and that data controller must then stop any data processing activity about this data subject.

 

Understand the rights of the people concerned

 

In accordance with the GDPR, any person whose data you process is given new rights, including the right of access to personal data, the right to correct and delete such data, or the right to portability of personal data.

Can your business easily locate, delete, and move customer data? Is it able to respond quickly to requests for personal data? Does your company, and the third parties that work for it, keep track of where these data are stored, how they are processed, and who they were shared with?

 

Ensure confidentiality from conception

 

As part of the GDPR, companies are required to implement a confidentiality strategy from the design stage when developing a new project, process, or product. The goal is to ensure the confidentiality of a data’s project as soon as it is launched, rather than implementing retrospective confidentiality measures, with the aim of reducing the risk of violation.

Have you limited access to personal data to those who need it in your business? A data protection impact assessment is sometimes necessary before processing personal data.

 

Be prepared for violation

 

Your company will need to implement appropriate policies and processes to handle data breaches. Make sure you know which authorities you will need to report any data breaches, as well as the deadlines. Any breach may result in a fine. Put in place clear policies and well-practiced procedures to ensure that you can react quickly to any data breach and notify in time where required.

 

Communicate the main information

 

In accordance with the GDPR, you will be required to provide the data subject with the legal basis for the processing of their data and to ensure that they are aware of the authorities from which they may lodge a complaint in the case of any problem. Make sure your online privacy policy is up to date.

 

Collaborate with your suppliers

 

GDPR compliance requires an end-to-end strategy that contains vendors processing personal data on your behalf. The use of a third party for data processing does not exempt companies from the obligations incumbent on them under the GDPR.

 

With any international data transfers, including intra-group transfers, it will be important to ensure that you have a legitimate basis for transferring personal data to jurisdictions that are not recognized as having adequate data protection regulation. Verify that the third-party data processor on your behalf has established strict data protection standards, has extensive experience in the field of large-scale data security management, and it has tools to help improve data governance and reduce the risk of breach.

 

Ensure your vendor meets globally recognized standards for security and data protection, including ISO 27018 – Code of Practice for Protecting Personal Data in the Cloud. Ask your vendor to provide you with all information about the network and data security who resides there (for example, its encryption policies and controls in place at the application level), its security policies, as well as its training, risk analysis, and testing strategies.

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From Data to Knowledge: #BigData and #DataMining

The increasing digitization of our activities, the constantly accumulating capacity to store digital data, the accumulation of data of all kind resulting therefrom, generates a new sector of activity whose purpose is the analysis of large quantities of data. New approaches, new methods, new knowledge are emerging, and ultimately no doubt new ways of thinking and working. Thus, this very large amount of data, (=big data), and its processing, (=data mining), affect different sectors such as the economy, marketing, but also research and knowledge.

The economic, scientific and ethical implications of this data are quite significant. The fact that we are in a constantly evolving sector, where changes are frequent and rapid doesn’t make the analysis easy … However, a deep knowledge of data is necessary in order to better understand what data mining is.

 

1 – What is data mining?             

 

Explore very large amounts of data. The purpose of data mining is to extract knowledge from large quantities of data by automatic or semiautomatic methods. Data mining, data drilling, knowledge Discovery from Data (KDD), are also referred as data mining.

 

  • How and why are such quantities of new data generated? Every minute 149519 e-mails are sent worldwide, 3.3 posts are published on Facebook, 3.8 million quarries are booked on Google, 65k photos are loaded on Instagram, 448k tweets are sent, 1400 posts are published via WordPress, 500 videos are uploaded on YouTube and last but not the least 29 million messages are sent via WhatsApp. These numbers can make one’s head go spin around, but important thing to note is that humans aren’t the only producers of data, machines also contribute with their sim cards, their sensors, and so on.
  • What to do with these data? If one understands the contemporary phenomenon of data accumulation, it is perhaps more difficult to perceive in what way these data, are changing the world. Depends how one is able to treat them. Science, IT, Medical sector relies heavily on statistics, on counting, and so on. From the moment when a set of data can be dealt with exhaustively, where cross-breeding and sorting can be carried out on a scale scarcely imaginable a few decades ago, these are analysis of our environment that are changing and being multiplied. In short, data is a tool for management and decision support and evaluation every sector and the raw material of the information is allowing the understanding of a phenomenon, a reality.

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2 – Value of Data

 

While IT organizations are best able to grasp the market potential of data accumulation and processing, this is not the case everywhere, where the idea that data is new oil is making its way more slowly than one might have imagined.

  • What is the market value of the data? Building data through a variety of IT operations is a valuable potential that companies are not always aware of or using it. Even if they do not necessarily know how to exploit data themselves, they have resources that aren’t profitable for them yet. These gathered data and their use is a key issue for companies. The Big Data is a real source of marketing opportunities.
  • Data to be protect that is complex to exploit: Personal data poses many problems for researchers specialized in their analysis. First, they point to the need to better protect them and ensure their conservation. Moreover, it requires very specialized skills to be treated in order to produce interesting results.

 

3 – Data mining and targeted marketing 

 

One of the most significant applications of data mining is undoubtedly in the regeneration of marketing, because data mining allows companies to reach consumers very precisely by establishing precise and reliable profiles of their interest, purchasing methods, their standard of living, etc. Moreover, there is no need to go through a complicated process of search, each of the Internet users leaves enough traces when surfing, tweeting, publishing on Facebook, so that his profiling is possible, without his knowledge most of the time…

  • A new space for social science research: Viewed from another angle, this accumulated data is a gold mine for researchers. Some behavioral researchers have looked at the attitudes of Internet users using dating sites. In addition to finding that the data they use is more reliable than that obtained by meeting individuals (they are easier to lie to an investigator than to a machine …), they can make analyzes that are not politically correct but very informative!

 

4 – The data mining forecast tool

Data mining is also a tool that allows to multiply the properties related to the calculation of probability. Indeed, because it makes it possible to cross a volume of data, but above all, because it makes it possible to apply these calculations to many different fields, it appears today as able to make Forecasts. Plus, Data mining for forecasting offers the opportunity to leverage the numerous sources of time-series data, both internal and external, available to the business decision-maker, into actionable strategies that can directly impact profitability. Deciding what to make, when to make it and for whom is a complex process. Understanding what factors drive demand and how these factors interact with production processes or demand and change over time are keys to deriving value in this context.  Today scientists do not hesitate to announce that they will soon be able to predict the future. All this, thanks to the Data!

  • Probabilities and predictions: Today, predictive statistics tackle all sorts of issues: natural disasters, health, delinquency, climate … Statistical tools are numerous and are combined to improve outcomes, such as when using “random checks”. Even more fascinating, software is capable of improving itself and accumulating ever more data to boost their performance … In the meantime, it is possible to rely on these analyzes to try to avoid the flu or get vaccinated wisely.
  • Anticipating or Preventing Crimes: If the idea that a software would be able to predict crimes and misdemeanors reminds one of Spielberg’s film “Minority report”, reality has now caught up with the fiction: the PredPol (predictive policing) software makes it possible to estimate better than other human technique or analysis, places where crime is likely to occur, and consequently better place police patrols and other preventive measures.
  • Preventing fraud: Other perspectives offered by data mining, improve the fight against fraud and “scams” in insurances sector. Here again, it is a matter of better targeting the controls and apparently it works: This technique gives very clear results. In more than half of cases, when a controller will do a targeted control on the basis of the data mining, he’ll find good results. Insurance companies also apply this type of analysis to detect scams.

Managing Data Traceability: Impact and Benefits

Data is at the heart of digital transformation. A company can’t support data lacking integrity if they aim to advance in their digital transformations initiatives.  The integrity of the data lies primarily in the confidence that users can have in the latter. Most of this trust rests on the traceability of data. In the absence of traceability, it is not possible to know if these data are trustworthy.

 

Data traceability is a concept that companies have been trying to understand for some time.  You might be asking for which reasons do today’s companies need more traceability? Well with a large amount of data from unmanaged external sources (sensors, data streams, Internet of objects), it is essential, for companies, to monitor these data when they are collected, processed and moved to be able to use them effectively. Digital transformation requires higher levels of data integrity. Indeed, companies need to have better data, which can be a basis they can trust.

 

Previously, data traceability was based on two dimensions: “where” and “how”. The need for better analysis and exploitation of the data leads to new demands and extends the definition of data, adding the following dimensions: “what”, “when”, “why” and “who”. Faced with these new requirements, it is necessary to master the bases of the primary components of the type: “where” and “how, especially as regards the impact and the value to be realized.

 

The “where” component of traceability focus on the origin of the data. The “how” component focus on how the data source was manipulated to produce its result. It is also possible to refine these two types of dimension via their level of granularity: “low” granularity and “high” granularity. The “where” component at the “low” granularity level focus on defining an upstream output dataset at the point of consumption to understand which dataset have been used to produce a result. The “how” component of the “low” granularity level focus on the transformations applied to the set of data source to produce the output dataset. On the other hand, “high” granularity level of traceability is concerned with the values ​​of data in the “low” level granularity: instead of where they were created and how they were modified to produce the result.

 

An example will better illustrate the types and granularities of traceability. Let’s take an accounting report, showing the total amount paid to suppliers over a given period. The “where” component of the “low” granularity level would trace all output data from the source invoice to the supplier tables from the accounting application. The traceability component “how” of “low” granularity level would look at how the supplier and billing tables were linked together with the calculation functions that were performed on the billing table to produce the total amount paid to each supplier. Traceability “where” at the “high” granularity level could (to search for the amount paid to a vendor) trace back to the invoices provided by the supplier. In order to take interest in the entire process, traceability at the “high” granularity level could also link to the original request: the purchase order, the receipt operations, in addition to the payment approvals.

 

Benefits of using data traceability

 

Declined in many forms, traceability can provide many benefits in terms of impact and added value to the companies that implement it. Such as:

  • Governance: Ensure the traceability of upstream data to provide owners and data sources with quality and access control results. This will allow data owners to manage their procedure in addition to downstream traceability (integrated with a corporate glossary, data traceability can allow data managers to control current definitions and understanding of terms and fields of data).
  • Conformity: Provide regulatory authorities with information to govern data sources, users and their behavior.
  • Change Management: Enabling users and developers to understand the impact of modifying certain data on downstream systems and reports.
  • Development of Solutions: Improve design, testing and deliverables of better quality. This is achieved through the sharing of traceability metadata, glossaries, and relationship among distributed development teams.
  • Storage Optimization: Provide as an input to archive decisions and provisions, an overview of the data being accessed and indicate where, how often and by whom access is permitted.
  • Data Quality: Improvement of the quality scores defined by the application of business rules and standardization on data, added to the metadata population as input of algorithms and decision making.
  • Problems Resolution: Helps in the analysis of root causes in repair-type processes.

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Traceability also brings a deeper advantage such as focus on the changed values ​​of the basic data entities that are shared between processes, services, and applications. For example, the impact that a change in, position, service, address or even the employer of a contact might have on the marketing, business or maintenance service of a business. According to the “U.S. Bureau of Labor Statistics,” an employee has on average 11 different employers throughout his career. Taking into account the speed at which US residents move and change their professions each year, the potential change in baseline data may be important in light of the adequacy of these statistics to the population of basic data within a company. The ability to collect, validate, distribute and track these changes in a timely manner could lead to better protection of existing revenue streams and the ability to capitalize on new revenue in B2B or B2C business relationships.

So, the companies which take advantage of traceability, are able to find data faster and are better able to support security and privacy requirements.

Challenges of #BigData

Behind the name of #BigData is hidden an astronomical amount of data produced anywhere, everywhere at any moment by men and machines to each action they perform together and separately.

This production is exploding because 90% of the available data was created only in the last two years. Big Data today is being analyzed to discover the insights that lead to better decisions and strategic business moves.

 

Big data apps are being used to improve offers, service levels and customer support and many more. The following numbers will certainly show you the economic potential of well-established data: Only 17% of companies haven’t plan at all to launch a Big Data project but over 70% of companies have already made use of Big Data, either by integrating their business or as part of a pilot project process. The Data technologies are maturing to a point in which more and more organizations are prepared to pilot and adopt big data as a core component of the information management and analytics infrastructure. It’s an area of research that is booming but still faces many challenges in leveraging the value that data have to offer.

 

Here are so called “big challenges” of Big Data.

 

Find a language for Big Data:

All sciences, chemistry and mathematics have experienced a tremendous boost by adopting a specific language. Don’t you think we must follow the same path in the area of Big Data and invent an algebraic notation and an adapted programming language to better share and facilitate its analysis?

 

Work on reliable data: 

With the explosion in the volume of available data, the challenge is how to separate the “signal” of “data” and “valuable information”. Unfortunately at this point, a lot of companies have difficulty to identify the right data and determine how to best use it. The fight against “spam data” and data quality is a crucial problem. Companies must think outside of the box and look for revenue models that are very different from the traditional business.

 

Data access: 

Data access and connectivity can be an obstacle. McKinsey survey shows that still a lot of data points aren‘t yet connected today, and companies often do not have the right platforms to manage the data across the enterprise.

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Embedding increasingly complex data: 

If the Big Data was first concerned the “simple” data (tables of numbers, graphs …), the processed data is now more and more complex and varied: images, videos, representations of the physical world and the living world. It is therefore necessary to rethink and reinvent the big data tools and architectures to capture, store and analyze this data diversity.

 

Better integrate time variable: 

The time dimension is also an important challenge for the development of Big Data, both to analyze causalities in the long term than to treat accurate information in real time in a large data flow. Finally, the problem also arises in terms of storage. The volume of created data will exceed the storage capacities and will require careful selection.

 

IT architecture: 

 The technology landscape in the data world is changing extremely fast. Delivering valuable data means collaboration with a strong and innovative technology partner that can help create the right IT architecture that can adapt to changes in the landscape in an efficient manner.

 

Security: 

Last but not the least, we’ve security issue. Keeping such vast lake of date secure is a big challenge itself. But if companies limit data access based on a user’s need, make user authentication for every team and team member accessing the data and make a proper use of encryption on data, we can avoid a lot of problems.

 

The change of scale offered by the technologies of Big Data have generated profound paradigm shifts in scientific, economic and political fields. But it also impacts the human field.

 

Xorlogics cognitive abilities are indeed developed to treat and represent all number of data. Big Data thus puts us to the test to challenge our analytical capabilities and our perception of the world. As we change and grow, the beliefs that are most vital to us is to put the people first, follow excellence, embrace the change and act with integrity to serve the world.We at Xorlogics have exceptional expertise in the domain of Big Data like Hadoop EcoSystem (HDFS), MapReduce, Pig, Spark, Storm HBase, Cassandra, MongoDB, Hive, Sqoop, Thrift, Zookeeper, HUE, Nutch Tika, Kafka.

 

So if you are looking for more information or to gain a better understanding of big data terms, tools and methodologies don’t hesitate to contact our experts in the data field!

How to succeed your Big Data project?

big data project

Big Data is sweeping the business world, there’s no doubt that data-driven decisions and applications create immense value by utilizing data sources to discover, present and operationalize important business insights.

 

Let’s see the list below how you can implement, manage and succeed your Big Data project.

 

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If your goal is not clear from the very beginning, you may not only be wasting your time but also money on wrong tools so you can easily penalize your project in terms of time and consumed resources. Keep in mind that your goal isn’t to develop a BIG, FAT database but to be able to collect useful information and analyze it in order to take good decisions for your organization.

Many companies focus on collecting as much data as possible from as many sources as possible. While gathering data is important, the second half of the equation — the “science” part — is too often forgotten. You need to approach your big data efforts from a scientific perspective to gain the most benefit from them. If not, you’re at risk of basing your decisions off of bad models, poor data quality, and erroneous assumptions.

 

  • The concept of uncertainty

One of the most significant developments of Big Data compared to more traditional data is the management of the uncertainty. This does not mean that nothing is planned or the Big Data project is launched without preparation. This means, however – and this is particularly true in marketing – that the Big Data project must take into account this uncertainty from the beginning of its design, and operate on a self-learning model. Again, you must, from the start, create goals that allow you to measure your progress along the way. You’ll also need to take into account what data you need, what existing data you have, and how it all applies to your business objectives.

 

  • Intelligent Big Data

Big Data is not a matter of robots. It’s primarily the result of crossing human intelligence, technology and automation. We all know that collecting information into a data lake is one thing, but finding the business value hidden in heaps of structured and unstructured data is quite another. To have big impact of big data and to deliver phenomenal results to meet expectations, they require new profiles at the intersection of different disciplines: computer science, databases, statistics, artificial intelligence, and last but not least, business knowledge (marketing, finance, logistics, etc.).

 

  • Impact of Big Data on organizations

Big data is becoming an effective basis of competition in pretty much every industry. Not only because of new professions emerged, for which the training is still largely to be create. But also because organizations “craft” of business are strongly rethought. One of the more significant impacts of big data is the organizational change or transformation necessary to support and exploit the big data opportunity. Old roles must be redefined and new roles must be introduced, creating both opportunities and anxiety for individuals and organizations alike.

 

  • Big Data technologies are available

Big Data is not only a buzzword but already available here and now. Many of the technologies used in the Big Data have indeed been invented and popularized by Web giants (Google and Yahoo! are among the pioneers) and are now made available to all who are able to implement them.

 

  • The data is the new oil

The distinction between information system (all processes and organizations between data, their process and archiving) and computer system (hardware and especially software used to process the data) is a classic.

The data is still a largely unknown area by the management of who still consider computer systems like magic formulas capable of transforming the business effortlessly. However, the data is capricious, and it requires a lot of work. It’s growing importance in a society where computerization is presence in all sectors, strength to change the perception of this data by the user. Much remains to be done for this change to be fully realized.

 

  • A Big Data project must be managed differently

Big Data is not only a marketing buzz word to describe existing and new technologies but they have their vocabulary, their professionals, their methods, algorithms, and specific projects approaches. Each Big Data project has its specificities. Beyond the technical approach, it induces specific methodology, an appropriate legal framework and a good measure of social impacts.  Learning will be necessary because Big Data are in constant reconfiguration.

 

What can we learn from Big Data? Certainly first of all we should understand what it is and what its value is because working with data is nothing like it was before. The reality is quite different. Above all, we must get rid of some myths like wishing to analyze everything in Big Data. Similarly, the idea of storing everything in order to “do something someday” is just a waste of time. Companies have never been in a better position to leverage the mountains of data available today to quickly gain insights for real business results.

Big Data, Big Problem?

In order to measure the progress of companies in the exploitation of their customer data, EY surveyed some known European companies. The purpose was to see the difference between the “buzz” generated by the fuzzy concept of big data and the reality of large companies. The results of this survey, conducted among more than 150 European companies, reveal that despite a largely positive perception, the “Big Data” hasn’t really taken place in reality unlike it has to be the case.
Let’s check the list below to know more about these issues that’s coming in the way of companies in order to integrate big data in their strategy.

 

Data collection via traditional channels:

Every businesses and organizations accumulate various type of data, such as financial information related to revenues and expenses, data about their customers, vendors and also about their employees. We’ve noticed that traditional file systems are still used by companies to gather data in order to increase their knowledge, understanding of customers, products and deploy marketing strategies. (Storing data in paper files, folders is a form of traditional system).

 

Unstructured data:

Companies collects huge volume of data and need valuable knowledge extracted from these data to improve their business results, still the survey reveals that 45% of respondents agrees that the data collected isn’t sufficiently exploited and only 27% of companies are equipped to manage and analyze the gathered data from many sources.

 

Analytical skills:

One of the top adoption challenges of big data is obtaining the skills and capabilities to interpret it. EY survey shows that for 70% of respondents, a team of less the 10 employees is dedicated to analyze the gathered data. Only 6% of companies have a staff of more than 50 dedicated people to decode useful information from the data.

 

A lack of data processing tools:

Good thing is that most of these large companies are aware of the progress of these unstructured data. 59% of respondents claim to anticipate an increase in the volume of data reliability within 18 months. At the same time, only 27% of them affirm that they have established internal processes to operate reliable or unstructured data.

 

Analysis of the data still (too) little predictive and real-time oriented:

Only 10% of companies operate their customer data for predictive purposes and 5% of them do it to optimize the technical process or to increase timeliness and increased storage capacities (key elements to exploit growing volumes and ever faster data and information flows).

 

The lack of mainstreaming in project management (Big) data:

39% of the respondents recognize that internal silos remain a drag on the optimal use of customer data. Each business has a habit of using and transforming data from its databases to meet its own goals or business issues, capital data cannot flow in the company, which explains the lack of a unified vision.

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The absence of ROI of Big Data projects:

Only 29% of respondents consider that the Big data is a major milestone and has the potential of big impact on business. As for the establishment of a “Big data Action Plan with concrete actions,” they are only 18% who actually did. Regarding the return on investment in particular, 58% of companies surveyed did not seek to quantify the contribution of solutions to their business performance. Again, the gap is huge between the most mature (77%) and “not mature” (3%).

 

Lack of sponsorship from the managing director:

The lack of ROI measurement, coupled with unfavorable economic conditions explain the caution of most CEO on the subject. The majority of small-medium size companies considered perception of top management as a brake on the optimal use of data within their business, 57% of them, against only 11% for the big size companies.

 

The reluctance to share personal data:

The issue related to data security, in which we can add the protection of privacy is the key to the future of the big data. The study of EY highlights that 70% of consumers are reluctant to share their personal data with companies and 49% say they are less likely to do so in the next five years.

 

Low awareness of safety issues and protection of data:

Among the companies EY surveyed, 30% believe they are not concerned with the protection of privacy issues during the operation of their customer data. 92.3% Companies, identified as the most mature in the Index EY Maturity Data, consider that the issue of protection of privacy is a priority. While for 58.6% of those who have been identified as less mature, don’t care about protection issue.

 

To resume:

Two-thirds of European companies (63%) consider that the big data is a valuable and interesting concept but still too vague, difficult to integrate within companies, in terms of organizational transformation, ROI strategy, management and training skills.

 

Even though big data is the petrol of this century, currently, half of the companies did not even studied any opportunities related to big data. Only 9% of companies surveyed have launched both the big data opportunity to study and put in place a comprehensive strategy to manage their customer data. Half of the respondents acknowledged that the absence of “a clear plan of action that constitutes a road map for the entire company” is an impediment to the optimal use of customer data. 57% of companies consider perception of top management as a brake against 11%.

 

The big data approach can be useful and beneficial for every businesses, but without a solid plan aligned with your business objectives you may miss out an elegant solution with a guaranteed return on investment.

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