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As today's enterprises strive to integrate their data assets in a secure and compliant manner while delivering on their digital transformation journey, the combined capabilities of Collibra and Bluemetrix will help global customers eliminate complexity across the entire data policy enforcement and management process. Read our product brief to learn how the platform streamlines and scales the enforcement of data policies enabling data users to create, capture and maintain the data governance state of all data assets processed in the pipeline. Click here to learn more

Bluemetrix Data Manager (BDM) is a suite of interoperable modules that allow a non-technical resource to build, schedule, transform, ingest and manage data pipelines inside a Data Lake without having to write any code or be an expert in the underlying environment. Built with simpler operations and data security in mind, it applies automation to various tasks so that the necessary code and commands are created and deployed as required. Click here to learn more

In this blog, we’d like to give you an overview of the steps required to tokenize sensitive data, migrate the data to the cloud, and then de-tokenize the data once in the cloud. We are using our BDM platform and the cloud provider is AWS. Step 1. The pipeline Let’s start with the pipeline. In our example, we’re getting data from an Oracle data warehouse which includes sensitive employees’ data, such as emails, credit cards details, and banking information among other data. We can select a column we can define a routine tokenization for the data therein. Once the routine tokenization has been implemented, the email format will still exist, but the content will be masked (for more on this topic, see here ). Each column in the table is tokenized individually and done via ‘no-code’ and in a user-friendly UI. This is advantageous for organisations, as developers are not required to perform the tokenization. In our example below, we are saving our anonymized data as a CSV output to an S3 bucket, and then we run the job. Step 2. The integrity of a dataset By using BDM, the platform allows you to preserve the integrity of a dataset while moving data to the cloud. This is ideal for departments that are concerned about data migration such as HR. Typically, a HR Department will have a lot of sensitive data covering BICs, IBANs, emails, social security numbers, along with contractual details. This, in turn, can make Data Protection Officers (DPOs) become wary of moving data to the cloud and insist on sensitive data being removed before the migration. However, with BDM, you can migrate all your data safely and securely via ‘in memory’ tokenization to the cloud. And once that data is on the cloud, any organisation can take advantage of, for example, the analytics which cloud platforms offer. Also, the data can be shared with third parties such as marketing companies in an entirely safe and trustworthy manner. Another feature of BDM is the ability to have ‘opt-out data’. Similar to the opt-out clause that people can avail of when it comes to marketing information, clients can choose what data is migrated to the cloud or not. Step 3. Migration – the view from AWS Once the data has been migrated, we can check the results in AWS, which is the destination for our example. The below screenshot is the view from AWS, and it details the successful migration. Note how the email column is tokenized while other columns, such as people’s names, are not. Furthermore, BDM allows you to de-tokenize your data in memory and write it back to your cloud destination, which is all captured as part of your data lineage. Data lineage is the process of recording - and the visualisation - of the data flow as it moves from the pipeline through its various stages. As tokenization is part of this process, it too is captured and represented within the lineage. For over a decade, Bluemetrix has worked with some of the largest health and financial organisations in the world and has worked on over 400 data lake projects. Connect with us here today and learn how to automate your data lake operation and management with trusted, governed, and cleaned data. Our experts at this online event, ‘Secure Your Cloud Migration By Leveraging In-Memory Tokenization’ will share their insights and best practices for securely moving your data into the cloud. You can watch it on-demand here.

To effectively understand healthcare insights, a data lake is optimal for predictive and advanced analytics when the volume and variety of data rapidly grow like the actual COVID situation. However, building a high-performance and fully functional operational data lake can take years before providing solutions.

Since the start of the pandemic, we have often heard officials say that ‘we’re following the science’. From politicians explaining lockdown measures to scientists discussing the logic behind vaccine rollouts, all eyes have been on the ‘science’. Yet what’s at the very heart of the science? When it comes down to it, it’s data. If you take the UK’s largest healthcare provider, they have gathered millions of data points about tens of thousands of patients who have suffered in a variety of ways from Covid-19. Now, just because we’re living in extraordinary times, you might think that the healthcare provider might forego data protection policies to possibly speed up their processes. However, nothing could be further from the truth. Teaming up with Bluemetrix Last year, the healthcare provider teamed up with Bluemetrix to support their cloud-based informatics solution which, in turn, would allow their staff to access de-identified data. Once the data was migrated successfully to the cloud, the healthcare provider was able to manage the control of the data in terms of access, security while allowing a full audit of every action that healthcare staff did while interacting with the data. Once the data was in the cloud, it not only allowed the healthcare provider to analyse the data, for the first time, they could share the data with third parties. This was a vital and much-needed step as the research into Covid-19 involved many stakeholders. Furthermore, the audited data was accessible to both the healthcare provider and the Data Protection Officer. Cloud migration Early on in the pandemic, the healthcare provider realised that by moving their data to the cloud, they could create a solution that would allow for the fast processing of data. However, moving such large volumes of critical data – critical both in terms of what the data had captured, and its end purpose i.e. supporting patient care and Covid research – required a robust solution. By implementing Bluemetrix’s BDM platform, data masking and tokenization was used to secure the data before it was migrated to the cloud, which then could be shared with the researchers and staff within the healthcare provider and with third parties. Also, when any stakeholders interacted with the data – from the healthcare provider to third parties – the integrity of the data remained constant and reliable. Data masking and tokenization Data masking and tokenization allowed the original data to be masked in such a way that it kept in line with data protection policies yet are useful to researchers. Technically, the data could be safely and securely accessed using R and Python to connect to the Hadoop cluster, where the relevant Covid-19 data sat. In conjunction with the healthcare provider and other key stakeholders, Bluemetrix created a data analytics platform that brought together data from multiple providers and data owners thus creating a complete picture of all Covid-19 related information. This dataset included primary care, acute, mental health, community and social care data to provide a linked dataset that was depersonalised. As the data had many owners and was from many sources and was changing frequently, the Bluemetrix solution had to ensure that the integrity of the data and its corresponding policy rules, captured such changes in real-time. The platform was also used for researchers to analyse de-identified data in order to help understand the disease and improve the healthcare response throughout the UK. Pandemic response: Data-driven insights Not only is it an example of data-driven insights being used in the middle of a pandemic, but the platform will also be used on an ongoing basis as Covid-19 strains may become a regular pathogen that researchers will be dealing with in the long term. ‘Working with the healthcare provider is a great example of migrating data to the cloud while implementing data masking and tokenization to support the migration. With our BDM platform, the healthcare provider had complete confidence in the entire process. We are delighted to have been able to work on this project which is delivering profound insights and research into Covid-19,’ says Richard Fox, Commercial Director at Bluemetrix. For more information about how Bluemetrix provides a unified data lake for healthcare research and analytics, download the datasheet below: #healthcare #bigdata #digitalhealth #datascience #analytics #cloudmigration

Of the many types of so-called ‘disruptors’ that have emerged over the years, the cloud has to be one of the best examples. For a start, its disruption is so vast that many of its users are not actually aware that the data which they consume is most likely cloud-based. From small offices losing their on-premises servers to large organisations moving lock, stock and barrel to the cloud, the benefits have been enjoyed by many. However, the mass migration to the cloud coincides with increased regulation governing data privacy, such as the GDPR. Not surprisingly, this is a huge concern for the custodians of our data – the data architects. Watch On-Demand Webinar: Secure Your Cloud Migration by Leveraging In-Memory Tokenization A perfect storm Migrating data to the cloud against this regulatory backdrop has become a perfect storm for data architects; while appreciating the advantages that the cloud brings, they know only too well of the issues which can affect their data. Yet the pressure is on from within their organisations to move to the cloud as everything from the cost to the convenience is being cited, and usually by those who are unaware of the issues that cloud migration brings. Take hybrid data processing, for example. If you adopt cloud at scale across multiple environments, you run into the problem of trying to manage the native data processing services of each cloud vendor. Furthermore, there are also issues when it comes to managing data and pipelines in a hybrid architecture. Tokenization versus In-Memory Tokenization The traditional approach to data tokenization is to substitute sensitive data with non-sensitive equivalents or tokens. It is a process that is widely used to protect credit card information (PCI) and personal health information (PHI), among other data. With this approach, you are also required to make copies of the data and store it on a disk before the data is tokenized. However, if the data is tokenized in memory, it ensures that a copy of your data is not backed up to a disk. By using secure stateful and stateless tokenization algorithms applied with strict user access policies, you can take full control over your data. Bluemetrix BDM Control Bluemetrix’s BDM solution is a data processing layer that sits across all cloud environments, thus giving you a single standard processing capability that you can control. It enables autonomous processing, whether on-premises, cloud, multi-cloud, or hybrid cloud. Also, it gives organisations the ability to decide where to store their data while knowing that they can more easily manage the data across the various environments. Bluemetrix Webinar Watch our on-demand webinar below, where Bluemetrix experts will explore the benefits of in-memory tokenisation, among other topics. ‘The purpose of the webinar is for attendees to get a really clear idea of the role of data masking and tokenisation when securing data, and then migrating the data to the cloud using a single no-code platform. Ultimately, all organisations what to be in a position to share sensitive data in a transparent, secured, and protected way, while taking full advantage of what the cloud has to offer,’ explains Leonardo Dias, the Principal Architect at Bluemetrix. For more information, watch the webinar to discover how data architects can leverage the best use of In-Memory Tokenization for cloud migration.

While it’s possibly a fool’s game to try and predict the future, there’s one aspect of it we can all safely agree on - the amount of data that we are all dealing with will exponentially grow, year on year. Data is being generated, analysed, stored, and regulated in unprecedented ways. And while it’s the lifeblood of many organisations, the governance of such data is a serious challenge. >> Download Now: Operationalise Your Data Governance for Analytics Bearing that in mind, the following are the seven key data governance challenges that organisations are facing. 1. Keeping data governance in sync Capturing the changes that happen to data is a perennial issue for data governors. Typically, this occurs when governance runs separately to the data pipeline which leads to the data falling out of sync. An ideal system will capture daily or real-time data changes and thus keep the data governors up to date. 2. Quality and Compliance rules The failure to tie data lineage, governance, and quality to Quality and Compliance rules lead to poor data governance outcomes. This often happens when there is not a standard approach or when data engineers are too busy and therefore are not applying lineage and/or quality checks to the data. 3. Data security and privacy It’s essential that data is only seen by those within your organisation who have the permission to see it, and when data is being accessed for analytics, it is masked in such a way that the underlying data is not revealed. Knowing where the data came from, what happened to it while it was being processed and who interacted with it, is a vital governance issue. 4. Applying data governance in hybrid cloud The benefits of moving to the cloud should not mean an interruption to your data governance. Before your migration, you should apply tagging, security, anonymisation, validation, and the transformation of that data to make it more transparent, better governed and cloud-ready. This means that your migration, from a data governance perspective, will allow you to seamlessly transition and benefit from the cloud immediately. 5. Data architecture – disparate systems As most organisations use disparate systems that interact with data, this naturally creates more work for data governors as they grapple with the multiple systems. While many systems will always be in place, data governors need to use a single data governance and operations tool which is ‘system agnostic’ in order to ingest, tag, and perform masking, tokenisation and transformation, among other tasks. 6. Data self-service – data requests and reports A big issue with reports is both the building of them and what data are people allowed to see. Instead of a data governor spending their time creating reports, an ideal system enables business analysts to prepare their own pipeline which are in line with governance procedures and then execute the report on their own by using self-service data sharing, intelligent data masking and governance approvals. 40.3% Percentage of Executives cite having a lack of organizational alignment as a challenge for data and analytics adoption. Site: Harvard Business Review 7. Unsure of data policies and procedures A lack of communication within an organisation can lead a general lack of understanding of data policies and procedures. However, instead of relying on people to study such documentation, your system should have in-built rules which automatically match a user’s permission levels thus enforcing regulations such as GDPR. This way data is matched to end users, which makes governance projects more likely to succeed, be quicker to implement and be more manageable. Bluemetrix’s BDM Control is the most comprehensive data and governance operations solution available. It will future proof your data while helping you track, capture and inform data governors about data changes as they happen. To learn more about how an enterprise data and governance automation solution can help you solve the organisational challenges above and more, schedule a free demo with one of our product specialist at your convenience!

When it comes to your organisation’s data, governance and compliance do not have to be at odds with the demands of your data. Over the years, computing innovations have coined various terms which try to capture the essence of the technology. Phrases such as the ‘cloud’ are an example as it attempts to portray where data is stored yet it often confuses people, and even disappoints them when they realise that their data is just being saved to a functional data centre somewhere in the suburbs. However, the phrase ‘data lake’ is a fitting metaphor for how big data can be stored. Simply put, it’s a vast pool of raw data which may or may not be structured. Certainly, data sloshing around in this vast pool is easier to imagine than it floating gently in a cloud. And to continue with this metaphor, there’s something lurking dangerously in the water – the twin threats of data governance and compliance. It’s commonplace to think that governance and compliance are at odds with data, that one gets in the way of the other, that data stakeholders could interact with data and get more done with it, if only such waters were rid of governance and compliance. Download now: Free checklist for Successful Data Lake Implementation The burden of big data Undoubtedly, dealing with data can be burdensome and lead to data issues, but it all depends on how your end-users actually interact with the data, and how such interaction is recorded by a data governance specialist. The traditional system and manual enforcement meant the end user had to record their interaction with the data via governance tools and catalogues. The issue with this approach is one of human error; people can easily forget to update their actions or incorrectly code the solution on the system. While double-checking for human error may have been an acceptable process when your data lake was more like a pond, the amount of data which is being collected, stored and subsequently analysed is vast. Also, data is no longer just data; it’s legally protected, has rights, and your organisation has to safeguard it or else face severe consequences. The ease of data automation Without doubt, some tasks are better automated. From a governance standpoint, it ensures that key actions are recorded correctly thus eliminating gaps in the recorded data. Furthermore, it will record lineage and tagging data when required. As for compliance, it offers end users a ruleset that enforces behaviour around regulations such as General Data Protection Regulations (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). BDM Solution – Operationalising Data Governance Another key element from an automation perspective, is that a lot more can be achieved. With the most comprehensive data and governance operations solution, BDM Control allows users to record all data activities along with data taxonomy and relationships. The data can be easily audited while sensitive data can be masked, anonymized or tokenized on ingest, as required. And data management policies can be programmed an enforced, all which is carried out in an easy-to-use GUI without the need to write code or program any business logic. Ultimately, this will remove the risk of data breaches, subsequent fines and reputational damage to your organisation, while ensuring data is used consistently and is easily audited. In other words, data lake placid. For more information, please contact us to find out how Bluemetrix can help your organisation automate data and governance operations.

Financial services organizations are a prime target for data breaches because of the potentially lucrative pickings for criminals, and data security in the banking sector is under siege. As a result, and because of a number of other negative consequences – financial, legal and reputational – of data breaches, all of which affect a company’s bottom line, the banking industry is compelled to focus on protecting customers’ personal data, or potentially lose money and customers. The Future of Data Privacy in the Banking Sector In the past few years, laws like the GDPR (Europe’s General Data Protection Regulations), CCPA (the California Consumer Privacy Act), LGPD (Brazil’s Lei Geral de Proteção de Dados), POPII (South Africa’s Protection of Personal information regulations), and PIPL (China's Personal Information Protection Law) have come into effect or been tightened. Privacy regulations do not only affect banks and other financial organizations operating nationally. Most of these laws affect any organisation targeting a foreign market even if they do not have a presence there. These laws are also designed to protect anyone living or visiting a country, not only nationalised citizens. Unless a business is community based, they are likely to be affected by privacy laws. Experts predict that privacy legislation is set to grow exponentially as countries, and independent states, adopt ever more stringent regulations. It behoves businesses to take the initiative in securing customer data before government forces them to do so, before they have to pay heavy fines for not doing so, and before they are sued by customers whose data is breached. In addition, without complying with data privacy regulations, banks and financial institutions may well find their aspirations to expand their products and services globally curtailed, their reputations bombing on social media sites, and their ability to compete with other financial service organizations throttled. Levelling the Privacy Playing Fields According to Gartner, by 2023 65 percent of the world’s population will have its personal data covered by some privacy regulations. A 2020 DLA Piper: GDPR data breach survey reported that between May 2018 and January 2020, there were 160,921 personal data breaches, with total fines of about 220-million Euros. Even without taking potential lawsuits into account, there is a large cost involved in protecting personal data. Security and Privacy Issues in Banking Data privacy automation can help banks and financial enterprises to keep pace with new regulations, utilizing the latest technologies. Gaining ground are techniques like anonymization, the process of protecting personal or sensitive information by de-identifying or encrypting information that connects people to their data. Anonymization is not just a guard against cybercrime. For example, banks may use data anonymization to share information externally – like statistics about customer loans – without disclosing individual customers' indebtedness. Google, for instance, uses anonymization to share information about buying trends to marketers and retailers without revealing the identity of the platforms' users. In these examples, anonymization becomes not only vital for security, but a powerful and competitive marketing tool. BDM Data Masking and Tokenization Solution Bluemetrix offers a range of data security solutions to help organizations meet data privacy and protection compliance obligations, whether in the cloud or on-premise, ensuring data is anonymised in as secure and efficient manner as possible. High-tech anonymization is at the heart of the BDM Data Masking and Tokenization solution.

As we slowly emerge from the pandemic, organisations are taking stock of a year which has pushed data compliance to its limits. It is exactly a year ago that Covid-19 struck and more people than ever before started working remotely. While researchers, no doubt, will look back at 2020 to examine how companies managed to keep afloat in such turbulent times, auditors have started assessing the year for different reasons: data compliance, or the lack thereof. Securing data is hard at the best of times. Over the last year, it’s certainly been the worst of times as various corporate departments grapple with the challenge of strained working environments and remote working while making sure businesses comply with the General Data Protection Regulations (GDPR), Payment Card Industry (PCI) standards and the Health Insurance Portability and Accountability Act (HIPAA). Data security Any rapid or prolonged change in operational practices or processes will inevitably lead to subsequent business challenges. Chief among such challenges is the security of your data, while ensuring that the security does not get in the way of using your data for the obvious benefit of your company and clients. While terms such as data masking and data tokenization were once only discussed behind the closed doors of your IT Department, they are increasingly being understood throughout entire organisations. Simply put – a failure to appreciate the importance of data masking and data tokenization and to understand the security risks that your organisation faces can lead to substantial regulatory fines and the ensuing fallout on your corporate reputation. Data Masking and Data Tokenization In general, data masking and data tokenization allows for the anonymization, pseudonymization, data de-identification, encryption, and obfuscation of information. The main reason why organisations want to anonymise data is to allow their data to be used in a secure manner, while preventing loss in the event of a security breach. The implementation of a robust data masking and data tokenization strategy means that companies can operate efficiently and securely, while being in full compliance with data regulations. Data is ‘masked’ in order to hide its original content and protect the information. Different levels of security can be deployed to mask the data depending on the specific algorithms used. Our BDM Masking tools allows for 12 out-of-the-box algorithms, while custom masking algorithms can also be developed. In most cases, data masking is non-reversible and it is not possible to return to the original state of the data. Whereas, data tokenization allows data to be anonymised and reversible to the original value of the data. What this means for your business Ultimately, masking and tokenization secures your data in a way that is scalable and available. It also reduces the chances of sensitive data exposure while maintaining compliance. By using our BDM Data Masking and Tokenization module, it removes the need for in-house development and minimises data-security training. Typically, expertise and knowledge are siloed within companies. We don’t expect IT to understand corporate law, nor do we expect HR professionals to appreciate the complexities of IT systems. However, this is changing due to the importance of securing data and regulatory compliance. If you are working in HR, Legal, or Finance and you are advised by IT about data masking and tokenization, it’s vital that you understand such terms in order to make an informed decision about their use. Companies face security threats continuously. If and when a security breach happens, your failure to understand the importance of data masking and tokenization will not be a good enough excuse. For more information, please contact us to find out how Bluemetrix can help you meet your data protection and compliance goals.

More than 90 percent of all data lakes are deployed to cloud environments because of the ease of use in which a physical environment can be set up and made available. A challenge remains configuring and customizing these environments cost-effectively and ingesting relevant and disparate types of data where it can become rapidly productive, appropriate to an organization’s industry and unique business requirements. In this article, we will explain when you should be using an automated data processing platform to build and integrate a true data lake into your data strategy, and do it cost effectively. Free checklist for a successful data lake implementation Two Different Approaches – Two Different Solutions Nearly three-quarters of respondents in a 2018 Eckerson survey said the data lake they used “fosters better decisions and actions by business users.” Typically, an organization will require both a data warehouse and a data lake (and data hubs, for that matter) as they serve different needs and use cases. But what makes data lakes smarter for many of today’s enterprise-level organisations? Perceived Disadvantages of Data Lakes The main criticism of data lakes has been that exploring large amounts of raw data can be difficult without specialised tools and (often expensive) skills to organise and catalogue the data. Compared to the traditional use of data warehousing, some organisations may find they do not have sufficient in-house data science expertise or the physical infrastructure to develop effective data lake solutions. This could, they predict, result in higher costs and a high time-to-market, resulting in years before benefits can be realised. The Solution to Data Warehouse Challenges However, organisations should consider the multiple advantages of data lakes over data warehouses in the context of current digital transformation trends and the adoption of machine learning processes and techniques if they want to remain competitive in their industry. While data warehouses provide a familiar interface for business users, data warehouse solutions are expensive, complicated to make changes to, lock companies into specific vendor solutions, and cannot deal efficiently with unstructured data. Unlike data warehouses, data lakes offer flexible, scalable solutions that, when implemented on an automated data processing platform, eliminate the perceived disadvantages of high skills requirements and a costly infrastructure. The platform provides the infrastructure as-a-service, and the skills to maintain it. Data lakes are also highly accessible and easy to update, providing increasingly advanced levels of data lake maturity, from simple data reservoir to interesting exploratory tool to complete big data analytical solution. Harnessing the power of AI transformation Critically, unlike data warehouses, data lakes allow the ingestion of raw data obtained from multiple disparate sources, necessary for machine learning application and the rapid development of AI solutions. This can result in enormous benefits to an organisation, including increased profits and efficiency and greater customer satisfaction. To put this into perspective, where it would take a data warehouse system 24 hours to create a data model for machine learning, the same process could take a data lake system 24 minutes. Data Lakes – a Modern Solution to Modern Problems "In our experience, an agile approach can help companies realize advantages from their data lakes within months rather than years. Quick wins and evidence of near-term impact can go a long way toward keeping IT and business leaders engaged and focused on data-management issues—thereby limiting the need for future rework and endless tweaking of protocols associated with populating, managing, and accessing the data lake." McKinsey When should organisations be using data lakes? For faster predictive and advanced analytics across multiple sectors, from health and finance to smart cities and marketing To create experimental machine learning models and AI algorithms for testing new ideas For applications where there are consistently high volumes of data Where the nature of the data keeps changing (as in the case of the current Covid-19 pandemic) Where the ingested data is raw and unstructured, or mixed As a self-service tool for business users to create their own queries and reports To create agile, data-driven applications Financial Systems Use Case Traditional proponents of data warehouse solutions, for financial systems, a data lake managed on a cloud-based platform can offer new opportunities in the industry, and: Cut down the amount of time to create and deploy machine learning models for new and advanced banking practices, like self-service Enable more secure centralised data storage Support increasingly complex global compliance regulations Allow machine learning analytics to create more accurate financial forecasts and risk assessments for different customer needs Promote experimentation and innovation to provide new financial offerings Use custom on-demand microservices to change the way banking is perceived, and used, by customers Analyse billions of financial transactions faster The Bluemetrix Approach Innovation and advancement in industries like healthcare, finance, and mission-critical industries is hampered by the fragmentation of different types and uses of data across projects, organizations, and countries in their sectors. Different governance rules create additional challenges. Bluemetrix has been building and deploying data lakes for over 400 enterprises since 2016, giving you unique insights into how to advance data lake maturity from a state of fragmentation to a state providing advanced analytical capabilities, according to the business needs of organisations. Bluemetrix provides the technology infrastructure, skills, tools, and services to create, manage, and maintain custom data lakes, so that enterprises are free to focus on the business bottom line. Bluemetrix helps organisations to quickly and easily create data pipelines using automation tools to: Ingest structured and unstructured data from any source Validate and quality check ingested data Secure and anonymise the data Carry out governance and GDPR compliance on the data Transform the data Automatically update your Data catalogue with all operations carried out on the lake

Think of your organisation’s data lakes as large water bodies with lots of H2O molecules (raw data in its native format). As a data owner, you decide if these water molecules get to flow freely and irrigate your organisational decisions or sit stagnantly. Obviously, democratised access to data is what will drive growth and change. But, if data pipelines are not appropriately managed, you get saddled with worries about the quality and security of the data. In turn, it would mean that you hold on tighter to the dataset. This just won't do. You need to feel confident about the data and how it will be used -- so that the organisation can benefit from it. In other words, you need to establish trust in data. This is the only way you will feel empowered enough to share data more freely. Do People ‘Trust your Data’? Today, companies need to sift through vast amounts of data every day. And with great data volumes comes great responsibilities and remarkable risks. To leverage and protect said data, data owners need to build higher levels of trust in their datasets. But, what does this ‘trust’ really mean? You can trust data that is consistent, accurate, complete, timely, traceable, unique and orderly. Once these factors are met, data owners become more comfortable sharing their data. It is also a measure of how confident each department or the analytics team is of the fidelity of datasets. The data should also be protected to satisfy both stakeholder and legal expectations. Click here to download top 15 questions for evaluating your data trustworthiness. Data Trust Challenges to Overcome Data trust issues are not unusual, nor are they unique to any particular industry. They are both technological and cultural in nature and signify a disconnect between the data requestor and the data owner, and their perception of trust. According to a study by PwC, data owners’ worries range from data theft and leakage (34%), quality of data (34%), privacy risk from authorized data processing (29%) and data integrity (31%). Thus, they cannot share data if they: Don't know what data they have - What sort of data is stored in each file? Is there PII (personal) data within the dateset? Is it secured and cleaned? Don't have absolute control over who can access or change the data Don't have confidence in the quality and validity of data that will be used to make business decisions. Data requestors, on the other hand, are equally frustrated for not gaining easy access to data. Unable to prove compliance while processing the data, to data owner; their requests for data access are regularly dismissed. Ultimately, poor access to data means less accurate data for analytics and reduced decision making capabilities. This is why it's critical to manage data access and assure data understanding, validity and quality. In this way, you create trusted data sources that you don't need to question every time. 5 Ways to Build Trust in Data The biggest barrier to data ambition lies in convincing data owners to share their precious resources. So, the need of the hour is to capture and process the data in a secure, transparent, governed manner, giving data owners the confidence to share. This ultimately leads to better analytics and compliance. Here are some of the best tactics that can help build the health of your data pipelines: Get data cleaned and validated: Analytics teams wouldn’t be able to trust in the data to make business decisions if it is fraught with duplications, lack of consistency and timeliness, etc. Basically, if you ingest garbage data, you will receive garbage business insights. Therefore, you need to strive for accurate, consistent, complete and reliable data to build user trust. Add metadata and business logic: Metadata and business logic add context to each piece of data so that data owners can precisely map the contents of pipelines. By significantly improving the business and technical understanding of your data, it enhances the data searchability and sensitive data discovery. Secure sensitive data: Often, you need to mask and hide PII data to avoid non-compliance around GDPR. Dynamic data masking solution by Bluemetrix allows you to consistently anonymise, de-tokenise & de-identify such data before disclosure. Monitor and track data: To trust your data, you must have the ability to prove that trust. For instance, you must have the ability to check where a record has been and the journey it has taken within your systems. You should also be able to tell who has accessed it throughout that journey. Bluemetrix’s schema evolution and versioning system scalably monitor and track what happens to data. This ensures that data consistency is guaranteed between data sources and destinations. Gain complete visibility and control over the process: Chaos would reign if data owners have no control over or view of who can access or change the data. Hence, data owners should strive to ensure a detailed audit trail reporting to know how and when the data has evolved. Also, by applying tags to different parts of the pipeline, you can devise unique data access policies. By optimising every step of the data value chain, you turn questionable data into valuable data. Once you secure stakeholder’s trust in data, you can harness it while respecting customer privacy and honouring regulations. With data lakes ingesting thousands of different pipelines from multiple departments, manually undertaking such data optimisation can be exhausting. The best workaround would be to invest time to identify an automated data governance solution that enables your stakeholders to establish trust in data.