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AUGMENTED DATA QUALITY: AN AI-FUELED APPROACH FOR YOUR DATA ZEN MOMENT

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    Data’s effectiveness hinges on its quality and here’s where Augmented Data Quality (ADQ) steps in, revolutionizing how we ensure our information assets are accurate, reliable, and ready to use.

    Traditional Data Quality: A Manual Marathon

    For years, data quality relied on automated but nevertheless manual processes. Data stewards meticulously combed through datasets, identifying and correcting errors like inconsistencies, missing values, and formatting issues. This painstaking approach, while crucial, becomes increasingly inefficient as data volumes explode.

    Augmented Data Quality: AI-Powered Efficiency

    Augmented Data Quality tackles this challenge head-on by leveraging artificial intelligence (AI) and machine learning (ML). These powerful tools automate data quality tasks, freeing up human experts for more strategic endeavors.

    Here’s how ADQ makes a difference:

    • Automated anomaly detection: AI algorithms can scan huge datasets, pinpointing anomalies and potential errors that might escape manual analysis.
    • Intelligent data cleansing: ADQ can suggest corrections for identified issues, streamlining the cleaning process. Machine learning even allows the system to “learn” from past corrections, continuously improving its accuracy.
    • Proactive monitoring: ADQ can be configured for real-time monitoring, enabling early detection and rectification of data quality issues before they impact downstream processes.

    Benefits Beyond Efficiency

    The advantages of ADQ extend far beyond simply saving time and resources. Here’s what organizations can expect:

    • Enhanced data trust: ADQ fosters a culture of data trust within an organization. With a high degree of confidence in data quality, employees across departments can make informed decisions based on reliable information.
    • Improved decision-making: Clean, accurate data leads to better insights. ADQ empowers businesses to leverage data for strategic planning, risk management, and optimized operations.
    • Reduced costs: Data quality issues can lead to costly rework and missed opportunities. ADQ proactively addresses these challenges, minimizing associated costs.

    Conclusion

    ADQ represents a significant step forward in data management. By harnessing the power of AI and automation, organizations can unlock the full potential of their data assets. As data continues to be the cornerstone of success, ADQ will be a critical differentiator for businesses that prioritize reliable information and data-driven decision making.



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    AI & DATA GOVERNANCE: THE INTERSECTION YOU CAN’T MISS TO MAKE AI RESPONSIBLE & TRUSTWORTHY

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    Artificial Intelligence (AI) has become a transformative force across industries, offering significant benefits such as increased efficiency, personalized services, and better decision-making. However, the adoption of AI also raises ethical, legal, and social concerns, necessitating effective governance mechanisms. AI governance involves establishing policies, regulations, and best practices to ensure the responsible development, deployment, and use of AI. A crucial aspect of AI governance is data governance, which focuses on managing and ensuring the quality, security, and ethical use of data.

    The Importance of Data Governance for AI

    Data governance is the foundation of any AI system, as AI models rely on data to learn, make predictions, and provide insights. The quality, diversity, and fairness of the data used in AI models significantly impact the accuracy, reliability, and fairness of AI outcomes. Therefore, robust data governance is essential for building trustworthy AI systems that deliver value while respecting ethical considerations and legal requirements.


    Effective Data Governance for Trustworthy AI

    Effective data governance includes several key elements:

    1. Data quality:
      Ensuring the accuracy, completeness, consistency, and timeliness of data used in AI models is crucial for generating reliable outcomes. Data cleansing, validation, and normalization techniques can help improve data quality.
    2. Data security:
      Protecting data from unauthorized access, theft, and misuse is essential for maintaining trust and complying with data protection regulations. Encryption, access controls, and monitoring can help ensure data security.
    3. Data privacy:
      Respecting individuals’ privacy rights and complying with data protection regulations, such as GDPR, is essential for ethical AI development. Techniques such as differential privacy, data anonymization, and user consent management can help protect individual privacy.
    4. Data bias and fairness:
      Ensuring that data used in AI models is representative, unbiased, and free from discrimination is critical for building fair and equitable AI systems. Techniques such as bias detection, mitigation, and fairness-aware machine learning can help address data bias and promote fairness.
    5. Data provenance and transparency:
      Providing clear documentation and explanations of data sources, processing, and usage is essential for building trust and accountability in AI systems. Techniques such as data lineage, model cards, and interpretability methods can help improve data and model transparency.

    AI Governance: Building on Data Governance Foundations

    Effective AI governance builds on these data governance principles and includes additional considerations: 

    1. AI model transparency and explainability:
      Providing clear explanations and justifications for AI model outcomes is essential for building trust, ensuring accountability, and facilitating auditability. Techniques such as SHAP, LIME, and decision trees can help improve model explainability.
    2. AI model validation and testing:
      Ensuring the accuracy, reliability, and robustness of AI models through rigorous testing, validation, and monitoring is crucial for building trust and ensuring safe and effective AI systems. Techniques such as cross-validation, stress testing, and model monitoring can help ensure model performance and reliability.
    3. AI model risk management:
      Identifying, assessing, and mitigating risks associated with AI models, such as safety, security, and reputational risks, is essential for responsible AI development. Techniques such as risk assessment frameworks, risk mitigation plans, and incident response plans can help manage AI risks.
    4. AI ethics and social responsibility:
      Ensuring that AI systems align with ethical principles, such as fairness, accountability, transparency, and social responsibility, is crucial for building trust and ensuring societal acceptance. Techniques such as ethical frameworks, social impact assessments, and multi-stakeholder engagement can help promote AI ethics and social responsibility.

    Conclusion

    AI governance and data governance are interconnected and interdependent, as effective data governance is essential for building trustworthy AI systems. By adopting robust data and AI governance practices, organizations can ensure the responsible development, deployment, and use of AI systems, while delivering value, building trust, and maintaining compliance with legal and ethical requirements. As AI continues to evolve and transform industries, effective governance will be crucial for achieving responsible and trustworthy AI that delivers long-term value and benefits for all stakeholders.

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    AGILE DATA GOVERNANCE – THE SMART WAY TO UPGRADE YOUR DATA DYNAMICS?

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    In the dynamics of today’s business, data is key for organizational vitality. While the imperative of data-driven decision-making is paramount, traditional old school data governance methodologies can prove ponderous, impeding progress. Enter agile data governance, a transformative paradigm inspired by principles from agile software development.

    Understanding Agile Data Governance

    Agile data governance represents a contemporary and adaptable approach to data management, drawing inspiration from the agility of software development methodologies. It prioritizes collaboration, adaptability, and continual improvement, aiming to streamline decision-making and enhance communication across diverse departments and stakeholders.

    Traditional Data Governance – The challenges & the case for the agile approach

    Conventional data governance potentially encounters several challenges:

    • Sluggish Processes: Extensive documentation and prolonged approval cycles can substantially delay data initiatives.
    • Inflexibility: Rigid frameworks struggle to keep pace with the ever-evolving demands of the business.
    • Top-Down Structure: Lack of collaboration leads to isolated information, hindering effective data utilization.
    • Low Engagement: Complex procedures create disconnection and discouragement among data users.

    Agile Data Governance – Distinct Advantages

    • Accelerated Value Realization: Break down extensive governance projects into manageable sprints for swift implementation and feedback loops, ensuring alignment with evolving needs. Prioritize business value at each stage, concentrating on crucial data elements and processes for rapid wins and showcasing the value of data governance to stakeholders.
    • Collaboration as a Cornerstone: Cultivate an environment where data producers and consumers collaborate, fostering a shared understanding of data definitions, usage guidelines, and ownership for improved data quality and accuracy. Leverage open communication channels and collaborative tools to encourage discussions, feedback, and shared ownership, dismantling silos and nurturing a data-driven culture.
    • Embracing Continuous Enhancement: Adopt an agile mindset, emphasizing learning and adaptation based on feedback to keep the data governance framework relevant, efficient, and aligned with changing business landscapes and technological advancements. Regularly review and refine policies and procedures based on real-world experiences and user feedback, ensuring ongoing effectiveness and support for organizational evolution.
    • Empowering Teams: Move away from a top-down, bureaucratic approach by equipping team members with the knowledge and tools needed to make data-informed decisions within defined boundaries. Promote ownership and accountability among data users, instilling a sense of responsibility for data quality and compliance, thereby fostering an engaged and data-driven workforce.

    Implementing Agile Data Governance – Key Steps

    While there is no one-size-fits-all approach, consider these key steps:

    • Define business goals and objectives, clearly understanding desired outcomes from adopting an agile data governance framework.
    • Identify key stakeholders and roles, involving data owners, stewards, consumers, and Business & IT representatives in the process.
    • Prioritize data assets and processes, focusing on critical data elements aligned with business goals.
    • Develop an iterative framework with clear principles, roles, responsibilities, and communication channels.
    • Establish a continuous improvement process, regularly reviewing framework effectiveness and adapting based on feedback and emerging needs.
    • Make optimal usage of fit-for-purpose tooling. While success isn’t solely dictated by technology, its impact on the degree to which agile data governance can be implemented is undeniable. It’s crucial to have a business-centric platform rather than one solely focused on IT to ensure a flexible and collaborative approach.

    Conclusion

    By embracing an agile approach to data governance, organizations can unlock the full potential of their data assets. Increased collaboration, faster time to value, and a culture of continuous improvement empower teams to make data-driven decisions and drive innovation in today’s dynamic business environment. Embark on your journey toward an agile data governance mindset and harness the power of data to propel your organization to success.

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    Interested in elevating your data governance initiative to the next level? Discover how Datalumen can assist you getting there. 

     

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    CHANGE & DATA GOVERNANCE – TAKE A LEAP FORWARD

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    A successful data governance initiative is based on properly managing the People, Process, Data & Technology square. The most important element of these four is undoubtedly People. The reason for that is that at the end it boils down to people in your organization to act in a new business environment. This always implies change so make sure that you have an enabling framework for managing also the people side of change. Prepare, support and equip individuals at different levels in your organization to drive change and data governance success.

    Change & the critical ingredient for data governance success.


    Change is crucial in the success or failure of a data governance initiative for two reasons:

    1First of all you should realize that with data governance you are going to tilt an organization. What we mean by this is that the situation before data governance is usually a silo-oriented organization. Individual employees, teams, departments, etc are the exclusive owner of their systems and associated data. With the implementation of data governance you will tilt that typical vertical data approach and align data flows with business processes that also run horizontally through an entire organization. This means that you need to help the organization to arrive at an environment where the data sharing & collaboration concept  is the new normal.

    2The second important reason is the so-called data governance heartbeat. What we see in many organizations is that there is a lot of enthusiasm at the start of a program. However, without the necessary framework, read also a change management plan, you run the fundamental risk that such an initiative will eventually die a silent death. People lose interest, no longer feel involved, no longer see the point of it. From that perspective, it is necessary to create a framework that keeps data governance’s heart beating.

    How to approach change?


    Change goes beyond training & communication. To facilitate the necessary changes, ChangeLab and Datalumen designed the ADKAR-based LEAP approach. LEAP is an acronym that stands for Learn, Envision, Apply & Poll. Each of these important steps help realize successful and lasting change.

    • Organizational culture and risk mitigation culture; “Readiness” for change

    • Scope and type of the (sub)projects

    • Timing and path for the change

    • Set up the change team

    • Stakeholder assessment & impact analysis (personnel analysis)

    • Sponsorship analysis

    • Risk assessment



    Need help covering change in the context of your data initiatives?

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    CALCULATING DATA GOVERNANCE ROI

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    THE GDPR BUSINESS VALUE ROADMAP

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    Getting a good understanding of the requirements but also the opportunities and business value is not easy. We designed a GDPR business value roadmap to help you with this and also make you understand what capabilities you need to get the job done.  


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    • How will you understand what in-scope data is used for, for what purpose and by whom?
    • How will you demonstrate how you’re aligning to the principles?
    • Is your approach mostly manual, using interviews, questionnaires & static documentation?
    • Is your approach inaccurate, time consuming, resource consuming, out-of-date –or all of the these?


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    • Do you understand where in-scope data is across your organisation and how it is shared?
    • How will you demonstrate you understand the size & shape of the data problem across domains and data subjects?
    • Is your approach mostly manual, using interviews, questionnaires & static documentation?
    • Is this approach inaccurate, time consuming, resource consuming, out-of-date –or all of the these?

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    • How will you capture, manage and distribute consents across channels and business units?
    • How will you demonstrate you have captured the lawfulness of processing across all in-scope data sources?
    • Do you have anything in place already? Or are you planning on extending existing preferences capabilities?

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    • How will you put protections and controls around identified in-scope data?
    • Can you demonstrate you have relevant control over the relevant in-scope data?
    • Are you planning to manually apply controls? Or apply masking, deletion & archiving solutions as required?
    • Will this approach give you a holistic view around the protections & controls you have in place?





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    SUMMER READING TIP

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    Summer is here and the longer days it brings means more time available to spend with a ripping read. That’s how it ideally works at least. We selected 3 valuable books worth your extra time.

     

    The Chief Data Officer’s Playbook

    The issues and profession of the Chief Data Officer (CDO) are of significant interest and relevance to organisations and data professionals internationally. Written by two practicing CDOs, this new book offers a practical, direct and engaging discussion of the role, its place and importance within organisations. Chief Data Officer is a new and rapidly expanding role and many organisations are finding that it is an uncomfortable fit into the existing C-suite. Bringing together views, opinions and practitioners experience for the first time, The Chief Data Officer’s Playbook offers a compelling guide to anyone looking to understand the current (and possible future) CDO landscape.

    Search on Google


    Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility

    Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility, the first book ever written on the topic of data virtualization, introduces the technology that enables data virtualization and presents ten real-world case studies that demonstrate the significant value and tangible business agility benefits that can be achieved through the implementation of data virtualization solutions. The book introduces the relationship between data virtualization and business agility but also gives you  a more thorough exploration of data virtualization technology. Topics include what is data virtualization, why use it, how it works and how enterprises typically adopt it. 

    Search on Google


    Start With Why

    Simon Sinek started a movement to help people become more inspired at work, and in turn inspire their colleagues and customers. Since then, millions have been touched by the power of his ideas, including more than 28 million who’ve watched his TED Talk based on ‘Start With Why’ — the third most popular TED video of all time. Sinek starts with a fundamental question: Why are some people and organizations more innovative, more influential, and more profitable than others? Why do some command greater loyalty from customers and employees alike? Even among the successful, why are so few able to repeat their success over and over? 
     
    People like Martin Luther King, Steve Jobs, and the Wright Brothers had little in common, but they all started with Why. They realized that people won’t truly buy into a product, service, movement, or idea until they understand the Why behind it.  ‘Start With Why’ shows that the leaders who’ve had the greatest influence in the world all think, act, and communicate the same way — and it’s the opposite of what everyone else does. Sinek calls this powerful idea The Golden Circle, and it provides a framework upon which organizations can be built, movements can be led, and people can be inspired. And it all starts with Why.

    Search on Google


    Summer Giveaways

    We’re giving away 50 copies of ‘Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility’.  Want to win? Just complete the form and cross your fingers. Good luck!


    Winners are picked randomly at the end of the giveaway. Our privacy policy is available here.

    GARTNER SURVEY FINDS CHIEF DATA OFFICERS ARE DELIVERING BUSINESS IMPACT AND ENABLING DIGITAL TRANSFORMATION

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    By 2021, the CDO Role Will Be the Most Gender Diverse of All Technology-Affiliated C-level Positions.

    As the role of chief data officer (CDO) continues to gain traction within organizations, a recent survey by Gartner, Inc. found that these data and analytics leaders are proving to be a linchpin of digital business transformation. 

    The third annual Gartner Chief Data Officer survey was conducted July through September 2017 with 287 CDOs, chief analytics officers and other high-level data and analytics leaders from across the world. Respondents were required to have the title of CDO, chief analytics officer or be a senior leader with responsibility for leading data and/or analytics in their organization. 

    “While the early crop of CDOs was focused on data governance, data quality and regulatory drivers, today’s CDOs are now also delivering tangible business value, and enabling a data-driven culture,” said Valerie Logan, research director at Gartner. “Aligned with this shift in focus, the survey also showed that for the first time, more than half of CDOs now report directly to a top business leader such as the CEO, COO, CFO, president/owner or board/shareholders. By 2021, the office of the CDO will be seen as a mission-critical function comparable to IT, business operations, HR and finance in 75 percent of large enterprises.” 

    The survey found that support for the CDO role and business function is rising globally. A majority of survey respondents reported holding the formal title of CDO, revealing a steady increase over 2016 (57 percent in 2017 compared with 50 percent in 2016). Those organizations implementing an Office of the CDO also rose since last year, with 47 percent reporting an Office of the CDO implemented (either formally or informally) in 2017, compared with 23 percent fully implemented in 2016. 

    “The steady maturation of the office of the CDO underlines the acceptance and broader understanding of the role and recognizes the impact and value CDOs worldwide are providing,” said Michael Moran, research director at Gartner. “The addition of new talent for increasing responsibilities, growing budgets and increasing positive engagement across the C-suite illustrate how central the role of CDO is becoming to more and more organizations.” 

    Budgets are also on the rise. Respondents to the 2017 survey report an average CDO office budget of $8 million, representing a 23 percent increase from the average of $6.5 million reported in 2016. Fifteen percent of respondents report budgets more than $20 million, contrasting with 7 percent last year. A further indicator of maturity is the size of the office of the CDO organization. Last year’s study reported total full time employees at an average of 38 (not distinguishing between direct and indirect reporting), while this year reports an average of 54 direct and indirect employees, representing the federated nature of the office of the CDO design. 

    Gartner CDO Survey Results

    Key Findings

    CDO shift from defense to offense to drive digital transformation

    With more than one-third of respondents saying “increase revenue” is a top three measure of success, the survey findings show a clear bias developing in favor of value creation over risk mitigation as the key measure of success for a CDO. The survey also looked at how CDOs allocate their time. On a mean basis, 45 percent of the CDO’s time is allocated to value creation and/or revenue generation, 28 percent to cost savings and efficiency, and 27 percent to risk mitigation. 

    “CDOs and any data and analytics leader must take responsibility to put data governance and analytics principles on the digital agenda. They have the right and obligation to do it,” said Mario Faria, managing vice president at Gartner. 

    CDO are responsible for more than just data governance

    According to the survey, in 2017, CDOs are not just focused on data as the title may imply. Their responsibilities span data management, analytics, data science, ethics and digital transformation. A larger than expected percentage of respondents (36 percent) also report responsibility for profit and loss (P&L) ownership. “This increased level of reported responsibility by CDOs reflects the growing importance and pervasive nature of data and analytics across organizations, and the maturity of the CDO role and function,” said Ms. Logan. 

    In the 2017 survey, 86 percent of respondents ranked “defining data and analytics strategy for the organization” as their top responsibility, up from 64 percent in 2016. This reflects a need for creating or modernizing data and analytics strategies within an increasing dependence on data and insights within a digital business context. 

    CDO are becoming impactful change agents leading the data-driven transformation

    The survey results provided insight into the kind of activities CDOs are taking on in order to drive change in their organizations. Several areas seem to have a notable increase in CDO responsibilities compared with last year:

    • Serving as a digital advisor: 71 percent of respondents are acting as a thought leader on emerging digital models, and helping to create the digital business vision for the enterprise.
    • Providing an external pulse and liaison: 60 percent of respondents are assessing external opportunities and threats as input to business strategy, and 75 percent of respondents are building and maintaining external relationships across the organization’s ecosystem.
    • Exploiting data for competitive edge: 77 percent of respondents are developing new data and analytics solutions to compete in new ways.

    CDO are diverse and tackling a wide array of internal challenges

    Gartner predicts that by 2021, the CDO role will be the most gender diverse of all technology-affiliated C-level positions and the survey results reflect that position. Of the respondents to Gartner’s 2017 CDO survey who provided their gender, 19 percent were female and this proportion is even higher within large organizations — 25 percent in organizations with worldwide revenue of more than $1 billion. This contrasts with 13 percent of CIOs who are women, per the 2018 Gartner CIO Agenda Survey. When it comes to average age of CDOs, 29 percent of respondents said they were 40 or younger. 

    The survey respondents reported that there is no shortage of internal roadblocks challenging CDOs. The top internal roadblock to the success of the Office of the CDO is “culture challenges to accept change” — a top three challenge for 40 percent of respondents in 2017. A new roadblock, “poor data literacy,” debuted as the second biggest challenge (35 percent), suggesting that a top CDO priority is ensuring commonality of shared language and fluency with data, analytics and business outcomes across a wide range of organizational roles. When asked about engagement with other C-level executives, respondents ranked the relationship with the CIO and CTO as the strongest, followed by a broad, healthy degree of positive engagement across the C-Suite. 


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    FISHING FOR BETTER BIG DATA INSIGHTS WITH AN INTELLIGENT DATA LAKE

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    Fishing in a lake and a data lake are much the same.
    Data scientists must not only go where the fish are for big data insights, but also find a way to quickly build the data pipeline that turns raw data into business results.

    When fishing it doesn’t matter how good of a fisherman you are—you’re not going to catch anything if you’re not fishing where the fish are. This same bit of advice extends to data lakes. 

    Not even the best data scientists in the world can find insights in data lakes that are nothing but data swamps. But that’s what most data analysts are using today—swamps filled with databases, file systems, and Hadoop clusters containing vast amounts of siloed data, but no efficient way to find, prepare, and analyze that data. That is why ideally you have collaborative self-service data preparation capabilities with governance and security controls.

    With this in mind, Informatica launched Big Data Management, which included a Live Data Map component to collect, store, and manage the metadata of many types of big data and deliver universal metadata services to power intelligent data solutions, such as the Intelligent Data Lake and Secure@Source. Intelligent Data Lake leverages the universal metadata services of Live Data Map to provide semantic and faceted search and a 360-degree-view of data assets such as end-to-end data lineage and relationships.



    In addition to smart search and a 360-degree-view of your data, Intelligent Data Lake provides analysts with a project workspace, schema-on-read data preparation tools, data profiling, automated data discovery, user annotation and tagging, and data set recommendations based on user behavior using machine learning. These capabilities make it much easier for analysts to “fish where the fish are” for big data insights.  

    In order to “land the fish” and turn these insights into big value, there needs to be a way to quickly build the data pipeline that turns raw data into business results. Intelligent Data Lake does this automatically by recording all the actions of a data analyst as they prepare data assets in what is called a “recipe.” These recipes then generate data pipelines (called mappings in Informatica) that IT can automatically deploy into production. What better way to turn insights into business value and fry up those fish you just caught?

    If you want to see how an Intelligent Data Lake works through a live demo, please contact us or have a chat with us at the upcoming Big Data & Analytics 2017 event.