Solution to Manage the Provenance &#038; Governance of Big Data Analytics
“Big Data and data science are riddled with data governance and provenance issues, making analytics that are computed downstream suspect of quality even if the ML or Graph analytics in and of themselves are competent and solving the core analysis problem, at least, in a lab environment.” What is the way industry at-large is finding to fix this problem, are there new technologies or techniques or best practices that address the problem? Introduction Though modern day and excellent mathematical and technological interventions to solve several Big Data analytics challenges, the output from Machine Learning or Graph Analytics is still subject to scrutiny. The only pervasive reason for this disparity is the controlled environments within which these solutions often produce results. The process of integrating the solution in to a larger data pool, often enamored by multitudes of data sources and data formats, is anxiously awaited to succeed. Organizations would much rather such high ticket solutions produce desired outcomes from the get-go. This anxiety are two uncertainties: knowing the provenance of data and putting a governance framework around that data The single-source-of-truth has become a shifting paradigm. From creation of related/integrated databases to federated Datawarehouses to conceptualizing Big Data, defining the single-source-of-truth has remained unique. However, with the new data sources (ex. social media) and the unprecedented volumes of data, the place where the ‘truth’ resides has changed and so should the means to look for it. In essence, the most significant change in the analytics world has been the realization that the provenance of data is no longer in a RDBMS or Graph database but in the contextual implications that the data provides. The significance of quantifying and qualifying data has been a tremendous force in paving way to solve the challenge of ‘Contextualizing the truth’. While several products offer solutions which address this challenge, they often fall short in guaranteeing the scalability or longevity of the solution. Logically, algorithms implementing NLP and AI address the challenge – however, the real solution is the actual ability of a technology to interpret and build the outcomes from interventions such as NLP or AI in to a solution unto itself. Semantic Graph Technology plays squarely to fill this exact space. The mode of sorting, defining, storing, retrieving, applying and smartening the data is what makes this technology exceptionally easy to understand and implement. More advanced than property graph solutions, Semantic Graph Technology alleviates most of the problems such as creating linear relationships which is otherwise complex. The other important aspect of data in modern analytics is the extensive need for governance. At the heart of any good governance model is a problem-solving approach led by data, rather than instincts. Organizations are interested in making data driven decisions that improve effective distribution of services and resources to end users.  Data driven decisions require deeper analysis of integrated data from all departments to be deeply analyzed and insights drawn through easy and free exploration, discovery and computing predictions. Increasing automation & smart devices have resulted in collection of vast amounts of data. Unfortunately this data is cannot be used effectively for analysis purposes due to immense complexity involved in pre-processing, organization, integration across various channels at the source level and at inter-department level and at the user level across historical, present and future data. Deep analysis requires that all data must be collected and indexed for analysis purposes. The other problem is the availability of such tools/products. Semantic Graph Technology provides such tools & solutions. In summary, the practical problem is that though everyone believes that Data is Gold, no one has a clear answer on how this Gold can be mined to help improve the business performance or address larger goals. Challenges in Solving the Problem are most definitely but not solely centered on the nature of data. Modern enterprises are collecting vast amounts structured, semi-structured and unstructured data. Most vendors deploy relational technologies to solve the analytics problem. Unfortunately, Relational technologies are only effective for tabular data and not efficient. They remain in-effective for semi and unstructured data which is categorized as Big Data! Big Data remains a major challenge as advanced analytics that organizations aspiring to, are difficult and even more problematic to deliver in an integrated context. What can solve the Problem is the Semantic Graph Technology which provides comprehensive solutions for the Big Data Analytics issue – seamlessly integrating advanced AI – Machine Learning, Natural Language processing, etc. Semantic analytics helps the users in two ways: precisely & easily analyze data and deliver value through analysis of the meta-knowledge itself to find previously unknown relationships and trends. This makes it easier and quicker for users to perform analyses and understand and share the results, without worrying about underlying databases or complex data science principles. For example, this technology can now help Governmental organizations to help track a citizens’ general acceptance to policies, “what-if” possibilities of certain policy implementations by modeling, predict to improve public health systems, etc., as the technology platform provides an integrated platform to mine and harness all types of data (structured, unstructured, voice, video, sensor, etc.). Some differentiators that solutions based in Semantic Graph Technology afford are Productized solutions include standard Business Intelligence as well as the newer Big Data and AI based Intelligence and Analytics in a single platform Integrate data across departments seamlessly, enabling deep insights by auto-correlations that are possible across departmental data Available in both Cloud based and on-premise solution that scales incrementally to largest amounts of data volumes In conclusion There is a solution that ensures the sanctity of data and the findings thereof – Semantic Graph Technology. The adoption of the solution, however, requires the highest level of organizational commitment and investment. Semantic Graph Technology enabled Big Data Analytics definitely provides a scalable and viable solution that addresses all the provenance and governance requirements. The above write up is a point of view by hiddime, a Cloud Based Big Data Analytics Solutions provider. Hiddime, the product, is based on Semantic Technology. It provides an easy interface to visualize complex outcomes from concepts such as Machine Learning. In addition, integrating with Allegrograph, a Semantic Graph Database provided by its partner Franz, hiddime offers analytic solutions that cover a diverse range of requirements. Visit www.hiddime.com and www.franz.com for more details and case examples.
“Big Data and data science are riddled with data governance and provenance issues, making analytics that are computed downstream suspect of quality even if the ML or Graph analytics in and of themselves are competent and solving the core analysis problem, at least, in a lab environment.”  What is the way industry at-large is finding […]
“Big Data and data science are riddled with data governance and provenance issues, making analytics that are computed downstream suspect of quality even
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["AI", "machine learning", "ML", "NLP", "data science", "analytics", "TPU", "R", "Go", "Scala"]
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