One of biggest challenges facing enterprises today is Data Security and Data Privacy. The emergence of potent data technologies such as Big Data, Machine Learning (ML), and the Internet of Things (IoT) in the Data Management landscape has now sparked a new interest in Data Governance. With so much multi-channel data flowing into an average organization, the issues of Data Quality and Data Governance are assuming prime importance. The current enterprise data, thanks to advanced data technologies, is now collected, organized, and deposited in multi-layered, analytics platforms, which makes overall data handling and Data Management strategies more complex than ever. In short, enterprise data cannot be considered trustworthy without a good Data Strategy in place.
The article titled Three Forces Driving Enterprise Data Strategy in 2017 describes how the mountains of transactional data from sensor-driven networks and unstructured data emerging from mobile and social platforms have necessitated consistent data usage practices, which in other words, means Data Governance.
Data Governance, which forms a core part of overall organizational Data Strategy, consists of many layers such as the physical system layer, the process layer, or the application layer. Without going into too many technical details, one can say that the ultimate goal of good Data Governance is to bring all data silos into a common platform and standardize data usage across the enterprise.
How Does Machine Learning Impact Data Strategy?
This clear explanation from SAS Institute helps clarify the differences between different types of Machine Learning. As the underlying operating principle of ML requires the teaching data to be of very high quality, so that smart algorithms can learn from such available data models and improve themselves.
Both in supervised and semi-supervised learning processes, the learning algorithm has to rely on the accuracy of the input and output data. Thus, it is imperative that the data is clean, consistent, and accurate. This is where organizational Data Strategy comes in. When the Data Quality, Data Security, Data Governance, Data Stewardship, and Data Sharing strategies in an organization are transparent and solid, then only Machine Learning algorithms can succeed in delivering the intended business outcomes. If you review a blog post titled Machine Learning Business Ideas from the New McKinsey Report, you will see how Machine Learning has the potential to take over most of human processes currently in practice.
The DATAVERSITY® article titled 2017 Trends in Data Strategy includes the opinions of and feedback from top industry leaders about the current status of Data Strategy in their organizations.
What Does Machine Learning Bring to Organizational Data Management?
For starters, the readers of this article can assume the following to be typical organizational headaches, so far as overall Data Strategy is concerned:
- Machine Learning requires exposure to vast amounts of data for its learning algorithms, thus parallel technologies like Big Data, Hadoop, or R must also be implemented. This indicates complex data-management strategies for the organization
- ML-enabled solutions typically involve multi-layered data processing, thus organizational Data Strategy teams must play special attention to Data Quality and Data Governance, and Data Security issues.
- ML solutions aim to provide real-time solutions based on multi-channel or sensor-aided data inflows. This adds to the already overburdened data cleansing, data standardization, and data governance practice in the organization.
- Data Stewardship will enforce clear accountability and responsiveness for all data team members, which means another layer of monitoring in the organizational Data Strategy architecture.
The Computer World article titled Machine Learning Is the New Face of Enterprise Data, which introduces the readers to the data handling complexity of an ML-driven AI system known as Siri that emulates a human data analyst. Take another example from this same article – Larry from Amazon Cloud, designed to think like a human analyst for real-time decision making. If ML-powered AI Data Analytics platforms have their way, soon human Data Scientists will get replaced by smart, self-thinking systems. You must also review a video blog titled How Machine Learning and AI Are Impacting the Data Industry from DATAVERSITY® to learn about the opinions of industry thought leaders.
How Does Machine Learning Impact Organizational Data Quality?
The blog post titled Machine Learning Impacts Data Quality Matching indicates that automation can vastly improve the data matching process in Machine Learning systems. Here the author of the post talks about Spark, another technology that has the potential to fully automate the data-matching process for superior Data Quality. As Data Quality and Data Governance are serious issues for high volumes of business data, a technology like Spark can greatly aid the data cleansing. On one hand, while Big Data Is Empowering AI and Machine Learning at Scale, the growing concern for Data Quality and Data Governance is leaving the industry leaders and operators frantic about implementing solid data strategies in their organizations. Yes, it is true that finally, enterprises will win from ML-driven insights, but before that happens, core Data Strategies protecting the future value of the data assets must be in place.
How Does Big Data Impact Organizational Data Strategy?
The newscast titled McKinsey Finds Hard Work to Do in Big Data Revisited from Computer Weekly seems to suggest that according to a McKinsey Report, most businesses derive only 30% value from their business data. This report also indicates that although Big Data enabled analytics solutions have helped enterprises to derive competitive intelligence, but the shortfall is in the application of such intelligence for improved results. This reported anomaly seems to indicate that enterprises not only need automated analytics solutions, but also automated or semi-automated decision-making platforms.
Does that Mean Machines are Better at Decision Making than Humans?
In this report, Gartner discusses how to create ML- powered Data Strategy, where the general belief is that Machine Learning-Powered Artificial Intelligence (MLpAI) can deliver solid results with automated systems. The report indicates that even in fully automated analytics platforms, the industry operators will have to think of a solid Data Strategy to support the aims of Data Quality, Data Governance, and Data Security. Read McKinsey’s 2016 Analytics Study Defines the Future of Machine Learning to understand how Machine Learning and Deep Learning have transformed traditional data analytics and Business Intelligence in global enterprises. With ML’s immense power to “predict and prescribe” future outcomes, organizations must pay more attention to Data Strategy to get the maximum benefits.
Data Quality not Algorithms are Crucial for Machine Learning Success
This sentiment expressed in the above heading is reinforced in the article tiled Data Not Algorithms Is key to Machine Learning Success. While most organizations are excited about finding new opportunities in ML-driven Data Analytics, the biggest hurdle in their journey ahead is “data” and not algorithms.
The implication is that enterprises who invest in sound data strategies will win the race in future. Automation will offer advanced Data Analytics to big and small organizations alike, but the core market differentiator will be access to “clean and well-governed data.” Read this interesting LinkedIn Pulse post titled Your Data Strategy Defensive or Offensive Why Both, which discusses the limitations of legacy MDM tools vastly unsuitable for advanced Big Data Management. As higher volumes of data help shape the ML algorithms, Data Quality becomes a key criterion for the success of ML-powered systems. Read this KDNugget post to understand Machine Learning – More Data better Algorithms.
Is Machine Learning Redefining Organizational Data Strategy?
In the Forbes blog post titled Machine Learning Is Redefining the Enterprise in 2016, you will discover that the primary goal of ML-powered Analytics systems is to uncover hidden opportunities for the most lucrative business decision. This post indicates that Machine Learning’s unlimited potential for learning to improve makes it the unbeatable choice for predictive analytics and competitive BI. The predictive patterns lying hidden in “unstructured data” of the social platforms, e-mails, customer service logs, mobile eCommerce networks, and sensor-driven data flows would remain hidden had it not been the combined effects of Machine Learning, IoT, Cloud, and other related technologies.
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